Predicting Prostate Adenocarcinoma Patients’ Survival and Immune Signature: A Novel Risk Model Based on Telomere-Related Genes. Jief Zheng, Jiah Chen et al.

Telomere-related genes (TRGs) play an essential role in the carcinogenesis and progression of prostate adenocarcinoma（PRAD）. The prognostic value of TRGs remains unclear in PRAD. 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. The integrated nomogram can be a good predictor of 3- and 5-year survival in patients with PRAD. This risk model is valuable for guiding the selection of immunotherapy and chemotherapy in the clinical treatment of patients with PRAD.

端粒相关基因（Telomere-related genes, TRGs）在前列腺腺癌（Prostate Adenocarcinoma, PRAD）的发生与进展中发挥关键作用，但其在前列腺腺癌中的预后价值仍不明确。本研究以癌症基因组图谱-前列腺腺癌（TCGA-PRAD）数据集作为训练集，以纪念斯隆-凯特琳癌症中心（MSKCC）与基因表达综合数据库（GEO）数据集作为验证集开展研究。我们构建了一款风险模型与列线图，用于预测前列腺腺癌患者的生存率；随后分析了模型基因的表达水平及其潜在调控机制。此外，本研究借助CIBERSORT算法、癌症药物敏感性基因组学（GDSC）数据库以及基序富集分析，探究了该风险模型与免疫细胞浸润、化疗药物敏感性及特定信号通路之间的关联。该整合式列线图可有效预测前列腺腺癌患者的3年及5年生存率，且该风险模型可为前列腺腺癌患者临床治疗中免疫治疗与化疗方案的选择提供重要参考价值。