Xiang Yuan, Zhou Zijian, Mu Tong, Zhang Shunyao, Xie Lei, Zhou Yajie, Zhang Wenxiong, Fu Liuxiang
Department of Thoracic Surgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, China.
The Second Clinical Medical School, Jiangxi Medical College, Nanchang University, Nanchang, 330088, China.
Biochem Biophys Rep. 2025 Jul 12;43:102102. doi: 10.1016/j.bbrep.2025.102102. eCollection 2025 Sep.
Ferroptosis and disulfidptosis, two programmed cell death pathways, critically drive tumor growth by affecting metastasis. Although the prognostic value of disulfidptosis and ferroptosis had been separately validated in kidney renal clear cell carcinoma (KIRC), prognostic effect of integrating two programmed death genes remains unclear in KIRC. Our objective is to establish an innovative prognostic model for KIRC.
We sourced KIRC patients' information that contains clinical and genomic from The Cancer Genome Atlas (TCGA) database. We selected disulfidptosis-related and ferroptosis-related genes (DRFs) to construct a prognostic model. By combining clinical features and prognostic models, we developed the nomogram. Additionally, the mechanism of DRF was explored in KIRC, including tumor immune dysfunction and exclusion (TIDE), Kaplan-Meier (K-M) analysis, tumor microenvironment (TME) analysis, and more. Drug sensitivity analysis shows which drugs are sensitive to tumors. Experiment with RT-PCR to confirm DRFs gene expression in the cell line.
Constructing risk score with five DRFs, all tumor samples were categorized into high-risk group (HG) and low-risk group (LG). The HG samples demonstrated lower survival rates according to K-M survival curves. The nomogram with risk score demonstrated significant predictive value than nomogram without the risk score. TME analysis indicated that the proportion of T cells follicular helper and Tregs was higher in HG, while Macrophages M1 and Mast cells resting were higher in LG. GSEA analysis demonstrated Retinol metabolism pathway, drug metabolism other enzymes pathway, etc. were enriched in HG, while endocytosis-related pathway, neurotrophin signaling pathway, etc. were enriched in LG. TIDE analysis showed tumors in HG are more prone to immune evasion. The drug sensitivity analysis indicated that the HG is sensitive to antitumor drugs such as Cedrane and Osimertinib, while the LG is sensitive to antitumor drugs such as 5-Fluorouracil and Entinostat. RT-qPCR have confirmed expression of DRFs in KIRC cell lines.
Our DRFs-based prognostic model and nomogram effectively predict survival and guide treatment decisions.
铁死亡和二硫化物诱导的细胞死亡是两种程序性细胞死亡途径，通过影响转移严重驱动肿瘤生长。尽管二硫化物诱导的细胞死亡和铁死亡的预后价值已在肾透明细胞癌（KIRC）中分别得到验证，但整合这两种程序性死亡基因的预后效果在KIRC中仍不清楚。我们的目标是建立一种创新的KIRC预后模型。
我们从癌症基因组图谱（TCGA）数据库中获取了包含临床和基因组信息的KIRC患者信息。我们选择了二硫化物诱导的细胞死亡相关基因和铁死亡相关基因（DRFs）来构建预后模型。通过结合临床特征和预后模型，我们开发了列线图。此外，还在KIRC中探索了DRF的机制，包括肿瘤免疫功能障碍和排除（TIDE）、Kaplan-Meier（K-M）分析、肿瘤微环境（TME）分析等。药物敏感性分析显示哪些药物对肿瘤敏感。通过RT-PCR实验确认DRFs基因在细胞系中的表达。
用五个DRFs构建风险评分，所有肿瘤样本被分为高风险组（HG）和低风险组（LG）。根据K-M生存曲线，HG样本的生存率较低。带有风险评分的列线图比没有风险评分的列线图具有显著的预测价值。TME分析表明，HG中滤泡辅助性T细胞和调节性T细胞的比例较高，而LG中M1巨噬细胞和静息肥大细胞的比例较高。基因集富集分析（GSEA）表明，视黄醇代谢途径、药物代谢其他酶途径等在HG中富集，而内吞作用相关途径、神经营养因子信号通路等在LG中富集。TIDE分析表明，HG中的肿瘤更容易发生免疫逃逸。药物敏感性分析表明，HG对雪松烷和奥希替尼等抗肿瘤药物敏感，而LG对5-氟尿嘧啶和恩替诺特等抗肿瘤药物敏感。RT-qPCR已证实DRFs在KIRC细胞系中的表达。
我们基于DRFs的预后模型和列线图能有效预测生存并指导治疗决策。
