Identification of Prostate Cancer Risk Genetics Biomarkers Based on Intergraded Bioinformatics Analysis

Abstract

BackgroundProstate cancer (PCa) is one of the most popular cancer types in men. Nevertheless, the pathogenic mechanisms of PCa are poorly understood. Hence, we aimed to identify the potential genetic biomarker of PCa in the present study.MethodsHigh-throughput data set GSE46602 was obtained from the comprehensive gene expression database (GEO) for screening differentially expressed genes (DEGs). The common DEGs were further screened out using The Cancer Genome Atlas (TCGA) dataset. Functional enrichment analysis includes Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) to study related mechanisms. The Cox and Lasso regression analyses were carried out to compress the target genes and construct the high-risk and low-risk gene model. Survival analyses were performed based on the gene risk signature model. The CIBERSORT algorithm was performed to clarify the correlation of the high- and low-risk gene model in risk and infiltration of immune cells in PCa.ResultsA total of 385 common DEGs were obtained. The results of functional enrichment analysis show that common DEGs play an important role in PCa. A three-gene signature model (KCNK3, AK5, and ARHGEF38) was established, and the model was significantly associated with cancer-related pathways, overall survival (OS), and tumor microenvironment (TME)-related immune cells in PCa.ConclusionThis new risk model may contribute to further investigation in the immune-related pathogenesis in progression of PCa.

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“…Compared with other reported models, the AUC value and gene numbers of our model are basically consistent. In different cancers, the AUC values of models were 0.622-0.88 across the immunerelated genes and autophagy-related genes, containing 3-15 genes [13][14][15][16][17] . Thus, our model is relatively stable.…”

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“…For instance, the re-analysis of high throughput expression data has led to the identification of several differentially expressed genes (DEGs) between cancerous and normal tissues. Further functional enrichment analyses have shown the relationship between these genes and clinical outcomes of patients [ 6 ]. Similar approaches have identified differentially expressed circular RNAs in this type of cancer and the main signaling pathways being controlled by these transcripts [ 7 ].…”