Suggestions or feedback? his dad took him to all the nearest air shows so he could see all the planes. And when he learned to drive he joked with his parents that he shouldn’t drive near the airport because he would get distracted He always looks up at the sky when he hears airplanes pass.  “I can't even tell you the first time I got interested in airplanes,” he says Caldelas is an MIT senior majoring in aeronautics and astronautics but he came into the university thinking he’d go into nuclear science and engineering. He used to think of his love of flying as a hobby but not a profession — that is until his friends convinced him to take a tour of the MIT’s Department of Aeronautics and Astronautics (AeroAstro) he learned of a semiserious requirement for every professor candidate the candidate is taken outside; if a plane flies overhead and the candidate doesn’t look up he knew AeroAstro would be his home.  ‘If that's the passion here in the department then that's where I should be.’ And I haven't regretted that decision since,” he says It feels like a home just because I can nerd out with people about all the airplane and space things.” Caldelas has focused on both air and space travel and hopes his career will go in both directions Caldelas has been involved with the Reserve Officers’ Training Corps (ROTC) during his four years at MIT and after graduation will join the Navy as a naval aviator After serving for his country and working with airplanes Caldelas is the kind of person to arrive at the airport well before his flight he loves sitting in a seat where he can look out the window and watch the engine function.   “There's a quote that goes ‘with understanding comes appreciation and with appreciation comes respect.’ So after studying how a jet engine works it makes [an airplane] even more incredible.”  The aeronautics part of his MIT education gave Caldelas a background on the theory and mechanics of airplane flight and tested equipment through wind tunnels.  gaining hands-on experience with the 737 and P-8A Poseidon aircraft He also got to see how understanding the mechanics of an airplane will help him when he is a pilot.  when they were testing some iterations of the new 777X one of the test pilots — who had both flying experience and and understood what was going on inside the plane — easily identified an issue with the plane because she was in tune with how an airplane is constructed he wants to commission as an officer in the Navy and be a fighter pilot He flew an airplane for the first time and has never gotten over that thrill he’s been involved with Naval ROTC and often wears the classic “summer whites” uniform with the gold buttons; this semester Caldelas says test pilots know how to fly and have a technical understanding of airplanes which helps them communicate with the engineers on what they need to tweak The AeroAstro hallway displays photos of many illustrious alumni of the department including a number of astronauts — a group Caldelas ultimately hopes to join His fascination with astronauts began early: When he was 4 years old his family went to NASA’s Kennedy Space Center.  The admiration with astronauts skyrocketed as he grew up When MIT was celebrating the 50th anniversary of the Apollo 11 mission Caldelas received an email from the department asking for students to help escort astronauts around the events he filled out the form — if there is an opportunity to meet an astronaut Caldelas was assigned to Mark Lee, a former Air Force Colonel and NASA astronaut who flew on four Space Shuttle missions Lee stopped in the middle of the hallway of photographs and nonchalantly said “that’s me,” pointing to a large photograph of a man in a white space suit with Earth in the background Caldelas looked at the frame and saw the name “Mark Lee” on it He immediately asked for a photograph of the two of them with the historic image in the background.  Who else can say they met the astronaut in a famous photograph?” Caldelas says Caldelas kept saying how he can’t believe he is in the same space as so many MIT legends A national Hispanic Scholarship Fund recipient Caldelas is also a first-generation American one of the first Hispanic students to be accepted into the engineering program at his high school and the first person to get into MIT from his New Jersey high school He’s constantly grateful for his opportunities and hopes to inspire the next generation just as the MIT astronauts and their photographs inspired him.  “You don’t have to be perfect to go to this school “It’s really humbling for me live out my dreams to come to MIT And I want to honor this opportunity by inspiring others to keep going and reach for their dreams.” This website is managed by the MIT News Office, part of the Institute Office of Communications Massachusetts Institute of Technology77 Massachusetts Avenue, Cambridge, MA, USA Volume 15 - 2024 | https://doi.org/10.3389/fimmu.2024.1357726 characterized by its complexity and diversity presents significant challenges in understanding its underlying biology we employed gene co-expression network analysis to investigate the gene composition and functional patterns in breast cancer subtypes and normal breast tissue Our objective was to elucidate the detailed immunological features distinguishing these tumors at the transcriptional level and to explore their implications for diagnosis and treatment The analysis identified nine distinct gene module clusters each representing unique transcriptional signatures within breast cancer subtypes and normal tissue while some clusters exhibited high similarity in gene composition between normal tissue and certain subtypes others showed lower similarity and shared traits These clusters provided insights into the immune responses within breast cancer subtypes including innate and adaptive immune responses Our findings contribute to a deeper understanding of the molecular mechanisms underlying breast cancer subtypes and highlight their unique characteristics The immunological signatures identified in this study hold potential implications for diagnostic and therapeutic strategies the network-based approach introduced herein presents a valuable framework for understanding the complexities of other diseases and elucidating their underlying biology Understanding the intricate connections between immune responses and breast cancer is vital for unraveling the complexities of this disease and developing innovative immunotherapeutic approaches that hold promise in improving patient outcomes The intricate interplay between inflammation and immune responses in the context of breast cancer has emerged as a critical avenue of research with the potential to shed light on the underlying molecular mechanisms driving this complex disease we delve into the gene expression profiles within distinct molecular subtypes of breast cancer to elucidate the subtle yet pivotal variations in the coordination of inflammation and immune responses By dissecting these intricate interactions at the gene expression level we aim to uncover novel insights that may pave the way for more tailored therapeutic strategies and a deeper understanding of the disease’s heterogeneity This investigation aims to contribute towards refining our comprehension of breast cancer biology and ultimately enhancing patient care Table 1 Number of samples corresponding to each breast cancer molecular intrinsic subtype the network serves as a representation of the co-expression patterns found within the tissue from which the data originate One notable structural feature is the presence of highly interconnected groups of co-expressed genes or communities if two genes are linked to a third gene within the same community it is likely that they are also connected to each other communities tend to have sparser connections with the rest of the network These communities can offer valuable insights into the potential co-occurrence of proteins within cells hinting at possible interactions and contributions to biological processes depending on the specificities of the background noise of the underlying experiments and the intrinsic variability of the samples between 0.01% and 0.1% of these dependencies are strong enough to represent biologically relevant co-expression relations in cancer thus forming the basis for co-expression networks That was the rationale used here to retain the top 100,000 higher MI links in the different networks Enrichment values were assessed for statistical significance by means of hypergeometric permutation tests with FDR-corrected p-values less than 0.05 we will use the term enriched communities as a short-hand for Community (or module) with statistically significant categories on a gene set enrichment analysis Table 2 General comparison between the top 100k interactions networks We compared communities at three complementary levels we looked at functional enrichment to see functional patterns within the same network and between networks we compared the gene composition of the communities between networks to see if the similarities at the functional level were a reflection of similar gene composition we compared the structure of the communities looking at the gene-to-gene associations (also called interactions or edges) Community similarity was determined using the Jaccard similarity index which is the size of their intersection divided by the size of their union Differential expression analysis was performed with the Limma package (34) from Bioconductor Ver The statistical method used was empirical Bayes and the criteria for differential expression was −1≤log fold change ≥1 and B statistic ≥6 Figure 1 Significantly enriched categories in GO Biological Process for the communities in the Luminal A breast cancer subtype network. Although only one breast cancer subtype is presented, it is representative of the pattern of enrichment classes in which each community presents characteristic, non-shared enrichment categories, which suggests functional specialization of the communities (see Supplementary Figures 1–3) Processes that are shared by multiple modules are few and defined in a very general manner A line spanning one of the lower rows through many communities labeled with genes for the ZNF family of proteins Such communities contain multiple genes annotated as transcription factors and contribute to the significance in the enrichment of the rather general process GO:0006357 Regulation of transcription by RNA polymerase II Figure 2 Significantly enriched categories in GO Biological Process for the communities in the Normal (healthy) tissue network Statistically significant categories differ from the Cancer networks (see Supplementary data for further details) An overview of the enriched categories on each network provides a general idea of the complexity of underlying functions driving a phenotype Each one of the enrichment sets includes a wide array of processes Processes enriched in the normal phenotype are biased towards cellular metabolism these functions are congruent with tissue maintenance and metabolism tumor phenotypes show a number of processes associated to an active immune response We found also a number of processes associated to immune cell migration This pattern is shared although with differences between each tumor subtype a recurring theme is the presence of multiple instances of regulation of gene expression where multiple communities show one or more GO terms associated with it These communities contain genes for transcription factors An interesting difference in tumor phenotypes is the presence to a higher degree of multiple process enrichments for mRNA processing and post-translational regulation by protein degradation Figure 3 Comparison of the communities with inflammation and immune system enrichment categories from all networks Clusters of communities with similar enrichment patterns can be observed; some of the enriched categories appear in all networks Figure 4 Visualization of the Jaccard Similarity index between communities with immune system enrichment categories from all networks Groups of communities with high similarity can be observed and such groups correspond to the clusters in the left panel which are color coded and named based on the network of origin Node