Integration of eQTL and GEO Datasets to Identify Genes Associated with Breast Ductal Carcinoma In Situ.

Abstract

Background: Breast ductal carcinoma in situ (DCIS), a common precursor of breast cancer, has poorly understood susceptible driver genes. This study aimed to identify genes influencing DCIS progression by integrating Mendelian randomization (MR) and Gene Expression Omnibus (GEO) datasets.

Methods: The GEO database was searched for DCIS-related datasets to extract differentially expressed genes (DEGs). MR was employed to find exposure single-nucleotide polymorphisms (SNPs) of expression quantitative trait locus (eQTL) gene expression from Genome-Wide Association Study database (GWAS) (IEU openGWAS project). DCIS was designated as the outcome variable. The intersection of genes was used for GO, KEGG and CIBERSORT analyses. The functional validation of selected DEGs was performed using Transwell invasion assays.

Results: Four datasets (GSE7782, GSE16873, GSE21422, and GSE59246) and 19,943 eQTL exposure data were obtained from GEO and the IEU openGWAS project, respectively. By intersecting DEGs, 13 genes (LGALS8, PTPN12, YTHDC2, RNGTT, CYB5R2, KLHDC4, APOBEC3G, GPX3, RASA3, TSPAN4, MAPKAPK3, ZFP37, and RAB3IL1) were incorporated into subsequent KEGG and GO analyses. Functional assays confirmed that silencing PTPN12, YTHDC2 and MAPKAPK3, or overexpressing GPX3, RASA3 and TSPAN4, significantly suppressed DCIS cell invasion. These DEGs were linked to immune functions, such as antigen processing and presentation and the tumor microenvironment (TME), and they showed associations with dendritic cell activation differences.

Conclusions: Thirteen genes were associated with DCIS progression, and six genes were validated in the cell experiments. KEGG and GO analyses highlight TME's role in early breast cancer, enhancing understanding of DCIS occurrence and aiding identification of high-risk tumors.