Exploring new targets for the treatment of hepatitis-B virus and hepatitis-B virus-associated hepatocellular carcinoma: A new perspective in bioinformatics.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34414947", "kind": "science-record", "revision": 5, "content_hash": "sha256:75f491024b8acce200b76df5570a4695d543c8ea7698aa3a07fcc7131f7871b1", "created_at": "2026-11-01T15:41:48Z", "updated_at": "2026-11-01T22:04:30Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Exploring new targets for the treatment of hepatitis-B virus and hepatitis-B virus-associated hepatocellular carcinoma: A new perspective in bioinformatics.", "doi": "10.1097/MD.0000000000026917", "pmid": "34414947", "authors": ["Yang Wang", "ShanShan Wang", "Yang Che", "DeXi Chen", "YaLi Liu", "Ying Shi"], "journal": "Medicine", "year": 2021, "abstract": "BACKGROUND: Hepatitis B Virus (HBV) infection is a global public health problem. After infection, patients experience a natural course from chronic hepatitis to cirrhosis and even Hepatitis B associated Hepatocellular Carcinoma (HBV-HCC). With the multi-omics research, many differentially expressed genes from chronic hepatitis to HCC stages have been discovered. All these provide important clues for new biomarkers and therapeutic targets. The purpose of this study is to explore the differential gene expression of HBV and HBV-related liver cancer, and analyze their enrichments and significance of related pathways.\nMETHODS: In this study, we downloaded four microarray datasets GSE121248, GSE67764, GSE55092, GSE55092 and GSE83148 from the Gene Expression Omnibus (GEO) database. Using these four datasets, patients with chronic hepatitis B (CHB) differentially expressed genes (CHB DEGs) and patients with HBV-related HCC differentially expressed genes (HBV-HCC DEGs) were identified. Then Protein-protein Interaction (PPI) network analysis, Gene Ontology (GO) functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed to excavate the functional interaction of these two groups of DEGs and the common DEGs. Finally, the Kaplan website was used to analyze the role of these genes in HCC prognostic.\nRESULTS: A total of 241 CHB DEGs, 276 HBV-HCC DEGs, and 4 common DEGs (cytochrome P450 family 26 subfamily A member 1 (CYP26A1), family with sequence similarity 110 member C(FAM110C), SET and MYND domain containing 3(SMYD3) and zymogen granule protein 16(ZG16)) were identified. CYP26A1, FAM110C, SMYD3 and ZG16 exist in 4 models and interact with 33 genes in the PPI network of CHB and HBV-HCC DEGs,. GO function analysis showed that: CYP26A1, FAM110C, SMYD3, ZG16, and the 33 genes in their models mainly affect the regulation of synaptic vesicle transport, tangential migration from the subventricular zone to the olfactory bulb, cellular response to manganese ion, protein localization to mitochondrion, cellular response to dopamine, negative regulation of neuron death in the biological process of CHB. In the biological process of HBV-HCC, they mainly affect tryptophan catabolic process, ethanol oxidation, drug metabolic process, tryptophan catabolic process to kynurenine, xenobiotic metabolic process, retinoic acid metabolic process, steroid metabolic process, retinoid metabolic process, steroid catabolic process, retinal metabolic process, and rogen metabolic process. The analysis of the 4 common DEGs related to the prognosis of liver cancer showed that: CYP26A1, FAM110C, SMYD3 and ZG16 are closely related to the development of liver cancer and patient survival. Besides, further investigation of the research status of the four genes showed that CYP26A1 and SMYD3 could also affect HBV replication and the prognosis of liver cancer.\nCONCLUSION: CYP26A1, FAM110C, SMYD3 and ZG16 are unique genes to differentiate HBV infection and HBV-related HCC, and expected to be novel targets for HBV-related HCC occurrence and prognostic judgement.", "claims": [{"id": "claim-method", "statement": "The P values were adjusted to correct false-positive results using the Benjamini and Hochberg False Discovery Rate method.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34414947", "locator": "sections[4] \"Identification of DEGs\" paragraph 2", "quote": "The P values were adjusted to correct false-positive results using the Benjamini and Hochberg False Discovery Rate method."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated during and/or analyzed during the study are available in the GEO database (http://www.ncbi.nlm.nih.gov/geo).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34414947", "locator": "sections[3] \"Microarray data\" paragraph 2", "quote": "The datasets generated during and/or analyzed during the study are available in the GEO database (http://www.ncbi.nlm.nih.gov/geo)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:34414947"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE121248"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE121248"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:34414947", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1097/MD.0000000000026917", "sha256": 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