size is proportional to the number of genes it contains Edges represent the degree of similarity and edge thickness is proportional to the similarity between modules Given the numerous coincidences in enriched categories between phenotypes we wanted to know whether similar enrichment patterns between distinct networks were the product of them having equivalent communities formed by similar sets of genes connected in a similar way We constructed a meta-network with nodes from immune-enriched communities in all networks and defined connections between them based on their structural similarity The comparison by gene composition shows a pattern similar to enriched categories Communities from distinct phenotypes form interconnected groups some of which have more than 50% of shared genes and correspond to the core of the functional groups In all cases which means no module has an identical module in any other network The community structure similarity, in which we compare the degree in which genes in similar communities are connected in the same way, confirms the observed patterns, but with overall smaller Jaccard index values, compared to those of gene composition. In this comparison, some of the communities that share genes do not have any equivalent connections, which reflects differences in gene associations between networks at a finer structural level (30) we refer to these groups of communities with similar gene composition and functional enrichment as similarity clusters we present a summary for each similarity cluster Similarity cluster 1 consists of communities: Normal 97 (18 genes) This cluster contains communities from the four subtypes and normal tissue The communities in this cluster have a high gene composition similarity between breast cancer subtypes with Jaccard similarity values between 0.7 and 0.82 and comparatively lower similarity between the normal tissue and the cancer subtypes with values between 0.34 and 0.41 The structural similarity of the communities shows a similar pattern with the highest similarity values between the four breast cancer subtypes and the lowest values between normal tissue and the subtypes All the enrichment categories for this cluster are in association with histone function None of the communities in this cluster have exclusive enrichment classes Enrichment classes include processes associated to antiviral response mediated by interferons Interestingly, similarity cluster 5, which includes communities Normal 58, LumA 75, LumB 69, Her2 77, and Basal 78, comprises genes for cytokine receptors, components of the T-cell receptor, and the T-cell signaling pathway (61, 62). It also contains genes for the CD8 co-receptor and enzymes of the cytotoxic effector function (Granzymes A, B, H and PRF1) (63, 64) IFNG gene is also part of the communities in the cluster Similarity cluster 6 includes the following communities: Normal 56 and LumA 122 It includes mostly genes for extracellular matrix components many of them members of the collagen family Enrichment categories refer mostly to extracellular matrix organization and the process Regulation of immune response (GO:0050776) appears perhaps as an artifact of the annotation due to the interaction between immune cells and the extracellular matrix necessary for recruitment Because of the heterogeneity in gene composition this cluster presents numerous enrichment classes that are not shared between communities or shared by only two or three of them Similarity cluster 8 includes the following communities: Normal 10, LumA 84, LumB 120, and Basal 156. This cluster has low levels of similitude between communities and the enrichment categories and genes are not shared across all communities. Enrichment classes in this group include cell signaling molecules, particularly chemokines like CCL2, CCL3, and CCL4, and components of the MAPK pathway including DUSP1, FOSB, JUN, and JUNB (66–68) Finally, similarity cluster 9 includes the following communities: Normal 61, LumA 112, LumB 119, Her2 104, and Basal 96. Enrichment classes for this cluster share CD79A, SLAMF7, and TNFRSF17 genes in the process Adaptive immune response (GO:0002250). These molecules play a role in cell signaling in lymphocytes (10) it is notable that Cluster 4 exhibits the highest degree of gene composition similarity between normal and its subtypes Her2 shows lower similarity and shares genes with cluster 7 as well Cluster 1 displays less similarity with tumors but it exhibits similar immunologic functions within itself Cluster 3 does not exhibit enriched immunologic functions in normal tissue Through the comparison of statistically enriched GO processes between networks we can identify a number of GO terms that are statistically significant in one network but not significant in any of the other networks Basal subtype GO exclusive categories include processes related to immune cell recruitment and activation, mostly through cytokines in the CCL family: CCL2, CCL3, CCL4, CCL19, and CCL21. Other exclusive processes suggest functions of lymphocytes through molecules like FOXP3 and EOMES, which are associated with lymphocyte homeostasis, or BTK, which is part of the B-cell receptor signaling pathway (69, 70) Other processes not directly associated to the immune response but with chromatin structure are represented in module 102 which contains predominantly histone genes Her2 subtype GO exclusive categories include processes related to immune cell recruitment and activation, including antigen processing by immunoproteasome, leukocyte migration signals by S100 family genes, and transcriptional control and antiviral response by TRIM family members (71, 72) Normal tissue GO exclusive processes are predominantly associated with cell cycle control These include molecules involved with DNA replication as well as components of the mitotic control and mitotic effector machinery We compared average gene expression levels in the four breast cancer subtypes against the normal phenotype and mapped them to each one of the selected communities in the breast cancer subtypes The modules show various patterns of differential expression although many genes show expression levels not significantly different than normal tissue and are the majority in some of the communities Differential expression patterns are not completely uniform within each community. Modules show distinct differential expression patterns. In some communities, we can even find examples of over-expressed, under-expressed, and non-differentially expressed genes (Figures 5–8) Figure 5 Differential gene expression with respect to normal breast tissue in the communities enriched in inflammation and immune response GO processes from the Luminal A subtype network Differential expression is similar along genes in the same community although most communities have genes with many distinct differential expression patterns with respect to normal tissue Figure 6 Differential gene expression with respect to normal breast tissue in the communities enriched in inflammation and immune response GO processes from the Luminal B subtype network Figure 7 Differential gene expression with respect to normal breast tissue in the communities enriched in inflammation and immune response GO processes from the Her2-enriched subtype network Figure 8 Differential gene expression with respect to normal breast tissue in the communities enriched in inflammation and immune response GO processes from the Basal subtype network Differential expression patterns tend to be roughly similar between communities belonging to the same similarity cluster The most evident of which is similarity cluster 1 Modules in this cluster contain mostly over-expressed genes of the histone family This is the only cluster with both consistent differential expression level and high gene composition similarity across all four breast cancer subtypes The gene compositions of the similarity clusters display some distinctive patterns. We can see in Figure 9 that most of the clusters are different, sharing none or just a few genes (see also Supplementary Tables 2, 3) a few clusters (yellow and orange pixels) may have significantly large intersections with Jaccard indices up to 0.8125 in the case of the Basal 102 and Her2 107 clusters Figure 9 Heatmap matrix presenting Jaccard indices showing the relative intersection of the gene compositions for all similarity clusters discussed It can be seen that there are a few clusters with relatively high intersections but most of these have characteristic gene compositions Other clusters showing significant overlap are among the Basal ones: Basal 102 with Lum A 98 (Jaccard index ≃ 0.743) Basal 110 with Normal 96 (Jaccard index ≃ 0.682) and Basal 137 with Lum B 130 (Jaccard index ≃ 0.647) the aforementioned similarities were with Basal 102 and Her 2 127 with Basal 137 (Jaccard index ≃ 0.625) For Lum A clusters: Lum A 111 significantly overlaps with Lum B 130 (Jaccard index ≃ 0.519) and Lum A 114 significantly overlaps with Lum B 116 (Jaccard index ≃ 0.577) Lum B 11 significantly overlaps with Her 2 107 (Jaccard index = 0.75) We can notice (even by a glimpse at Figure 9) that in the case of the normal tissue most clusters have a very small overlap with those of the cancer subtypes with the notable exception of the intersection of cluster Normal 86 with Basal 110 and Lum A 114 (both with Jaccard index ≃ 0.682) and with Lum B 116 (Jaccard index ≃ 0.714) This study delves into the complex world of transcriptional regulation within breast cancer molecular subtypes and normal breast tissue shedding light on how the gene networks differ between these categories One of the significant findings is the variation in gene composition and structural characteristics of the networks across different subtypes and normal breast tissue subtle differences in how genes are associated into co-expression groups referred to here as communities and the internal structure of these communities It is relevant to highlight that the present study represents a coarse-grained view that although quite useful is expected to be complemented and in many cases superseded by analysis of single-cell and spatial transcriptomic experiments but since higher costs and logistic and experimental complexities continue to prevent them from being applied to large sample sets (many individuals and not just many cells) we believe that the broad view presented here is still quite valuable we did not just consider genes present in immune cells This is so since we are convinced that the underlying information that we can gather from analyzing the different cell types in the tumor environment would further illuminate our understanding on these matters hence we did not focus only on gene expressed in immune cells since it is very likely that those genes will have associated with other genes and pathways and by being restricted to this information without considering differences in cell populations they devised a novel framework to identify distinct patterns of gene co-expression networks and inflammation-related modules from genome-scale microarray data following viral infection these modules were categorized into oncogenic and dysfunctional types The core of the framework involves the comparative examination of viral infection modules across various disease stages and types Module preservation during disease progression is assessed based on alterations in network connectivity across different stages The evaluation of similarities and differences in HBV and HCV involved comparing the overlap of gene compositions and functional annotations in their respective modules The identification of co-expression modules allows researchers to uncover key genes that may act in concert to drive cancer development These modules often comprise genes with related functions participating in common biological pathways or cellular processes Dissecting gene co-expression networks in cancer can reveal potential biomarkers and insights into the heterogeneity of tumors which was able to provide novel insights into viral hepatocarcinogenesis and disease progression underscoring the advantages of an integrative and comparative network analysis over existing approaches reliant on differential expression and virus–host interactome-based