Coordinated inflammation and immune response transcriptional regulation in breast cancer molecular subtypes.

Breast cancer, characterized by its complexity and diversity, presents significant challenges in understanding its underlying biology. In this study, 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. Interestingly, 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, revealing diverse immunological functions, 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. Additionally, the network-based approach introduced herein presents a valuable framework for understanding the complexities of other diseases and elucidating their underlying biology.

Metformin induces ZFP36 by mTORC1 inhibition in cervical cancer-derived cell lines.

BACKGROUND: Metformin, a widely prescribed antidiabetic drug, has shown several promising effects for cancer treatment. These effects have been shown to be mediated by dual modulation of the AMPK-mTORC1 axis, where AMPK acts upstream of mTORC1 to decrease its activity. Nevertheless, alternative pathways have been recently discovered suggesting that metformin can act through of different targets regulation. METHODS: We performed a transcriptome screening analysis using HeLa xenograft tumors generated in NOD-SCID mice treated with or without metformin to examine genes regulated by metformin. Western Blot analysis, Immunohistochemical staining, and RT-qPCR were used to confirm alterations in gene expression. The TNMplot and GEPIA2 platform were used for in silico analysis of genes found up-regulated by metformin, in cervical cancer patients. We performed an AMPK knock-down using AMPK-targeted siRNAs and mTOR inhibition with rapamycin to investigate the molecular mechanisms underlying the effect of metformin in cervical cancer cell lines. RESULTS: We shown that metformin decreases tumor growth and increased the expression of a group of antitumoral genes involved in DNA-binding transcription activator activity, hormonal response, and Dcp1-Dcp2 mRNA-decapping complex. We demonstrated that ZFP36 could act as a new molecular target increased by metformin. mTORC1 inhibition using rapamycin induces ZFP36 expression, which could suggest that metformin increases ZFP36 expression and requires mTORC1 inhibition for such effect. Surprisingly, in HeLa cells AMPK inhibition did not affect ZFP36 expression, suggesting that additional signal transducers related to suppressing mTORC1 activity, could be involved. CONCLUSIONS: These results highlight the importance of ZFP36 activation in response to metformin treatment involving mTORC1 inhibition.

Methylation-related genes involved in renal carcinoma progression.

Renal carcinomas are a group of malignant tumors often originating in the cells lining the small tubes in the kidney responsible for filtering waste from the blood and urine production. Kidney tumors arise from the uncontrolled growth of cells in the kidneys and are responsible for a large share of global cancer-related morbidity and mortality. Understanding the molecular mechanisms driving renal carcinoma progression results crucial for the development of targeted therapies leading to an improvement of patient outcomes. Epigenetic mechanisms such as DNA methylation are known factors underlying the development of several cancer types. There is solid experimental evidence of relevant biological functions modulated by methylation-related genes, associated with the progression of different carcinomas. Those mechanisms can often be associated to different epigenetic marks, such as DNA methylation sites or chromatin conformation patterns. Currently, there is no definitive method to establish clear relations between genetic and epigenetic factors that influence the progression of cancer. Here, we developed a data-driven method to find methylation-related genes, so we could find relevant bonds between gene co-expression and methylation-wide-genome regulation patterns able to drive biological processes during the progression of clear cell renal carcinoma (ccRC). With this approach, we found out genes such as ITK oncogene that appear hypomethylated during all four stages of ccRC progression and are strongly involved in immune response functions. Also, we found out relevant tumor suppressor genes such as RAB25 hypermethylated, thus potentially avoiding repressed functions in the AKT signaling pathway during the evolution of ccRC. Our results have relevant implications to further understand some epigenetic-genetic-affected roles underlying the progression of renal cancer.

Oncogenic Role of miR-217 During Clear Cell Renal Carcinoma Progression.

