A robust twelve-gene signature for prognosis prediction of hepatocellular carcinoma

Ouyang, Guoqing; Yi, Bin; Pan, Guangdong; Chen, Xiang

doi:10.1186/s12935-020-01294-9

Cited by 57 publications

(58 citation statements)

References 50 publications

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“…In contrast, for some cancers, such as malignant peripheral nerve sheath tumors 13 , 14 and seminomas 15 , HMMR expression is downregulated and its low expression associates with poor patient survival. Very recently, HMMR was found to be one of the hub genes responsible for prognosis prediction of hepatocellular carcinoma (HCC) by bioinformatic analysis 16 - 19 . However, the clinical significance of HMMR linking normal liver to NASH to HCC and the biological function of HMMR in HCC remains unclear.…”

Section: Introductionmentioning

confidence: 99%

Oleate acid-stimulated HMMR expression by CEBPα is associated with nonalcoholic steatohepatitis and hepatocellular carcinoma

Zhang¹,

Liu²,

Xie³

et al. 2020

Int. J. Biol. Sci.

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Non-alcoholic steatohepatitis (NASH) is a type of nonalcoholic fatty liver disease and has become a major risk factor for hepatocellular carcinoma (HCC). However, the underlying pathophysiological mechanisms are still elusive. Here, we identify hyaluronan-mediated motility receptor (HMMR) as a critical gene associated with NASH/HCC by combination of bioinformatic analysis and functional experiments. Analysis of differentially expressed genes (DEGs) between normal controls and NASH/HCC identified 5 hub genes (HMMR, UBE2T, TYMS, PTTG1 and GINS2). Based on the common DEGs, analyses of univariate and multivariate Cox regression and the area under the curve (AUC) value of the receiver operating characteristic (ROC) indicate that HMMR is the most significant gene associated with NASH/HCC among five hub genes. Oleate acid (OA), one of fatty acids that induce cellular adipogenesis, stimulates HMMR expression via CCAAT/enhancer-binding protein α (CEBPα). CEBPα increases the expression of HMMR through binding to its promoter. HMMR promotes HCC cell proliferation in vitro via activation of G1/S and G2/M checkpoint transitions, concomitant with a marked increase of the positive cell cycle regulators, including cyclin D1, cyclin E, and cyclin B1. Knockdown of HMMR suppresses HCC tumor growth in nude mice. Our study identifies an important role of HMMR in NASH/HCC, and suggests that HMMR may be a useful target for therapy and prognostic prediction of NASH/HCC patients.

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Section: Introductionmentioning

confidence: 99%

Oleate acid-stimulated HMMR expression by CEBPα is associated with nonalcoholic steatohepatitis and hepatocellular carcinoma

Zhang¹,

Liu²,

Xie³

et al. 2020

Int. J. Biol. Sci.

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“…Several prior analyses have also shown that certain gene signatures may predict patient survival (20)(21)(22)(23)(24)(25)(26); however, our study has some differences and/or advantages compared with similar analyses. First, the gene signatures built in previous studies require many genes (20)(21)(22)(23), which possibly leads to some difficulties in real-world practice. Our novel signature requires only 4 genes, and the predictive ability of our signature is acceptable, which increases the feasibility of the use of our signature in real-world practice.…”

Section: Discussionmentioning

confidence: 80%

Identification of a Novel Four-Gene Signature Correlated With the Prognosis of Patients With Hepatocellular Carcinoma: A Comprehensive Analysis

Zhu

2021

Front. Oncol.

