Comprehensive analysis to identify the neurotransmitter receptor-related genes as prognostic and therapeutic biomarkers in hepatocellular carcinoma

Background: Hepatocellular carcinoma (HCC) is a highly heterogeneous disease with high morbidity and mortality, which accounts for the fourth most common cause of cancer-related deaths. Reports suggest that the neurotransmitter receptor-related genes (NRGs) may influence the tumor microenvironment and the prognosis of patients with HCC. Methods: The clinical information and RNA-seq data of patients with HCC were acquired from the ICGC-LIRI-JP dataset and the TCGA-LIHC dataset. Effects of 115 NRGs on the prognosis of HCC patients were analyzed in the ICGC-LIRI-JP dataset. The least absolute shrinkage and selection operator (LASSO) regression model was utilized to generate a risk score formula based on the critical NRGs. Next, the risk score effectiveness was validated both in the TCGA-LIHC dataset and in our clinical HCC samples. Based on the risk scores, patients with HCC were divided into two groups. Moreover, differentially expressed genes (DEGs) were screened. The gene ontology (GO) was used to analyze the functional enrichments of DEGs and to identify potential signaling pathways. To test the diagnostic effectiveness of our model, the receiver operator characteristic curve (ROC) analysis and nomogram were used. Finally, potential targeted drug prediction was performed based on DEGs of nine clinical HCC samples. Results: Nine NRGs were correlated significantly with the prognosis of patients with HCC, and eight NRGs were successfully included in the LASSO regression model. The Kaplan-Meier analysis of overall survival (OS) suggested that patients in the high-risk score group had worse prognosis; on the other hand, ROC analysis revealed a high prognostic value of the risk score in HCC. Several critical signaling pathways, such as lipid metabolism, organic acid metabolism, cell migration, cell adhesion, and immune response, were enriched both in public datasets and clinical samples. Nomogram results also suggested that the risk scores correlated well with the patients' prognosis. Potential targeted drugs prediction revealed that tubulin inhibitors might be the promising drugs for patients with HCC who have high risk scores based on the NRGs. Conclusion: We established a prognostic model based on critical NRGs. NRGs show a promising prognostic prediction value in HCC and are potential therapeutic targets for the disease treatment.