A Novel Early-Stage Lung Adenocarcinoma Prognostic Model Based on Feature Selection With Orthogonal Regression - Tang, Binhua; Wang, Yuqi; Chen, Yu; Li, Ming; Tao, Yongfeng (Frontiers in Cell and Developmental Biology, 2021)
Carcinoma diagnosis and prognosis are still hindered by lack of effective prediction model and integration methodology. We proposed a novel feature selection with orthogonal regression (FSOR) method to resolve predictor selection and performance optimization. Functional enrichment and clinical outcome analyses with multi-omics information validated the method’s robustness in the early-stage prognosis of lung adenocarcinoma. Furthermore, compared with the classic LASSO regression method (the averaged 1-4-year predictive AUC measure, 0.6998), the proposed one outperforms more accurately by 0.7208 with fewer predictors; particularly its averaged 1-3-year AUC reaches 0.723, versus classic 0.6917 on TCGA. In sum, the proposed method can deliver better prediction performance for early-stage prognosis and improve therapy strategy, but with less predictor consideration and computation burden. The self-composed running scripts, together with the processed results are available at https://github.com/gladex/PM-FSOR.