Identification of novel blood-based HCC-specific diagnostic biomarkers for human hepatocellular carcinoma

Li, Yin; He, Na; Chen, Chuizhe; Zhang, Nan; Lin, Yuan; Xia, Qianfeng

doi:10.1080/21691401.2019.1613421

Cited by 28 publications

(27 citation statements)

References 41 publications

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“…Although surgery, transcatheter arterial chemoembolization (TACE), radiofrequency ablation, radiotherapy and other therapeutic approaches can control the progress of HCC, generally speaking, HCC is still under exploration in the areas of molecular typing, molecular diagnosis and gene target selection. Although CDH 21 , LDH 22 , NCOR1 23 and other mutant genes are new biomarkers in HCC subtype classification 24,25 , identification of gene mutations that affect recurrence is complex but will have clinical significance. Meanwhile, various probability theories and linear algebra algorithms can be used for comprehensive analysis.…”

Section: Discussionmentioning

confidence: 99%

Identification of a novel gene signature for the prediction of recurrence in HCC patients by machine learning of genome-wide databases

Shen

Liang

Zou

et al. 2020

Sci Rep

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Hepatocellular carcinoma (HCC) is a common malignant tumor in China. In the present study, we aimed to construct and verify a prediction model of recurrence in HCC patients using databases (TCGA, AMC and Inserm) and machine learning methods and obtain the gene signature that could predict early relapse of HCC. Statistical methods, such as feature selection, survival analysis and Chi-Square test in R software, were used to analyze and select mutant genes related to disease free survival (DFS), race and vascular invasion. In addition, whole-exome sequencing was performed on 10 HCC patients recruited from our center, and the sequencing results were compared with the databases. Using the databases and machine learning methods, the prediction model of recurrence was constructed and optimized, and the selected mutant genes were verified in the test group. The accuracy of prediction was 74.19%. Moreover, these 10 patients from our center were used to verify these mutant genes and the prediction model, and a success rate of 80% was achieved. Collectively, we discovered recurrence-related genes and established recurrence prediction model of recurrence for HCC patients, which could provide significant guidance for clinical prediction of recurrence. Hepatocellular carcinoma (HCC) is a common malignant tumor in China, which ranks the third in morbidity and the second in mortality. Its morbidity is usually associated with specific risk factors, including infections with HBV and HCV, high alcohol intake, obesity and consumption of aflatoxin-containing food 1. With the development of the second-generation sequencing techniques increasing research on HCC has been conducted on the molecular level. In 2014, Totoki et al. 2 have reported the whole-genome sequencing of 608 HCC patients from Asia and Europe. In 2015, Schulze et al. 3 have reported the whole-genome sequencing of 243 HCC patients from Europe and America. In 2016, Fujimoto et al. 4 have reported the whole-genome sequencing of 300 HCC patients from Japan. The molecular blueprint of HCC including somatic mutation, mRNA expression, methylation and miRNA regulation has been gradually outlined, which could be used for the diagnosis, treatment, and prediction of recurrence and survival of liver cancer patients. In 2017, TCGA working group 5 has systematically analyzed the sequencing results of the whole exome of more than 360 HCC patients in TCGA database and compared these data with other published HCC sequencing samples. Various statistical methods, related classification and clustering algorithms of machine learning have been used. TERT, TP53, CTNNB1, AXIN1, ARID1A, ARID2, RB1, ALB, APOB, PTEN, CDKN2A, DOCK2 6-15 and other somatic cells with significantly mutant genes (SMGs) and driver mutation have been identified. These findings have been rapidly applied as potential therapeutic targets and prognostic indicators in clinical practice. However, the high cost of whole-exome sequencing and whole-genome sequencing limits its use in clinical practice. Actually, patients often ...

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Section: Discussionmentioning

confidence: 99%

Identification of a novel gene signature for the prediction of recurrence in HCC patients by machine learning of genome-wide databases

Shen

Liang

Zou

et al. 2020

Sci Rep

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“…Li et al explored the Cancer Genome Atlas database (TGCA) to identify Hub genes involved in key tumorigenic pathways 14 . Similarly, Yin et al 15 analyzed peripheral blood mononuclear cells (PBMC)-derived RNA to identify diagnostic blood-based biomarkers for HCC. More recently, Li et al found a six-gene signature that predicts OS in patients with HCC, however using tissue expression data.…”

Section: Discussionmentioning

confidence: 99%

Weighted miRNA co-expression networks analysis identifies circulating miRNA predicting overall survival in hepatocellular carcinoma patients

