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.