Clear cell renal carcinoma (ccRC) comprises a set of heterogeneous, fast-progressing pathologies with poor prognosis. Analyzing ccRC progression in terms of modifications at the molecular level may provide us with a broader understanding of the disease, paving the way for improved diagnostics and therapeutics. The role of micro-RNAs (miRs) in cancer by targeting both oncogenes and tumor suppressor genes is widely known. Despite this knowledge, the role of specific miRs and their targets in the progression of ccRC is still unknown. To evaluate the action of miRs and their target genes during ccRC progression, here we implemented a three-step method for constructing miR-gene co-expression networks for each progression stage of ccRC as well as for adjacent-normal renal tissue (NT). In the first step, we inferred all miR-gene co-expression interactions for each progression stage of ccRC and for NT. Afterwards, we filtered the whole miR-gene networks by differential gene and miR expression between successive stages: stage I with non-tumor, stage II with stage I, and so on. Finally, all miR-gene interactions whose relationships were inversely proportional (overexpressed miR and underexpressed genes and vice versa) were kept and removed otherwise. We found that miR-217 is differentially expressed in all contrasts; however, its targets were different depending on the ccRC stage. Furthermore, the target genes of miR-217 have a known role in cancer progression-for instance, in stage II network, GALNTL6 is overexpressed, and it is related to cell signaling, survival, and proliferation. In the stage III network, WNK2, a widely known tumor suppressor, is underexpressed. For the stage IV network, IGF2BP2, a post-transcriptional regulator of MYC and PTEN, is overexpressed. This data-driven network approach has allowed us to discover miRs that have different targets through ccRC progression, thus providing a method for searching possible stage-dependent therapeutic targets in this and other types of cancer.

Gene Co-Expression in Breast Cancer: A Matter of Distance.

Gene regulatory and signaling phenomena are known to be relevant players underlying the establishment of cellular phenotypes. It is also known that such regulatory programs are disrupted in cancer, leading to the onset and development of malignant phenotypes. Gene co-expression matrices have allowed us to compare and analyze complex phenotypes such as breast cancer (BrCa) and their control counterparts. Global co-expression patterns have revealed, for instance, that the highest gene-gene co-expression interactions often occur between genes from the same chromosome (cis-), meanwhile inter-chromosome (trans-) interactions are scarce and have lower correlation values. Furthermore, strength of cis- correlations have been shown to decay with the chromosome distance of gene couples. Despite this loss of long-distance co-expression has been clearly identified, it has been observed only in a small fraction of the whole co-expression landscape, namely the most significant interactions. For that reason, an approach that takes into account the whole interaction set results appealing. In this work, we developed a hybrid method to analyze whole-chromosome Pearson correlation matrices for the four BrCa subtypes (Luminal A, Luminal B, HER2+ and Basal), as well as adjacent normal breast tissue derived matrices. We implemented a systematic method for clustering gene couples, by using eigenvalue spectral decomposition and the k-medoids algorithm, allowing us to determine a number of clusters without removing any interaction. With this method we compared, for each chromosome in the five phenotypes: a) Whether or not the gene-gene co-expression decays with the distance in the breast cancer subtypes b) the chromosome location of cis- clusters of gene couples, and c) whether or not the loss of long-distance co-expression is observed in the whole range of interactions. We found that in the correlation matrix for the control phenotype, positive and negative Pearson correlations deviate from a random null model independently of the distance between couples. Conversely, for all BrCa subtypes, in all chromosomes, positive correlations decay with distance, and negative correlations do not differ from the null model. We also found that BrCa clusters are distance-dependent, meanwhile for the control phenotype, chromosome location does not determine the clustering. To our knowledge, this is the first time that a dependence on distance is reported for gene clusters in breast cancer. Since this method uses the whole cis- interaction geneset, combination with other -omics approaches may provide further evidence to understand in a more integrative fashion, the mechanisms that disrupt gene regulation in cancer.

Loss of Long Distance Co-Expression in Lung Cancer.