Pascut

Pratama

Gilardi

et al. 2020

Sci Rep

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The weighted gene co-expression network analysis (WGCNA) has been used to explore gene expression datasets by constructing biological networks based on the likelihood expression profile among genes. In recent years, WGCNA found application in biomarker discovery studies, including miRNA. Serum samples from 20 patients with hepatocellular carcinoma (HCC) were profiled through miRNA 3.0 gene array and miRNAs biomarker candidates were identified through WGCNA. Results were validated by qRT-PCR in 102 HCC serum samples collected at diagnosis. WGCNA identified 16 miRNA modules, nine of them were significantly associated with the clinical characteristics of the patient. The Red module had a significant negative correlation with patients Survival (− 0.59, p = 0.007) and albumin (− 0.52, p = 0.02), and a positive correlation with PCR (0.61, p = 0.004) and alpha-fetoprotein (0.51, p = 0.02). In the red module, 16 circulating miRNAs were significantly associated with patient survival. MiR-3185 and miR-4507 were identified as predictors of patient survival after the validation phase. At diagnosis, high expression of circulating miR-3185 and miR-4507 identifies patients with longer survival (HR 2.02, 95% CI 1.10–3.73, p = 0.0086, and HR of 1.75, 95% CI 1.02–3.02, p = 0.037, respectively). Thought a WGCNA we identified miR-3185 and miR-4507 as promising candidate biomarkers predicting a longer survival in HCC patients.

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“…No single biomarker has adequate sensitivity or speci city for HCC diagnosis. More and more studies are beginning to notice the use of genome-wide expression analysis to predict and diagnose diseases [35,36]. Compared with the single-gene biomarker, our multiple-genes based risk score-related prediction model got high accuracy with high sensitivity and speci city, which could be also used in other studies.…”

Section: Disscusionmentioning

confidence: 94%

Identification of Key Genes in HCV-induced HCC at Early Stage on the Background of HCV-cirrhosis

nie

et al. 2020

Preprint

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Background: Hepatocellular carcinoma (HCC) is one of the most common and deadly malignant tumors worldwide. Hepatitis C virus (HCV)-induced cirrhosis (HCV-cirrhosis) is one of the leading causes of HCC occurrence. Due to the lack of effective biomarkers, most patients with HCV-induced HCC (HCV-HCC) are at advanced stages when they are first diagnosed. Our study aims to employ transcriptomic profiling dataset to identify potential biomarkers that can predict HCV-HCC at early stage on the background of HCV-cirrhosis. Methods: The dataset incorporating HCV-cirrhosis and HCV-HCC subjects at different stages from Gene Expression Omnibus was analyzed to identify gene signature-related to HCV-HCC on the background of HCV-cirrhosis. Multiple-genes based risk score-related prediction model was established to predict HCV-HCC samples at different stages, and receiver-operating characteristic (ROC) curve analysis was used to select the best cutoff value of risk scores for prediction and to evaluate the prediction model. Samples with risk scores higher than cutoff value were defined as high score group.Results: Highly clustered 20 genes were identified and were all gradually increased as HCV-HCC progressed from HCV-cirrhosis to very advanced HCC. Compared with HCV-cirrhosis, the prevalence of HCV-HCC at any of the stages in high score group was 100%. The risk score-related prediction model based on the 20 genes-biomarker got the accuracy of over 95.2 % with area under ROC (AUC) over 0.94. To implement the biomarker easily in real life and make it economical, we tried to limit the gene numbers. Interestingly, CDKN3 and FAM83D were significantly decreased in HCV-cirrhosis tissues as compared to normal tissues and then increased as HCV-HCC progressed. The results were attractive that the prediction model based on this 2 genes-biomarker indicated that prevalence of HCV-HCC in high score group was still 100% and got the predictive accuracy of over 95.2 % with AUC over 0.96, revealing even a better performance.Conclusion: Our study indicated a 2-genes-based biomarker that could identify HCV-HCC at earlier stages on the background of HCV-cirrhosis, which might be a promising biomarker for early diagnosis of HCV-HCC and potential novel treatment target.

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Identification of novel blood-based HCC-specific diagnostic biomarkers for human hepatocellular carcinoma

Cited by 28 publications

References 41 publications

Identification of a novel gene signature for the prediction of recurrence in HCC patients by machine learning of genome-wide databases

Identification of a novel gene signature for the prediction of recurrence in HCC patients by machine learning of genome-wide databases

Weighted miRNA co-expression networks analysis identifies circulating miRNA predicting overall survival in hepatocellular carcinoma patients

Identification of Key Genes in HCV-induced HCC at Early Stage on the Background of HCV-cirrhosis

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