失巢凋亡是一种程序性细胞死亡形式，对于预防癌症转移至关重要。在一些实体癌中，失巢凋亡抵抗可以促进肿瘤进展。然而，这种现象在透明细胞肾细胞癌 (ccRCC) 中尚未得到充分研究。利用 SVM 机器学习，我们从 ccRCC 患者转录组数据中鉴定了核心失巢凋亡相关基因 (ARG)。 LASSO Cox 回归模型将患者分为风险组，为预后模型提供信息。 GSVA 和 ssGSEA 评估了免疫浸润，单细胞分析检查了免疫细胞中的 ARG 表达。定量 PCR 和免疫组织化学验证了 ccRCC 中免疫治疗应答者和无应答者之间的 ARG 表达差异。CCND1、CDKN3、PLK1 和 BID 等 ARG 是预测 ccRCC 结果的关键，将较高​​的风险与 Treg 浸润增加和 M1 巨噬细胞存在减少联系起来。表明失巢凋亡抵抗促进了免疫抑制环境。单细胞研究表明 ARG 在 Tregs 和树突状细胞中富集，影响免疫检查点。免疫组织化学分析显示，在对免疫治疗有反应的 ccRCC 组织中，ARG 蛋白表达显着升高。这项研究建立了一种新的失巢凋亡抗性基因特征，可以预测 ccRCC 的生存和免疫治疗反应，表明通过这些 ARG 操纵免疫环境可以改善治疗策略和预后ccRCC.版权所有 © 2024 Wen、Hou、Qi、Cheng、Liao、Fang、Xiao、Qiu 和 Wei。

Anoikis is a form of programmed cell death essential for preventing cancer metastasis. In some solid cancer, anoikis resistance can facilitate tumor progression. However, this phenomenon is underexplored in clear-cell renal cell carcinoma (ccRCC).Using SVM machine learning, we identified core anoikis-related genes (ARGs) from ccRCC patient transcriptomic data. A LASSO Cox regression model stratified patients into risk groups, informing a prognostic model. GSVA and ssGSEA assessed immune infiltration, and single-cell analysis examined ARG expression across immune cells. Quantitative PCR and immunohistochemistry validated ARG expression differences between immune therapy responders and non-responders in ccRCC.ARGs such as CCND1, CDKN3, PLK1, and BID were key in predicting ccRCC outcomes, linking higher risk with increased Treg infiltration and reduced M1 macrophage presence, indicating an immunosuppressive environment facilitated by anoikis resistance. Single-cell insights showed ARG enrichment in Tregs and dendritic cells, affecting immune checkpoints. Immunohistochemical analysis reveals that ARGs protein expression is markedly elevated in ccRCC tissues responsive to immunotherapy.This study establishes a novel anoikis resistance gene signature that predicts survival and immunotherapy response in ccRCC, suggesting that manipulating the immune environment through these ARGs could improve therapeutic strategies and prognostication in ccRCC.Copyright © 2024 Wen, Hou, Qi, Cheng, Liao, Fang, Xiao, Qiu and Wei.