methodologies Regarding the identification of communities related to inflammation and immune responses, the comparison of community enrichment profiles uncovers shared enrichment classes among different networks, even including the normal tissue network (36, 82) These shared functions are organized into clusters that exhibit similar enrichment patterns Our findings reveal interconnected groups of communities across various subtypes it is crucial to note that no community is identical to another in any network The results of this analysis reveal fascinating insights into the transcriptional regulation of genes within different breast cancer molecular subtypes and normal breast tissue Each similarity cluster presents distinct characteristics and functional enrichment patterns that are of great significance for our understanding of breast cancer biology Similarity Cluster 1 encompasses modules from all breast cancer subtypes and normal tissue gene composition similarity is highest between breast cancer subtypes while normal tissue exhibits lower similarity with the cancer subtypes The cluster predominantly features genes coding for histone proteins and is associated with immune response due to the role of histones in antimicrobial responses communities from all breast cancer subtypes and normal tissue are grouped together Gene composition similarity is more pronounced between the breast cancer subtype communities whereas lower similarity values exist between normal tissue and cancer subtypes This cluster is enriched with genes associated with antiviral responses mediated by interferons and Basal subtypes are part of this cluster Gene composition similarity varies within this cluster with a few shared genes and enriched categories from Normal 392 This cluster predominantly contains genes related to MHC class I molecules and antigen processing and presentation and Normal are represented in Similarity Cluster 4 with the smallest community (Normal 86) displaying the highest similarity values This cluster features genes for MHC class II molecules This result (relatively high similarity between normal tissue and PAM50 subtypes in MHC II molecules) appears paradoxical given that it is known that here is a low level of immune-related surveillance in normal tissue we do not have a definite explanation to this fact but perhaps this will be better understood in the future by examining single-cell and spatial transcriptomics data One possible explanation is that some of the molecules involved in cancer-related immune surveillance may play additional roles in normal cells of the four main stages of immune surveillance (antigen recognition the first and the last (antigen recognition and memory response) may be active even in normal cells Similarity Cluster 5 comprises genes for cytokine receptors along with genes related to cytotoxic effector functions common to all subtypes This cluster is notable for its association with genes promoting immune response Similarity Cluster 6 includes communities from Normal and LumA subtypes primarily characterized by genes related to extracellular matrix components Enrichment categories focus on extracellular matrix organization and potential immune responses likely due to interactions between immune cells and the extracellular matrix Similarity Cluster 7 exhibits comparatively lower similarity values The variety in gene composition results in numerous enrichment classes that may not be shared across all communities A similar trend is seen in Similarity Cluster 8 and Basal subtypes; this cluster displays low similarity levels between modules Enrichment categories include cell signaling molecules and chemokines like CCL2 and Basal subtypes make up Similarity Cluster 9 It features enrichment classes shared by genes like CD79A and TNFRSF17 in the context of adaptive immune response these findings highlight the unique gene composition and enrichment patterns within each similarity cluster offering a comprehensive view of the complex immunological functions associated with breast cancer molecular subtypes and normal breast tissue Cluster 4 exhibits the highest gene composition similarity between normal tissue and its subtypes whereas Her2 displays lower similarity and shares features with Cluster 7 showcases dissimilarity with tumors but retains shared immunologic functions within its communities These results deepen our understanding of the molecular mechanisms at play in breast cancer subtypes shedding light on their unique characteristics and potential implications for diagnosis and treatment We can notice the subtle differences in gene composition and network structure as well as the importance of shared functional enrichments which can be interpreted as a global context of the phenotype and providing a foundation for further understanding the regulation of inflammation and immune responses in these phenotypes communities in the network do not correspond to groups of uniformly differentially expressed genes genes with similar differential expression tendencies are scattered across a number of communities in the network We believe this is evidence for the notion that differential expression is not a characteristic that uniquely determines the formation of co-expressed groups This also opens the question of what other factors may contribute in the formation of such co-expression patterns especially if we consider the occurrence of distinct co-expressed but non differentially expressed genes a subtlety that is lost when screening only differentially expressed genes We can argue that differences in behavior may arise also from differences in the co-expression context of genes could lead to changes in molecular dynamics and behavior contributing to the expression of a particular phenotype is not optimal in the identification of individual genetic actors but rather to discern general differences in expression patterns between phenotypes Gene products often have pleiotropic effects and it seems reasonable they be regulated in many different contexts This could be one of the causes of the observed multiple connections in our co-expression networks We would like to highlight a recurrent observation in genome-wide co-expression enrichment patterns highly statistically significant instances of enrichment of immune system categories each cell can be seen as the result of a particular gene-expression program taking effect in the context of the organism the presence of mutations at various levels is recognized as a recurrent and important cause that drives changes in gene expression patterns and the phenotype these transformed cells interact with other non-transformed cells affecting their behavior This is frequently proposed in the form of cancer hijacking normal biological mechanisms to favor its own development We believe that the recurrence of gene co-expression patterns may be the result of this appropriation of mechanisms by tumors from otherwise genetically normal immune and adjacent normal cells This may also be one factor causing the observed similarity clusters where we find numerous genes whose expression is restricted to specific cell lineages The integration of multi-omics approaches, including DNA methylation assays (84), copy number variants (CNVs) (85, 86), miRNA expression profiling (87–89), transcription factor binding site analysis (90), and proteomics (91), can further improve our understanding of gene regulation in breast cancer tumors (84, 92) These comprehensive techniques offer unprecedented insights into the intricate molecular mechanisms underlying tumorigenesis and progression they illuminate how alterations in DNA methylation patterns and dysregulated protein expression contribute to the development and behavior of breast cancer these multi-omics approaches also shed light on the potential role of gene regulation in modulating immune responses within the tumor microenvironment By uncovering key molecular players involved in immune evasion or activation multi-omics analyses provide valuable insights for the development of novel immunotherapeutic strategies aimed at harnessing the immune system to combat breast cancer and in contrast with analyses based purely on gene composition which show a relatively low number of significant similarities between the similarity clusters but more closely in line with functional enrichment analyses it was found that a number of multi-omic regulatory interactions exists for these clusters and there is a non-trivial overlap between those Our studies highlight the roles that multitargeted transcription factors and epigenomic phenomena (mostly in the form of hypomethylated regions) play in consolidating biological functions in the similarity clusters For brevity, we must only comment on some of these relationships; however, the full set of SGCCA statistically significant multi-omic calculated associations can be found in Supplementary Table 4. Note that the microRNA hsa-mir-146a-5p is a highly significant common multi-omic regulator of the Basal 104, Her2 88, LumA 83, Lum B 93, and Normal 26 similarity clusters (technical note: p-values have been capped in Supplementary Table 4 so that any p-value less than 1E−16 appears displayed as 0 [zero]) hsa-mir-152–3p is a common regulator of the Her2 127 and Lum A 111 clusters Perhaps the most consistent finding (though not surprising at all) is that transcription factors of the IRF family are master regulators of many similarity clusters most of them in cancer subtypes (with the exception of the Normal 26 cluster) we found that there is significant hypomethylation in promoter regions of the MTF-1 gene associated with the expression patterns in the Basal 102 and Lum B 116 clusters more comprehensive analyses need to be performed in order to unveil the full potential of multi-omics to reveal the extent of immune-related transcriptional regulatory processes in breast tumor subtypes It is very likely that these future studies may involve single-cell multi-omic descriptions These studies are on themselves challenging long-term projects before strong conclusions can be reached we can argue that the effect of CNVs in breast cancer co-expression may likely explain just a fraction of the associated variance the ultimate benchmark in natural sciences remains experimental validation Several potential experimental methodologies may include proteomic Breast cancer is a highly heterogeneous disease Among the sources of variability in phenotype presentation some are related to ancestry and the genetic makeup of the underlying populations The TCGA–Genomic Data Commons database is one of the largest most comprehensive and curated repositories of omic data for cancer most samples belong to US residents and are thus enriched in Caucasian ancestry In spite of this and other design considerations it spans a lot of variability that can be indeed stratified for its well-curated metadata both clinically and regarding other determinants of health we used TCGA as our primary dataset and indeed we were able to adapt this to our own designs by further classifying and prioritizing the particular samples we used in our study That was the reason to further reclassify samples with more stringent algorithms Although the issue of generalizability of results just described is quite relevant we believe that these analyses will provide enough insight to serve as a starting point for deeper We consider relevant to recognize the origin of the data and the limitations of the variables measured Our data come from fine needle aspiration (FNA) samples taken from living tissue These samples contain a number of different cells that are representative of the cells present within the tissue and processed in bulk measured mRNA abundances reflect small regions of the tissue of origin and not specific cell types This is a technical limitation we hope gets resolved with more recent single-cell sequencing technologies we have measured abundances of RNA species assigned to known genes which we assume as a proxy for protein abundance although the active modulation of cell behavior through these processes is hinted in the structure and gene composition of the networks our study allowed us to shed some light to further understand the complex and distinctive transcriptional