Integrative GEO and Mendelian randomization analysis reveals transcriptomic and lipidomic features of esophageal...

The incidence of esophageal adenocarcinoma (EA) has significantly increased in developed Western countries. Despite medical advancements, the prognosis remains poor, with a 5-year survival rate of less than 20%. By 2024, the global incidence is expected to reach 141,300 new cases annually, underscoring the urgent need to elucidate the mechanisms underlying EA pathogenesis to develop effective preventive and therapeutic strategies.

To identify differentially expressed genes (DEGs) linked to EA, microarray datasets sourced from the Gene Expression Omnibus (GEO) database were scrutinized, incorporating 4 datasets that met the defined criteria. Using expression quantitative trait loci and Mendelian randomization (MR) analyses, the contribution of genetic factors to EA development was evaluated. Functional pathways were explored using Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis, which revealed enrichment in lipid metabolism. Consequently, Bayesian-weighted MR analysis was performed on 179 plasma lipid subgroups.

We identified 492 DEGs, 211 of which were downregulated and 281 were upregulated. The MR analysis identified 178 genes with significant causal effects on EA. Four co-expressed genes were ultimately determined: FZD2, KRT23, and CES1 were significantly upregulated in EA and positively associated with its occurrence, whereas ALDOC (aldolase, fructose-bisphosphate C) was inversely associated with EA risk. Elevated levels of sphingomyelins, sterol esters, diacylglycerols, and triacylglycerols were linked to a reduced risk of EA, whereas high levels of phosphatidylethanolamine correlated with a heightened risk.

Integration of DEGs, expression quantitative trait loci, and lipidomics data provides robust insights into the molecular mechanisms of EA. These findings provide a promising foundation for the development of novel targeted therapies.