Integrated analysis of genes shared between type 2 diabetes mellitus and osteoporosis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38966541", "kind": "science-record", "revision": 3, "content_hash": "sha256:3977ded7666d49d1e7b71afc8a069090cc7e997c9a3ed25e2667ea865b96f390", "created_at": "2026-11-01T20:29:02Z", "updated_at": "2026-11-01T22:12:11Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integrated analysis of genes shared between type 2 diabetes mellitus and osteoporosis.", "doi": "10.3389/fphar.2024.1388205", "pmid": "38966541", "authors": ["Fangyu Li", "Ying Wang", "Jie Cao", "Qi Chen", "Yuanyuan Gao", "Rui Li", "Li Yuan"], "journal": "Frontiers in pharmacology", "year": 2024, "abstract": "Background: The relationship between type 2 diabetes mellitus (T2DM) and osteoporosis (OP) has been widely recognized in recent years, but the mechanism of interaction remains unknown. The aim of this study was to investigate the genetic features and signaling pathways that are shared between T2DM and OP.\nMethods: We analyzed the GSE76894 and GSE76895 datasets for T2DM and GSE56815 and GSE7429 for OP from the Gene Expression Omnibus (GEO) database to identify shared genes in T2DM and OP, and we constructed coexpression networks based on weighted gene coexpression network analysis (WGCNA). Shared genes were then further analyzed for functional pathway enrichment. We selected the best common biomarkers using the least absolute shrinkage and selection operator (LASSO) algorithm and validated the common biomarkers, followed by RT-PCR, immunofluorescence, Western blotting, and enzyme-linked immunosorbent assay (ELISA) to validate the expression of these hub genes in T2DM and OP mouse models and patients.\nResults: We found 8,506 and 2,030 DEGs in T2DM and OP, respectively. Four modules were identified as significant for T2DM and OP using WGCNA. A total of 19 genes overlapped with the strongest positive and negative modules of T2DM and OP. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed these genes may be involved in pantothenate and CoA biosynthesis and the glycosaminoglycan biosynthesis-chondroitin sulfate/dermatan sulfate and renin-angiotensin system signaling pathway. The LASSO algorithm calculates the six optimal common biomarkers. RT-PCR results show that LTB, TPBG, and VNN1 were upregulated in T2DM and OP. Immunofluorescence and Western blot show that VNN1 is upregulated in the pancreas and bones of T2DM model mice and osteoporosis model mice. Similarly, the level of VNN1 in the sera of patients with T2DM, OP, and T2DM and OP was higher than that in the healthy group.\nConclusion: Based on the WGCNA and LASSO algorithms, we identified genes and pathways that were shared between T2DM and OP. Both pantothenate and CoA biosynthesis and the glycosaminoglycan biosynthesis-chondroitin sulfate/dermatan sulfate and renin-angiotensin systems may be associated with the pathogenesis of T2DM and OP. Moreover, VNN1 may be a potential diagnostic marker for patients with T2DM complicated by OP. This study provides a new perspective for the systematic study of possible mechanisms of combined OP and T2DM.", "claims": [{"id": "claim-method", "statement": "To better screen the risk of crosstalk genes between T2DM and OP, LASSO regression was performed in R project.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38966541", "locator": "sections[7] \"2.5 Identification of optimal diagnostic shared genes\" paragraph 2", "quote": "To better screen the risk of crosstalk genes between T2DM and OP, LASSO regression was performed in R project."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38966541", "locator": "sections[27] \"Data availability statement\" paragraph 2", "quote": "The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation."}], "state": "complete"}, {"id": "claim-target-result", "statement": "LASSO analysis of gene-expression data from four GEO datasets identified six shared diagnostic genes: LGALS2, LTB, STEAP4, TPBG, VGF, and VNN1.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38966541", "locator": "sections[21] \"3.4 Prediction of optimal diagnostic genes\" paragraph 2", "quote": "We then extracted the expression data of the 19 genes from the T2DM gene expression profile using the LASSO algorithms with an optimal lambda value of lambda.min = 0.0055 (Figures 4A, B)."}, {"asset": "source:pmid:38966541", "locator": "sections[21] \"3.4 Prediction of optimal diagnostic genes\" paragraph 2", "quote": "We used the same method to obtain eight genes for the diagnosis of OP (lambda.min = 0.0385; Figures 4C, D)."}, {"asset": "source:pmid:38966541", "locator": "sections[21] \"3.4 Prediction of optimal diagnostic genes\" paragraph 2", "quote": "The diagnostic genes of the two diseases were intersected to obtain six common diagnostic genes, namely, LGALS2, LTB, STEAP4, TPBG, VGF, and VNN1."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "LASSO analysis of gene-expression data from four GEO datasets identified six shared diagnostic genes: LGALS2, LTB, STEAP4, TPBG, VGF, and VNN1.", "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:38966541"], "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:GSE56815"], "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": {}, "inputs": ["data:geo:GSE56815"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38966541", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fphar.2024.1388205", "sha256": 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