networks within various breast cancer molecular subtypes and normal breast tissue we have identified nine similarity clusters of gene communities whose transcriptional signatures (some of them similar among themselves and even to normal healthy tumor-adjacent tissue in terms of co-expression patterns) may contribute to characterize the immunological response patterns (as reflected in gene co-expression activity) shared The presence of both innate and adaptive immune responses may reflect a coordinated immunological defense mechanism against the disease These immunological signatures not only may deepen our comprehension of the similarities and differences among subtypes but also are able to potentially advance our understanding of the relevant functional features towards the development of personalized diagnostic and therapeutic strategies our network-based approach provides a valuable framework for dissecting the complexities involved in breast cancer-associated immune responses paving the way to uncover their underlying biology we hope that these insights may be gradually translated into tangible clinical benefits ultimately improving patient outcomes and transforming the current approach to cancer management The original contributions presented in the study are included in the article/Supplementary Material Further inquiries can be directed to the corresponding author The studies involving humans were approved by NCI/NHGRI TCGA Data Access Committee -for further information please communicate todGNnYWRhY0BtYWlsLm5paC5nb3Y= The studies were conducted in accordance with the local legislation and institutional requirements Written informed consent for participation was not required from the participants or the participants’ legal guardians/next of kin in accordance with the national legislation and institutional requirements The author(s) declare that no financial support was received for the research The results published here are based on data generated by the TCGA Research Network: https://www.cancer.gov/tcga Guillermo de Anda-Jauregui´ for valuable technical advice The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2024.1357726/full#supplementary-material Supplementary File 1 | Excel spreadsheet with enrichment results for communities with immune-related categories Supplementary Figure 1 | Heatmap comparing all enriched categories from all networks Communities with immune -related enrichments are labelled in red Supplementary Figure 2 | Heatmap comparing enrichments in all immune-related modules between all networks Supplementary Figure 3 | Chart comparing DE status of genes from cluster 1 present in all cancer phenotypes Scripts used to perform these analyses are publicly available at the following repositories: https://github.com/tadeito/breastcancerimmunenetworks https://github.com/josemaz/omicsBRCA https://github.com/CSB-IG/SGCCA balancing immune response: crosstalk between adaptive and innate immune cells during breast cancer progression CrossRef Full Text | Google Scholar Gene-expression molecular subtyping of triple-negative breast cancer tumours: importance of immune response Assessment of host immune response in breast cancer patients Google Scholar The genomic landscape of breast cancer and its interaction with host immunity Clinical relevance of host immunity in breast cancer: from tils to the clinic Mechanism of immune evasion in breast cancer CrossRef Full Text | Google Scholar Mechanisms of immune evasion in breast cancer PubMed Abstract | CrossRef Full Text | Google Scholar Stromal cell diversity associated with immune evasion in human triple-negative breast cancer The immune system and inflammation in breast cancer PubMed Abstract | CrossRef Full Text | Google Scholar If we build it they will come: targeting the immune response to breast cancer Tumor heterogeneity correlates with less immune response and worse survival in breast cancer patients Role of immune regulatory cells in breast cancer: foe or friend Molecular portraits of human breast tumours Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications A 50-gene intrinsic subtype classifier for prognosis and prediction of benefit from adjuvant tamoxifen Gene co-expression network analysis reveals common system-level properties of prognostic genes across cancer types Prognostic genes of breast cancer identified by gene co-expression network analysis Tissue classification with gene expression profiles in: Proceedings of the fourth annual international conference on Computational molecular biology Google Scholar Reprogrammed genetic decoding in cellular gene expression Regulatory r-loops as facilitators of gene expression and genome stability PubMed Abstract | CrossRef Full Text | Google Scholar Six novel immunoglobulin genes as biomarkers for better prognosis in triple-negative breast cancer by gene co-expression network analysis Gene co-expression is distance-dependent in breast cancer Gene co-expression in breast cancer: A matter of distance The cancer genome atlas (tcga): an immeasurable source of knowledge CrossRef Full Text | Google Scholar Rna-seq based genome-wide analysis reveals loss of inter-chromosomal regulation in breast cancer A novel non-parametric method for uncertainty evaluation of correlation-based molecular signatures: its application on pam50 algorithm Information theoretical methods for complex network structure reconstruction CrossRef Full Text | Google Scholar Aracne: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context Google Scholar Functional and transcriptional connectivity of communities in breast cancer co-expression networks Maps of information flow reveal community structure in complex networks Google Scholar Maps of random walks on complex networks reveal community structure PubMed Abstract | CrossRef Full Text | Google Scholar PubMed Abstract | CrossRef Full Text | Google Scholar limma powers differential expression analyses for rna-sequencing and microarray studies Transcriptional network architecture of breast cancer molecular subtypes Network modularity in breast cancer molecular subtypes A signature of immune function genes associated with recurrence-free survival in breast cancer patients Expression of the mhc class ii pathway in triple-negative breast cancer tumor cells is associated with a good prognosis and infiltrating lymphocytes Expression of the mhc class ii in triple-negative breast cancer is associated with tumor-infiltrating lymphocytes and interferon signaling Biological consequences of mhc-ii expression by tumor cells in cancer The role of oxidative stress on breast cancer development and therapy CrossRef Full Text | Google Scholar The role of the complement system in cancer PubMed Abstract | CrossRef Full Text | Google Scholar Complement in cancer: untangling an intricate relationship Alterations in histone deacetylase 8 lead to cell migration and poor prognosis in breast cancer Histone modification and histone modification-targeted anti-cancer drugs in breast cancer: Fundamentals and beyond Intranuclear and higher-order chromatin organization of the major histone gene cluster in breast cancer Neutrophil extracellular traps associate with clinical stages in breast cancer CrossRef Full Text | Google Scholar Tlrs: linking inflammation and breast cancer PubMed Abstract | CrossRef Full Text | Google Scholar Exosome rna unshielding couples stromal activation to pattern recognition receptor signaling in cancer Function of hnrnpc in breast cancer cells by controlling the dsrna-induced interferon response Interferon signaling is diminished with age and is associated with immune checkpoint blockade efficacy in triple-negative breast cancer Dna methyltransferase inhibition upregulates mhc-i to potentiate cytotoxic t lymphocyte responses in breast cancer Downregulation of tap1 and tap2 in early stage breast cancer Tradeoff between metabolic i-proteasome addiction and immune evasion in triple-negative breast cancer Increased expression of the immunoproteasome subunits psmb8 and psmb9 by cancer cells correlate with better outcomes for triple-negative breast cancers Activation of erα signaling differentially modulates ifn-γ induced hla-class ii expression in breast cancer cells Involvement of macrophage migration inhibitory factor and its receptor (cd74) in human breast cancer Cd74 and intratumoral immune response in breast cancer PubMed Abstract | CrossRef Full Text | Google Scholar The role of cytokines in breast cancer development and progression breast cancer stem cells (bcscs) and chemoresistance CrossRef Full Text | Google Scholar Synergistic effect of granzyme b-azurin fusion protein on breast cancer cells Tumor-infiltrating cd8 t cells predict clinical breast cancer outcomes in young women PubMed Abstract | CrossRef Full Text | Google Scholar Is the complement protein c1q a pro-or anti-tumorigenic factor bioinformatics analysis involving human carcinomas Differential roles for dusp family members in epithelial-to-mesenchymal transition and cancer stem cell regulation in breast cancer Ezh2 inhibitors-mediated epigenetic reactivation of fosb inhibits triple-negative breast cancer progress High endogenous ccl2 expression promotes the aggressive phenotype of human inflammatory breast cancer Foxp3 transcription factor: a candidate marker for susceptibility and prognosis in triple negative breast cancer Metaplastic breast cancers frequently express immune checkpoint markers foxp3 and pd-l1 Telomere length assessment in leukocytes presents potential diagnostic value in patients with breast cancer Trim proteins in breast cancer: Function and mechanism PubMed Abstract | CrossRef Full Text | Google Scholar The role of extracellular matrix proteins in breast cancer Apobec3b expression and its prognostic potential in breast cancer Germline apobec3b deletion influences clinicopathological parameters in luminal-a breast cancer: Evidences from a southern Brazilian cohort Identification of differentially expressed lncrnas and mrnas in luminal-b breast cancer by rna-sequencing Modulation of jakstat signaling by lnk: A forgotten oncogenic pathway in hormone receptor-positive breast cancer Heat shock proteins create a signature to predict the clinical outcome in breast cancer Gene co-expression modules as clinically relevant hallmarks of breast cancer diversity PubMed Abstract | Google Scholar Coexpression network analysis in chronic hepatitis b and c hepatic lesions reveals distinct patterns of disease progression to hepatocellular carcinoma The emerging potential for network analysis to inform precision cancer medicine Guideline for comparing functional enrichment of biological network modular structures CrossRef Full Text | Google Scholar Functional impact of multi-omic interactions in breast cancer subtypes PubMed Abstract | CrossRef Full Text | Google Scholar 3 do not influence gene co-expression in breast cancer subtypes The role of copy number variants in gene co-expression patterns for luminal b breast tumors non-redundant microrna functional control in breast cancer molecular subtypes highly connected micrornas control functionality in breast cancer networks Network analysis of emt and met micro-rna regulation in breast cancer The role of transcription factors in the loss of inter-chromosomal co-expression for breast cancer subtypes The breast cancer protein co-expression landscape PubMed Abstract | CrossRef Full Text | Google Scholar Molecular mechanisms of multi-omic regulation in breast cancer PubMed Abstract | CrossRef Full Text | Google Scholar Loss of long-range co-expression is a common trait in cancer CrossRef Full Text | Google Scholar Tumor immune subtypes distinguish tumor subclasses with clinical implications in breast cancer patients Master regulators of signaling pathways: an application to the analysis of gene regulation in breast cancer An independent poor-prognosis subtype of breast cancer defined by a distinct tumor immune microenvironment Luminal a breast cancer co-expression network: Structural and functional alterations The great immune escape: understanding the divergent immune response in breast cancer subtypes Deep (phospho) proteomics profiling of pre-treatment needle biopsies identifies signatures of treatment resistance in her2+ breast cancer Phosphorylation of rab7 by tbk1/ikk-e regulates innate immune signaling in triple-negative breast cancer phosphoproteome and kinome characterization of luminal a breast cancer Proteomic profiling reveals that esr1 mutations enhance cyclin-dependent kinase signaling High-throughput proteomics of breast cancer interstitial fluid: identification of tumor subtype-specific serologically relevant biomarkers Regulated phosphosignaling associated with breast cancer subtypes and druggability*[s] The tale of tils in breast cancer: a report from the international immuno-oncology biomarker working group Cancer-associated fibroblasts-derived exosomes suppress immune cell function in breast cancer via the mir-92/pd-l1 pathway Lncrna gata3-as1 facilitates tumour progression and immune escape in triple-negative breast cancer through destabilization of gata3 but stabilization of pd-l1 Functional th1-oriented t follicular helper cells that infiltrate human breast cancer promote effective adaptive immunity Zamora-Fuentes JM and Hernandez-Lemus E (2024) Coordinated inflammation and immune response transcriptional regulation in breast cancer molecular subtypes Received: 18 December 2023; Accepted: 03 June 2024;Published: 25 June 2024 Copyright © 2024 Velazquez-Caldelas, Zamora-Fuentes and Hernandez-Lemus. 