Identification of Novel Cancer-Related Genes with a Prognostic Role Using Gene Expression and Protein-Protein Interaction Network Data

Abstract

Early cancer diagnosis and prognosis prediction are necessary for cancer patients. Effective identification of cancer-related genes and biomarkers and survival prediction for cancer patients would facilitate personalized treatment of cancer patients. This study aimed to investigate a method for integrating data regarding gene expression and protein-protein interaction networks to identify cancer-related prognostic genes via random walk with restart algorithm and survival analysis. Known cancer-related genes in protein-protein interaction networks were considered seed genes, and the random walk algorithm was used to identify candidate cancer-related genes. Thereafter, using the univariant Cox regression model, gene expression data were screened to identify survival-related genes. Furthermore, candidate genes and survival-related genes were screened to identify cancer-related prognostic genes. Finally, the effectiveness of the method was verified through gene function analysis and survival prediction. The results indicate that the cancer-related genes can be considered prognostic cancer biomarkers and provide a basis for cancer diagnosis.