Identifying core genes in keloid and investigating immune infiltration and pan-cancer associations using eQTL and machine learning

Firstly, bioinformatics analysis identified differentially expressed genes (DEGs) from the keloid GEO datasets. Secondly, MR was applied to eQTL and keloid GWAS datasets to identify candidate genes. Overlapping genes were derived by intersecting DEGs with MR candidate genes. Causal relationships between overlapping genes and keloids were analyzed using five MR methods, identifying core genes significantly associated with keloid pathogenesis. Cochran's Q test and MR-Egger intercept analysis evaluated heterogeneity and pleiotropy in MR results. GO, KEGG, and GSEA enrichment analyses were conducted to explore core gene functions. Finally, validation and TCGA pan-cancer analyses were conducted on the core genes.