Hong Yude, Hu Xiao, Chen Libo, Li Mingyong, Zhang Mingxiao, Deng Weiming
Department of Urology, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China.
Department of Urology, Affiliated Beijing Chaoyang Hospital of Capital Medical University, Beijing, China.
Front Cell Dev Biol. 2025 Jun 4;13:1560095. doi: 10.3389/fcell.2025.1560095. eCollection 2025.
Clear cell renal cell carcinoma (ccRCC) is a highly aggressive renal cancer subtype and lacks highly precise individualized treatment options. Thus, we used a novel computational framework to construct a consensus machine learning-related signature (MLRS) to predict prognosis and screen patients effectively for immunotherapy.
An integrative machine learning procedure involving 10 methods was used to contract MLRS. Various methods were used to evaluate immune cell infiltration and biological characteristics. Moreover, we explored the response to immunotherapy and drug sensitivity. Single-cell RNA sequencing analysis, qRT-PCR, and a CCK-8 assay were used to clarify the biological functions of the hub gene.
MLRS demonstrated outstanding performance in predicting prognosis compared with the other published signatures, and the high-MLRS group had a favorable outcome in four independent datasets. Furthermore, the low-MLRS group displayed a greater possibility of responding to immunotherapy and had a "hot" tumor immunophenotype. The high-MLRS group was characterized by a phenotype of immune suppression and was less likely to benefit from immunotherapy, while some small molecule inhibitors might serve as promising treatment options. Single-cell analysis revealed that MLRS was highly enriched in endothelial cells. We also identified DLL4/Notch and JAG/Notch signaling as the key ligand-receptor pairs in ccRCC. EMCN was downregulated in ccRCC, and further functional experiments demonstrated that EMCN knockdown inhibited cell proliferation.
The MLRS can predict patient prognosis, may be utilized to screen potential populations that may benefit from immunotherapy, and predict potential drug targets, with broad significance for the clinical treatment of ccRCC.
透明细胞肾细胞癌（ccRCC）是一种侵袭性很强的肾癌亚型，缺乏高度精确的个体化治疗方案。因此，我们使用了一种新颖的计算框架来构建一个共识性机器学习相关特征（MLRS），以有效预测预后并筛选适合免疫治疗的患者。
采用一种涉及10种方法的综合机器学习程序来构建MLRS。使用各种方法评估免疫细胞浸润和生物学特征。此外，我们还探究了对免疫治疗的反应和药物敏感性。采用单细胞RNA测序分析、qRT-PCR和CCK-8测定法来阐明枢纽基因的生物学功能。
与其他已发表的特征相比，MLRS在预测预后方面表现出色，并且在四个独立数据集中，高MLRS组具有良好的预后。此外，低MLRS组对免疫治疗有更大的反应可能性，并且具有“热”肿瘤免疫表型。高MLRS组的特征是免疫抑制表型，从免疫治疗中获益的可能性较小，而一些小分子抑制剂可能是有前景的治疗选择。单细胞分析显示MLRS在内皮细胞中高度富集。我们还确定DLL4/Notch和JAG/Notch信号通路是ccRCC中的关键配体-受体对。EMCN在ccRCC中表达下调，进一步的功能实验表明敲低EMCN可抑制细胞增殖。
MLRS可以预测患者预后，可用于筛选可能从免疫治疗中获益的潜在人群，并预测潜在的药物靶点，对ccRCC的临床治疗具有广泛的意义。
