Wang Ming, Dai Bangshun, Liu Qiushi, Zhang Xiansheng
Department of Urology, First Affiliated Hospital of Anhui Medical University, Anhui Medical University, No. 218 Jixi Road, Hefei, Anhui, China.
Cancer Cell Int. 2024 Aug 24;24(1):297. doi: 10.1186/s12935-024-03462-7.
Prostate cancer is one of the most common cancers in men with a significant proportion of patients developing biochemical recurrence (BCR) after treatment. Programmed cell death (PCD) mechanisms are known to play critical roles in tumor progression and can potentially serve as prognostic and therapeutic biomarkers in PCa. This study aimed to develop a prognostic signature for BCR in PCa using PCD-related genes.
We conducted an analysis of 19 different modes of PCD to develop a comprehensive model. Bulk transcriptomic, single-cell transcriptomic, genomic, and clinical data were collected from multiple cohorts, including TCGA-PRAD, GSE58812, METABRIC, GSE21653, and GSE193337. We analyzed the expression and mutations of the 19 PCD modes and constructed, evaluated, and validated the model.
Ten PCD modes were found to be associated with BCR in PCa, with specific PCD patterns exhibited by various cell components within the tumor microenvironment. Through Lasso Cox regression analysis, we established a Programmed Cell Death Index (PCDI) utilizing an 11-gene signature. High PCDI values were validated in five independent datasets and were found to be associated with an increased risk of BCR in PCa patients. Notably, older age and advanced T and N staging were associated with higher PCDI values. By combining PCDI with T staging, we constructed a nomogram with enhanced predictive performance. Additionally, high PCDI values were significantly correlated with decreased drug sensitivity, including drugs such as Docetaxel and Methotrexate. Patients with lower PCDI values demonstrated higher immunophenoscores (IPS), suggesting a potentially higher response rate to immune therapy. Furthermore, PCDI was associated with immune checkpoint genes and key components of the tumor microenvironment, including macrophages, T cells, and NK cells. Finally, clinical specimens validated the differential expression of PCDI-related PCDRGs at both the gene and protein levels.
In conclusion, we developed a novel PCD-based prognostic feature that successfully predicted BCR in PCa patients and provided insights into drug sensitivity and potential response to immune therapy. These findings have significant clinical implications for the treatment of PCa.
前列腺癌是男性最常见的癌症之一，相当一部分患者在治疗后会出现生化复发（BCR）。已知程序性细胞死亡（PCD）机制在肿瘤进展中起关键作用，并有可能作为前列腺癌的预后和治疗生物标志物。本研究旨在利用PCD相关基因开发一种前列腺癌BCR的预后特征。
我们对19种不同的PCD模式进行了分析，以建立一个综合模型。从多个队列中收集了批量转录组、单细胞转录组、基因组和临床数据，包括TCGA-PRAD、GSE58812、METABRIC、GSE21653和GSE193337。我们分析了19种PCD模式的表达和突变情况，并构建、评估和验证了该模型。
发现10种PCD模式与前列腺癌的BCR相关，肿瘤微环境中的各种细胞成分表现出特定的PCD模式。通过Lasso Cox回归分析，我们利用一个由11个基因组成的特征建立了程序性细胞死亡指数（PCDI）。在五个独立数据集中验证了高PCDI值，并且发现其与前列腺癌患者BCR风险增加相关。值得注意的是，年龄较大以及T和N分期较晚与较高的PCDI值相关。通过将PCDI与T分期相结合，我们构建了一个预测性能增强的列线图。此外，高PCDI值与药物敏感性降低显著相关，包括多西他赛和甲氨蝶呤等药物。PCDI值较低的患者表现出较高的免疫表型评分（IPS），表明对免疫治疗的反应率可能更高。此外，PCDI与免疫检查点基因以及肿瘤微环境的关键成分有关，包括巨噬细胞、T细胞和NK细胞。最后，临床标本在基因和蛋白质水平上验证了PCDI相关PCDRG的差异表达。
总之，我们开发了一种基于PCD的新型预后特征，成功预测了前列腺癌患者的BCR，并为药物敏感性和对免疫治疗的潜在反应提供了见解。这些发现对前列腺癌的治疗具有重要的临床意义。
