Dataset Information

Model-based analysis of competing-endogenous pathways (MACPath) in human cancers.

ABSTRACT: Competing endogenous RNA (ceRNA) has emerged as an important post-transcriptional mechanism that simultaneously alters expressions of thousands genes in cancers. However, only a few ceRNA genes have been studied for their functions to date. To understand the major biological functions of thousands ceRNA genes as a whole, we designed Model-based Analysis of Competing-endogenous Pathways (MACPath) to infer pathways co-regulated through ceRNA mechanism (cePathways). Our analysis on breast tumors suggested that NGF (nerve growth factor)-induced tumor cell proliferation might be associated with tumor-related growth factor pathways through ceRNA. MACPath also identified indirect cePathways, whose ceRNA relationship is mediated by mediating ceRNAs. Finally, MACPath identified mediating ceRNAs that connect the indirect cePathways based on efficient integer linear programming technique. Mediating ceRNAs are unexpectedly enriched in tumor suppressor genes, whose down-regulation is suspected to disrupt indirect cePathways, such as between DNA replication and WNT signaling pathways. Altogether, MACPath is the first computational method to comprehensively understand functions of thousands ceRNA genes, both direct and indirect, at the pathway level.

SUBMITTER: Park HJ

PROVIDER: S-EPMC5882149 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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Model-based analysis of competing-endogenous pathways (MACPath) in human cancers.

Park Hyun Jung HJ Kim Soyeon S Kim Soyeon S Li Wei W

PLoS computational biology 20180322 3

Competing endogenous RNA (ceRNA) has emerged as an important post-transcriptional mechanism that simultaneously alters expressions of thousands genes in cancers. However, only a few ceRNA genes have been studied for their functions to date. To understand the major biological functions of thousands ceRNA genes as a whole, we designed Model-based Analysis of Competing-endogenous Pathways (MACPath) to infer pathways co-regulated through ceRNA mechanism (cePathways). Our analysis on breast tumors su ...[more]

PMID: 29565967

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Project description:BackgroundNon-small cell lung cancer (NSCLC) is a major subtype of lung cancer with high malignancy and bad prognosis, consisted of lung adenocarcinomas (LUAD) and lung squamous cell carcinomas (LUSC) chiefly. Multiple studies have indicated that competing endogenous RNA (ceRNA) network centered long noncoding RNAs (lncRNAs) can regulate gene expression and the progression of various cancers. However, the research about lncRNAs-mediated ceRNA network in LUAD is still lacking.MethodsIn this study, we analyzed the RNA-seq database from The Cancer Genome Atlas (TCGA) and obtained dysregulated lncRNAs in NSCLC, then further identified survival associated lncRNAs through Kaplan-Meier analysis. Quantitative real time PCR (qRT-PCR) was performed to confirm their expression in LUAD tissues and cell lines. The ceRNA networks were constructed based on DIANA-TarBase and TargetScan databases and visualized with OmicShare tools. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to investigate the potential function of ceRNA networks.ResultsIn total, 1,437 and 1,699 lncRNAs were found to be up-regulated in LUAD and LUSC respectively with 895 lncRNAs overlapping (|log2FC| > 3, adjusted P value <0.01). Among which, 222 lncRNAs and 46 lncRNAs were associated with the overall survival (OS) of LUAD and LUSC, and 18 out of 222 up-regulated lncRNAs were found to have inverse correlation with LUAD patients' OS (|log2FC| > 3, adjusted P value < 0.02). We selected 3 lncRNAs (CASC8, LINC01842 and VPS9D1-AS1) out of these 18 lncRNAs and confirmed their overexpression in lung cancer tissues and cells. CeRNA networks were further constructed centered CASC8, LINC01842 and VPS9D1-AS1 with 3 miRNAs and 100 mRNAs included respectively.ConclusionThrough comprehensively analyses of TCGA, our study identified specific lncRNAs as candidate diagnostic and prognostic biomarkers for LUAD. The novel ceRNA network we created provided more insights into the regulatory mechanisms underlying LUAD.

Project description:In addition to direct targeting and repressing mRNAs, recent studies reported that microRNAs (miRNAs) can bridge up an alternative layer of post-transcriptional gene regulatory networks. The competing endogenous RNA (ceRNA) regulation depicts the scenario where pairs of genes (ceRNAs) sharing, fully or partially, common binding miRNAs (miRNA program) can establish coexpression through competition for a limited pool of the miRNA program. While the dynamics of ceRNA regulation among cellular conditions have been verified based on in silico and in vitro experiments, comprehensive investigation into the strength of ceRNA regulation in human datasets remains largely unexplored. Furthermore, pan-cancer analysis of ceRNA regulation, to our knowledge, has not been systematically investigated.In the present study we explored optimal conditions for ceRNA regulation, investigated functions governed by ceRNA regulation, and evaluated pan-cancer effects. We started by investigating how essential factors, such as the size of miRNA programs, the number of miRNA program binding sites, and expression levels of miRNA programs and ceRNAs affect the ceRNA regulation capacity in tumors derived from glioblastoma multiforme patients captured by The Cancer Genome Atlas (TCGA). We demonstrated that increased numbers of common targeting miRNAs as well as the abundance of binding sites enhance ceRNA regulation and strengthen coexpression of ceRNA pairs. Also, our investigation revealed that the strength of ceRNA regulation is dependent on expression levels of both miRNA programs and ceRNAs. Through functional annotation analysis, our results indicated that ceRNA regulation is highly associated with essential cellular functions and diseases including cancer. Furthermore, the highly intertwined ceRNA regulatory relationship enables constitutive and effective intra-function regulation of genes in diverse types of cancer.Using gene and microRNA expression datasets from TCGA, we successfully quantified the optimal conditions for ceRNA regulation, which hinge on four essential parameters of ceRNAs. Our analysis suggests optimized ceRNA regulation is related to disease pathways and essential cellular functions. Furthermore, although the strength of ceRNA regulation is dynamic among cancers, its governing functions are stably maintained. The findings of this report contribute to better understanding of ceRNA dynamics and its crucial roles in cancers.

Dataset Information

Model-based analysis of competing-endogenous pathways (MACPath) in human cancers.

Publications

Model-based analysis of competing-endogenous pathways (MACPath) in human cancers.

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