Novel bioinformatic approaches show the role of driver genes in the progression of cervical cancer: An in-silico study.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39634417", "kind": "science-record", "revision": 3, "content_hash": "sha256:7501496bc2a619d1ee5f0ce37452033ede88bec23d373eb8ea9ce8881187ad40", "created_at": "2026-11-01T20:29:54Z", "updated_at": "2026-11-01T22:13:15Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Novel bioinformatic approaches show the role of driver genes in the progression of cervical cancer: An in-silico study.", "doi": "10.1016/j.heliyon.2024.e40179", "pmid": "39634417", "authors": ["Amir Hossein Yari", "Parisa Shiri Aghbash", "Mobina Bayat", "Shiva Lahouti", "Nazila Jalilzadeh", "Leila Nariman Zadeh", "Amir Mohammad Yari", "Parinaz Tabrizi-Nezhadi", "Javid Sadri Nahand", "Habib MotieGhader", "Hossein Bannazadeh Baghi"], "journal": "Heliyon", "year": 2024, "abstract": "Background: The goal of this bioinformatics research is to get a comprehensive understanding of the driver genes and their function in the development, progression, and treatment of cervical cancer. This study constitutes a pioneering attempt, adding to our knowledge of genetic diversity and its ramifications.\nMaterial and methods: In this project, we use bioinformatics and systems biology methods to identify candidate transcription factors and the genes they regulate in order to identify microRNAs and LncRNAs that regulate these transcription factors and lead to the discovery of new medicines for the treatment of cervical cancer. From the differentially expressed genes available via GEO's GSE63514 accession, we use driver genes to choose these candidates. We then used the WGCNA tool in R to rebuild the co-expression network and its modules. The hub genes of each module were determined using CytoHubba, a Cystoscope plugin. The biomarker potential of hub genes was analyzed using the UCSC Xena browser and the GraphPad prism program. The TRRUST database is used to locate the TFs that regulate the expression of these genes. In order to learn how drugs, MicroRNAs, and LncRNAs interact with transcription factors, we consulted the Drug Target Information Database (DGIDB), the miRWalk database, and the LncHub database. Finally, the online database Enrichr is utilized to analyze the enrichment of Gene Ontology and KEGG pathways.\nResults: By combining the mRNA expression levels of 2041 driver genes from 14 early-stage Cervical cancer and 24 control samples, a co-expression network was built. The cluster analysis shows that the collection of shared genes may be broken down into seven distinct groups, or \"modules.\" According to the average linkage hierarchical clustering and Summary smaller than 2, we found five modules (represented by the colors blue, brown, red, green, and grey) in our research. Then, we identify 5 high-degree genes from these modules that may serve as diagnostic biomarkers (ZBBX, PLCH1, TTC7B, DNAH7, and ZMYND10). In addition, we identify four transcription factors (SRF, RELA, NFKB1, and SP1) that regulate the expression of genes in the co-expression module. Drugs, microRNAs, and long noncoding RNAs are then shown to cooperate with transcription factors. At last, the KEGG database's pathways were mined for information on how the co-expression module fits within them. More clinical trials are required for more trustworthy outcomes, and we collected this data using bioinformatics methods.\nConclusion: The major goal of this research was to identify diagnostic and therapeutic targets for cervical cancer by learning more about the involvement of driver genes in cancer's earliest stages.", "claims": [{"id": "claim-method", "statement": "The gene expression levels of cervical cancer samples and normal controls were compared using the student's t-test, with a significance threshold of p < 0.05. Utilizing GraphPad Prism software, a Receiver Operating Characteristic (ROC) curve analysis was performed to assess the diagnostic capability of the discovered hub genes by computing the area under the curve (AUC) for each gene.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39634417", "locator": "sections[12] \"Statistical analysis\" paragraph 2", "quote": "The gene expression levels of cervical cancer samples and normal controls were compared using the student's t-test, with a significance threshold of p < 0.05. Utilizing GraphPad Prism software, a Receiver Operating Characteristic (ROC) curve analysis was performed to assess the diagnostic capability of the discovered hub genes by computing the area under the curve (AUC) for each gene."