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PurposeHepatocellular carcinoma (HCC) is a common solid-tumor malignancy with high heterogeneity, and accurate prognostic prediction in HCC remains difficult. This analysis was performed to find a novel prognostic multigene signature.MethodsThe TCGA-LIHC dataset was analyzed for differentially coexpressed genes through weighted gene coexpression network analysis (WGCNA) and differential gene expression analysis. A protein-protein interaction (PPI) network and univariate Cox regression analysis of overall survival (OS) were utilized to identify their prognostic value. Next, we used least absolute shrinkage and selection operator (LASSO) Cox regression to establish a prognostic module. Subsequently, the ICGC-LIRI-JP dataset was applied for further validation. Based on this module, HCC cases were stratified into high-risk and low-risk groups, and differentially expressed genes (DEGs) were identified. Functional enrichment analyses of these DEGs were conducted. Finally, single-sample gene set enrichment analysis (ssGSEA) was performed to explore the correlation between the prognostic signature and immune status.ResultsA total of 393 differentially coexpressed genes were obtained. Forty differentially coexpressed hub genes were identified using the CytoHubba plugin, and 38 of them were closely correlated with OS. Afterward, we established the four-gene prognostic signature with an acceptable accuracy (area under the curve [AUC] of 1-year survival: 0.739). The ICGC-LIRI-JP dataset also supported the acceptable accuracy (AUC of 1-year survival:0.752). Compared with low-risk cohort, HCC cases in the high-risk cohort had shorter OS, higher tumor grades, and higher T stages. The risk scores of this signature still act as independent predictors of OS (P<0.001). Functional enrichment analyses suggest that it was mainly organelle fission and nuclear division that were enriched. Finally, ssGSEA revealed that this signature is strongly associated with the immune status of HCC patients.ConclusionsThe proposed prognostic signature of four differentially coexpressed hub genes has satisfactory prognostic ability, providing important insight into the prediction of HCC prognosis.

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“…Overexpressed of FAM83D leads to amplified cell proliferation, migration and metastasis of ovarian cancer cells and high expression correlated with tumor stage and grade [18]. Downregulated LCAT is strongly related to poor survival in HCC [19]. ADH4 is significantly downregulated in HCC when compared with non-cancerous tissue, besides the ADH4 protein expression is lower in HCC [20].…”

Section: Discussionmentioning

confidence: 99%

Construction and Validation of a Prognostic Gene-Based Model for Overall Survival Prediction in Hepatocellular Carcinoma Using an Integrated Statistical and Bioinformatic Approach

Dessie

Chiang

et al. 2021

IJMS

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Hepatocellular carcinoma (HCC) is one of the most common lethal cancers worldwide and is often related to late diagnosis and poor survival outcome. More evidence is demonstrating that gene-based prognostic models can be used to predict high-risk HCC patients. Therefore, our study aimed to construct a novel prognostic model for predicting the prognosis of HCC patients. We used multivariate Cox regression model with three hybrid penalties approach including least absolute shrinkage and selection operator (Lasso), adaptive lasso and elastic net algorithms for informative prognostic-related genes selection. Then, the best subset regression was used to identify the best prognostic gene signature. The prognostic gene-based risk score was constructed using the Cox coefficient of the prognostic gene signature. The model was evaluated by Kaplan–Meier (KM) and receiver operating characteristic curve (ROC) analyses. A novel four-gene signature associated with prognosis was identified and the risk score was constructed based on the four-gene signature. The risk score efficiently distinguished the patients into a high-risk group with poor prognosis. The time-dependent ROC analysis revealed that the risk model had a good performance with an area under the curve (AUC) of 0.780, 0.732, 0.733 in 1-, 2- and 3-year prognosis prediction in The Cancer Genome Atlas (TCGA) dataset. Moreover, the risk score revealed a high diagnostic performance to classify HCC from normal samples. The prognosis and diagnosis prediction performances of risk scores were verified in external validation datasets. Functional enrichment analysis of the four-gene signature and its co-expressed genes involved in the metabolic and cell cycle pathways was constructed. Overall, we developed a novel-gene-based prognostic model to predict high-risk HCC patients and we hope that our findings can provide promising insight to explore the role of the four-gene signature in HCC patients and aid risk classification.

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A robust twelve-gene signature for prognosis prediction of hepatocellular carcinoma

Cited by 57 publications

References 50 publications

Oleate acid-stimulated HMMR expression by CEBPα is associated with nonalcoholic steatohepatitis and hepatocellular carcinoma

Oleate acid-stimulated HMMR expression by CEBPα is associated with nonalcoholic steatohepatitis and hepatocellular carcinoma

Identification of a Novel Four-Gene Signature Correlated With the Prognosis of Patients With Hepatocellular Carcinoma: A Comprehensive Analysis

Construction and Validation of a Prognostic Gene-Based Model for Overall Survival Prediction in Hepatocellular Carcinoma Using an Integrated Statistical and Bioinformatic Approach

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