A multi-algorithm machine learning framework identifies FGD5, LRRC36, C8B, and MYOC as novel diagnostic biomarkers in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer and exhibits complex heterogeneity. Despite extensive research on driver gene mutations such as in EGFR and TP53, resistance mechanisms and co-mutations underscore the need to elucidate the molecular mechanisms of LUAD and to discover novel biomarkers for personalized treatment. We integrated transcriptomic data from 1398 samples (1160 LUAD and 238 normal tissues) obtained from the GEO and TCGA databases. Analytical procedures included differential expression, functional enrichment (GO/KEGG/GSEA), and immune cell infiltration assessment. We applied 113 machine learning algorithms to identify core genes and develop a diagnostic model. The relationship between core gene expression and prognosis was assessed using TCGA data, and downregulation of these genes was experimentally verified by RT-PCR in A549 and Beas-2B cell lines. Our analysis revealed significant enrichment of the “cytoskeleton in muscle cells” pathway and activation of pyrimidine metabolism in LUAD. Immune profiling showed reduced proportions of monocytes and resting mast cells. We identified four core genes—FGD5, LRRC36, C8B, and MYOC—that were consistently downregulated in LUAD. A diagnostic model based on these genes demonstrated strong predictive performance, and low expression of each core gene was correlated with poor patient prognosis.Multivariate Cox regression confirmed their status as independent prognostic factors. This study identifies FGD5, LRRC36, C8B, and MYOC as novel core genes and independent prognostic biomarkers in LUAD, and underscores the potential role of the “cytoskeleton in muscle cells” pathway. Our multi-faceted strategy provides new insights into LUAD pathogenesis and yields promising biomarker candidates for improving clinical diagnostics.