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data supporting this study's findings are available from the corresponding author upon request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39634417", "locator": "sections[29] \"Data availability\" paragraph 2", "quote": "The data supporting this study's findings are available from the corresponding author upon request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Then, we identify 5 high-degree genes from these modules that may serve as diagnostic biomarkers (ZBBX, PLCH1, TTC7B, DNAH7, and ZMYND10).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39634417", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Then, we identify 5 high-degree genes from these modules that may serve as diagnostic biomarkers (ZBBX, PLCH1, TTC7B, DNAH7, and ZMYND10)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Then, we identify 5 high-degree genes from these modules that may serve as diagnostic biomarkers (ZBBX, PLCH1, TTC7B, DNAH7, and ZMYND10).", "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:39634417"], "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:GSE63514"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "blocked", "blocked_on": "method:student_t", "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:GSE63514"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:student_t"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39634417", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1016/j.heliyon.2024.e40179", "sha256": "sha256:176371a4897539324f097c1548e4f670f599d9e1d753bb6b4dde0e663e0d634e", "bytes": 68090, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1016/j.heliyon.2024.e40179", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11616557", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE63514", "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=GSE63514", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE63514", "version": null, "note": "The data supporting this study's findings are available from the corresponding author upon request.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The data supporting this study's findings are available from the corresponding author upon request.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM1551311", "GSM1551312", "GSM1551313", "GSM1551314", "GSM1551315", "GSM1551316", "GSM1551317", "GSM1551318", "GSM1551319", "GSM1551320", "GSM1551321", "GSM1551322", "GSM1551323", "GSM1551324", "GSM1551325", "GSM1551326", "GSM1551327", "GSM1551328", "GSM1551329", "GSM1551330", "GSM1551331", "GSM1551332", "GSM1551333", "GSM1551334", "GSM1551335", "GSM1551336", "GSM1551337", "GSM1551338", "GSM1551339", "GSM1551340", "GSM1551341", "GSM1551342", "GSM1551343", "GSM1551344", "GSM1551345", "GSM1551346", "GSM1551347", "GSM1551348", "GSM1551349", "GSM1551350", "GSM1551351", "GSM1551352", "GSM1551353", "GSM1551354", "GSM1551355", "GSM1551356", "GSM1551357", "GSM1551358", "GSM1551359", "GSM1551360", "GSM1551361", "GSM1551362", "GSM1551363", "GSM1551364", "GSM1551365", "GSM1551366", "GSM1551367", "GSM1551368", "GSM1551369", "GSM1551370", "GSM1551371", "GSM1551372", "GSM1551373", "GSM1551374", "GSM1551375", "GSM1551376", "GSM1551377", "GSM1551378", "GSM1551379", "GSM1551380", "GSM1551381", "GSM1551382", "GSM1551383", "GSM1551384", "GSM1551385", "GSM1551386", "GSM1551387", "GSM1551388", "GSM1551389", "GSM1551390", "GSM1551391", "GSM1551392", "GSM1551393", "GSM1551394", "GSM1551395", "GSM1551396", "GSM1551397", "GSM1551398", "GSM1551399", "GSM1551400", "GSM1551401", "GSM1551402", "GSM1551403", "GSM1551404", "GSM1551405", "GSM1551406", "GSM1551407", "GSM1551408", "GSM1551409", "GSM1551410", "GSM1551411", "GSM1551412", "GSM1551413", "GSM1551414", "GSM1551415", "GSM1551416", "GSM1551417", "GSM1551418", "GSM1551419", "GSM1551420", "GSM1551421", "GSM1551422", "GSM1551423", "GSM1551424", "GSM1551425", "GSM1551426", "GSM1551427", "GSM1551428", "GSM1551429", "GSM1551430", "GSM1551431", "GSM1551432", "GSM1551433", "GSM1551434", "GSM1551435", "GSM1551436", "GSM1551437", "GSM1551438"], "sample_count": 128, "group_sizes": null, "group_sizes_basis": null, "platform": "570", "taxon": "Homo sapiens", "title": "Gene expression analysis of cervical cancer progression", "resolution_checked_at": "2026-11-01T15:39:55Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE63514 not downloaded", "asset:data:geo:GSE63514 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "group_comparison", "specific": "student_t_test", "label": "Group comparison", "dsl_operations": [], "missing_operations": ["student_t"], "expressible": false, "parameters": {}, "data_shapes": ["two_or_more_groups_continuous"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "The gene expression levels of cervical cancer samples and normal controls were compared using the student's t-test, with a significance threshold of p < 0.05. 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