ArticleMolecular genetics & genomic medicine2019
Transcriptome analysis identifies metallothionein as biomarkers to predict recurrence in hepatocellular cacinoma.
Article in Molecular genetics & genomic medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 11 citations in OpenAlex.
- Metallothioneins in the Pathogenesis of Liver Diseases: A Review.International journal of hepatology · 2025Review
- Metallothionein: A Comprehensive Review of Its Classification, Structure, Biological Functions, and Applications.Antioxidants (Basel, Switzerland) · 2024Review
- Integrative Analysis of Metallothioneins Identifies MT1H as Candidate Prognostic Biomarker in Hepatocellular Carcinoma.Frontiers in molecular biosciences · 2021Article
- [Bioinformatics analysis of key genes and prognosis-related genes during the onset of hepatocellular carcinoma].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2020Article
- Transcriptome analysis identifies metallothionein as biomarkers to predict recurrence in hepatocellular cacinoma.Molecular genetics & genomic medicine · 2019Article
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Authors and funding
2 authors at 2 institutions in 2 countries.
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Abstract
backgroundLiver cancer is the fifth most common cancer, and hepatocellular carcinoma (HCC) is the major liver tumor type seen in adults. HCC is usually caused by chronic liver disease such as hepatitis B virus or hepatitis C virus infection. One of the promising treatments for HCC is liver transplantation, in which a diseased liver is replaced with a healthy liver from another person. However, recurrence of HCC after surgery is a significant problem. Therefore, it is important to discover reliable cellular biomarkers that can predict recurrence in HCC.
methodsWe analyzed previously published HCC RNA-Seq data that includes 21 paired tumor and normal samples, in which nine tumors were recurrent after orthotopic liver transplantation and 12 were nonrecurrent tumors with their paired normal samples. We used both the reference genome and de novo transcriptome assembly based analyses to identify differentially expressed genes (DEG) and used RandomForest to discover biomarkers.
resultsWe obtained 398 DEG using the Reference approach and 412 DEG using de novo assembly approach. Among these DEG, 258 genes were identified by both approaches. We further identified 30 biomarkers that could predict the recurrence. We used another independent HCC study that includes 50 patients normal and tumor samples. By using these 30 biomarkers, the prediction accuracy was 100% for normal condition and 98% for tumor condition. A group of Metallothionein was specifically discovered as biomarkers in both reference and de novo assembly approaches.
conclusionWe identified a group of Metallothionein genes as biomarkers to predict recurrence. The metallothionein genes were all down-regulated in tumor samples, suggesting that low metallothionein expression may be a promoter of tumor growth. In addition, using de novo assembly identified some unique biomarkers, further confirmed the necessity of conducting a de novo assembly in human cancer study.
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