Neurotransmitter receptor-associated gene signature: prognostic and immunosuppressive microenvironment in NSCLC

OBJECTIVE: This study sought to identify neurotransmitter receptor-related genes (NR-RGs) that are critically involved in non-small cell lung cancer (NSCLC) through bioinformatics approaches. METHODS: The TCGA-NSCLC dataset was utilized as the training cohort, while the GSE50081 dataset served as the validation cohort. NR-RGs were curated, and single-sample gene set enrichment analysis (ssGSEA) scores were computed. Subsequently, weighted gene co-expression network analysis (WGCNA) and functional enrichment analyses were conducted. A risk prediction model and a prognostic model were constructed based on identified gene signatures. Finally, a competing endogenous RNA (ceRNA) network was established, and gene expression levels were experimentally validated. RESULTS: 192 differentially expressed genes were identified as candidate NR-RGs. The risk model ultimately highlighted six genes: CPS1, CDH17, NIPAL4, SOX2, CALB2, and KREMEN2 as potential biomarkers. The prognostic model demonstrated robust predictive performance for patient outcomes. Immune infiltration analysis revealed a significant positive correlation between neutrophil abundance and the risk score. Expression analysis indicated that CPS1 and CALB2 were downregulated in NSCLC samples, whereas CDH17, NIPAL4, SOX2, and KREMEN2 were upregulated. CONCLUSION: The genes CPS1, CDH17, NIPAL4, SOX2, CALB2, and KREMEN2 were identified as prognostic biomarkers in NSCLC, providing insights into their potential roles in disease progression and therapeutic targeting.