Integrating differential gene expression and Mendelian randomization analyses reveal novel gene for prognosis, immune response, and drug sensitivity in lung cancer

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OBJECTIVE: To integrate transcriptomic and genomic data to identify lung cancer biomarkers, evaluate their prognostic significance, and explore their potential in guiding personalized therapy. METHOD: We integrated transcriptomic and genomic data to uncover genes associated with lung cancer risk using differential expression and Mendelian randomization (MR) analyses. Gene expression profiles from three GEO datasets (GSE19804, GSE18842, GSE19188) were batch-corrected with ComBat and analyzed to identify differentially expressed genes (DEGs). Two-sample MR was performed using lung cancer GWAS summary statistics. Functional enrichment, immune infiltration, survival analysis, and drug sensitivity prediction were conducted. The results were validated using The Cancer Genome Atlas (TCGA) lung cancer cohort. RESULT: A total of 1,193 DEGs (409 upregulated, 784 downregulated) were detected between examined groups. MR analysis revealed 253 genes associated with lung cancer risk, including 118 increasing and 135 decreasing genes. Eleven genes overlapped between DEGs and MR results, with SAPCD2 and SPP1 being found to be associated with increased risk of lung cancer, whereas GPX3, C1orf162, CTSW, TPSAB1, CD93, HK3, TFPI, NEBL, and STEAP4 exhibiting inverse association. Enrichment analysis highlighted functions in vesicle lumen, carbohydrate metabolism, and immune processes. SAPCD2 and SPP1 were correlated with pro-tumor immune infiltration. Seven genes were associated with overall survival, and a derived prognostic score effectively stratified patients and correlated with TIDE and drug response profiles. CONCLUSION: This integrative genomic-transcriptomic analysis uncovered genes with suggestive causal and prognostic relevance in lung cancer, emphasizing their immune roles and therapeutic potential. These insights advance the understanding of lung cancer biology and support precision oncology strategies.

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