Predicting Prostate Adenocarcinoma Patients’ Survival and Immune Signature: A Novel Risk Model Based on Telomere-Related Genes

Abstract

Abstract Background Telomere-related genes (TRGs) play an essential role in the carcinogenesis and progression of prostate adenocarcinoma(PRAD). However, the prognostic value of TRGs remains unclear in PRAD. Methods We conducted a study using The Cancer Genome Atlas-Prostate Adenocarcinoma (TCGA-PRAD) dataset as the training group and the Memorial Sloan-Kettering Cancer Center (MSKCC) and Gene Expression Omnibus (GEO) datasets as the validation group. We developed a risk model and a nomogram to predict survival rates in patients with PRAD. The expression of model genes and their possible regulatory mechanisms were then analyzed. Furthermore, we explored the relationship between the risk model and immune cell infiltration, chemotherapy drug sensitivity, and specific signaling pathways using the CIBERSORT algorithm, the Genomics of Drug Sensitivity in Cancer (GDSC) database, and motif enrichment. Results The developed risk model was based on seven key TRGs (HELLS, TOP3A, SRC, LARP7, BUB3, THRSP, and GTF2H4). Moreover, this model was an independent prognostic factor for patients with PRAD and was significantly associated with T and N stages. Among seven TRGs, TOP3A and BUB3 were not only significantly positively correlated with the clinical T and N phases of PRAD, but also expression was significantly increased in PRAD tissues compared to adjacent normal tissues. The model was a good predictor of 1-, 3-, and 5-year survival, and patients in the high-risk group had significantly shorter overall survival than those in the low-risk group. The integrated nomogram can be a good predictor of 3- and 5-year survival in patients with PRAD. Finally, compared to the low-risk group, the high-risk group had a higher response to chemotherapy and immunosuppression, which provided potential guidance to treatment options for patients in the high-risk group. Conclusion In summary, a new risk model based on TRGs was successfully developed in PRAD. This risk model is valuable for guiding the selection of immunotherapy and chemotherapy in the clinical treatment of patients with PRAD.