Differentially expressed discriminative genes and significant meta-hub genes based key genes identification for hepatocellular carcinoma using statistical machine learning.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36882493", "kind": "science-record", "revision": 4, "content_hash": "sha256:f1657196eafe7b420e18687051c64d77e462e01d368e34567c7abea7c7c19c4b", "created_at": "2026-11-01T19:01:31Z", "updated_at": "2026-11-01T22:51:26Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Differentially expressed discriminative genes and significant meta-hub genes based key genes identification for hepatocellular carcinoma using statistical machine learning.", "doi": "10.1038/s41598-023-30851-1", "pmid": "36882493", "authors": ["Md Al Mehedi Hasan", "Md Maniruzzaman", "Jungpil Shin"], "journal": "Scientific reports", "year": 2023, "abstract": "Hepatocellular carcinoma (HCC) is the most common lethal malignancy of the liver worldwide. Thus, it is important to dig the key genes for uncovering the molecular mechanisms and to improve diagnostic and therapeutic options for HCC. This study aimed to encompass a set of statistical and machine learning computational approaches for identifying the key candidate genes for HCC. Three microarray datasets were used in this work, which were downloaded from the Gene Expression Omnibus Database. At first, normalization and differentially expressed genes (DEGs) identification were performed using limma for each dataset. Then, support vector machine (SVM) was implemented to determine the differentially expressed discriminative genes (DEDGs) from DEGs of each dataset and select overlapping DEDGs genes among identified three sets of DEDGs. Enrichment analysis was performed on common DEDGs using DAVID. A protein-protein interaction (PPI) network was constructed using STRING and the central hub genes were identified depending on the degree, maximum neighborhood component (MNC), maximal clique centrality (MCC), centralities of closeness, and betweenness criteria using CytoHubba. Simultaneously, significant modules were selected using MCODE scores and identified their associated genes from the PPI networks. Moreover, metadata were created by listing all hub genes from previous studies and identified significant meta-hub genes whose occurrence frequency was greater than 3 among previous studies. Finally, six key candidate genes (TOP2A, CDC20, ASPM, PRC1, NUSAP1, and UBE2C) were determined by intersecting shared genes among central hub genes, hub module genes, and significant meta-hub genes. Two independent test datasets (GSE76427 and TCGA-LIHC) were utilized to validate these key candidate genes using the area under the curve. Moreover, the prognostic potential of these six key candidate genes was also evaluated on the TCGA-LIHC cohort using survival analysis.", "claims": [{"id": "claim-method", "statement": "SVM with RBF kernel was applied on the identified DEGs (699 DEGs for GSE36376; 428 DEGs for GSE39791; and 413 DEGs for GSE57957) of each dataset in order to identify the DEDGs of HCC patients.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36882493", "locator": "sections[4] \"Identification of common DEDGs using SVM\" paragraph 2", "quote": "SVM with RBF kernel was applied on the identified DEGs (699 DEGs for GSE36376; 428 DEGs for GSE39791; and 413 DEGs for GSE57957) of each dataset in order to identify the DEDGs of HCC patients."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated and/or analyzed during the current study are available in the Gene Expression Omnibus (GEO) repository with accession numbers: GSE36376, GSE39791, GSE57957, and GSE76427 with GPL10558 platforms. One can easily download these datasets from the link: www.ncbi.nlm.nih.gov/geo/.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36882493", "locator": "sections[31] \"Data availability\" paragraph 2", "quote": "The datasets generated and/or analyzed during the current study are available in the Gene Expression Omnibus (GEO) repository with accession numbers: GSE36376, GSE39791, GSE57957, and GSE76427 with GPL10558 platforms. One can easily download these datasets from the link: www.ncbi.nlm.nih.gov/geo/."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study identified six key candidate genes for hepatocellular carcinoma: TOP2A, CDC20, ASPM, PRC1, UBE2C, and NUSAP1.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36882493", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Finally, six key candidate genes (TOP2A, CDC20, ASPM, PRC1, NUSAP1, and UBE2C) were determined by intersecting shared genes among central hub genes, hub module genes, and significant meta-hub genes."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study identified six key candidate genes for hepatocellular carcinoma: TOP2A, CDC20, ASPM, PRC1, UBE2C, and NUSAP1.", "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:36882493"], "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:GSE36376"], "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:GSE36376"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36882493", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1038/s41598-023-30851-1", "sha256": "sha256:42a833914c2a07016f383d07084ac166f5ff11a4e60ebfd622118f5cf3bb5150", "bytes": 62906, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1038/s41598-023-30851-1", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9992474", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE36376", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE36376", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE36376", "version": null, "note": "The datasets generated and/or analyzed during the current study are available in the Gene Expression Omnibus (GEO) repository with accession numbers: GSE36376, GSE39791, GSE57957, and GSE76427 with GPL10558 platforms. 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