Identification of a ferroptosis-related gene signature predicting recurrence in stage II/III colorectal cancer based on machine learning algorithms

作者：Ze Wang, Chenghao Ma, Qiong Teng, Jinyu Man, Xuening Zhang, Xinjie Liu, Tongchao Zhang, Wei Chong, Hao Chen, Ming Lü · 发表于：Frontiers in Pharmacology · 年份：2023 · DOI：10.3389/fphar.2023.1260697 · 被引用次数：8 · 研究领域：Ferroptosis and cancer prognosis、Cancer, Lipids, and Metabolism、Genetic factors in colorectal cancer

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 signa...