Identification a novel cuproptosis-related signature and molecular subtypes based on comprehensive bioinformatics analysis for predicting the prognosis and immunotherapy response of hepatocellular carcinoma

• DOI: 10.21203/rs.3.rs-3218590/v1
• Publication Date: 2023-08-09T06:28:49Z
• Authors: Shuo Wang; Xinzi Xue; Hongyan Bai; Junwen Qi; Lin Liu; Mengting Zhuang; Su juan Fei; Bei Miao
• Keywords: Nomogram; Univariate

Abstract Background This study aims to identify a novel cuproptosis-related model using comprehensive bioinformatics analysis, which will offer new insights into hepatocellular carcinoma (HCC) classification. Additionally, it seeks comprehensively analyze the correlation between risk score and various aspects, including prognosis, tumor mutation burden (TMB), biological function, microenvironment (TME), immune efficacy of HCC. Methods In this study, we integrated HCC gene expression profile data from TCGA GEO databases. Based on 49 genes (CRG), unsupervised clustering analysis was used construct molecular subtypes obtain differentially expressed genes. Through univariate Cox regression identified genes(DEGs) associated with prognosis. Using selected DEGs, established through lasso multivariate analysis. Furthermore, conducted additional validation GSE14520 International Cancer Genome Consortium (ICGC) datasets. We assessed prognostic value methods, survival ROC curve nomogram. validated differences in functions among different groups features, functional enrichment, cell infiltration other utilized TIDE score, checkpoint, drug sensitivity, immunophenoscore(IPS), (TME) evaluate patients' response immunotherapy. These evaluations were further Mvigor210 dataset. these analyses, aimed gain valuable effectiveness immunotherapy for patients provide potential guidance personalized treatment approaches. Results distinct prognosis function subtype carcinoma, built by GMPS, DNAJC6, BAMBI, MPZL2, ASPHD1, IL7R, EPO, BBOX1 CXCL9 (CRGRM). as an independent predictor based combined TCGA-LIHC GSE76427 cohorts, verified ICGC strongly correlated clinicopathological features age, sex, stage, status histological grade. Our demonstrated that lower had higher probability survival, better genetic mutations. Conclusions The integration statistical datasets ensured accuracy reliability our findings. By following steps, objective is classification perspective cuproptosis explore factors relevant thereby offering more targeted management patients.