Bioinformatic identification of hub genes Myd88 and Ccl3 and TWS-119 as a potential agent for the treatment of massive cerebral infarction.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37234258", "kind": "science-record", "revision": 4, "content_hash": "sha256:80bf2ff9e452b0d4fcf890bd5c3559a1a2ef2359e9ff1b083801ca5260dbadde", "created_at": "2026-11-01T19:01:56Z", "updated_at": "2026-11-01T22:52:00Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Bioinformatic identification of hub genes Myd88 and Ccl3 and TWS-119 as a potential agent for the treatment of massive cerebral infarction.", "doi": "10.3389/fnins.2023.1171112", "pmid": "37234258", "authors": ["Ai Guo", "Bin Gao", "Mengting Zhang", "Xiaoyu Shi", "Weina Jin", "Decai Tian"], "journal": "Frontiers in neuroscience", "year": 2023, "abstract": "Background: Massive cerebral infarction (MCI) causes severe neurological deficits, coma and can even result in death. Here, we identified hub genes and pathways after MCI by analyzing microarray data from a murine model of ischemic stroke and identified potential therapeutic agents for the treatment of MCI.\nMethods: Microarray expression profiling was performed using the GSE28731 and GSE32529 datasets from the Gene Expression Omnibus (GEO) database. Data from a sham group (n = 6 mice) and a middle cerebral artery occlusion (MCAO) group (n = 7 mice) were extracted to identify common differentially expressed genes (DEGs). After identifying gene interactions, we generated a protein-protein interaction (PPI) network with Cytoscape software. Then, the MCODE plug-in in Cytoscape was used to determine key sub-modules according to MCODE scores. Enrichment analyses were then conducted on DEGs in the key sub-modules to evaluate their biological functions. Furthermore, hub genes were identified by generating the intersections of several algorithms in the cytohubba plug-in; these genes were then verified in other datasets. Finally, we used Connectivity MAP (CMap) to identify potential agents for MCI therapy.\nResults: A total of 215 common DEGs were identified and a PPI network was generated with 154 nodes and 947 edges. The most significant key sub-module had 24 nodes and 221 edges. Gene ontology (GO) analysis showed that the DEGs in this sub-module showed enrichment in inflammatory response, extracellular space and cytokine activity in terms of biological process, cellular component and molecular function, respectively. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that TNF signaling was the most enriched pathway. Myd88 and Ccl3 were identified as hub genes and TWS-119 was identified as the most potential therapeutic agent by CMap.\nConclusions: Bioinformatic analysis identified two hub genes (Myd88 and Ccl3) for ischemic injury. Further analysis identified TWS-119 as the best potential candidate for MCI therapy and that this target may be associated with TLR/MyD88 signaling.", "claims": [{"id": "claim-method", "statement": "Differentially expressed RNAs between the MCAO and sham groups were identified as DEGs by the limma (Linear Models for Microarray Analysis) package in R (GEO2R; http://www.ncbi.nlm.nih.gov/geo/geo2r). The limma package was used to apply multiple-testing corrections on P-values to help correct for the occurrence of false positives. The Benjamini and Hochberg (false discovery rate) method was then used to adjust P values to limit false positive errors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37234258", "locator": "sections[4] \"2.2. Identification of DEGs\" paragraph 2", "quote": "Differentially expressed RNAs between the MCAO and sham groups were identified as DEGs by the limma (Linear Models for Microarray Analysis) package in R (GEO2R; http://www.ncbi.nlm.nih.gov/geo/geo2r). The limma package was used to apply multiple-testing corrections on P-values to help correct for the occurrence of false positives. The Benjamini and Hochberg (false discovery rate) method was then used to adjust P values to limit false positive errors."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37234258", "locator": "sections[17] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A total of 215 common DEGs were identified and a PPI network was generated with 154 nodes and 947 edges.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37234258", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "A total of 215 common DEGs were identified and a PPI network was generated with 154 nodes and 947 edges."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A total of 215 common DEGs were identified and a PPI network was generated with 154 nodes and 947 edges.", "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:37234258"], "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:GSE28731"], "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": "Benjamini and Hochberg false discovery rate adjustment"}, "inputs": ["data:geo:GSE28731"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37234258", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": 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