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When San Diego Metropolitan Transit System Security Officer Carlos Paredes collapsed from cardiac arrest at the 12th & Imperial Transit Center on February 9 the quick actions taken by seven of his colleagues in MTS Security helped save his life Thanks to his colleagues’ training in CPR and Automated External Defibrillator (AED) use Officer Paredes was able to attend today’s MTS Board of Directors meeting during which the seven officers that worked together to save his life were recognized MTS Board Chair Georgette Gómez presented the officers with a plaque recognizing their life-saving efforts “It is not an exaggeration to state that Officer Paredes would likely not be here today without the efforts of the seven officers,” said Gómez they performed a heroic act to save the life of a fellow human being.” Officer Paredes collapsed at the 12th & Imperial Station near the MTS headquarters He was quickly moved to a safe location by fellow officers Jeremy Echeverria and Brandon Garcia who radioed MTS Central Control and requested paramedics Officer Alex Masciovecchio arrived at the scene determined that Paredes had no pulse and began to perform CPR for several minutes retrieved an AED and used the device on Officer Paredes who was moments later resuscitated by paramedics arriving at the scene The seven officers were recognized for their heroic actions by the MTS Board of Directors during today’s public meeting The actions taken by the officers are an excellent example of the dedication and professionalism of the MTS Transit Enforcement personnel Castro Caldelas will be the second place in Galicia declared as one of the most beautiful villages in Spain The Association of the Most Beautiful Villages in Spain has decided to include in its select list the town of Castro Caldelas which will officially be proclaimed on Friday It will be conducted by the television presenter Lucía Rodríguez and will be chaired by the main political leaders of the province the event will end with the performance of the tenor Bernardino Atienza the Real Banda de Gaitas de Ourense and the Banda de Gaitas Os Trabazos © 2018 Ribeira Sacra Tourism Consortium Spanish shipping company Elcano has taken delivery of its two ME-GI liquefied natural gas (LNG) carriers from the Japanese shipbuilder Imabari The duo capable of transporting 178,000 cubic meters of the chilled fuel each will operate under a charter deal with Naturgy transporting US LNG volumes from Cheniere’s Sabine Pass export facility in Louisiana Castilo de Caldelas departed the Imabari’s Saijo shipyard in mid-June The vessel delivered a cargo from Qatari LNG complex in Ras Laffan to Portuguese REN-operated Sines terminal on July 26 the vessel delivered 63,000 tons of the chilled fuel to the Portuguese market Three days later the vessel docked at the Elengy-operated Montoir-de-Bretagne LNG terminal in France and is currently sailing in the Mediterranean Sea Castillo de Caldelas joins the first vessel in Elcano’s pair built at Imabari the Castillo de Merida that was delivered earlier in March Daily news and in-depth stories in your inbox Ingersoll Rand Engineering Project Solutions At Ingersoll Rand’s Engineering Project Solutions we have been managing and implementing engineered to-order air packages for complex technical requirements for over 60 years We provide specialized custom compressed air and gas compressors as well as nitrogen generation packages to international EPC contractors and engineering companies across a range of […] Essentials Stroll around the city centre, visit this great museum and enjoy a drink or a meal at any of its squares Views with the Cíes Islands in the background Visit the old towns of Vigo and Baiona and immerse yourself in their fascinating history Enjoy the thermal springs of Mondariz and discover the wines from O Condado de Tea This fish is tasted in the alameda (tree-lined avenue), by the river. It is accompanied by several interesting activities such as exhibitions, concerts, sport events, fishing courses, a motorcycle rally and a choral competition. The International Fishing Competition under the category of salmonids contributed to the creation and consolidation of the Trout Festival. Fishermen and fisherwoman coming from different places of Galicia and Portugal gather in Ponte Caldelas to get a good catch and to win some of the cherished trophies in the different categories. The competition, one of the most awaited of the fishing season in the Euroregion, attracted, from the very beginning, many participants who, in turn, contributed to the promotion of Ponte Caldelas. Apart from celebrating a fellowship meal that also extols the trout, ludic and cultural activities were also organised and they contributed to consolidate it in the decades ahead. There has been no let-up in the pace of LNG carrier shipyard activity so far in 2018. In the two months since LNG World Shipping’s previous fleet review, 10 LNG carriers have been handed over to their owners and nine further such vessels have been ordered. In the four months to 30 April 2018, 23 LNG carriers were delivered and 18 vessels added to the orderbook. Japanese owners, charterers and shipbuilders have been at the fore in the latest batch of ship completions. The portfolio of ships delivered in the last two months includes the first Moss spherical tank LNG carrier to be powered by a dual-fuel diesel-electric (DFDE) propulsion system and the largest Moss spherical tank ship ever built. Both breakthrough vessels were constructed by the Sakaide yard of Kawasaki Heavy Industries (KHI) in Japan. All the large, conventional-size LNG carriers built by KHI to date have Moss containment systems and, prior to the recent deliveries, all KHI-built LNG carriers had been steam-turbine-powered. KHI’s first DFDE ship is LNG Sakura, a 177,000 m3 vessel delivered to a 70/30 joint venture partnership comprising Kansai Electric Power (Kepco) and NYK Line, respectively. Kepco will charter the ship for 20 years, to lift cargoes from Dominion Energy’s newly commissioned Cove Point export terminal in the US, among other facilities, while NYK Line will operate the vessel. KHI has utilised its proprietary polyurethane foam Kawasaki Panel System to insulate the four spherical tanks on the twin-screw LNG Sakura (Sakura means “cherry blossom” in Japanese). The technology yields a comparatively low cargo boil-off gas (BOG) rate of 0.08% per day, an attractive attribute considering the long transpacific voyages on which the vessel will be engaged. LNG Sakura departed Cove Point on 22 April with its inaugural cargo from the facility. The construction of a 5.1 million tonnes per annum (mta) liquefaction train at what was previously an LNG import terminal has given the Maryland facility a bi-directional capability. While LNG Sakura marks a step up in size from KHI’s earlier LNG carriers, its next completion, the 182,000 m3 Pacific Breeze commissioned a few weeks later, is the world’s largest Moss ship. Pacific Breeze is owned by K Line and chartered by IT Marine Transport, a joint Inpex/Total shipping operation, for use in the carriage of LNG from the new 8.9 mta Ichthys LNG export terminal near Darwin in Australia to Taiwan’s CPC Corporation. As a result of the ship’s capacity and the relatively short voyage distance, the charterers anticipate that Pacific Breeze will lift 1.75 mta at the Ichthys terminal. Like LNG Sakura, Pacific Breeze is powered by a DFDE propulsion system and its Kawasaki Panel System insulation yields a cargo BOG rate of 0.08%. As part of the Ichthys LNG terminal commissioning process, Pacific Breeze delivered a cooldown cargo loaded in Singapore to the Darwin site on 26 April 2018. Pacific Breeze remains in the area following discharge of the cooldown shipment and there is a possibility that the vessel is lined up to load the inaugural Ichthys LNG export cargo. In March 2018 the Nagasaki yard of Mitsubishi Heavy Industries (MHI) delivered a Moss spherical tank ship for service with a new Australian export project. MHI handed over the 155,000 m3 Pacific Mimosa to LNG Marine Transport, a ship operating company in which Jera holds a 70% stake, Mitsubishi Corp 15% and NYK Line 15%. NYK is responsible for the ship’s technical management. The vessel has been taken on long-term charter by Jera for lifting cargoes from the Chevron-led, 8.9 mta Wheatstone LNG project in northwestern Australia. Jera, a joint venture responsible for the combined LNG marketing activities of Tokyo Electric Power and Chubu Electric Power, is the largest buyer of Wheatstone’s output and lifted its first cargo from the project in November 2017. Pacific Mimosa is powered by an ultra-steam-turbine (UST) propulsion system that makes use of reheated steam to achieve fuel efficiency gains of 15% over traditional steam turbines. With the UST technology the boiler generates high-pressure steam at 12 MPa and 565˚C by transferring the heat from the combustion of the cargo BOG. To the non-LNG community there is no immediate indication that Pacific Mimosa has four cargo tanks. In contrast to traditional Moss ships, where almost one-half of each spherical cargo tank protrudes above the main deck, the newbuilding is one of the new generation of “peas-in-a-pod” Sayaendo system design from MHI. That means that the top half of each cargo tank is obscured by the continuous tank cover the extends the length of the main deck in way of the cargo tanks. The cover not only protects the cargo tanks and deck pipework, but also adds to the vessel’s structural integrity. Two other recent newbuildings have Japanese connections. Castillo de Merida is the first of a pair of 178,000 m3 newbuildings for Spain’s Naviera Elcano from the Saijo yard of Imabari, while the Samsung-built Marvel Falcon is destined to lift Cameron LNG exports under charter to Mitsui & Co and the technical management of NYK Line. The capacity of Castillo de Merida marks a significant step up from the 154,000 m3 size of the previous LNG carriers built by Imabari. Castillo de Merida and its imminent Imabari newbuilding sistership, Castillo de Caldelas, have been taken on charter by Gas Natural Fenosa (GNF) for the lifting of Sabine Pass LNG cargoes in the US state of Louisiana; as an ability to optimise Panama Canal transit opportunities is paramount, the principals have decided on the 178,000 m3 size for the pair. Castillo de Merida is propelled by a pair of MAN Diesel & Turbo’s M-type electronically controlled gas-injection (ME-GI) engines, a two-stroke propulsion system particularly popular in the LNG carrier sector at the moment. The ship also has GTT Mark III Flex membrane tanks, a containment system that provides a reduction in cargo BOG rates down to the 0.10% level. While Imabari has achieved considerable success in the domestic shipbuilding sector in recent years, not least in the construction of next-generation ultra-large container ships for domestic shipowners, the fabrication