Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, China.
Department of Urology, Shanghai Changhai Hospital, Shanghai, China.
Front Endocrinol (Lausanne). 2022 Oct 21;13:1037099. doi: 10.3389/fendo.2022.1037099. eCollection 2022.
Prostate cancer (PCa) is a common malignancy that poses a major threat to the health of men. Prostate-specific antigen (PSA) and its derivatives, as FDA-approved detection assays, are insufficient to serve as optimal markers for patient prognosis and clinical decision-making. It is widely acknowledged that aberrant glycolytic metabolism in PCa is related to tumor progression and acidifies the tumor microenvironment (TME). Considering the non-negligible impacts of glycolysis and immune functions on PCa, we developed a combined classifier in prostate cancer. The Glycolysis Score containing 19 genes and TME Score including three immune cells were created, using the univariate and multivariate Cox proportional hazards model, log-rank test, least absolute shrinkage and selection operator (LASSO) regression analysis and the bootstrap approach. Combining the glycolysis and immunological landscape, the Glycolysis-TME Classifier was then constructed. It was observed that the classifier was more accurate in predicting the prognosis of patients than the current biomarkers. Notably, there were significant differences in metabolic activity, signaling pathways, mutational landscape, immunotherapeutic response, and drug sensitivity among the Glycolysis/TME, Mixed group and Glycolysis/TME identified by this classifier. Overall, due to the significant prognostic value and potential therapeutic guidance of the Glycolysis-TME Classifier, we anticipate that this classifier will be clinically beneficial in the management of patients with PCa.
前列腺癌（PCa）是一种常见的恶性肿瘤，对男性健康构成重大威胁。前列腺特异性抗原（PSA）及其衍生物作为 FDA 批准的检测方法，不足以作为患者预后和临床决策的最佳标志物。人们普遍认为，PCa 中异常的糖酵解代谢与肿瘤进展有关，并使肿瘤微环境（TME）酸化。考虑到糖酵解和免疫功能对 PCa 的不可忽视的影响，我们开发了一种用于前列腺癌的联合分类器。使用单变量和多变量 Cox 比例风险模型、对数秩检验、最小绝对收缩和选择算子（LASSO）回归分析和自举方法，创建了包含 19 个基因的糖酵解评分和包含三种免疫细胞的 TME 评分。结合糖酵解和免疫景观，然后构建了糖酵解-TME 分类器。结果表明，该分类器在预测患者预后方面比当前的生物标志物更准确。值得注意的是，在代谢活性、信号通路、突变景观、免疫治疗反应和药物敏感性方面，糖酵解/TME、混合组和通过该分类器确定的糖酵解/TME 之间存在显著差异。总体而言，由于糖酵解-TME 分类器具有显著的预后价值和潜在的治疗指导意义，我们预计该分类器将在 PCa 患者的管理中具有临床益处。
