ArticleOncology letters2020
High-throughput sequencing-based analysis of gene expression of hepatitis B virus infection-associated human hepatocellular carcinoma.
Article in Oncology letters, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed, 12 citations in OpenAlex.
- Identification of RAD51AP1 as a key gene in hepatitis B virus-associated hepatocellular carcinoma.Heliyon · 2025Article
- Machine Learning Approach and Bioinformatics Analysis Discovered Key Genomic Signatures for Hepatitis B Virus-Associated Hepatocyte Remodeling and Hepatocellular Carcinoma.Cancer informatics · 2025Review
- Analysis of Somatic Mutations in the TCGA-LIHC Whole Exome Sequence to Identify the Neoantigen for Immunotherapy in Hepatocellular Carcinoma.Current issues in molecular biology · 2023Article
- Liver-specific LINC01146, a promising prognostic indicator, inhibits the malignant phenotype of hepatocellular carcinoma cells both in vitro and in vivo.Journal of translational medicine · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hepatitis B virus (HBV) infection is a critical factor for the initiation and progression of hepatocellular carcinoma (HCC). Gene expression profiles for HBV-associated HCC may provide valuable insight for the diagnosis and treatment of this type of HCC. The present study aimed to screen the differential genes in human HCC tissues based on high-throughput sequencing and to predict the potential therapeutic targets. Total mRNA was extracted from human HCC tissues and paracancerous tissues and sequenced using the Hiseq4000 sequencing platform. Differential gene expressions were screened and further analyzed using quantitative PCR and immunohistochemistry. A total of 2,386 differentially expressed genes were screened. Of these, 1119 were upregulated and 1,267 were downregulated in paracancerous tissues compared with tumor tissues. Gene Ontology term analysis demonstrated that differentially expressed genes were involved in carboxylic acid catabolism, monocarboxylic acid metabolic processes and α-amino acid metabolic processes. Molecular functional analysis revealed that the differentially expressed genes functioned in oxidoreductase activity, for example acting on CH-OH group of donors and permitting identical protein binding, anion binding, coenzyme binding and monocarxylic acid transporter activity. The Kyoto Encyclopedia of Genes and Genomes analysis reported that the differentially expressed genes were primarily concentrated in 20 signaling pathways, such as valine, leucine and leucine degradation, retinol metabolism and the cell cycle. Differential expression of proteins regulating the cell cycle, including stratifin, cyclin B1 and cyclin-dependent kinase 1, were significantly higher in tumor tissue compared with those in paracancerous tissue at both the mRNA and protein levels. These results were consistent with those obtained from high-throughput sequencing, indicating the reliability of the high-throughput sequencing. Together, these results identified differentially expressed genes and predicted the subsequent signaling pathways, which may be involved in the occurrence and development of HCC. Therefore, the present study may provide novel implications in the therapeutic and diagnosis of HCC.
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