Microarray Data Mining and Preliminary Bioinformatics Analysis of Hepatitis D Virus-Associated Hepatocellular Carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33564675", "kind": "science-record", "revision": 8, "content_hash": "sha256:ba3de45678841b9f2a453ea380aab789ca62cc1ed9e19e479c773431ac676ce6", "created_at": "2026-11-01T15:41:24Z", "updated_at": "2026-11-01T22:46:06Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Microarray Data Mining and Preliminary Bioinformatics Analysis of Hepatitis D Virus-Associated Hepatocellular Carcinoma.", "doi": "10.1155/2021/1093702", "pmid": "33564675", "authors": ["Zhe Yu", "Xuemei Ma", "Wei Zhang", "Xiujuan Chang", "Linjing An", "Ming Niu", "Yan Chen", "Chao Sun", "Yongping Yang"], "journal": "BioMed research international", "year": 2021, "abstract": "Several studies have demonstrated that chronic hepatitis delta virus (HDV) infection is associated with a worsening of hepatitis B virus (HBV) infection and increased risk of hepatocellular carcinoma (HCC). However, there is limited data on the role of HDV in the oncogenesis of HCC. This study is aimed at assessing the potential mechanisms of HDV-associated hepatocarcinogenesis, especially to screen and identify key genes and pathways possibly involved in the pathogenesis of HCC. We selected three microarray datasets: GSE55092 contains 39 cancer specimens and 81 paracancer specimens from 11 HBV-associated HCC patients, GSE98383 contains 11 cancer specimens and 24 paracancer specimens from 5 HDV-associated HCC patients, and 371 HCC patients with the RNA-sequencing data combined with their clinical data from the Cancer Genome Atlas (TCGA). Afterwards, 948 differentially expressed genes (DEGs) closely related to HDV-associated HCC were obtained using the R package and filtering with a Venn diagram. We then performed gene ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis to determine the biological processes (BP), cellular component (CC), molecular function (MF), and KEGG signaling pathways most enriched for DEGs. Additionally, we performed Weighted Gene Coexpression Network Analysis (WGCNA) and protein-to-protein interaction (PPI) network construction with 948 DEGs, from which one module was identified by WGCNA and three modules were identified by the PPI network. Subsequently, we validated the expression of 52 hub genes from the PPI network with an independent set of HCC dataset stored in the Gene Expression Profiling Interactive Analysis (GEPIA) database. Finally, seven potential key genes were identified by intersecting with key modules from WGCNA, including 3 reported genes, namely, CDCA5, CENPH, and MCM7, and 4 novel genes, namely, CDC6, CDC45, CDCA8, and MCM4, which are associated with nucleoplasm, cell cycle, DNA replication, and mitotic cell cycle. The CDCA8 and stage of HCC were the independent factors associated with overall survival of HDV-associated HCC. All the related findings of these genes can help gain a better understanding of the role of HDV in the underlying mechanism of HCC carcinogenesis.", "claims": [{"id": "claim-method", "statement": "We performed differential expression analysis using Bayes t-statistics from the LIMMA implemented in the R Bioconductor and corrected p values for multiple testing using the Benjamini-Hochberg method [20].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33564675", "locator": "sections[4] \"2.2. Identification of DEGs\" paragraph 2", "quote": "We performed differential expression analysis using Bayes t-statistics from the LIMMA implemented in the R Bioconductor and corrected p values for multiple testing using the Benjamini-Hochberg method [20]."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets used to support the findings of this study are available from GEO (https://www.ncbi.nlm.nih.gov/geo/) and TCGA (https://portal.gdc.cancer.gov/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33564675", "locator": "sections[21] \"Data Availability\" paragraph 2", "quote": "The datasets used to support the findings of this study are available from GEO (https://www.ncbi.nlm.nih.gov/geo/) and TCGA (https://portal.gdc.cancer.gov/)."}], "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:33564675"], "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. 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We selected three microarray datasets: GSE55092 contains 39 cancer specim", "s Supplementary MaterialsTable S1: DEGs from microarray datasets GSE55092 and GSE98383. Table S2: 94"], "order_basis": "paper_text_order:0", "id": "assay:1"}, {"role": "data_generation", "definition_id": "rna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["HCC patients, and 371 HCC patients with the RNA-sequencing data combined with their clinical data from"], "order_basis": "paper_text_order:836", "id": "assay:2"}, {"role": "analysis", "definition_id": "differential_expression", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["We performed differential expression analysis using Bayes t-statistics from the LIMMA implemented in the R Bioconductor and corrected p values for multiple testing using the Benjamini-Hochberg method [20]."], "order_basis": "consumes_earlier_output", "id": "assay:3"}]}