Guo Kaixuan, Lai Cong, Shi Juanyi, Tang Zhuang, Liu Cheng, Li Kuiqing, Xu Kewei
Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
Front Oncol. 2021 Sep 14;11:605810. doi: 10.3389/fonc.2021.605810. eCollection 2021.
Prostate cancer (PCa) is one of the most prevalent cancers among males, and its mortality rate is increasing due to biochemical recurrence (BCR). Glycolysis has been proven to play an important regulatory role in tumorigenesis. Although several key regulators or predictors involved in PCa progression have been found, the relationship between glycolysis and PCa is unclear; we aimed to develop a novel glycolysis-associated multifactor prediction model for better predicting the prognosis of PCa.
Differential mRNA expression profiles derived from the Cancer Genome Atlas (TCGA) PCa cohort were generated through the "edgeR" package. Glycolysis-related genes were obtained from the GSEA database. Univariate Cox and LASSO regression analyses were used to identify genes significantly associated with disease-free survival. ROC curves were applied to evaluate the predictive value of the model. An external dataset derived from Gene Expression Omnibus (GEO) was used to verify the predictive ability. Glucose consumption and lactic production assays were used to assess changes in metabolic capacity, and Transwell assays were used to assess the invasion and migration of PC3 cells.
Five glycolysis-related genes were applied to construct a risk score prediction model. Patients with PCa derived from TCGA and GEO (GSE70770) were divided into high-risk and low-risk groups according to the median. In the TCGA cohort, the high-risk group had a poorer prognosis than the low-risk group, and the results were further verified in the GSE70770 cohort. experiments demonstrated that knocking down HMMR, KIF20A, PGM2L1, and ANKZF1 separately led to less glucose consumption, less lactic production, and inhibition of cell migration and invasion, and the results were the opposite with GPR87 knockdown.
The risk score based on five glycolysis-related genes may serve as an accurate prognostic marker for PCa patients with BCR.
前列腺癌（PCa）是男性中最常见的癌症之一，由于生化复发（BCR），其死亡率正在上升。糖酵解已被证明在肿瘤发生中起重要的调节作用。尽管已经发现了一些参与PCa进展的关键调节因子或预测因子，但糖酵解与PCa之间的关系尚不清楚；我们旨在开发一种新的糖酵解相关多因素预测模型，以更好地预测PCa的预后。
通过“edgeR”软件包生成来自癌症基因组图谱（TCGA）PCa队列的差异mRNA表达谱。糖酵解相关基因从GSEA数据库中获得。单因素Cox和LASSO回归分析用于鉴定与无病生存期显著相关的基因。应用ROC曲线评估模型的预测价值。来自基因表达综合数据库（GEO）的外部数据集用于验证预测能力。葡萄糖消耗和乳酸生成测定用于评估代谢能力的变化，Transwell测定用于评估PC3细胞的侵袭和迁移。
应用五个糖酵解相关基因构建风险评分预测模型。根据中位数将来自TCGA和GEO（GSE70770）的PCa患者分为高风险组和低风险组。在TCGA队列中，高风险组的预后比低风险组差，并且在GSE70770队列中进一步验证了结果。实验表明，分别敲低HMMR、KIF20A、PGM2L1和ANKZF1会导致葡萄糖消耗减少、乳酸生成减少以及细胞迁移和侵袭受到抑制，而敲低GPR87的结果则相反。
基于五个糖酵解相关基因的风险评分可能作为BCR的PCa患者的准确预后标志物。
