本研究致力于利用二硫死亡相关铁死亡（SRF）基因构建一个新的特征，以预测结直肠癌（CRC）个体对免疫治疗的反应、预后和药物敏感性。RNA测序数据以及个体相应的临床信息与 CRC 一起从癌症基因组图谱 (TCGA) 数据集中提取。 SRF 是在随机森林 (RF)、最小绝对收缩和选择算子 (LASSO) 以及逐步回归算法的帮助下构建的。为了验证 SRF 模型，我们将其应用于外部队列 GSE38832。比较高风险组和低风险组（类别）的预后、免疫治疗反应、药物敏感性、基因的分子功能和基因的体细胞突变。此后，所有统计分析均借助 R（版本 4.23）软件和 Cytoscape（版本 3.8.0）工具的各种软件包进行。SRF 是基于五个基因（ATG7、USP7、MMD、PLIN4 和总谐波失真 (THDC)2)。单变量和多变量 Cox 回归分析均证实 SRF 是一个独立的、与预后相关的危险因素。高风险类别的个体预后更差，肿瘤突变负荷（TMB）升高，并且免疫抑制状态明显。因此，他们在免疫治疗后可能有更好的结果，并且可能受益于帕唑帕尼、拉帕替尼和舒尼替尼的给药。总之，SRF可以作为预后评估的新生物标志物。此外，它也是 CRC 药物敏感性和免疫治疗反应的良好预测因子，但在临床环境中实施之前应进行优化。© 2024。作者。

This research strived to construct a new signature utilizing disulfidptosis-related ferroptosis (SRF) genes to anticipate response to immunotherapy, prognosis, and drug sensitivity in individuals with colorectal cancer (CRC).The data for RNA sequencing as well as corresponding clinical information of individuals with CRC, were extracted from The Cancer Genome Atlas (TCGA) dataset. SRF were constructed with the help of the random forest (RF), least absolute shrinkage and selection operator (LASSO), and stepwise regression algorithms. To validate the SRF model, we applied it to an external cohort, GSE38832. Prognosis, immunotherapy response, drug sensitivity, molecular functions of genes, and somatic mutations of genes were compared across the high- and low-risk groups (categories). Following this, all statistical analyses were conducted with the aid of the R (version 4.23) software and various packages of the Cytoscape (version 3.8.0) tool.SRF was developed based on five genes (ATG7, USP7, MMD, PLIN4, and THDC2). Both univariate and multivariate Cox regression analyses established SRF as an independent, prognosis-related risk factor. Individuals from the high-risk category had a more unfavorable prognosis, elevated tumor mutational burden (TMB), and significant immunosuppressive status. Hence, they might have better outcomes post-immunotherapy and might benefit from the administration of pazopanib, lapatinib, and sunitinib.In conclusion, SRF can act as a new biomarker for prognosis assessment. Moreover, it is also a good predictor of drug sensitivity and immunotherapy response in CRC but should undergo optimization before implementation in clinical settings.© 2024. The Author(s).