Integrated bioinformatics and feature selection-based discovery of ADP-ribosylation-associated biomarkers in non-small cell lung cancer.

Abstract

Non-small cell lung cancer (NSCLC) remains a significant global health concern. ADP-ribosylation, a key post-translational modification, may influence the tumor immune microenvironment; however, the genes transcriptionally linked to ADP-ribosylation pathway activity in NSCLC are poorly understood. In the present study, transcriptomic data from the Gene Expression Omnibus database were analyzed to identify differentially expressed genes (DEGs) associated with ADP-ribosylation via differential expression analysis. Weighted gene co-expression network analysis was subsequently applied to refine these DEGs and extract ADP-ribosylation-related module genes. Feature selection methods aided by machine learning, including LASSO regression and the Boruta algorithm, were employed to identify core signature genes. Enzyme-linked immunosorbent assay (ELISA) validated poly(ADP-ribose) polymerase (PARP) protein levels in NSCLC tissues. Immune infiltration analysis, single-gene gene-set enrichment analysis and regulatory network construction were used to explore gene functions. ELISA confirmed significantly higher PARP concentrations in NSCLC tissues compared to para-cancerous tissues (P<0.01), indicating dysregulated ADP-ribosylation pathway activity. Three signature genes (HBZ, HLA-DRA and CCL4) were identified, all of which were significantly associated with immune-cell infiltration and immune factor expression. Reverse transcription-quantitative PCR validation in 10 paired NSCLC patient samples revealed a significant downregulation of HBZ (P<0.0001) and CCL4 (P<0.01), while HLA-DRA showed a non-significant downward trend. This study provides preliminary evidence that HBZ, HLA-DRA and CCL4 are transcriptionally linked to ADP-ribosylation pathway activity and may modulate the NSCLC immune microenvironment, offering potential avenues for future immunotherapeutic research.