Fig. 3 | Scientific Reports

Fig. 3

From: Integrated single-cell and machine learning analysis identifies PMAIP1 as a novel biomarker for predicting prognosis and immunotherapy response in colorectal cancer

Fig. 3The alternative text for this image may have been generated using AI.

WGCNA Analysis Reveals Apoptosis-Related Gene Modules Associated with Tumor Clinical Traits. (A) Sample clustering dendrogram for identifying outlier samples. (B) Soft-thresholding power selection plot for network construction. The left panel displays the scale-free topology fit index (R²) under different soft-threshold values, while the right panel shows the mean connectivity trend. A soft-threshold of 9 was selected to ensure the network approximates a scale-free distribution. (C) Gene clustering dendrogram with modules obtained from dynamic tree cutting, where colors represent different modules. (D) Sample clustering heatmap integrated with phenotypic traits (such as gender, age, stage, etc.). (E) Heatmap of correlations between modules and clinical traits, with colors representing Pearson correlation coefficients and p-values in parentheses. Among them, the black module (MEblack) is significantly correlated with tumor stage (r = −0.24, p = 0.002).

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