Identification of Potential Biomarkers of Type 2 Diabetes Mellitus-Related Immune Infiltration Using Weighted Gene Coexpression Network Analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35187175", "kind": "science-record", "revision": 4, "content_hash": "sha256:c6614d65fa0a34615ca2688727e414cd4d53950bae6effda5b921393a47e558d", "created_at": "2026-11-01T17:36:57Z", "updated_at": "2026-11-01T22:05:57Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of Potential Biomarkers of Type 2 Diabetes Mellitus-Related Immune Infiltration Using Weighted Gene Coexpression Network Analysis.", "doi": "10.1155/2022/9920744", "pmid": "35187175", "authors": ["Ji Zhou", "Xiaoyi Zhang", "Lixia Ji", "Guohui Jiang"], "journal": "BioMed research international", "year": 2022, "abstract": "BACKGROUND: Type 2 diabetes mellitus (T2DM) is characterized by chronic low-grade inflammation, showing an increasing trend. The infiltration of immune cells into adipose tissue has been shown to be an important pathogenic cause of T2DM. The purpose of this study is to use the relevant database to identify some abnormally expressed or dysfunctional genes related to diabetes from the perspective of immune infiltration.\nMETHODS: Weighted gene coexpression network analysis (WGCNA) was employed to systematically identify the coexpressed gene modules and hub genes associated with T2DM development based on a microarray dataset (GSE23561) from the Gene Expression Omnibus (GEO) database. The key genes in modules highly related to clinical features were calculated and screened by using R software, and their participation in T2DM was determined by gene enrichment analysis. The mRNA levels of CSF1R, H2AFV, LCK, and TLR9 in pre-T2DM mice and normal wild-type mice were detected by WGCNA screening and real-time quantitative reverse transcriptase polymerase chain reaction (qRT-PCR).\nRESULTS: We constructed 14 coexpressed gene modules, and the brown module was shown to be significantly related to T2DM. Through verification of the protein-protein interaction (PPI) network, four upregulated hub genes, CSF1R, H2AFV, LCK, and TLR9, were screened from the brown module and successfully distinguishedT2DM patients from healthy people. These hub genes may be used as biomarkers and important indicators for patient diagnosis. Enrichment analysis showed that these hub genes were highly associated with IL-6-related inflammatory metabolism, immune regulation, and immune cell infiltration. Finally, we verified the hub genes CSF1R, LCK, and TLR9 in a T2DM animal model and found that their mRNA levels were significantly higher in animals with T2DM than in control group mice (NC).\nCONCLUSIONS: In summary, our results suggest that these hub genes (CSF1R, LCK, and TLR9) can serve as biomarkers and immunotherapeutic targets for T2DM.", "claims": [{"id": "claim-method", "statement": "Weighted gene coexpression network analysis (WGCNA) was employed to systematically identify the coexpressed gene modules and hub genes associated with T2DM development based on a microarray dataset (GSE23561) from the Gene Expression Omnibus (GEO) database.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35187175", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Weighted gene coexpression network analysis (WGCNA) was employed to systematically identify the coexpressed gene modules and hub genes associated with T2DM development based on a microarray dataset (GSE23561) from the Gene Expression Omnibus (GEO) database."}], "state": "complete"}, {"id": "claim-data-access", "statement": "We wish not to share the raw data as the authors are aiming for future publications from the data. However, the data used to support the findings of this study are available from the corresponding author upon request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35187175", "locator": "sections[21] \"Data Availability\" paragraph 2", "quote": "We wish not to share the raw data as the authors are aiming for future publications from the data. However, the data used to support the findings of this study are available from the corresponding author upon request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "WGCNA of the GSE23561 GEO expression dataset identified 14 modules, with the brown module most strongly correlated with T2DM (R2 = 0.75, p = 9e − 04) and selected as the key module.", "kind": "quantitative", "target": {"value": 2.0, "unit": "correlation coefficient", "tolerance": null, "source": "Among them, both the brown module (R2 = 0.75, p = 9e − 04) and the light blue module were highly correlated with T2DM (R2 = 0.51, p = 0.04) (Figure 3(a))."}, "evidence": [{"asset": "source:pmid:35187175", "locator": "sections[14] \"3.3. Identification of Key Modules and Enrichment Analysis\" paragraph 2", "quote": "Among them, both the brown module (R2 = 0.75, p = 9e − 04) and the light blue module were highly correlated with T2DM (R2 = 0.51, p = 0.04) (Figure 3(a))."}, {"asset": "source:pmid:35187175", "locator": "sections[14] \"3.3. 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