Gao Feng, Huang Yasheng, Yang Mei, He Liping, Yu Qiqi, Cai Yueshu, Shen Jie, Lu Bingjun
Department of Urology, Hangzhou Hospital of Traditional Chinese Medicine, Hangzhou, Zhejiang, 310007, China.
Heliyon. 2024 Sep 6;10(18):e37554. doi: 10.1016/j.heliyon.2024.e37554. eCollection 2024 Sep 30.
Prostate cancer (PCa) incidence and mortality rates are rising, necessitating precise prognostic tools to guide personalized treatment. Dysregulation of programmed cell death pathways in tumor suppression and cancer development has garnered increasing attention, providing a new research direction for identifying biomarkers and potential therapeutic targets.
Integrating multiple database resources, we constructed and optimized a prognostic signature based on the expression of programmed cell death-related genes (PCDRG) using ten machine learning algorithms. Model performance and prognostic effects were further evaluated. We analyzed the relationships between signature and clinicopathological features, somatic mutations, drug sensitivity, and the tumor immune microenvironment, and constructed a nomogram. The expression level of PCDRGs were evaluated and compared.
Of 1560 PCDRGs, 149 were differentially expressed in PCa, with 34 associated with biochemical recurrence. The PCDRG-derived index (PCDI), constructed using the random forest algorithm, exhibited optimal prognostic performance, successfully stratifying PCa patients into two groups with significant prognostic differences. Patients with high PCDI scores exhibited poorer survival and lower immunotherapy benefit. PCDI was closely associated with the infiltration of specific immune cells, particularly positive correlations with macrophages and T helper cells, and negative correlations with neutrophils, suggesting that PCDI may influence the tumor immune microenvironment, thereby affecting patient prognosis and treatment response. PCDI was associated with age, pathological stage, somatic mutations, and drug sensitivity. The PCDI-based nomogram demonstrated excellent performance in predicting biochemical recurrence in PCa patients. Finally, the differential expression of these PCDRGs was verified based on cell lines and PCa patient expression profile data.
This study developed an effective prognostic indicator for prostate cancer, PCDI, using machine learning approaches. PCDI reflects the link between aberrant programmed cell death pathways and disease progression and treatment response.
前列腺癌（PCa）的发病率和死亡率正在上升，因此需要精确的预后工具来指导个性化治疗。肿瘤抑制和癌症发展过程中程序性细胞死亡途径的失调日益受到关注，为识别生物标志物和潜在治疗靶点提供了新的研究方向。
整合多个数据库资源，我们使用十种机器学习算法，基于程序性细胞死亡相关基因（PCDRG）的表达构建并优化了一个预后特征模型。进一步评估了模型性能和预后效果。我们分析了该特征模型与临床病理特征、体细胞突变、药物敏感性以及肿瘤免疫微环境之间的关系，并构建了列线图。对PCDRG的表达水平进行了评估和比较。
在1560个PCDRG中，有149个在PCa中差异表达，其中34个与生化复发相关。使用随机森林算法构建的PCDRG衍生指数（PCDI）表现出最佳的预后性能，成功地将PCa患者分为两组，两组预后差异显著。PCDI评分高的患者生存率较差，免疫治疗获益较低。PCDI与特定免疫细胞的浸润密切相关，特别是与巨噬细胞和辅助性T细胞呈正相关，与中性粒细胞呈负相关，这表明PCDI可能影响肿瘤免疫微环境，从而影响患者的预后和治疗反应。PCDI与年龄、病理分期、体细胞突变和药物敏感性相关。基于PCDI的列线图在预测PCa患者生化复发方面表现优异。最后，基于细胞系和PCa患者表达谱数据验证了这些PCDRG的差异表达。
本研究使用机器学习方法开发了一种有效的前列腺癌预后指标PCDI。PCDI反映了异常程序性细胞死亡途径与疾病进展和治疗反应之间的联系。
