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• W4385702946 abstract "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 to comprehensively analyze the correlation between the cuproptosis-related risk score and various aspects, including prognosis, tumor mutation burden (TMB), biological function, tumor microenvironment (TME), and immune efficacy of HCC. Methods In this study, we integrated the HCC gene expression profile data from TCGA and GEO databases. Based on the expression of 49 cuproptosis-related genes (CRG), unsupervised clustering analysis was used to construct cuproptosis-related molecular subtypes and obtain differentially expressed genes. Through univariate Cox regression analysis, we identified differentially expressed genes(DEGs) associated with prognosis. Using the selected DEGs, we established a model through lasso Cox regression analysis and multivariate Cox regression analysis. Furthermore, we conducted additional validation of the model using data from the GSE14520 and International Cancer Genome Consortium (ICGC) datasets. We assessed the prognostic value of the model through various methods, including survival analysis, ROC curve analysis, and prognostic nomogram. We validated the differences in biological functions among different risk groups using immune features, functional enrichment, and immune cell infiltration analysis, among other analysis. Additionally, we utilized the TIDE score, immune checkpoint, drug sensitivity, immunophenoscore(IPS), and tumor microenvironment (TME) to evaluate patients' response to immunotherapy. These evaluations were further validated using data from the Mvigor210 dataset. Through these comprehensive analyses, we aimed to gain valuable insights into the effectiveness of immunotherapy for patients with hepatocellular carcinoma (HCC) and provide potential guidance for personalized treatment approaches. Results This study identified with distinct prognosis and biological function of molecular subtype of hepatocellular carcinoma, built by GMPS, DNAJC6, BAMBI, MPZL2, ASPHD1, IL7R, EPO, BBOX1 and CXCL9 cuproptosis-related gene risk score model (CRGRM). We validated the risk score as an independent predictor of HCC prognosis and immune response based on the combined TCGA-LIHC and GSE76427 cohorts, and verified the prognostic value of the risk score in GSE14520 and ICGC datasets. This model was strongly correlated with clinicopathological features including age, sex, tumor stage, survival status and histological grade. Our analysis demonstrated that patients with a lower risk score had a higher probability of survival, better response to immunotherapy and a lower probability of genetic mutations. Conclusions The comprehensive integration and statistical analysis of these datasets ensured the accuracy and reliability of our findings. By following these steps, Our objective is to provide new insights into the classification of hepatocellular carcinoma (HCC) from the perspective of cuproptosis and explore factors relevant to prognosis, thereby offering more targeted guidance for the treatment and management of HCC patients." @default.
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• W4385702946 date "2023-08-09" @default.
• W4385702946 modified "2023-10-18" @default.
• W4385702946 title "Identification a novel cuproptosis-related signature and molecular subtypes based on comprehensive bioinformatics analysis for predicting the prognosis and immunotherapy response of hepatocellular carcinoma" @default.
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• W4385702946 doi "https://doi.org/10.21203/rs.3.rs-3218590/v1" @default.
• W4385702946 hasPublicationYear "2023" @default.
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