A two-gene prognostic model for lung adenocarcinoma using summary-data-based Mendelian randomization analysis and Cox proportional hazards regression

The Kaplan–Meier plot and univariate and multivariate Cox regression models illustrated that patients in the high-risk group had a poorer clinical outcome. The low-risk group showed enrichment of several immune-related pathways and showed higher infiltration levels of 15 immune cell types.</p> <p>A two-gene prognostic model was developed to predict outcomes in patients with LUAD, which was also highly associated with immune status.</p> <p>Lung adenocarcinoma (LUAD) is the most common pathological subtype of lung cancer.

In this study, we conducted SMR analysis to explore genetic associations between genes and LUAD, and analyzed DEGs between normal and LUAD tissue. Then, univariate Cox regression and survival analyses were performed to identify prognosis-related genes. A multivariate Cox regression analysis was used to construct a prognostic model, with individual gene contributions clarified through Shapley Additive Explanations (SHAP) analysis.

Two prognosis-related genes (PKP2 and SLC7A11) were identified and used to develop the prognostic model. Survival analysis and the univariate and multivariate Cox regression models illustrated that patients in the high-risk group had a poorer clinical outcome. The model was also highly related to immune status.</p> <p>Two prognostic genes (PKP2 and SLC7A11) were identified with Summary-data-based Mendelian Randomization Analysis and Cox Proportional Hazards Regression.</p> <p>A prognostic model was constructed to predict outcomes in LUAD.</p> <p>The prognostic model was highly related to immune state.</p>