Zhang Xinyu, Li Pan, Ji Luhua, Zhang Yuanfeng, Zhang Ze, Guo Yufeng, Zhang Luyang, Jing Suoshi, Dong Zhilong, Tian Junqiang, Yang Li, Ding Hui, Yang Enguang, Wang Zhiping
Institute of Urology, Lanzhou University Second Hospital, Key Laboratory of Gansu Province for Urological Diseases, Gansu Urological Clinical Center, Lanzhou, China.
Institute of Urology, Lanzhou University Second Hospital, Key Laboratory of Gansu Province for Urological Diseases, Gansu Urological Clinical Center, Lanzhou, China.
Transl Oncol. 2025 Apr;54:102349. doi: 10.1016/j.tranon.2025.102349. Epub 2025 Mar 11.
Mesenchymal stem cells (MSCs), due to their tumor-targeting homing properties, are present in the tumor microenvironment (TME) and influence the biological behaviors of tumors. The purpose of this paper is to establish a signature based on the MSC secretome to predict the prognosis and treatment of bladder cancer (BLCA).
The presence of MSCs in BLCA was validated through flow cytometry and multiplex fluorescence immunohistochemistry (mFIHC), and the relationships between MSCs and clinical characteristics were explored. Unsupervised clustering analysis was performed on BLCA according to the differential proteins detected in MSC-conditioned medium (MSCCM) using a cytokine array. Using the TCGA-BLCA, GSE32548, and GSE32894 datasets as background data, a risk signature was constructed according to the differential proteins in MSCCM through machine learning. For the risk groups with high and low prognoses, we calculated Kaplan-Meier (K-M) curves. Additionally, we explored the relationships between the signature and the tumor immune landscape, response to immunotherapy, and chemotherapy drugs.
Both flow cytometry and mFIHC confirmed the presence of MSCs in bladder tumors, and clinical samples revealed correlations between MSCs and the pathological grade, T stage, and Ki67 in BLCA. Based on differential proteins and unsupervised clustering analysis, BLCA patients were divided into two groups, and significant differences were found between these groups in terms of TME, immune response, and clinical treatments. Using machine learning, a signature was constructed with the combination algorithm Stepcox (both) + plsRcox, revealing significant survival differences between the high- and low-risk MSC groups. Regression analyses, along with ROC curves, further demonstrated that risk score independently predict the prognosis of patients with high predictive performance. Moreover, there were notable differences between the high- and low-risk groups in terms of the TME scores, immune infiltration, and immune checkpoints. For BLCA immunotherapy, the low-risk group suggested better efficacy, while conventional chemotherapy drugs such as gemcitabine and cisplatin might be less effective in the low-risk group.
The signature based on MSC secreted protein profiles could effectively predict the prognosis of BLCA and provided valuable guidance for treatment and drug resistance.
间充质干细胞（MSCs）因其肿瘤靶向归巢特性，存在于肿瘤微环境（TME）中，并影响肿瘤的生物学行为。本文旨在建立基于MSCs分泌组的特征，以预测膀胱癌（BLCA）的预后和治疗效果。
通过流式细胞术和多重荧光免疫组织化学（mFIHC）验证BLCA中MSCs的存在，并探讨MSCs与临床特征之间的关系。根据使用细胞因子阵列在MSC条件培养基（MSCCM）中检测到的差异蛋白，对BLCA进行无监督聚类分析。以TCGA-BLCA、GSE32548和GSE32894数据集作为背景数据，通过机器学习根据MSCCM中的差异蛋白构建风险特征。对于高、低预后风险组，我们计算了Kaplan-Meier（K-M）曲线。此外，我们还探讨了该特征与肿瘤免疫景观、免疫治疗反应和化疗药物之间的关系。
流式细胞术和mFIHC均证实膀胱肿瘤中存在MSCs，临床样本显示BLCA中MSCs与病理分级、T分期和Ki67之间存在相关性。基于差异蛋白和无监督聚类分析，将BLCA患者分为两组，这些组在TME、免疫反应和临床治疗方面存在显著差异。使用机器学习，结合Stepcox（两者）+plsRcox算法构建了一个特征，揭示了高、低风险MSC组之间存在显著的生存差异。回归分析以及ROC曲线进一步表明，风险评分能够独立预测具有高预测性能的患者的预后。此外，高、低风险组在TME评分、免疫浸润和免疫检查点方面存在显著差异。对于BLCA免疫治疗，低风险组显示出更好的疗效，而吉西他滨和顺铂等传统化疗药物在低风险组可能效果较差。
基于MSCs分泌蛋白谱的特征能够有效预测BLCA的预后，并为治疗和耐药性提供有价值的指导。
