Innovative Biomarkers for Lung Cancer Classification and Prediction Using High-Dimensional Machine Learning: A Novel Approach to Targeted Therapies

Lung adenocarcinoma (LUAD) represents the most prevalent pathological subtype of lung cancer. Unfortunately, a significant proportion of LUAD cases are detected at advanced stages, where the prognosis remains unfavorable. Therefore, our objective was to discover innovative biomarkers to enhance the diagnosis and treatment of early-stage LUAD and to develop targeted therapeutic strategies. In this study, two microarray datasets, GSE۷۵۰۳۷ and GSE۳۲۸۶۳, were sourced from the Gene Expression Omnibus (GEO) database for analysis. Data preprocessing and meta-analysis were conducted using the R statistical programming language. Feature selection was carried out through analysis of variance (ANOVA). To predict cancer stages, ordinal regression, ordinal tree, and ordinal forest models were developed, and their predictive performance was evaluated. Differentially expressed genes (DEGs) were identified and subjected to gene set enrichment analysis to uncover significant biological pathways. The validation and diagnostic accuracy of the DEGs were further examined using UALCAN and ROC curve analysis. Additionally, the selected genes were validated using an independent, comprehensive LUAD RNA-Seq dataset from The Cancer Genome Atlas (TCGA). To identify cell types associated with these genes, scRNA-Seq data from the GEO database (dataset GSE۱۳۱۹۰۷) including lung cancer and normal samples were analyzed. Differential gene expression was examined using the FindMarkers function from the Seurat package. Finally, potential therapeutic agents were identified through the DGIdb database, followed by molecular docking and molecular dynamics simulations to evaluate their potential efficacy as treatments. Analysis revealed that the top ۴۰ differentially expressed genes (DEGs) were primarily associated with pathways involving drug metabolism via cytochrome P۴۵۰, xenobiotic metabolism by cytochrome P۴۵۰, and retinol metabolism. Among these, genes ADH۱A, ADH۱B, and F۱۰ stood out due to

کلیدواژه ها:

Lung adenocarcinoma ، biomarkers ، machine learning ، gene expression ، therapeutic strategies