Development and Validation of a Metabolic Gene-Based Prognostic Signature for Hepatocellular Carcinoma

Weng, Juyang; Zhou, Chixing; Zhou, Qiang; Chen, Wanyong; Yin, Yuehui; Atyah, Manar; Dong, Qiongzhu; Shi, Yi; Ren, Ning

doi:10.2147/jhc.s300633

Cited by 3 publications

(2 citation statements)

References 45 publications

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“…To further clarify the relationship between the signature and clinicopathological characteristics and prognosis in HCC patients, we performed univariate and multivariate Cox regression analysis. Univariate Cox regression analyses indicated that TNM stage (HR = 1.865, 95% CI: 1.456‐2.388) and our signature (HR = 2.794, 95% CI: 1.991‐3.920) were independent risk factors for OS in the entire TCGA cohort, similar with that of a published 10‐gene metabolic signature (Weng_ signature, HR = 3.203, 95% CI: 2.383‐4.304) 17 18 …”

Section: Resultssupporting

confidence: 76%

“…With the swift development of high‐throughput sequencing technology, it may be conceivable to focus on systematic exploration of a class of genes associated with prediction of patient’ survival 35 . For instance, Weng, et al 17 developed a prognostic signature based on 10 metabolic genes, which reflects the metabolic and immune characteristics of tumours. Another study has identified a novel robust four‐gene metabolic signature to explain the dysregulated metabolic microenvironment and pathways 18 …”

Section: Discussionmentioning

confidence: 99%

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Prognostic and predictive role of a metabolic rate‐limiting enzyme signature in hepatocellular carcinoma

Wang

Xia

et al. 2021

Cell Proliferation

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Objectives Abnormal expression of metabolic rate‐limiting enzymes drives the occurrence and progression of hepatocellular carcinoma (HCC). This study aimed to elucidate the comprehensive model of metabolic rate‐limiting enzymes associated with the prognosis of HCC. Materials and Methods HCC animal model and TCGA project were used to screen out differentially expressed metabolic rate‐limiting enzyme. Cox regression, least absolute shrinkage and selection operation (LASSO) and experimentally verification were performed to identify metabolic rate‐limiting enzyme signature. The area under the receiver operating characteristic curve (AUC) and prognostic nomogram were used to assess the efficacy of the signature in the three HCC cohorts (TCGA training cohort, internal cohort and an independent validation cohort). Results A classifier based on three rate‐limiting enzymes (RRM1, UCK2 and G6PD) was conducted and serves as independent prognostic factor. This effect was further confirmed in an independent cohort, which indicated that the AUC at year 5 was 0.715 (95% CI: 0.653‐0.777) for clinical risk score, whereas it was significantly increased to 0.852 (95% CI: 0.798‐0.906) when combination of the clinical with signature risk score. Moreover, a comprehensive nomogram including the signature and clinicopathological aspects resulted in significantly predict the individual outcomes. Conclusions Our results highlighted the prognostic value of rate‐limiting enzymes in HCC, which may be useful for accurate risk assessment in guiding clinical management and treatment decisions.

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Section: Resultssupporting

confidence: 76%

Section: Discussionmentioning

confidence: 99%

Prognostic and predictive role of a metabolic rate‐limiting enzyme signature in hepatocellular carcinoma

Wang

Xia

et al. 2021

Cell Proliferation

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Construction of A Novel Prognostic Model with Molecular Targets for Hepatocellular Carcinoma Based on Bioinformatics Applications

