Yang Enguang, Ji Luhua, Zhang Xinyu, Jing Suoshi, Li Pan, Wang Hanzhang, Zhang Luyang, Zhang Yuanfeng, Yang Li, Tian Junqiang, Wang Zhiping
Institute of Urology, Key Laboratory of Gansu Province for Urological Diseases, Gansu Urological Clinical Center, Lanzhou University Second Hospital, Lanzhou 730030, China.
Department of Pathology and Laboratory Medicine, Legorreta Cancer Center at Brown University, The Warren Alpert Medical School of Brown University, Brown University Health, Providence 02912, Rhode Island, USA.
Stem Cells Int. 2024 Nov 15;2024:6064671. doi: 10.1155/sci/6064671. eCollection 2024.
Mesenchymal stem cells (MSCs) have been identified to have a unique migratory pattern toward tumor sites across diverse cancer types, playing a crucial role in cancer progression, treatment resistance, and immunosuppression. This study aims to formulate a prognostic model focused on MSC-associated markers to efficiently predict the clinical outcomes and responses to therapy in individuals with bladder cancer (BC). Clinical and transcriptome profiling data were extracted from The Cancer Genome Atlas Urothelial Bladder Carcinoma (TCGA-BLCA) and GSE31684 databases. Systematic quantification of MSC prevalences and stromal indices was undertaken, culminating in the discernment of genes correlated with stromal MSCs following a thorough application of weighted gene coexpression network analysis techniques. Subsequently, an exhaustive risk signature pertinent to MSC was formulated by amalgamating methods from univariate and Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression models. Drugs targeting genes associated with MSCs were screened using molecular docking. The prognostic model for MSC incorporated five critical genes: ZNF165, matrix remodeling-associated 7 (MXRA7), CEMIP, ADP-ribosylation factor-like 4C (ARL4C), and cerebral endothelial cell adhesion molecule (CERCAM). In the case of BC patients, stratification was performed into discrete risk categories, utilizing the median MSC risk score as a criterion. It was striking that those classified within the high-MSC-risk bracket demonstrated correlations with unfavorable prognostic implications. Enhanced responsiveness to immunotherapy in low-MSC-risk patients was delineated compared to their high-MSC-risk counterparts. A heightened receptivity was noted toward particular chemotherapy drugs, encompassing gemcitabine, vincristine, paclitaxel, gefitinib, and sorafenib, within this high-risk group. Conversely, a superior reaction to cisplatin was distinctly evident among those marked by low MSC scores. The results of molecular docking demonstrated that kaempferol exhibited favorable docking with ZNF165, quercetin exhibited favorable docking with MXRA7, mairin exhibited favorable docking with CEMIP, and limonin diosphenol exhibited favorable docking with ARL4C. The five-gene MSC prognostic model demonstrates substantial efficacy in prognosticating clinical outcomes and gauging responsiveness to chemotherapy and immunotherapy regimens. The genes ZNF165, MXRA7, CEMIP, ARL4C, and CERCAM are underscored as promising candidates warranting further exploration for anti-MSC therapeutic strategies, thereby offering novel insights for personalized treatment approaches in BC.
间充质干细胞（MSCs）已被证实对多种癌症类型的肿瘤部位具有独特的迁移模式，在癌症进展、治疗耐药性和免疫抑制中发挥着关键作用。本研究旨在构建一个以MSCs相关标志物为重点的预后模型，以有效预测膀胱癌（BC）患者的临床结局和对治疗的反应。从癌症基因组图谱尿路上皮膀胱癌（TCGA-BLCA）和GSE31684数据库中提取临床和转录组分析数据。对MSCs患病率和基质指数进行系统量化，在全面应用加权基因共表达网络分析技术后，最终识别出与基质MSCs相关的基因。随后，通过整合单变量和最小绝对收缩和选择算子（LASSO）Cox回归模型的方法，构建了一个与MSCs相关的详尽风险特征。使用分子对接筛选靶向与MSCs相关基因的药物。MSCs预后模型纳入了五个关键基因：锌指蛋白165（ZNF165）、基质重塑相关蛋白7（MXRA7）、CEMIP、ADP核糖基化因子样4C（ARL4C）和脑内皮细胞粘附分子（CERCAM）。对于BC患者，以MSCs风险评分中位数为标准，将其分为不同的风险类别。值得注意的是，那些被归类为高MSCs风险组的患者显示出与不良预后相关。与高MSCs风险患者相比，低MSCs风险患者对免疫治疗的反应性增强。在这个高风险组中，对包括吉西他滨、长春新碱、紫杉醇、吉非替尼和索拉非尼在内的特定化疗药物的敏感性更高。相反，在MSCs评分低的患者中，对顺铂的反应明显更好。分子对接结果表明，山奈酚与ZNF165表现出良好的对接，槲皮素与MXRA7表现出良好的对接，马里宁与CEMIP表现出良好的对接，柠檬苦素二酚与ARL4C表现出良好的对接。五基因MSCs预后模型在预测临床结局以及评估对化疗和免疫治疗方案的反应方面具有显著疗效。ZNF165、MXRA7、CEMIP、ARL4C和CERCAM基因被强调为有前景的候选基因，值得进一步探索抗MSCs治疗策略，从而为BC的个性化治疗方法提供新的见解。
