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Abstract：
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.
摘要：
背景：结直肠癌（CRC）是全球最普遍的癌症类型之一。由于II/III期CRC肿瘤生物学的固有异质性，存在生存悖论。铁凋亡与肿瘤的进展密切相关，铁凋亡相关基因可作为预测癌症预后的新生物标志物。方法：从FerrDb和KEGG数据库中检索铁凋亡相关基因。我们的研究共有1397个样本来自9个独立的数据集，其中四个被整合为训练数据集来训练和构建模型，并在其余数据集中进行验证。我们开发了一个机器学习框架，该框架具有基于10倍交叉验证（CV）或引导重采样算法的10种算法的83种组合，以识别最健壮和稳定的模型。进行C-indice和ROC分析以评估其预测准确性和辨别能力。进行生存分析，然后进行单变量和多变量Cox回归分析，以评估所识别特征的性能。结果：通过Lasso和plsRcox的组合鉴定出铁凋亡相关基因（FRG）特征，由23个基因组成。FRG特征表现出比常见临床病理特征更好的表现(例如，年龄和阶段)，分子特征(例如，BRAF突变和微卫星不稳定性）和一些已发表的预测CRC预后的标志。将签名进一步分层为高风险组和低风险亚组，其中高FRG特征表明在所有收集的数据集中预后不良。敏感性分析显示FRG特征仍然是重要的预后因素。最后,我们开发了列线图和决策树来增强预后评估。结论：FRG特征能够准确选择高危II/III期CRC人群，并有助于优化精准治疗以改善其临床结局。
