Current status of hepatocellular carcinoma detection: screening strategies and novel biomarkers

Ayoub, Walid; Steggerda, Justin; Yang, Ju Dong; Kuo, Alexander; Sundaram, Vinay; Lu, Shelly C.

doi:10.1177/1758835919869120

Cited by 60 publications

(48 citation statements)

References 69 publications

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“…Delayed diagnosis is a major factor responsible for the poor prognosis of hepatocellular carcinoma (HCC). Therefore, developing a novel strategy for early detection of HCC could improve outcomes of patients with HCC (Marrero et al, 2018;Ayoub et al, 2019). Alpha-fetoprotein (AFP) performs disappointingly in early HCC screening and surveillance because of its low sensitivity and specificity (Marrero et al, 2009;El-Bahrawy, 2010).…”

Section: Discussionmentioning

confidence: 99%

Identification of Gene Signatures for Diagnosis and Prognosis of Hepatocellular Carcinomas Patients at Early Stage

et al. 2020

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The onset of liver cancer is insidious. Currently, there is no effective method for the early detection of hepatocellular carcinoma (HCC). Transcriptomic profiles of 826 tissue samples from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), Genotype tissue expression (GTEx), and International Cancer Genome Consortium (ICGC) databases were utilized to establish models for early detection and surveillance of HCC. The overlapping differentially expressed genes (DEGs) were screened by elastic net and robust rank aggregation (RRA) analyses to construct the diagnostic prediction model for early HCC (DP.eHCC). Prognostic prediction genes were screened by univariate cox regression and lasso cox regression analyses to construct the survival risk prediction model for early HCC (SP.eHCC). The relationship between the variation of transcriptome profile and the oncogenic risk-score of early HCC was analyzed by combining Weighted Correlation Network Analysis (WGCNA), Gene Set Enrichment Analysis (GSEA), and genome networks (GeNets). The results showed that the AUC of DP.eHCC model for the diagnosis of early HCC was 0.956 (95% CI: 0.941-0.972; p < 0.001) with a sensitivity of 90.91%, a specificity of 92.97%. The SP.eHCC model performed well for predicting the overall survival risk of HCC patients (HR = 10.79; 95% CI: 6.16-18.89; p < 0.001). The oncogenesis of early HCC was revealed mainly involving in pathways associated with cell proliferation and tumor microenvironment. And the transcription factors including EZH2, EGR1, and SOX17 were screened in the genome networks as the promising targets used for precise treatment in patients with HCC. Our findings provide robust models for the early diagnosis and prognosis of HCC, and are crucial for the development of novel targets applied in the precision therapy of HCC.

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Section: Discussionmentioning

confidence: 99%

Identification of Gene Signatures for Diagnosis and Prognosis of Hepatocellular Carcinomas Patients at Early Stage

et al. 2020

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“…Therefore, the diagnosis of HCC becomes particularly important. Current methods for evaluating HCC include histological observation, serum alpha-fetoprotein (AFP) level detection, computed tomography (CT), and magnetic resonance imaging (MRI) (Ayoub et al, 2019). The major clinical methods used in the treatment of HCC include surgical resection, liver transplantation, ablative therapies, chemoembolization, and systemic therapy (Akateh et al, 2019).…”

Section: Nlrp3 Inflammasome and Hcc Hepatocellular Carcinomamentioning

confidence: 99%

Involvement of Nucleotide-Binding Oligomerization Domain-Like Receptor Family Pyrin Domain Containing 3 Inflammasome in the Pathogenesis of Liver Diseases

Shi

Yang

Zhang

2020

Front. Cell Dev. Biol.

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“…[ 2 ] Accumulating evidence indicated that the abnormal genes expression is associated with initiation and poor prognosis of HCC, [ 3 , 4 ] and numerous studies have been performed to search for suitable biomarkers of early detection and prognosis. [ 5 , 6 ] However, limit number of biomarkers are used in clinical practice. [ 7 ] Therefore, it is especially important to explore more reliable biomarkers for improving the early detection, prognosis prediction, and better understanding the potential mechanism of HCC.…”

Section: Introductionmentioning

confidence: 99%

Screening of significant biomarkers with poor prognosis in hepatocellular carcinoma via bioinformatics analysis

et al. 2020

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Hepatocellular carcinoma (HCC) is a malignant tumor with unsatisfactory prognosis. The abnormal genes expression is significantly associated with initiation and poor prognosis of HCC. The aim of the present study was to identify molecular biomarkers related to the initiation and development of HCC via bioinformatics analysis, so as to provide a certain molecular mechanism for individualized treatment of hepatocellular carcinoma. Three datasets (GSE101685, GSE112790, and GSE121248) from the GEO database were used for the bioinformatics analysis. Differentially expressed genes (DEGs) of HCC and normal liver samples were obtained using GEO2R online tools. Gene ontology term and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis were conducted via the Database for Annotation, Visualization, and Integrated Discovery online bioinformatics tool. The protein–protein interaction (PPI) network was constructed by the Search Tool for the Retrieval of Interacting Genes database and hub genes were visualized by Cytoscape. Survival analysis and RNA sequencing expression were conducted by UALCAN and Gene Expression Profiling Interactive Analysis. A total of 115 shared DEGs were identified, including 30 upregulated genes and 85 downregulated genes in HCC samples. P53 signaling pathway and cell cycle were the major enriched pathways for the upregulated DEGs whereas metabolism-related pathways were the major enriched pathways for the downregulated DEGs. The PPI network was established with 105 nodes and 249 edges and 3 significant modules were identified via molecular complex detection. Additionally, 17 candidate genes from these 3 modules were significantly correlated with HCC patient survival and 15 of 17 genes exhibited high expression level in HCC samples. Moreover, 4 hub genes (CCNB1, CDK1, RRM2, BUB1B) were identified for further reanalysis of KEGG pathway, and enriched in 2 pathways, the P53 signaling pathway and cell cycle pathway. Overexpression of CCNB1, CDK1, RRM2, and BUB1B in HCC samples was correlated with poor survival in HCC patients, which could be potential therapeutic targets for HCC.

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Current status of hepatocellular carcinoma detection: screening strategies and novel biomarkers

Cited by 60 publications

References 69 publications

Identification of Gene Signatures for Diagnosis and Prognosis of Hepatocellular Carcinomas Patients at Early Stage

Identification of Gene Signatures for Diagnosis and Prognosis of Hepatocellular Carcinomas Patients at Early Stage

Involvement of Nucleotide-Binding Oligomerization Domain-Like Receptor Family Pyrin Domain Containing 3 Inflammasome in the Pathogenesis of Liver Diseases

Screening of significant biomarkers with poor prognosis in hepatocellular carcinoma via bioinformatics analysis

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