Identification of key signaling pathways and novel computational drug target for oral cancer, metabolic disorders and periodontal disease.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39674633", "kind": "science-record", "revision": 4, "content_hash": "sha256:108588ec0f18700565e3c69ac18047243bc58db5ba4579650e28b865e49aa76f", "created_at": "2026-11-01T20:29:58Z", "updated_at": "2026-11-01T22:55:48Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of key signaling pathways and novel computational drug target for oral cancer, metabolic disorders and periodontal disease.", "doi": "10.1016/j.jgeb.2024.100431", "pmid": "39674633", "authors": ["Mohammad Khursheed Alam", "Md Faruk Hosen", "Kiran Kumar Ganji", "Kawsar Ahmed", "Francis M Bui"], "journal": "Journal, genetic engineering & biotechnology", "year": 2024, "abstract": "AIM: Due to conventional endocrinological methods, there is presently no shared work available, and no therapeutic options have been demonstrated in oral cancer (OC) and periodontal disease (PD), type 2 diabetes (T2D), and obese patients. The aim of this study is to determine the similar molecular pathways and potential therapeutic targets in PD, OC, T2D, and obesity that may be used to anticipate the progression of the disease.\nMETHODS: Four Gene Expression Omnibus (GEO) microarray datasets (GSE29221, GSE15773, GSE16134, and GSE13601) are used for finding differentially expressed genes (DEGs) for T2D, obese, and PD patients with OC in order to explore comparable pathways and therapeutic medications. Gene ontology (GO) and pathway analysis were used to investigate the functional annotations of the genes. The hub genes were then identified using protein-protein interaction (PPI) networks, and the most significant PPI components were evaluated using a clustering approach.\nRESULTS: These three gene expression-based datasets yielded a total of seven common DEGs. According to the GO annotation, the majority of the DEGs were connected with the microtubule cytoskeleton structure involved in mitosis. The KEGG pathways revealed that the concordant DEGs are connected to the cell cycle and progesterone-mediated oocyte maturation. Based on topological analysis of the PPI network, major hub genes (CCNB1, BUB1, TTK, PLAT, and AHNAK) and notable modules were revealed. This work additionally identified the connection of TF genes and miRNAs with common DEGs, as well as TF activity.\nCONCLUSION: Predictive drug analysis yielded concordant drug compounds involved with T2D, OC, PD, and obesity disorder, which might be beneficial for examining the diagnosis, treatment, and prognosis of metabolic disorders and Oral cancer.", "claims": [{"id": "claim-method", "statement": "The DEGs were discovered using the R-programming language's (R-PL) limma library. The DESeq236 and limma package37 are used in order to obtain the data that are generated from microarray analysis. In several testing scenarios, the false discovery rate was handled using the Benjamini-Hochberg method.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39674633", "locator": "sections[4] \"Extraction of DEGs and concordant DEGs among OC, PD, T2D, and obesity\" paragraph 2", "quote": "The DEGs were discovered using the R-programming language's (R-PL) limma library. The DESeq236 and limma package37 are used in order to obtain the data that are generated from microarray analysis. In several testing scenarios, the false discovery rate was handled using the Benjamini-Hochberg method."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets are publicly available in NCBI data repository.\nLink 1: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE29221.\nLink 2: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE15773.\nLink 3: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE16134.\nLink 4: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE13601.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39674633", "locator": "sections[24] \"Availability of data and materials\" paragraphs 1-6", "quote": "The datasets are publicly available in NCBI data repository.\nLink 1: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE29221.\nLink 2: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE15773.\nLink 3: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE16134.\nLink 4: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE13601."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The analysis identified seven common differentially expressed genes shared by the OC, T2D, PD, and obesity datasets.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39674633", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "These three gene expression-based datasets yielded a total of seven common DEGs."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The analysis identified seven common differentially expressed genes shared by the OC, T2D, PD, and obesity datasets.", "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:39674633"], "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:GSE13601"], "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": {"fdr_threshold": "with Benjamini-Hochberg false discovery rate control"}, "inputs": ["data:geo:GSE13601"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39674633", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": 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In several testing scenarios, the false discovery rate was handled using the Benjamini-Hochberg method."}}, {"id": "method-2", "family": "network_analysis", "specific": "ppi_network", "label": "Network / pathway analysis", "dsl_operations": ["build_graph", "centrality_scores", "pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.7, "band": "medium", "quote": "protein-protein interaction"}}], "targets": [], "gaps": []}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: limma_voom."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE29221", "GSE15773", "GSE16134", "GSE13601"], "resolved_accessions": ["GSE29221", "GSE15773", "GSE16134", "GSE13601"], "statements": ["The datasets are publicly available in NCBI data repository.\nLink 1: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE29221.\nLink 2: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE15773.\nLink 3: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE16134.\nLink 4: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE13601."], "basis": "The paper names GSE29221, GSE15773, GSE16134, GSE13601 in NCBI GEO and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "assets": ["source:pmid:39674633", "data:geo:GSE13601"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE13601"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T19:41:43Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 15381, "completion_tokens": 1544, "cached_tokens": 264}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:39674633", "type": "paper", "label": "pmid:39674633"}, {"id": "source:pmid:39674633", "type": "manuscript", "label": "PMID 39674633 full text"}, {"id": "data:geo:GSE13601", "type": "dataset", "label": "GEO GSE13601"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:39674633", "type": "paper", "label": "pmid:39674633"}, {"id": "source:pmid:39674633", "type": "manuscript", "label": "PMID 39674633 full text"}, {"id": "data:geo:GSE13601", "type": "dataset", "label": "GEO GSE13601"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-39674633", "type": "extraction", "used": ["source:pmid:39674633"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE13601", "type": "accession_resolution", "used": ["data:geo:GSE13601"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:39674633", "parent_hash": "sha256:03a77c253a19b68a383f834e6c3744f6a9fdcd8168434247f8b47c1173a777a9", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:GSE13601 licence not read", "asset:data:geo:GSE13601 not downloaded", "asset:data:geo:GSE15773 licence not read", "asset:data:geo:GSE15773 not downloaded", "asset:data:geo:GSE16134 licence not read", "asset:data:geo:GSE16134 not downloaded", "asset:data:geo:GSE29221 licence not read", "asset:data:geo:GSE29221 not downloaded", "execution.not_run", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "data_generation", "definition_id": "microarray", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["METHODS: Four Gene Expression Omnibus (GEO) microarray datasets (GSE29221, GSE15773, GSE16134, and"], "order_basis": "paper_text_order:572", "id": "assay:1"}, {"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": ["The DEGs were discovered using the R-programming language's (R-PL) limma library. The DESeq236 and limma package37 are used in order to obtain the data that are generated from microarray analysis. In several testing scenarios, the false discovery rate was handled using the Benjamini-Hochberg method."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}