of the latest LNG carriers has caused difficulties and the deliveries of Castillo de Meridas and Castillo de Caldedas were nine months later than originally planned. The 174,000 m3 Marvel Falcon was delivered by Samsung Heavy Industries (SHI) to NYK Line in April 2018. The ship is the first in a fleet of eight new vessels that Mitsui & Co is taking on 25-year charters, primarily to lift cargoes from the planned Cameron LNG export terminal in Louisiana. Marvel Falcon is powered by a pair of Generation X, low-pressure, dual-fuel, two-stroke (X-DF) engines developed by Winterthur Gas & Diesel (WinGD). Two-stroke engines are now the propulsion system of choice for conventional-size LNG carriers and, of the new ships ordered so far this year, preferences have been split fairly evenly between the ME-GI and X-DF engine types. Volume 10 - 2019 | https://doi.org/10.3389/fimmu.2019.00056 Inflammation has been recognized as an important driver in the development and growth of malignancies Inflammatory signaling in cancer emerges from the combinatorial interaction of several deregulated pathways Pathway deregulation is often driven by changes in the underlying gene regulatory networks it can be argued that a closer analysis of the structure of such regulatory networks will shed some light on how gene deregulation led to sustained inflammation in cancer we inferred an inflammation-associated gene regulatory network from 641 breast cancer and 78 healthy samples A modular structure analysis of the regulatory network was carried out revealing a hierarchical modular structure Modules show significant overrepresentation score p-values for biological processes unveiling a definite association between inflammatory processes and adaptive immunity Other modules are enriched for T-cell activation differentiation of CD8+ lymphocytes and immune cell migration thus reinforcing the aforementioned association These analyses suggest that in breast cancer tumors the balance between antitumor response and immune tolerance involving CD8+ T cells is tipped in favor of the tumor One possible mechanism is the induction of tolerance and anergization of these cells by persistent antigen exposure cancerous phenotypes result from a complex of interacting biological processes or pathways that are subverted in favor of tumor survival There is a strikingly similar scenario when we look at tumors This suggests that inflammatory response in conjunction with the immune system plays an important role on breast cancer maintenance Considering the intricate set of relationships that inflammation and immunity play in a complex phenotype such as a breast cancer an integrative approach capable of capturing at least part of the complexity of biological processes related to inflammation results appealing the construction of gene regulatory networks via the association between gene expression levels offers us a valuable view on how groups of genes are being collectively coordinated Previous work from our group has shown how network modularity structure is associated to biological functionality in transcription factor networks (20) and distinctive network structure for each of the major breast cancer molecular subtypes (24) by means of an automated analysis of network inference module detection and enrichment analysis we have been capable to detect specific processes in breast cancer molecular subtypes we approach the entangled nature of the inflammatory process in the maintenance of breast cancer phenotype through the inference of an inflammation-related gene regulatory network and their associated genes (their immediate or first neighbors in network terminology) then finding the modular structure of the network and associated biological processes for each module We found a hierarchical modular structure in transcriptional networks associated to inflammatory response where genes tend to be connected to others with similar differential expression patterns: overexpressed genes are more connected between them Modules of the network are mainly associated to immune system A comprehensive integrative framework allows us to observe a broader landscape of how inflammation may have influence in the establishment of pathological phenotypes this exploratory approach could help to direct research toward more specific questions about therapeutic options Graphical description of the methods followed in this work (1) Gene expression datasets from GEO were selected All datasets contained normal tissue samples and primary merged and normalized to obtain an expression matrix (Rows correspond to genes and columns correspond to samples) (3) Tumor samples expression matrix was used to calculate all statistical dependencies with the MI function between pairs of genes to construct the network (4) The network was filtered to obtain the top 10,000 interactions ranked by MI value of genes in the inflammatory response process and first neighbors (5) The inflammation network was analyzed to detect modules via the Infomap algorithm (6) The resulting modules were tested for functional enrichment of Gene Ontology GEO identifier and references for the data used here A network in this context is a mathematical object composed of a colection of nodes that represent genes and a colection of edges that represent the statistical dependency between pairs of genes We used MI calculations implemented in the ARACNe algorithm (34) to calculate pairwise statistical dependency for all gene pairs in the platform (MI threshold set with p ≤ 1) which gives us a completely connected graph (a network where all possible interactions exist) for the whole-genome Our network contains only the top 10,000 most stringent interactions between genes from the inflammatory response and their first neighbors which correspond to 0.005% of possible correlated gene pairs The reasoning behind this is that the genes defining the phenotype must have the strongest statistical dependencies between them and also in order to minimize the effect of false positives to start with a list of genes of interest can help us to discover other genes associated to them at the transcriptional level This however does not guarantee that we recover all genes in the list but the ones within our imposed threshold The network was curated starting from the complete graph using a reference gene list of the inflammatory process obtained from Gene Ontology (Inflammatory response process GO:0006954) with the following procedure: First we ranked all interactions in descending order (largest to smallest MI value) we searched for the genes sharing the strongest interactions with those in the inflammatory response process This was done by filtering those interactions where at least one gene is part of the inflammatory response From these interactions we took the top 10,000 interactions and obtained the names of all the genes involved which comprises the list of inflammatory response and associated genes because we wanted to recover interactions between inflammation gene first neighbors we extracted from the network the top 10,000 interactions between the genes of the list (inflammation and first neighbors) We showed that gene modules are representative of distinct and meaningful biological functions Thus, in order to find to find connectivity patterns in our network, here we used Infomap (19), a well-known flow-based information clustering method to determine the modular structure of complex networks. Likewise, we use the expanded version of Infomap to find a finer modular structure over the two-level modules using the hierarchical version of the map equation (35) Using the hierarchical map equation it possible to exploit the fact that the modules in a network are themselves organized into submodules and sub-submodules which can reveal a richer multilevel organization. This approach has been successfully applied as well in the case of GNRs associated with Her2+ cancer subtype (36) The number of genes contained in each module can amount to several hundred. Additionally, we have to deal with the fact that individual genes can be annotated for more than one function or pathway. To obtain biological insights from gene sets like these, we use statistical over representation analysis to reduce such large sets of individual gene names to identifiable biological functions (37) choosing a significance p ≤ 1 × 10−5 The Inflammation-associated Gene Regulatory Network (IGRN) contains the top 10,000 interactions ordered by MI value; this IGRN has 942 genes with three connected components that contain more than 10 genes (Figures 2A–C and Supplementary File 1) Information about the DE status for each node was mapped to the network This revealed a definite composition of DE genes for each component Network obtained from the MI highest interactions between inflammatory process genes and their first neighbors The network consists of 942 genes and 10,000 interactions (edges) Node color represent the differential expression status compared to healthy mammary tissue White or pale color means no differential expression (−1≥LogFch ≤ 1) This network consists of many connected components of which three of them (A–C) consists of more than 10 genes Indicated with (D) are small components of less than 10 genes By observing the way nodes aggregate in the largest component (A) it is evident that the network has an internal modular structure where differential expression levels seem to cluster with similar differential expression trends We further explored how this network is organized and which known biological processes are being regulated The largest component contains 787 out of the 942 genes in the network. We explored the module structure of this component, which revealed a hierarchical structure of 4 first level and 28 second level modules or submodules (Table 2 and Supplementary File 2). Modules and submodules were labeled based on the highest page-ranked gene (Figure 3A) modules and submodules are indicated with a subscript after the name of its highest ranked gene i.e Modules and submodules in the largest component of the network Modular structure of the largest component of the network Darkest colors and largest circles show those modules with more genes; link widths correspond to the flow between modules Node labels refer to genes with highest page rank in the module (B) inter-module edges preferentially link genes with similar expression patterns Modules are visualized by differential expression Red links are inter-module edges linking two overexpressed genes blue links are analogous but regarding underexpressed genes gray links show intra-module edges and inter-module edges between no-differentially expressed elements When we observe the DE status in individual submodules, it becomes evident how it tends to display characteristic patterns for each submodule. For instance, submodules such as LDB2sm are integrated by genes with a tendency to underexpression, and IFI44Lsm integrated by genes tending to overexpression (Figure 3B) all genes have a DE status of no change respect to normal mammary tissue other submodules such as SPARCsm and CCR5sm show a mixed DE profile Coordinated over and underexpression gene sets may account for modulation of opposing or conflicting pathways Modules are disjoint sets of nodes at the same hierarchical level We performed overrepresentaion analysis for these gene sets taking Gene Ontology:Biological Processes as the reference database Statistically significant enrichment was found Top enrichment scores for the CCR5m include immune response, innate immune response, cytokine-mediated signaling pathway, adaptive immune response, also included is inflammatory response (Figure 4) SPARCm top enrichments include extracellular matrix organization collagen catabolic process and angiogenesis Since the inflammatory response process was enriched only in the CCR5m we focused on the biological processes and pathways of its submodules Heatmap of GO Biological process enrichments for CCR5m submodules each column corresponds to a detected module according to the network structure Each row is the enriched biological process in the aforementioned modules The color code represents the enrichment p-value: red color takes account for the most significant values each module