ABSTRACT
Objective:To construct a novel cuproptosis-related gene signature (CRGS) for prediction of prognosis, immunotherapy response and drug sensitivity in patients with hepatocellular carcinoma (HCC).Methods:Data materials for this study were obtained from the international cancer genome consortium (ICGC), the cancer genome atlas (TCGA) database and Migort210 database, and protein expression profiles were obtained from the human protein atlas image classification database. Based on the TCGA cohort, the least absolute shrinkage and selection operator algorithm was applied to construct the CRGS and calculate the risk score for each HCC patient. HCC patients were grouped according to the median risk score: HCC patients in the TCGA cohort were divided into a high-risk group TCGA and a low-risk group TCGA with 184 cases in each group; HCC patients in the ICGC cohort were divided into a high-risk group ICGC and a low-risk group ICGC with 116 cases in each group. Patients in the Migort210 cohort were divided into a responder group ( n=68) and a non-responder group ( n=230) based on their response to immunotherapy. We assessed the value of CRGS in predicting the prognosis of HCC patients in the TCGA cohort and validated whether CRGS could be used to predict the prognosis of HCC patients in the ICGC dataset. To explore the role of CRGS in predicting immunotherapy response and drug sensitivity in HCC patients based on data from the TCGA cohort, and to apply the Migort210 immunotherapy cohort to validate the clinical value of CRGS in predicting immunotherapy in malignant tumors. Results:CRGS consists of four copper death-related genes: GLS, CDKN2A, LIPT1, and DLAT. Patients in the high-risk group TCGA had lower overall survival (OS), disease-specifical survival, and progression-free interval than those in the low-risk group TCGA (all P<0.01). OS of patients in the high-risk group ICGC was lower than that in the low-risk group ICGC ( P=0.022). Multivariate Cox regression analysis showed that CRGS was an independent risk factor for poor prognosis in HCC patients (TCGA: HR=2.991, 95% CI: 1.781-5.049, P<0.001; ICGC: HR=4.621, 95% CI: 1.685-12.674, P=0.033). Risk scores were positively correlated with the expression levels of CTLA4, PDCD1, CD80 and HLLA2 (all P<0.001). Patients in the high-risk group TCGA had lower tumor immune dysfunction and rejection scores than those in the low-risk group TCGA [-0.04(-0.07, -0.02) vs. -0.02(-0.04, 0) points], and the difference was statistically significant ( P<0.001). Patients in the responder group had a higher risk score than the non-responder group [1.70 (1.56, 1.90) vs. 1.63 (1.52, 1.80)], with a statistically significant difference ( P<0.05). The half-inhibitory concentrations (IC 50) for sunitinib, rapamycin and etanercept were higher in the high-risk group TCGA than that in the low-risk group TCGA, while the IC 50 for erlotinib was lower than that in the low-risk group TCGA, and the differences were all statistically significant (all P<0.001). Conclusion:The CRGS might be served as a potential biomarker to predict the prognoses, immunotherapy response, and drug sensitivity of patients with HCC.
ABSTRACT
Butyrate, one of the major products of the gut microbiota, has played notable roles in diverse therapies for multiple tumors. Our study aimed to determine the roles of genes that modulate butyrate metabolism (BM) in predicting the clinical prognosis and responses to systemic therapies in hepatocellular carcinoma (HCC). The genes modulating BM were available from the GeneCard database, and gene expression and clinical information were obtained from TCGA-LIHC, GEO, ICGC-JP, and CCLE databases. Candidate genes from these genes that regulate BM were then identified by univariate Cox analysis. According to candidate genes, the patients in TCGA were grouped into distinct subtypes. Moreover, BM- related gene signature (BMGs) was created via the LASSO Cox algorithm. The roles of BMGs in identifying high-risk patients of HCC, assessing the prognoses, and predicting systematic therapies were determined in various datasets. The statistical analyses were fulfilled with R 4.1.3, GraphPad Prism 8.0 and Perl 5.30.0.1 software. In the TCGA cohort, most butyrate-related genes were over-expressed in the B cluster, and patients in the B cluster showed worse prognoses. BMGs constructed by LASSO were composed of eight genes. BMGs exhibited a strong performance in evaluating the prognoses of HCC patients in various datasets, which may be superior to 33 published biomarkers. Furthermore, BMGs may contribute to the early surveillance of HCC, and BMGs could play active roles in assessing the effectiveness of immunotherapy, TACE, ablation therapy, and chemotherapeutic drugs for HCC. BMGs may be served as novel promising biomarkers for early identifying high-risk groups of HCC, as well as assessing prognoses, drug sensitivity, and the responses to immunotherapy, TACE, and ablation therapy in patients with HCC.
