Wu Yuni, Xu Ran, Wang Jing, Luo Zhibin
Department of Oncology, Chongqing General Hospital, Chongqing University, Chongqing, 401147, China.
School of Clinical Medicine, North Sichuan Medical College, Nanchong, 637100, China.
Discov Oncol. 2024 Sep 27;15(1):487. doi: 10.1007/s12672-024-01277-6.
Prostate cancer (PCa) is a prevalent malignancy among men, primarily originating from the prostate epithelium. It ranks first in global cancer incidence and second in mortality rates, with a rising trend in China. PCa's subtle initial symptoms, such as urinary issues, necessitate diagnostic measures like digital rectal examination, prostate-specific antigen (PSA) testing, and tissue biopsy. Advanced PCa management typically involves a multifaceted approach encompassing surgery, radiation, chemotherapy, and hormonal therapy. The involvement of aging genes in PCa development and progression, particularly through the mTOR pathway, has garnered increasing attention.
This study aimed to explore the association between aging genes and biochemical PCa recurrence and construct predictive models. Utilizing public gene expression datasets (GSE70768, GSE116918, and TCGA), we conducted extensive analyses, including Cox regression, functional enrichment, immune cell infiltration estimation, and drug sensitivity assessments. The constructed risk score model, based on aging-related genes (ARGs), demonstrated superior predictive capability for PCa prognosis compared to conventional clinical features. High-risk genes positively correlated with risk, while low-risk genes displayed a negative correlation.
An ARGs-based risk score model was developed and validated for predicting prognosis in prostate adenocarcinoma (PRAD) patients. LASSO regression analysis and cross-validation plots were employed to select ARGs with prognostic significance. The risk score outperformed traditional clinicopathological features in predicting PRAD prognosis, as evidenced by its high AUC (0.787). The model demonstrated good sensitivity and specificity, with AUC values of 0.67, 0.675, 0.696, and 0.696 at 1, 3, 5, and 8 years, respectively, in the GEO cohort. Similar AUC values were observed in the TCGA cohort at 1, 3, and 5 years (0.67, 0.659, 0.667, and 0.743). The model included 12 genes, with high-risk genes positively correlated with risk and low-risk genes negatively correlated.
This study presents a robust ARGs-based risk score model for predicting biochemical recurrence in PCa patients, highlighting the potential significance of aging genes in PCa prognosis and offering enhanced predictive accuracy compared to traditional clinical parameters. These findings open new avenues for research on PCa recurrence prediction and therapeutic strategies.
前列腺癌（PCa）是男性中常见的恶性肿瘤，主要起源于前列腺上皮。它在全球癌症发病率中排名第一，死亡率排名第二，在中国呈上升趋势。PCa的初始症状不明显，如泌尿系统问题，这就需要采取数字直肠检查、前列腺特异性抗原（PSA）检测和组织活检等诊断措施。晚期PCa的治疗通常涉及多方面的方法，包括手术、放疗、化疗和激素治疗。衰老基因在PCa发生和发展中的作用，特别是通过mTOR途径，越来越受到关注。
本研究旨在探讨衰老基因与生化性PCa复发之间的关联，并构建预测模型。利用公共基因表达数据集（GSE70768、GSE116918和TCGA），我们进行了广泛的分析，包括Cox回归、功能富集、免疫细胞浸润估计和药物敏感性评估。基于衰老相关基因（ARGs）构建的风险评分模型在预测PCa预后方面显示出比传统临床特征更好的预测能力。高风险基因与风险呈正相关，而低风险基因呈负相关。
开发并验证了基于ARGs的风险评分模型，用于预测前列腺腺癌（PRAD）患者的预后。采用LASSO回归分析和交叉验证图来选择具有预后意义的ARGs。风险评分在预测PRAD预后方面优于传统的临床病理特征，其高AUC（0.787）证明了这一点。该模型显示出良好的敏感性和特异性，在GEO队列中，1年、3年、5年和8年的AUC值分别为0.67、0.675、0.696和0.696。在TCGA队列中，1年、3年和5年也观察到类似的AUC值（0.67、0.659、0.667和0.743）。该模型包含12个基因，高风险基因与风险呈正相关，低风险基因呈负相关。
本研究提出了一个强大的基于ARGs的风险评分模型，用于预测PCa患者的生化复发，突出了衰老基因在PCa预后中的潜在意义，并提供了比传统临床参数更高的预测准确性。这些发现为PCa复发预测和治疗策略的研究开辟了新途径。