has a set of unique enriched processes which could be related to the specificity of functions by each submodule Seven out of eleven CCR5m submodules showed a statistically significant enrichment for GO Biological processes. Some of these enrichments were shared between submodules, with the largest category overlap between CCR5sm and CD2sm (Figure 4). These two submodules have numerous connections and are the two largest in terms of of gene and interaction counts (Figure 2) is enriched for inflammatory response and other adaptive immune response processes Genes coding for proteins involved in T lymphocyte signaling such as CD45 CD4 and CD28 which serve as coreceptors necessary for T cell activation PIK3CD and VAV1 participate in the B cell signaling pathway as well as in the Fcγ and Fcϵ signaling pathways through which receptor mediated endocytosis can be activated allowing antigen presenting cells to capture antigens bound to antibody molecules which allows APCs to activate CD8+ T cells who normally attack infected or transformed cells through presentation of internally-only produced antigens The molecules coordinated in this submodule suggest the coregulation of genes that elicit inflammatory cell recruitment mediated by cytokines These functions are at the beginning of adaptive immune response and complemented by characteristic processes of effector immune cells This result will be discussed in the following section and shares most of its GO enriched processes with the CCR5sm CD2sm is highly connected to the CCR5sm sharing 831 edges a chemotactic cytokine for lymphocytes recognized by CXCR3 and CCL8 also known as monocyte chemoattractant protein 2 (MCP-2) recognized by CCR5 which serves as a chemoattractant to monocytes which later can differentiate to phagocytic cells in this submodule we observe a funtional relationship with CCR5sm This is backed by the fact that CCR5sm triggers the adaptive immune response meanwhile CD2sm is related to effector functions mainly involved in CD8+ cells response which could imply a temporal relationship between both submodules The immunoproteasome has a distinct pattern of protein cleavage and the resulting peptides are more efficiently transported by TAP to the inside of the ER and more efficiently loaded in to MHC class I molecules This allows the presentation of a distinct repertoire of antigens to CD8+ T lymphocytes and is important for the recognition and elimination of infected or transformed cells via the Cytotoxic T Lymphocyte (CTL) induced cell death in which perforin and granzyme play effector roles This submodule contains genes that code cytokines CXCL10 and CXCL11 which are CXCR3 ligands involved in lymphocyte migration as chemoattractants and are induced by IFNγ signaling (49) known as MCP1 and MCP2 also are chemoattractants for leukocytes The coordinated expression of these genes suggests an active inflammatory process where leukocytes are recruited to the tumor In order to quantitatively evaluate the association between inflammation and adaptive response, we performed a pathway deregulation analysis of our data by implementing the ‘Pathifier” algorithm (55) to identify sets of samples significantly deregulated (this is PDS > 0.4) in the inflammation and adaptive response related pathways From these analyses we found that out of our 641 tumor samples a total of 395 (61.6%) were significantly deregulated in both whereas 210 (32.7%) were strongly deregulated in adaptive immunity but not in inflammation Also 22 (3.4%) tumor samples resulted in deregulated inflammation without significant adaptive immunity changes and just 14 tumors (2.18%) were not significantly affected in adaptive immunity nor in inflammation It is noticeable that most tumors in our study are significantly affected in their adaptive immune responses (94.3%) This group includes most of the inflammation affected tumors (395 out of 417 In contrast there are very few tumors without significant adaptive immune deregulation (36 out of 641 or 5.6% overall) Also scarce resulted tumors with notorious inflammation but with no significant adaptive immunity changes (22 out of 417 or 5.3%) intrinsically including immunosuppressive feed-back signaling to activated T cells expression of IL-10 and PD-L1 genes by activated myeloid cells is supposed to inhibit adaptive T cell response adaptive immune response comprises a variety of immunosuppressive events eventually elicited by T cell subsets expressing specific markers These events may be strongly associated to other relevant molecules in the inflammatory context we have performed a generalized linear model (a form of logistic multivariate regression) for the association (membership) of the four groups of samples with the following results: • FOXP3 expression is significantly associated (p-value = 0.0133 postive association) to adaptive immune response in breast tumors • FOXP3 and GZMB expression are significantly associated (p-value = 2.57 E-5 for FOXP3 and 8.14E-11 for GZMB both postive associations) to ‘adaptive immune response plus inflammation” in breast tumors • The group with ‘no adaptive immune response nor inflammation” showed a discrete (although significant) negative association with FOXP3 expresion (p-value = 0.027) in all tumors FOXP3 expression is highly significant positively associated to GZMB expression levels (p-value = 2.0E-16) This type of response is associated to virus or intracellular parasites as well as transformed cancerous cells and culminates in the elimination of targeted cells Modules in the network are enriched in processes of complementary biological functions CCR5sm contains genes involved in antigen acquisition via receptor mediated phagocytosis as well as a number of chemokines that mediate immune cell recruitment PSMB9sm contains genes of components of the immunoproteasome and MHC-I molecules involved in the presentation of internally produced antigens The immunoproteasome is induced by IFNγ signaling and is associated with a response to intracellular infections and transformation in tumor cells HLA−DRAsm contains MHC-II genes involved in antigen presentation to CD4+ T lymphocytes Other modules like IFI44Lsm also contain interferon-induced genes annotated with antiviral functions CD2sm has genes involved in T lymphocyte function including T cell receptor components and co-receptor molecule CD8A as well as other genes related to T cell cytotoxic function like perforin and granzymes antitumor mechanisms coexist with tolerance mechanisms which impair tumor immune destruction Our results suggest an active process of tolerance where cytotoxic CD8+ lymphocytes turn to anergic/memory cells that no longer fight the tumor This is supported by the coregulation of CD8A and IL7R genes in CD2sm but not of CD8B Other modules like IGLC2sm contain genes of immunoglobulin chains including constant and variable chains that may be indicative of the presence of B lymphocyte infiltrates Our dataset included samples from patients with developing tumors that received no neoadjuvant treatment where type I inflammatory response is associated to good prognosis and tumor elimination it seems paradoxical that a favorable immune response has been related to tumor growth The cellular composition of breast cancer tissue includes cancerous cells as well as immune cell infiltrates our subnetwork suggests the presence of phagocytic and antigen-presenting cells (i by the presence of MHC class II in HLA−DRAsm and Fc receptors in CCR5sm) and T CD8 and CD4 cells (TCR components CD3D as well as CD4 and CD28 correceptors in CXCR5sm) These are indicative of antigen-specific immune responses Primed cells are able to enter inflammed tissues and exert their effector functions when they encounter their specific antigen In some circumstances effector functions can be minimized or suppressed in the presence of antigen Tolerance to self-antigens is necessary to avoid autoimmune tissue destruction while tolerance to antigens derived from food and microbiome components keeps inflammation at bay tolerance may be part of the contributing factors of the pathology of chronic disease Redmod and Sherman (60) proposed a model that explains how chronic antigen exposure can lead to persistence of anergic T CD8+ cells strong signaling through the TCR eliminates many of the activated cells either throug IL7Rα receptor downregulation or a decrease in antiapoptotic molecules strong TCR signaling in the surviving cells produces an increase in free calcium levels triggering anergy and a reduction in the Ras-ERK proapoptotic signaling In our network CD8A and IL7R genes are coregulated in the CD2sm. IL7Rα and CD8αα are associated to the survival and development of memory CD8+ T cells (61, 62) given that our expression matrix contains both CD8A and CD8B genes for the CD8α and CD8β subunits of the CD8 correceptor This may point out to purely CD8αα signaling which is hypothesized to exert a weaker TCR signaling response Anergic CD8 T cells need to be exposed to antigen to maintain anergy interactions and genes were obtained from a background that included the whole genome (gene set defined by the HGU133 plus2 platform) and the strength and ranking of the statistical dependencies between gene pairs was not known a priori Only 76 out of a total of 942 genes in the network are annotated as part of the inflammatory response process which represents 8% of the network gene count Meanwhile 694 genes in the network are annotated as part of the immune response process The whole network was constructed taking into account all inflammation-associated genes, the modular and submodular structure of this network groups specific functional processes for particular modules (Figure 4) This could reflect the compartmentalization of immune response activating sets of genes depending on the type of response that should be triggered general modules could be active as a first response under cellular damage or an external influence CCR5 and SPARC modules are associated to two different general cell processes: immunity (CCR5) and extracellular remodeling (SPARC) Our interpretation here is that modules partially reflect cell types present in the tumor microenvironment and that are sampled as part of the biopsy This is specially suggested in the CD2sm where a number of genes characteristic of CD8+ T lymphocytes are grouped together the topology of modules in the network is strongly associated to specific and separated functions Concomitantly, the interconnection between submodules inside the modules could be associated to fluxes of information between processes or cooperativity between modules. This is the case of CCR5sm and CD2sm. In Figures 3A,B it is possible to observe that the strongest link between submodules occurs in this pair the number of common enriched processes between modules is the highest Processes related to T-cell activation and adaptive immune response are shared between these two submodules indicating possible cooperation mechanisms under the need to stimulate T-cell activity Interestingly enough, ZFPM1 module, despite the fact that it contains 173 genes does not result statistically enriched for any biological process at our chosen significance threshold. This results remarkable since the belonging genes are not differentially expressed (white genes clustered in left side of Figure 2) These genes may not be part of the global response that cell performs under an external stimulus The results obtained from our network of inflammation-associated genes suggest that in primary A possible mechanism being the induction of tolerance and anergization of these cells by persistent antigen exposure This hypothesis is supported by the presence of modules in the network with genes that enrich processes and pathways related to antigen acquisition as well as genes characteristic of CD8+ T effector cells several kind of efforts must be achieved in order to understand the complex interplay between factors shaping cancerous phenotypes the modular analysis of the network structure observed here provides us with an alternative framework of the aforementioned interplay between specific modules that take account of particular biological processes that are activated in response to external stimuli the observed results highlight