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

Hyun Jung Park1, Soyeon Kim2, Wei Li3,4

  • 1Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.

Plos Computational Biology
|March 23, 2018
PubMed

Insights

Competing endogenous RNA (ceRNA) regulates thousands of genes in cancer. Our new method, MACPath, reveals ceRNA-co-regulated pathways, including indirect ones mediated by tumor suppressor genes.

Area of Science:

  • Genomics
  • Computational Biology
  • Cancer Research

Background:

  • Competing endogenous RNA (ceRNA) is a post-transcriptional mechanism affecting gene expression in cancer.
  • The functional roles of most ceRNA genes remain largely uncharacterized.

Purpose of the Study:

  • To develop a computational method for inferring pathways co-regulated by ceRNA mechanisms (cePathways).
  • To comprehensively analyze the biological functions of thousands of ceRNA genes at the pathway level.

Main Methods:

  • Developed Model-based Analysis of Competing-endogenous Pathways (MACPath) to infer direct and indirect cePathways.
  • Utilized integer linear programming to identify mediating ceRNAs connecting indirect cePathways.
  • Applied MACPath to analyze breast tumor data.

Main Results:

  • Identified NGF-induced tumor cell proliferation linked to growth factor pathways via ceRNA in breast tumors.
  • Discovered indirect cePathways mediated by specific ceRNAs.
  • Found mediating ceRNAs are enriched in tumor suppressor genes, potentially disrupting pathways like DNA replication and WNT signaling upon downregulation.

Conclusions:

  • MACPath is the first computational tool to comprehensively understand the pathway-level functions of numerous ceRNA genes.
  • The study highlights the role of ceRNAs, including tumor suppressors, in complex pathway regulation within cancer.

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