Wang

2023

j biomed nanotechnol

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We aimed to analyze the differentially expressed genes associated with hepatocellular carcinoma (HCC) by bioinformatics method and to identify potential molecular targets for immunotherapy and molecular indicators for predicting HCC prognosis. Gene Expression Omnibus (GEO) was used to download the hepatocellular carcinoma related microarray data. The R language’s Limma tool was used to identify the genes with differential expression. For differentially expressed genes, GO (Gene Ontology) enrichment analysis, KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis, and protein–protein interaction analysis were carried out. PPI (protein–protein interaction) regulation network construction. In order to further evaluate HCC specific differentially expressed genes, HCC specific expression analysis was carried out at the same time by merging with other tumour RNA-seq transcriptome data in the TCGA database. The relationship between immune-related LncRNA and independent risk factors was examined using univariate and multivariate Cox regression as well as Least absolute shrinkage and selection operator (LASSO) analysis. The proportional Hazards model (COX model) was utilized to model the chosen important genes and predict the prognosis. We obtained important genes through additional screening, and the GSE6764 validation set discovered that the expression of these genes decreased with increasing tumor stage (P < 0.05). The prognosis analysis of the gene model revealed that the high-risk group had a dismal outcome. COX modelling was carried out for important genes. Meanwhile, the GSE76427 and GSE54236 validation sets validated the model’s survival analyses. By analyzing the gene expression profile of HCC utilizing cuttingedge bioinformatics techniques including Cox Regression and LASSO analysis, we were able to screen out the important modules and essential genes, create a predictive model for HCC, and propose possible biomarkers for the prediction of HCC.

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Identification and characterization of a 25-lncRNA prognostic signature for early recurrence in hepatocellular carcinoma

Wei

Han

et al. 2021

BMC Cancer

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Background Early recurrence is the major cause of poor prognosis in hepatocellular carcinoma (HCC). Long non-coding RNAs (lncRNAs) are deeply involved in HCC prognosis. In this study, we aimed to establish a prognostic lncRNA signature for HCC early recurrence. Methods The lncRNA expression profile and corresponding clinical data were retrieved from total 299 HCC patients in TCGA database. LncRNA candidates correlated to early recurrence were selected by differentially expressed gene (DEG), univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses. A 25-lncRNA prognostic signature was constructed according to receiver operating characteristic curve (ROC). Kaplan-Meier and multivariate Cox regression analyses were used to evaluate the performance of this signature. ROC and nomogram were used to evaluate the integrated models based on this signature with other independent clinical risk factors. Gene set enrichment analysis (GSEA) was used to reveal enriched gene sets in the high-risk group. Tumor infiltrating lymphocytes (TILs) levels were analyzed with single sample Gene Set Enrichment Analysis (ssGSEA). Immune therapy response prediction was performed with TIDE and SubMap. Chemotherapeutic response prediction was conducted by using Genomics of Drug Sensitivity in Cancer (GDSC) pharmacogenomics database. Results Compared to low-risk group, patients in high-risk group showed reduced disease-free survival (DFS) in the training (p < 0.0001) and validation cohort (p = 0.0132). The 25-lncRNA signature, AFP, TNM and vascular invasion could serve as independent risk factors for HCC early recurrence. Among them, the 25-lncRNA signature had the best predictive performance, and combination of those four risk factors further improves the prognostic potential. Moreover, GSEA showed significant enrichment of “E2F TARGETS”, “G2M CHECKPOINT”, “MYC TARGETS V1” and “DNA REPAIR” pathways in the high-risk group. In addition, increased TILs were observed in the low-risk group compared to the high-risk group. The 25-lncRNA signature negatively associates with the levels of some types of antitumor immune cells. Immunotherapies and chemotherapies prediction revealed differential responses to PD-1 inhibitor and several chemotherapeutic drugs in the low- and high-risk group. Conclusions Our study proposed a 25-lncRNA prognostic signature for predicting HCC early recurrence, which may guide postoperative treatment and recurrence surveillance in HCC patients.

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Development and Validation of a Metabolic Gene-Based Prognostic Signature for Hepatocellular Carcinoma

Cited by 3 publications

References 45 publications

Prognostic and predictive role of a metabolic rate‐limiting enzyme signature in hepatocellular carcinoma

Prognostic and predictive role of a metabolic rate‐limiting enzyme signature in hepatocellular carcinoma

Construction of A Novel Prognostic Model with Molecular Targets for Hepatocellular Carcinoma Based on Bioinformatics Applications

Identification and characterization of a 25-lncRNA prognostic signature for early recurrence in hepatocellular carcinoma

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