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Patients are frequently diagnosed at advanced stages, resulting in poor survival rates. Therefore, the identification of molecular markers for early detection and diagnosis is essential.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Methods<\/jats:title>\n            <jats:p>Three mesothelioma datasets were downloaded from the GEO database for differential gene expression analysis. Instrumental variables (IVs) were identified based on expression quantitative trait locus (eQTL) data for Mendelian randomization (MR) analysis using mesothelioma Genome-Wide Association Study (GWAS) data from the FINNGEN database. The intersecting genes from MR-identified risk genes and differentially expressed genes were identified as key co-expressed genes for mesothelioma. Functional enrichment analyses, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA), as well as immune cell correlation analysis, were performed to elucidate the roles of key genes in mesothelioma. Additionally, the differential expression of key genes in mesothelioma was validated in independent GEO datasets and TCGA datasets. This integrative research combining multiple databases and analytical methods established a robust model for identifying mesothelioma risk genes.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>The research conducted in our study identified 1608 genes that were expressed differentially in mesothelioma GEO datasets. By combining these genes with 192 genes from MR analysis, we identified 14 key genes. Notably, MPZL1, SOAT1, TACC3, and CYBRD1 are linked to a high risk of mesothelioma, while TGFBR3, NDRG2, EPAS1, CPA3, MNDA, PRKCD, MTUS1, ALOX15, LRRN3, and ITGAM are associated with a lower risk. These genes were found to be enriched in pathways associated with superoxide metabolism, cell cycle regulation, and proteasome function, all of which are linked to the development of mesothelioma. Noteworthy observations included a significant infiltration of M1 macrophages and CD4\u2009+\u2009T cells in mesothelioma, with genes SOAT1, MNDA, and ITGAM showing a positive correlation with the level of M1 macrophage infiltration. Furthermore, the differential expression analyses conducted on the GEO validation set and TCGA data confirmed the significance of the identified key genes.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>This integrative eQTL and Mendelian randomization analysis provides evidence of a positive causal association between 14 key co-expressed genes and mesothelioma genetically. These disease critical genes are implicated in correlations with biological processes and infiltrated immune cells related to mesothelioma. Moreover, our study lays a theoretical foundation for further research into the mechanisms of mesothelioma and potential clinical applications.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/s12931-025-03219-4","type":"journal-article","created":{"date-parts":[[2025,4,13]],"date-time":"2025-04-13T17:21:16Z","timestamp":1744564876000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Integrative eQTL and Mendelian randomization analysis reveals key genetic markers in mesothelioma"],"prefix":"10.1186","volume":"26","author":[{"given":"Jinsong","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingmeng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaru","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhengliang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Xiong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,4,13]]},"reference":[{"issue":"Suppl 1","key":"3219_CR1","doi-asserted-by":"publisher","first-page":"S28","DOI":"10.21037\/tlcr.2019.11.11","volume":"9","author":"N Alpert","year":"2020","unstructured":"Alpert N, et al. 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