Fig. 1

Constructing WGCNA and prognosis model with LASSO regression. (A) The relationship between the scale-free fit index (y-axis) and the soft thresholding power (x-axis). (B) The relationship between average connectivity (degree, y-axis) and soft thresholding power. (C) Sample clustering and immune infiltration characteristics. (D) To generate a correlation heatmap illustrating the correlation between module genes and macrophage infiltration as well as marker genes. (E) Selecting genes associated with gastric cancer prognosis with single factor regression analysis from key module genes identified by WGCNA. (F-G) LASSO Cox regression to identify the top 13 genes associated with prognosis. (H) Comparison of median survival time between high-risk and low-risk groups using KM survival curves. (I) Time-dependent receiver operating characteristic (ROC) analysis for evaluating the performance of the prognostic model. (J) Column chart depicting prognosis prediction for gastric cancer patients based on Cox regression analysis