Fig. 4: Construction and validation of a treg-associated prognostic model in NSCLC. | npj Precision Oncology

Fig. 4: Construction and validation of a treg-associated prognostic model in NSCLC.

From: G0S2: a potential target for NSCLC identified through prognostic models from multi-Omic analysis of regulatory T cell metabolic genes

Fig. 4

A univariate Cox regression analysis was performed on the expression profiles of 26 genes to evaluate their potential impact on the overall survival (OS) of patients, and seven genes were significantly associated with OS (A). Subsequently, the LASSO Cox regression method was used to develop a prognostic feature model based on these seven genes (BIRC3, TIMP1, G0S2, ACP5, PRKCB, PDE4B, CD52) (B–D). Patients were stratified into high-risk and low-risk groups based on median risk scores calculated by the model, and survival analysis revealed that the OS of patients in the high-risk group was significantly lower than that in the low-risk group (E). Time-dependent ROC curve analysis confirmed the model’s moderate accuracy in predicting 1-year, 3-year, and 5-year survival rates (F). Univariate and multivariate Cox regression analyses identified independent prognostic factors, presented in a forest plot where the final column displays the hazard ratio (HR), 95% confidence interval (CI), and p-value in a compact format (G). A nomogram was established based on independent prognostic factors to predict overall survival (H), and the calibration curves for the nomogram are presented (I).

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