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Identification modules of gastric cancer based on protein-protein interaction networks and gene expression data

Wei Cui1, Zhenfang Gu, Haiying Liu

  • 1Department of Oncology, Affiliated Hospital of Jining Medical University, Jining, Shandong Province, P.R. China.

Journal of B.U.ON. : Official Journal of the Balkan Union of Oncology
|October 26, 2018
PubMed
Abstract

Insights

This study identified novel molecular modules in gastric cancer using protein-protein interaction networks and gene expression data. These modules offer potential biomarkers and therapeutic targets for this complex disease.

Area of Science:

  • Oncology
  • Bioinformatics
  • Systems Biology

Background:

  • Gastric cancer development involves complex genetic and epigenetic alterations.
  • Understanding these changes requires analyzing gene interactions and expression patterns.

Purpose of the Study:

  • To identify novel molecular modules associated with gastric cancer.
  • To utilize protein-protein interaction networks and gene expression data for this identification.

Main Methods:

  • Downloaded microarray data (E-GEOD-15460) and protein-protein interaction data (STRING).
  • Employed a fast depth-first assay to detect maximal cliques in disease and control groups.
  • Applied Benjamini-Hochberg correction for multiple testing.

Main Results:

  • Identified 248 modules in the control group and 30 in the disease group.
  • Detected 734 similar/identical modules between groups via correlation density.
  • Constructed a protein-protein interaction network with 7899 genes and 48469 interactions.
  • Found 6 significantly different modules linked to gastric cancer.

Conclusions:

  • Identified novel modules with significant differences in gastric cancer.
  • These modules and their associated genes represent potential biomarkers.
  • These findings suggest new therapeutic targets for gastric cancer treatment.

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