PDF(Pubmed)
Abstract：
OBJECTIVE: This study aimed to explore the mechanisms of transient receptor potential (TRP) channels on the immune microenvironment and develop a TRP-related signature for predicting prognosis, immunotherapy response, and drug sensitivity in gliomas.
METHODS: Based on the unsupervised clustering algorithm, we identified novel TRP channel clusters and investigated their biological function, immune microenvironment, and genomic heterogeneity. In vitro and in vivo experiments revealed the association between TRPV2 and macrophages. Subsequently, based on 96 machine learning algorithms and six independent glioma cohorts, we constructed a machine learning-based TRP channel signature (MLTS). The performance of the MLTS in predicting prognosis, immunotherapy response, and drug sensitivity was evaluated.
RESULTS: Patients with high expression levels of TRP channel genes had worse prognoses, higher tumor mutation burden, and more activated immunosuppressive microenvironment. Meanwhile, TRPV2 was identified as the most essential regulator in TRP channels. TRPV2 activation could promote macrophages migration toward malignant cells and alleviate glioma prognosis. Furthermore, MLTS could work independently of common clinical features and present stable and superior prediction performance.
CONCLUSIONS: This study investigated the comprehensive effect of TRP channel genes in gliomas and provided a promising tool for designing effective, precise treatment strategies.
METHODS: Based on the unsupervised clustering algorithm, we identified novel TRP channel clusters and investigated their biological function, immune microenvironment, and genomic heterogeneity. In vitro and in vivo experiments revealed the association between TRPV2 and macrophages. Subsequently, based on 96 machine learning algorithms and six independent glioma cohorts, we constructed a machine learning-based TRP channel signature (MLTS). The performance of the MLTS in predicting prognosis, immunotherapy response, and drug sensitivity was evaluated.
RESULTS: Patients with high expression levels of TRP channel genes had worse prognoses, higher tumor mutation burden, and more activated immunosuppressive microenvironment. Meanwhile, TRPV2 was identified as the most essential regulator in TRP channels. TRPV2 activation could promote macrophages migration toward malignant cells and alleviate glioma prognosis. Furthermore, MLTS could work independently of common clinical features and present stable and superior prediction performance.
CONCLUSIONS: This study investigated the comprehensive effect of TRP channel genes in gliomas and provided a promising tool for designing effective, precise treatment strategies.
摘要：
目的：本研究旨在探讨瞬时受体电位（TRP）通道在免疫微环境中的作用机制，并开发与TRP相关的标记来预测预后，免疫治疗反应，和神经胶质瘤的药物敏感性。
方法：基于无监督聚类算法，我们鉴定了新的TRP通道簇，并研究了它们的生物学功能，免疫微环境，和基因组异质性。体外和体内实验揭示了TRPV2与巨噬细胞之间的关联。随后，基于96种机器学习算法和六个独立的神经胶质瘤队列，我们构建了基于机器学习的TRP通道签名(MLTS)。MLTS在预测预后方面的表现，免疫治疗反应，并对药物敏感性进行了评估。
结果：TRP通道基因高表达的患者预后较差，更高的肿瘤突变负担，和更活化的免疫抑制微环境。同时,TRPV2被确定为TRP通道中最重要的调节因子。TRPV2活化可促进巨噬细胞向恶性细胞迁移，减轻胶质瘤预后。此外，MLTS可以独立于常见的临床特征工作，并具有稳定和优越的预测性能。
结论：这项研究调查了TRP通道基因在神经胶质瘤中的综合作用，并为设计有效的，精确的治疗策略。
方法：基于无监督聚类算法，我们鉴定了新的TRP通道簇，并研究了它们的生物学功能，免疫微环境，和基因组异质性。体外和体内实验揭示了TRPV2与巨噬细胞之间的关联。随后，基于96种机器学习算法和六个独立的神经胶质瘤队列，我们构建了基于机器学习的TRP通道签名(MLTS)。MLTS在预测预后方面的表现，免疫治疗反应，并对药物敏感性进行了评估。
结果：TRP通道基因高表达的患者预后较差，更高的肿瘤突变负担，和更活化的免疫抑制微环境。同时,TRPV2被确定为TRP通道中最重要的调节因子。TRPV2活化可促进巨噬细胞向恶性细胞迁移，减轻胶质瘤预后。此外，MLTS可以独立于常见的临床特征工作，并具有稳定和优越的预测性能。
结论：这项研究调查了TRP通道基因在神经胶质瘤中的综合作用，并为设计有效的，精确的治疗策略。
