Fig 1: Exploring the biological functions of biomarker genes. (A) Comparison of biomarker gene expression between normal tissue and cancer tissue in TCGA COAD. (B–E) Comparison of biomarker gene expression between cancer tissues and paired normal tissues, statistically assessed using Wilcox test. (F) The representative immunohistochemical images of FABP4, SCG2, CALB2, SERPINE1, and HOXC6. A total of 16 pairs of CRC tissue (cancer and adjacent normal tissue) were collected for IHC. (G) The heatmap shows the Pearson’s correlation between five biomarker genes and immune cells in the combined GEO cohort. Red represents positive correlation, whereas blue represents negative correlation. (H) The correlation between five biomarker genes and four immunosuppressive indices (columns), including T cell dysfunction score (first column, T dysfunction value in core dataset), association with ICB survival outcome (second column, z-score in the Cox-PH regression in immunotherapy), log-fold change (logFC) in CRISPR screens (third column, helping identify regulators whose knockout can mediate the efficacy of lymphocyte-mediated tumor killing in cancer models), and T cell exclusion score (fourth column, assessing the gene expression levels in immunosuppressive cell types that drive T cell exclusion). Genes (rows) are ranked by average value across four immunosuppressive indices analyzed using the TIDE website.