the compartmentalized structure of co-regulated genes that may act and behave together in a well-defined time and space prognostic and therapeutics for breast cancer and contributed to the analysis of results JE-E contributed to the analysis of results All authors read and approved the final version of the manuscript This work was supported by CONACYT (grant no as well as by federal funding from the National Institute of Genomic Medicine (Mexico) Additional support has been granted by the National Laboratory of Complexity Sciences (grant no student from Programa de Maestría y Doctorado en Ciencias Bioquímicas Universidad Nacional Autónoma de México (UNAM) and received a fellowship from CONACYT (grant no 742069/59691) EH-L acknowledges additional support from the 2016 Marcos Moshinsky Fellowship in the Physical Sciences Authors want to thak José María Zamora-Fuentes for his technical support and comments on the final version of this manuscript The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2019.00056/full#supplementary-material A text file containing the edge list or connectivity map for the gene regulatory network (a Cytoscape SIF file) A text file with the definition of the modules and submodules for the largest component of the network (an Infomap MAP file) A multi-page PDF file with heatmaps presenting gene signatures related to the expression of genes related to specific pathways in the adaptive immunity and inflammation groups of samples in our original dataset A multi-page PDF file with heatmaps presenting gene signatures related to the expression of genes related to specific pathways in the adaptive immunity and inflammation groups of samples in the validation (TCGA) dataset A multi-page PDF containing gene expression boxplots for relevant genes in the different groups of inflammatory response Cancer Incidence and Mortality Worldwide: IARC CancerBase No Lyon: International Agency for Research on Cancer [accessed 17 October Comprehensive molecular portraits of human breast tumours CrossRef Full Text | Google Scholar Balancing immune response: crosstalk between adaptive and innate immune cells during breast cancer progression Dr William Coley and tumour regression: a place in history or in the future Google Scholar Colony-stimulating factor 1 promotes progression of mammary tumors to malignancy Gastroenterology (2010) 138:2101–14 The role of inflammation in the pathogenesis of gastric cancer Selective inhibition of cyclooxygenase-2 suppresses growth and induces apoptosis in human esophageal adenocarcinoma cells Leukocyte composition of human breast cancer Proc Natl Acad Sci USA (2012) 109:2796–801 Relevance of tumor-infiltrating lymphocytes in breast cancer The structure and function of complex networks CrossRef Full Text | Google Scholar PubMed Abstract | CrossRef Full Text | Google Scholar Network biology: understanding the cell's functional organization Finding and evaluating community structure in networks Proc Natl Acad Sci USA (2008) 105:1118–23 Community structure reveals biologically functional modules in MEF2C transcriptional regulatory network Modularity and community structure in networks Proc Natl Acad Sci USA (2006) 103:8577–82 A Literature-Based Approach to a Narco-Network Google Scholar Gene expression omnibus: NCBI gene expression and hybridization array data repository Correlating transcriptional networks to breast cancer survival: a large-scale coexpression analysis BRCA1-related gene signature in breast cancer: the role of ER status and molecular type Epithelial-mesenchymal transition spectrum quantification and its efficacy in deciphering survival and drug responses of cancer patients Gene expression signatures in breast cancer distinguish phenotype characteristics In: Bioinformatics and Computational Biology Solutions Using R and Bioconductor Google Scholar How to infer gene networks from expression profiles The role of information theory in gene regulatory network inference Information theoretical methods to deconvolute genetic regulatory networks applied to thyroid neoplasms aracne: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context Multilevel compression of random walks on networks reveals hierarchical organization in large integrated systems The hierarchical modular structure of HER2+ breast cancer network Gene ontology: tool for the unification of biology WEB-based GEne SeT anaLysis toolkit (WebGestalt): update 2013 HTSanalyzeR: an R/Bioconductor package for integrated network analysis of high-throughput screens C1q and mannose binding lectin engagement of cell surface calreticulin and CD91 initiates macropinocytosis and uptake of apoptotic cells IGM is required for efficient complement mediated phagocytosis of apoptotic cells in vivo Molecular mechanisms for synchronized transcription of three complement C1q subunit genes in dendritic cells and macrophages C1q binds directly and specifically to surface blebs of apoptotic human keratinocytes: complement deficiency and systemic lupus erythematosus revisited NADPH oxidase controls phagosomal pH and antigen cross-presentation in human dendritic cells Differential lysosomal proteolysis in antigen-presenting cells determines antigen fate A macrophage mRNA selectively induced by gamma-interferon encodes a member of the platelet factor 4 family of cytokines Proc Natl Acad Sci USA (1990) 87:5238–42 γ-Interferon transcriptionally regulates an early-response gene containing homology to platelet proteins Binding and functional properties of recombinant and endogenous CXCR3 chemokine receptors Control of effector CD8+ T cell function by the transcription factor Eomesodermin Understanding the biology of antigen cross-presentation for the design of vaccines against cancer the functional plasticity of the ubiquitin–proteasome system Characterization of apoptosis in a breast cancer cell line after IL-10 silencing Asian Pacific J Cancer Prevent (2018) 19:777 Interferon-γ: an overview of signals Pathway-based personalized analysis of cancer Proc Natl Acad Sci USA (2013) 110:6388–93 PubMed Abstract | CrossRef Full Text | Google Scholar Cross-presentation: inducing CD8 T cell immunity and tolerance PubMed Abstract | CrossRef Full Text | Google Scholar Cell death and immunity: the ABCs of granule-mediated cytotoxicity: new weapons in the arsenal CrossRef Full Text | Google Scholar Selective expression of the interleukin 7 receptor identifies effector CD8 T cells that give rise to long-lived memory cells CD8αα-mediated survival and differentiation of CD8 memory T cell precursors Network modularity and hierarchical structure in breast cancer molecular subtypes In: International Conference on Complex Systems Google Scholar RNA-Seq based genome-wide analysis reveals loss of inter-chromosomal regulation in breast cancer Pathway-based drug repositioning for breast cancer molecular subtypes Espinal-Enríquez J and Hernandez-Lemus E (2019) Unveiling the Link Between Inflammation and Adaptive Immunity in Breast Cancer Received: 17 October 2018; Accepted: 10 January 2019; Published: 29 January 2019 Copyright © 2019 Velazquez-Caldelas, Alcalá-Corona, Espinal-Enríquez and Hernandez-Lemus. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) The magic which envelops every part of the Ribeira Sacra is most clearly represented in the variety of festivals celebrated in its different municipalities Carnival and traditional trades and exalting wine and cuisine are pilgrimages: festivals which began as religious and commercial celebrations and which are now excellent excuses to come together and enjoy food Some of these celebrations have been declared Galician Festivals of Tourist Interest such as the Feria do Viño de Amandi (Amandi Wine Fair) in Sober or the Folión de Carros(Cart Festival) in Chantada: a celebration dating back to the Middle Ages which represents the agricultural trades and work carried out in the area The Festa das Fachas de Castelo (Castelo Torch Burning Festival) in Taboada has also been awarded this title seeing locals and visitors build a large torch made of straw before its flames light up the night we have compiled a list of the main festivals celebrated in the different municipalities of the Ribeira Sacra Fiesta de la carne ao caldeiro (Stewed Beef Festival) Fiestas de la Asunción (Assumption of Mary Festival) Magosto de San Martiño (San Martiño Chestnut Festival) Fiesta de la Ternera Gallega (Galician Beef Festival) Fiesta de la cereza y del aceite (Cherry and Olive Oil Festival) Feria del vino de Amandi (Amandi Wine Fair) Fiesta del caldo de huesos (Bone Broth Festival) Fiesta de las Fachas de Castelo(Castelo Torch Burning Festival) The Local Europe ABVästmannagatan 43113 25 StockholmSweden Storm Ivo to continue battering Spain on Thursday Stormy and extremely windy weather has been almost constant this week first with Storm Herminia and now with Storm Ivo which on Thursday will continue to lash Spain with very strong winds plenty of rain as well as snow at high altitudes Galicia and the Basque Country are on red alert due to coastal storms and the rest of the northern part of the country is on orange alert due to the strong gusts of wind most of Spain including the Canary Islands will experience very windy weather on Thursday and coastal areas can expect extremely choppy seas The Ministry of Labour has reached an agreement with the unions to raise the minimum wage (SMI in Spanish) by 4.4 percent in 2025 This increase translates into €50 more per month taking it up €1,184 per month in 14 payments (€16,576 gross per year) The increase will benefit just over two million workers representing around 12 percent of Spain's salaried population according to estimates by leading trade union CCOO Spain's top prosecutor denies leaking documents against opposition Spain's top prosecutor has denied leaking legal documents about the partner of the Madrid region's influential conservative leader in a case embarrassing Socialist Prime Minister Pedro Sanchez Álvaro García Ortiz refused to answer anyone but his lawyer during a 90-minute testimony at the Supreme Court in an appearance without precedent in Spanish legal history against the Madrid region's leader Isabel Díaz Ayuso and is one of several affairs undermining the minority leftist administration Spanish media published in March last year a draft agreement between the public prosecutor's office and the lawyer of businessman Alberto González Amador who is under investigation for alleged tax fraud Spanish economy shines in 2024 with 3.2% growth The Spanish economy expanded 3.2 percent last year thanks to buoyant exports and consumption that have made it one of the fastest-growing developed countries Spain has been consistently outstripping a mostly sluggish eurozone and the data published by the National Statistics Institute confirmed its standout performance with 0.8 percent growth in the final three months of 2024 The result slightly exceeded forecasts of 3.1 percent growth by the Bank of Spain and the International Monetary Fund Exports in the European Union's fourth-largest economy grew three percent year-on-year in the final quarter of 2024 and household consumption increased 3.7 percent The service sector continued to perform strongly from October to December with a jump of 3.9 percent year-on-year which represents around 13 percent of the economy has driven the sector as a record 94 million tourists flocked to the world's second most-visited country last year Please log in here to leave a comment Graduation exercises for the Class of 2016 at Monroe High School were conducted on June 23 at Sun National Bank Center in Trenton The class valedictoria was Kush Chandres Shah and the salutatorian was Raksha Dondapati READ, LOOK and WATCH: More Class of 2016 graduation coverage The following is the alphabetical list of 511 graduates: The TimesAysha Frade’s last words to her husband were preserved in an unsent text message written moments before she died She was texting as she walked across Westminster Bridge to pick up their two daughters said: “On that day Aysha had called me at 2.30pm but I was on a conference call at work and I couldn’t pick up give me a couple of minutes and I’ll call her back “When I finished that call I heard this chatter about something’s happened in Westminster