Identifying novel candidate biomarkers of RCC based on WGCNA analysis

Per Med. 2018 Sep;15(5):381-394. doi: 10.2217/pme-2017-0091. Epub 2018 Sep 27.

Abstract

Aim: Extracting differential expression genes (DEGs) is an effective approach to improve the accuracy of determining the candidate biomarker genes. However, the previous DEGs analysis methods ignore that the expression levels of genes in different pathology stages of cancers are complex and various.

Methods: In our study, staging DEGs analysis and weighted gene co-expression network analysis were applied to gene expression data of renal cell carcinoma (RCC).

Results: According to construct gene topology network for exploring hub genes, 12 genes were identified as hub genes.

Conclusion: Combining with the effect of hub gene expression level on RCC patient survival and different biological data analysis, three hub genes were found that they might be three novel candidate biomarkers of RCC.

Keywords: DEGs; RCC; WGCNA; biomarker; hub genes.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Basic Helix-Loop-Helix Transcription Factors / genetics
  • Biomarkers / blood
  • Biomarkers, Tumor / genetics
  • Carcinoma, Renal Cell / genetics*
  • Cell Cycle Proteins
  • DNA-Binding Proteins / genetics
  • Gene Expression / genetics
  • Gene Expression Profiling / methods*
  • Gene Expression Regulation, Neoplastic / genetics
  • Gene Regulatory Networks
  • Humans
  • Protein Interaction Maps / genetics
  • Transcription Factors / genetics
  • Transcriptome / genetics

Substances

  • Basic Helix-Loop-Helix Transcription Factors
  • Biomarkers
  • Biomarkers, Tumor
  • Cell Cycle Proteins
  • DNA-Binding Proteins
  • PPP1R35 protein, human
  • PRDM16 protein, human
  • SIM2 protein, human
  • Transcription Factors