Abstract
BACKGROUND: Our investigation seeks to elucidate the underlying biological mechanisms by which HIV infection might contribute to lung cancer development, thereby providing valuable insights for developing targeted prevention and treatment strategies for HIV-positive populations. METHODS: We integrated genetic datasets, identified causal SNPs through SMR/HEIDI analysis, and developed predictive models using multiple machine learning approaches with TCGA and GEO cohorts. Survival differences between risk groups were assessed with Kaplan-Meier analysis. We characterized gene functions using GO analysis, evaluated immune cell infiltration with CIBERSORT/ESTIMATE, and performed weighted gene co-expression network analysis to identify functional gene modules associated with phenotypic traits. RESULTS: The study analyzed the association between HIV infection and lung cancer using bioinformatics. METHODS: Manhattan plot analysis identified 53 significantly associated SNPs shared between the two diseases, suggesting a possible common genetic basis. GOKEGG enrichment analysis revealed that related genes primarily participate in various metabolic pathways, signal transduction, and cell cycle regulation. TCGA data analysis uncovered hundreds of differentially expressed genes between tumor and control groups, including 20 downregulated and 20 upregulated genes. WGCNA network analysis identified key gene modules and confirmed significant correlations between module membership and gene significance (correlation coefficients of 0.79 and 0.58). These findings provide new insights into the molecular mechanisms linking HIV infection and lung cancer development. CONCLUSION: This comprehensive investigation provides compelling evidence supporting early intervention strategies for lung cancer prevention in HIV-positive individuals.