Lung cancer is one of the deadliest, most aggressive cancers. Abrupt changes in gene expression represent an important challenge to understand and fight the disease. Gene co-expression networks (GCNs) have been widely used to study the genomic regulatory landscape of human cancer. Here, based on 1,143 RNA-Seq experiments from the TCGA collaboration, we constructed GCN for the most common types of lung tumors: adenocarcinoma (TAD) and squamous cells (TSCs) as well as their respective control networks (NAD and NSC). We compared the number of intra-chromosome (cis-) and inter-chromosome (trans-) co-expression interactions in normal and cancer GCNs. We compared the number of shared interactions between TAD and TSC, as well as in NAD and NSC, to observe which phenotypes were more alike. By means of an over-representation analysis, we associated network topology features with biological functions. We found that TAD and TSC present mostly cis- small disconnected components, whereas in control GCNs, both types have a giant trans- component. In both cancer networks, we observed cis- components in which genes not only belong to the same chromosome but to the same cytoband or to neighboring cytobands. This supports the hypothesis that in lung cancer, gene co-expression is constrained to small neighboring regions. Despite this loss of distant co-expression observed in TAD and TSC, there are some remaining trans- clusters. These clusters seem to play relevant roles in the carcinogenic processes. For instance, some clusters in TAD and TSC are associated with the immune system, response to virus, or control of gene expression. Additionally, other non-enriched trans- clusters are composed of one gene and several associated pseudo-genes, as in the case of the FTH1 gene. The appearance of those common trans- clusters reflects that the gene co-expression program in lung cancer conserves some aspects for cell maintenance. Unexpectedly, 0.48% of the edges are shared between control networks; conversely, 35% is shared between lung cancer GCNs, a 73-fold larger intersection. This suggests that in lung cancer a process of de-differentiation may be occurring. To further investigate the implications of the loss of distant co-expression, it will become necessary to broaden the investigation with other omic-based approaches. However, the present approach provides a basis for future work toward an integrative perspective of abnormal transcriptional regulatory programs in lung cancer.

Gene Expression and Co-expression Networks Are Strongly Altered Through Stages in Clear Cell Renal Carcinoma.

Clear cell renal carcinoma (ccRC) is a highly heterogeneous and progressively malignant disease. Analyzing ccRC progression in terms of modifications at the molecular and genetic level may help us to develop a broader understanding of its patho-physiology and may give us a glimpse toward improved therapeutics. In this work, by using TCGA data, we studied the molecular progression of the four main ccRC stages (i, ii, iii, iv) in two different yet complementary approaches: (a) gene expression and (b) gene co-expression. For (a) we analyzed the differential gene expression between each stage and the control non-cancer group. We compared the progression molecular signature between stages, and observed those genes that change their expression patterns through progression stages. For (b) we constructed and analyzed co-expression networks for the four ccRC progression stages, as well as for the control phenotype, to observe whether and how the co-expression landscape changes with progression. We separated genomic interactions into intra-chromosome (cis-) and inter-chromosome (trans-). Finally, we intersected those networks and performed functional enrichment analysis. All calculations were made over different network sizes, from the top 100 edges to top 1,000,000. We show that differential expression is quite similar between ccRC progression stages. However, interestingly, two genes, namely SLC6A19 and PLG show a significant progressive decrease in their expression according to ccRC stage, meanwhile two other genes, SAA2-SAA4 and CXCL13 show progressive increase. Despite the high similarity between gene expression profiles, all networks are substantially different between them in terms of their topological features. Control network has a larger proportion of trans- interactions, meanwhile for any stage, the amount of cis- interactions is higher, independent of the network cut-off. The majority of interactions in any network are phenotype-specific. Only 189 interactions are shared between the five networks, and 533 edges are ccRC-specific, independent of the stage. The small resulting connected components in both cases are formed by genes with the same differential expression trend, and are associated with important biological processes, such as cell cycle or immune system, suggesting that activity of these categories follows the differential expression trend. With this approach we have shown that, even if the expression program is similar during ccRC progression, the co-expression programs strongly differ. More research is needed to understand the delicate interplay between expression and co-expression, but this is a first approach to enclose both approaches in an integrative view aimed at a deeper understanding in gene regulation in tumor evolution.