Identification of Survival Associated Hub Genes in Prostate Cancer Patients From the TCGA Database

Author Type(s)

Faculty

Document Type

Abstract

Publication Date

7-1-2021

DOI

10.1158/1538-7445.AM2021-202

Journal Title

Cancer Research

Department

Pathology, Microbiology and Immunology

Abstract

Background: Prostate cancer is the most frequently diagnosed malignancy and the fourth leading cause of cancer-related death in the global male population. Although the disease has a relatively low mortality rate with some patients surviving for 10-20 years after treatment, others respond poorly to treatment and die of metastatic disease within 2-3 years. Therefore, there is an urgent need to develop strategies to identify patients with clinically significant prostate cancer requiring aggressive treatment to improve survival, while sparing others unnecessary side effects. The purpose of this study was to identify survival associated genes in prostate cancer patients from the TCGA database using bioinformatics tools.

Methods: Data from prostate cancer patients in the TCGA database were divided into two study groups: a high and a low expression group, relative to the median expression. The Gene Expression Profiling Interactive Analysis (GEPIA2) tool was used for the identification of the most differential survival genes. Metascape bioinformatics tool was subsequently used for clustering of genes based on processes, pathway enrichment analysis, and construction of Protein-Protein Interaction (PPI) network. Metascape was also used for molecular Complex Detection (MCODE) to identify the genes with the highest degree of connection, known as hub genes, and to screen modules of the PPI network.

Results: Bioinformatics analysis allowed the identification of 361 genes whose expression levels were significantly associated with overall survival in prostate cancer patients from the TCGA. Survival associated genes were primarily enriched in mRNA processing, DNA repair, ncRNA processing, DNA replication, macromolecule methylation, among others. The 12 most connected genes were selected as hub genes and Kaplan-Meier analysis was used to verify survival associated with this set of genes. Hub genes included several splicing factors and components of the processing machinery of cellular pre-mRNAs.

Conclusions: These hub genes may reveal basic mechanisms underlying the development of clinically relevant prostate cancer and contribute to the identification of novel markers for prognosis of this cancer.

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