[Identification of dynamic co-expression networks in peripheral blood of rats after middle cerebral artery occlusion].

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:31955531", "kind": "science-record", "revision": 3, "content_hash": "sha256:904fe5a4ff4df82bfe4122c6b3a3e08c6c3d1365eac9bcb1720259a694bee39e", "created_at": "2026-11-01T17:33:19Z", "updated_at": "2026-11-01T21:22:49Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "[Identification of dynamic co-expression networks in peripheral blood of rats after middle cerebral artery occlusion].", "doi": "10.3785/j.issn.1008-9292.2019.12.01", "pmid": "31955531", "authors": ["Zongfu Pan", "Xiaoping Hu", "Yiwen Zhang", "Li Li", "Ping Huang"], "journal": "Zhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences", "year": 2019, "abstract": "OBJECTIVE: To identify the time dependent profiles of gene expression and featured co-expression network modules in peripheral blood of rats after middle cerebral artery occlusion (MCAO).\nMETHODS: Microarray GSE119121 from GEO database was analyzed by R language to identify the significantly changed genes in peripheral blood at different time points (0, 1, 2, 3, 6 and 24 h) after MCAO. Gene expression patterns at different time courses were screened by STEM tools. Then, function annotation and pathway enrichment of differentially expressed genes (DEGs) were performed using the Gene Ontology (GO) database and the Kyoto Gene and Genomic Encyclopedia (KEGG) database. Depending on CEMiTool package, gene expression profile matrix was inputted into R to construct the co-expression networks and to analyze modules, and enrichment analysis was conducted to evaluate the correlation between the modules and different time points.\nRESULTS: Comparing with gene at 0 h, the numbers of DEGs in peripheral blood at different time points after MCAO were 163 (1 h), 502 (2 h), 527 (3 h), 550 (6 h), and 75 (24 h), respectively. Moreover, a total of 38 gene expression patterns were enriched, and pattern 65 and pattern 34 were specifically up-regulated or down-regulated at 2-6 h. Hp, Nos2, P2ry10, and Klf12 were representative genes of these two models. The co-expression network module analysis showed that the gene status in the early acute phase (1-6 h) was positively correlated with the Module 2. Module 3 and Module 4 was positively correlated with phase phase 1-3 h and 2-6 h, respectively. Noteworthy, Module 6 gradually changed from positive correlation (0-2 h) to negative correlation (3-24 h) with the MCAO time course, and Module 6 was mainly related to viral response and innate immune response. The hub genes of Module 6 included Mx1, Mx2, and Rtp4.\nCONCLUSIONS: Our study has identified the featured genes and dynamic co-expression network modules in peripheral blood after acute ischemic stroke, which provides a potential basis for judging the onset time of ischemic stroke.", "claims": [{"id": "claim-method", "statement": "利用GEO数据库的基因表达谱芯片GSE119121联合R语言分析MCAO造模后不同时间点（0、1、2、3、6及24 h）外周血的差异表达基因。通过STEM工具筛选不同造模时程基因表达模式。利用基因本体（GO）数据库和京都基因与基因组百科全书（KEGG）数据库对差异表达的基因进行功能注释和通路富集。在R语言环境下利用CEMiTool包对基因表达谱矩阵进行共表达网络构建及模块分析，并将模块与不同造模时间点进行富集分析。", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:31955531", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "利用GEO数据库的基因表达谱芯片GSE119121联合R语言分析MCAO造模后不同时间点（0、1、2、3、6及24 h）外周血的差异表达基因。通过STEM工具筛选不同造模时程基因表达模式。利用基因本体（GO）数据库和京都基因与基因组百科全书（KEGG）数据库对差异表达的基因进行功能注释和通路富集。在R语言环境下利用CEMiTool包对基因表达谱矩阵进行共表达网络构建及模块分析，并将模块与不同造模时间点进行富集分析。"}], "state": "complete"}, {"id": "claim-data-access", "statement": "利用GEO数据库的基因表达谱芯片GSE119121联合R语言分析MCAO造模后不同时间点（0、1、2、3、6及24 h）外周血的差异表达基因。", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:31955531", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "利用GEO数据库的基因表达谱芯片GSE119121联合R语言分析MCAO造模后不同时间点（0、1、2、3、6及24 h）外周血的差异表达基因。"}], "state": "complete"}, {"id": "claim-target-result", "statement": "利用GSE119121外周血基因表达谱分析，筛选出38种基因表达模式，并识别出与造模时间相关的动态共表达网络模块。", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:31955531", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "共有38种基因表达模式被富集，其中模式65和模式34分别在2~6 h特异上调或下调， Hp、 Nos2、 P2ry10及 Klf12为两种模式的代表性基因。"}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "利用GSE119121外周血基因表达谱分析，筛选出38种基因表达模式，并识别出与造模时间相关的动态共表达网络模块。", "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:31955531"], "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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