A curated list of immune system associated genes related to cancer

Abstract Immune system dysregulation is implicated in a wide range of human diseases, including cancer. Genetic studies have shown evidence of a link between immune diseases and cancer; therefore, deficits of immune system-related genes could be involved in cancer development. In this study, we compiled a curated list of immune system-related genes linked to cancer. In this study, we constructed a list of immune system-related genes by selecting relevant genes from Gene Autoimmune disease Association Database (GAAD), International Union of Immunological Societies (IUIS) and DisGeNET databases. After careful curation and deduplication, 722 genes were classified into three tiers according to gene-disease association scores from the DISEASES tool: Tier-1 (147 genes), Tier-2 (332 genes), and Tier-3 (243 genes), reflecting their links to immune diseases. Using online pathway analysis tools DAVID and Enrichr, we verified the biological functions of the genes in each tier. Functional validation analyses showed that Tier-1 genes were strongly enriched for cytokine-mediated signaling, interleukin pathways and innate/adaptive immune responses, reflecting important immune-cancer mechanisms. Tier-2 genes were mainly enriched for innate immune responses, chemokine signaling pathways, and T-cell differentiation and checkpoint pathways. This reflected the Tier-2 genes’ broader immune regulatory functions. Tier-3 showed less immune-related functional enrichment than Tier-1 and Tier-2, enriched for complement activation, telomere maintenance, and broad cellular pathways. We believe that this curated immune-cancer gene database of 722 genes will be a valuable and reliable source for scientists studying the relationship between immune system-related genes and cancer. It provides a biologically validated source that might complement automated gene-disease association sources and support the training and enhancement of current AI tools used to identify genes linked to cancer from large datasets.