Dataset Information

Novel lncRNAs Co-Expression Networks Identifies LINC00504 with Oncogenic Role in Luminal A Breast Cancer Cells.

ABSTRACT: Long non-coding RNAs (lncRNAs) are functional transcripts with more than 200 nucleotides. These molecules exhibit great regulatory capacity and may act at different levels of gene expression regulation. Despite this regulatory versatility, the biology of these molecules is still poorly understood. Computational approaches are being increasingly used to elucidate biological mechanisms in which these lncRNAs may be involved. Co-expression networks can serve as great allies in elucidating the possible regulatory contexts in which these molecules are involved. Herein, we propose the use of the pipeline deposited in the RTN package to build lncRNAs co-expression networks using TCGA breast cancer (BC) cohort data. Worldwide, BC is the most common cancer in women and has great molecular heterogeneity. We identified an enriched co-expression network for the validation of relevant cell processes in the context of BC, including LINC00504. This lncRNA has increased expression in luminal subtype A samples, and is associated with prognosis in basal-like subtype. Silencing this lncRNA in luminal A cell lines resulted in decreased cell viability and colony formation. These results highlight the relevance of the proposed method for the identification of lncRNAs in specific biological contexts.

SUBMITTER: Mathias C

PROVIDER: S-EPMC7957645 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Novel lncRNAs Co-Expression Networks Identifies LINC00504 with Oncogenic Role in Luminal A Breast Cancer Cells.

Mathias Carolina C Groeneveld Clarice S CS Trefflich Sheyla S Zambalde Erika P EP Lima Rubens S RS Urban Cícero A CA Prado Karin B KB Ribeiro Enilze M S F EMSF Castro Mauro A A MAA Gradia Daniela F DF de Oliveira Jaqueline C JC

International journal of molecular sciences 20210228 5

Long non-coding RNAs (lncRNAs) are functional transcripts with more than 200 nucleotides. These molecules exhibit great regulatory capacity and may act at different levels of gene expression regulation. Despite this regulatory versatility, the biology of these molecules is still poorly understood. Computational approaches are being increasingly used to elucidate biological mechanisms in which these lncRNAs may be involved. Co-expression networks can serve as great allies in elucidating the possi ...[more]

PMID: 33670895

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Project description:Gene co-expression networks have become a usual approach to integrate the vast amounts of information coming from gene expression studies in cancer cohorts. The reprogramming of the gene regulatory control and the molecular pathways depending on such control are central to the characterization of the disease, aiming to unveil the consequences for cancer prognosis and therapeutics. There is, however, a multitude of factors which have been associated with anomalous control of gene expression in cancer. In the particular case of co-expression patterns, we have previously documented a phenomenon of loss of long distance co-expression in several cancer types, including breast cancer. Of the many potential factors that may contribute to this phenomenology, copy number variants (CNVs) have been often discussed. However, no systematic assessment of the role that CNVs may play in shaping gene co-expression patterns in breast cancer has been performed to date. For this reason we have decided to develop such analysis. In this study, we focus on using probabilistic modeling techniques to evaluate to what extent CNVs affect the phenomenon of long/short range co-expression in Luminal B breast tumors. We analyzed the co-expression patterns in chromosome 8, since it is known to be affected by amplifications/deletions during cancer development. We found that the CNVs pattern in chromosome 8 of Luminal B network does not alter the co-expression patterns significantly, which means that the co-expression program in this cancer phenotype is not determined by CNV structure. Additionally, we found that region 8q24.3 is highly dense in interactions, as well as region p21.3. The most connected genes in this network belong to those cytobands and are associated with several manifestations of cancer in different tissues. Interestingly, among the most connected genes, we found MAF1 and POLR3D, which may constitute an axis of regulation of gene transcription, in particular for non-coding RNA species. We believe that by advancing on our knowledge of the molecular mechanisms behind gene regulation in cancer, we will be better equipped, not only to understand tumor biology, but also to broaden the scope of diagnostic, prognostic and therapeutic interventions to ultimately benefit oncologic patients.

Project description:IntroductionBreast cancer heterogeneity occurs as a consequence of the dysregulation of numerous oncogenic pathways as well as many non-genetic factors, including tumor microenvironmental stresses such as hypoxia, lactic acidosis, and glucose deprivation. Although the importance of these non-genetic factors is well recognized, it is not clear how to integrate these factors within the genetic framework of cancer as the next logical step in understanding tumor heterogeneity.MethodsWe report here the development of a series of gene expression signatures to measure the influences of microenvironmental stresses. The pathway activities of hypoxia, lactic acidosis, acidosis and glucose deprivation were investigated in a collection of 1,143 breast tumors, which have been separated into 17 breast tumor subgroups defined by their distinct patterns of oncogenic pathways. A validation dataset comprised of 547 breast tumors was also used to confirm the major findings, and representative breast cancer cell lines were utilized to validate in silico results and mechanistic studies.ResultsThrough the integrative pathway analysis of microenvironmental stresses and oncogenic events in breast tumors, we identified many known and novel correlations between these two sources of tumor heterogeneity. Focusing on differences between two human epidermal growth factor receptor 2 (HER2)-related subgroups, previously identified based on patterns of oncogenic pathway activity, we determined that these subgroups differ with regards to tumor microenvironmental signatures, including hypoxia. We further demonstrate that each of these subgroups have features consistent with basal and luminal breast tumors including patterns of oncogenic signaling pathways, expression of subtype specific genes, and cellular mechanisms that regulate the hypoxia response. Importantly, we also demonstrate that the correlated pattern of hypoxia-related gene expression and basal-associated gene expression are consistent across HER2-related tumors whether we analyze the tumors as a function of our pathway-based classification scheme, using the intrinsic gene list (ERBB2+), or based on HER2 IHC status. Our results demonstrate a cell lineage-specific phenomenon in which basal-like tumors, HER2-related tumors with high hypoxia, as well as normal basal epithelial cells express increased mRNA levels of HIF-1α compared to luminal types and silencing of HIF-1α results in decreased expression of hypoxia-induced genes.ConclusionsThis study demonstrates differences in microenvironmental conditions in HER2-related subgroups defined by distinct oncogenic pathway activities, and provides a mechanistic explanation for differences in the observed hypoxia response between these subgroups. Collectively, these data demonstrate the potential of a pathway-based classification strategy as a framework to integrate genetic and non-genetic factors to investigate the basis of tumor heterogeneity.

Dataset Information

Novel lncRNAs Co-Expression Networks Identifies LINC00504 with Oncogenic Role in Luminal A Breast Cancer Cells.

Publications

Novel lncRNAs Co-Expression Networks Identifies LINC00504 with Oncogenic Role in Luminal A Breast Cancer Cells.

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