背景：结直肠癌（CRC）是全球最常见的癌症类型之一。由于II/III期CRC肿瘤生物学上的内在异质性，存在生存悖论。铁死亡（ferroptosis）与肿瘤的进展密切相关，铁死亡相关基因可以作为预测癌症预后的新型生物标志物。方法：从FerrDb和KEGG数据库检索铁死亡相关基因。共纳入了来自9个独立数据集的1,397个样本，其中有4个数据集合并为训练数据集，用于训练和构建模型，并在其余数据集中进行验证。我们基于10折交叉验证（CV）或自助重采样算法，使用83种组合的10种算法开发了一个机器学习框架，以识别最稳健和稳定的模型。进行了C指数和ROC分析以评估其预测准确性和判别能力。随后进行了生存分析，并进行了单因素和多因素Cox回归分析，以评估所鉴定的标志物的性能。结果：通过Lasso和plsRcox的组合识别了与铁死亡相关的基因（FRG）标志，共由23个基因组成。与普通的临床病理特征（如年龄和分期）、分子特征（如BRAF突变和微卫星不稳定性）以及几个已发表的标志物相比，FRG标志在预测CRC预后方面表现更好。该标志进一步分为高风险组和低风险亚组，其中高FRG标志在所有收集的数据集中都表示预后不良。敏感性分析显示FRG标志仍然是一个显著的预后因素。最后，我们开发了一个评估预后的诊断图和决策树。结论：FRG标志可以准确选择高风险II/III期CRC人群，并帮助优化精准治疗以改善他们的临床预后。版权 © 2023 Wang, Ma, Teng, Man, Zhang, Liu, Zhang, Chong, Chen and Lu.

Background: Colorectal cancer (CRC) is one of the most prevalent cancer types globally. A survival paradox exists due to the inherent heterogeneity in stage II/III CRC tumor biology. Ferroptosis is closely related to the progression of tumors, and ferroptosis-related genes can be used as a novel biomarker in predicting cancer prognosis. Methods: Ferroptosis-related genes were retrieved from the FerrDb and KEGG databases. A total of 1,397 samples were enrolled in our study from nine independent datasets, four of which were integrated as the training dataset to train and construct the model, and validated in the remaining datasets. We developed a machine learning framework with 83 combinations of 10 algorithms based on 10-fold cross-validation (CV) or bootstrap resampling algorithm to identify the most robust and stable model. C-indice and ROC analysis were performed to gauge its predictive accuracy and discrimination capabilities. Survival analysis was conducted followed by univariate and multivariate Cox regression analyses to evaluate the performance of identified signature. Results: The ferroptosis-related gene (FRG) signature was identified by the combination of Lasso and plsRcox and composed of 23 genes. The FRG signature presented better performance than common clinicopathological features (e.g., age and stage), molecular characteristics (e.g., BRAF mutation and microsatellite instability) and several published signatures in predicting the prognosis of the CRC. The signature was further stratified into a high-risk group and low-risk subgroup, where a high FRG signature indicated poor prognosis among all collected datasets. Sensitivity analysis showed the FRG signature remained a significant prognostic factor. Finally, we have developed a nomogram and a decision tree to enhance prognosis evaluation. Conclusion: The FRG signature enabled the accurate selection of high-risk stage II/III CRC population and helped optimize precision treatment to improve their clinical outcomes.Copyright © 2023 Wang, Ma, Teng, Man, Zhang, Liu, Zhang, Chong, Chen and Lu.