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Identification of hub genes and outcome in colon cancer based on bioinformatics analysis

Wanli Yang1, Jiaojiao Ma1, Wei Zhou1

  • 1State Key Laboratory of Cancer Biology, National Clinical Research Center for Digestive Diseases, Xijing Hospital of Digestive Diseases, Air Force Military Medical University, Xi'an, China, hongliufmmu@163.com.

Cancer Management and Research
|January 16, 2019
PubMed
Abstract

Insights

This study identifies key genes driving colon cancer progression. These differentially expressed genes (DEGs) and hub genes may serve as novel biomarkers for colon cancer diagnosis and treatment strategies.

Area of Science:

  • Oncology
  • Genomics
  • Bioinformatics

Background:

  • Colon cancer is a leading global malignancy with poorly understood carcinogenesis mechanisms.
  • Identifying key genes and pathways is crucial for effective disease monitoring and treatment.
  • This research focuses on discovering potential biomarkers and therapeutic targets for colon cancer.

Purpose of the Study:

  • To explore potential biomarkers for colon cancer diagnosis.
  • To identify novel therapeutic targets for colon cancer treatment.
  • To elucidate the molecular mechanisms underlying colon cancer progression.

Main Methods:

  • Analysis of gene expression profile GSE44076 from the Gene Expression Omnibus database.
  • Screening of differentially expressed genes (DEGs) using GEO2R.
  • Functional enrichment analysis (Gene Ontology, KEGG) and protein-protein interaction network analysis (STRING, Cytoscape).

Main Results:

  • Identified 497 DEGs: 129 upregulated (e.g., Hippo signaling, Wnt signaling) and 368 downregulated (e.g., retinol metabolism).
  • Constructed a protein-protein interaction network, identifying three key modules and 20 hub genes (e.g., COL1A1, CXCL5).
  • Kaplan-Meier analysis and correlation analysis were performed on hub genes for survival insights.

Conclusions:

  • Differentially expressed genes (DEGs), particularly hub genes like COL1A1, are implicated in colon cancer progression.
  • These identified genes show potential as novel biomarkers for colon cancer diagnosis.
  • The findings suggest these genes could guide the development of new therapeutic strategies for colon cancer.
Keywords:
bioinformatics analysiscolon cancerdiagnosisprognosisprotein-protein interaction

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