Identification of gene biomarkers in patients with postmenopausal osteoporosis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:30569177", "kind": "science-record", "revision": 7, "content_hash": "sha256:efb49f53e4ee85e98cf27aa00c059039bb4e85cd75ced9afdce0bbd23a2c663c", "created_at": "2026-11-01T15:40:39Z", "updated_at": "2026-11-01T22:00:13Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of gene biomarkers in patients with postmenopausal osteoporosis.", "doi": "10.3892/mmr.2018.9752", "pmid": "30569177", "authors": ["Chenggang Yang", "Jing Ren", "Bangling Li", "Chuandi Jin", "Cui Ma", "Cheng Cheng", "Yaolan Sun", "Xiaofeng Shi"], "journal": "Molecular medicine reports", "year": 2019, "abstract": "Postmenopausal osteoporosis (PMOP) is a major public health concern worldwide. The present study aimed to provide evidence to assist in the development of specific novel biomarkers for PMOP. Differentially expressed genes (DEGs) were identified between PMOP and normal controls by integrated microarray analyses of the Gene Expression Omnibus (GEO) database, and the optimal diagnostic gene biomarkers for PMOP were identified with LASSO and Boruta algorithms. Classification models, including support vector machine (SVM), decision tree and random forests models, were established to test the diagnostic value of identified gene biomarkers for PMOP. Functional annotations and protein‑protein interaction (PPI) network constructions were also conducted. Integrated microarray analyses (GSE56815, GSE13850 and GSE7429) of the GEO database were employed, and 1,320 DEGs were identified between PMOP and normal controls. An 11‑gene combination was also identified as an optimal biomarker for PMOP by feature selection and classification methods using SVM, decision tree and random forest models. This combination was comprised of the following genes: Dehydrogenase E1 and transketolase domain containing 1 (DHTKD1), osteoclast stimulating factor 1 (OSTF1), G protein‑coupled receptor 116 (GPR116), BCL2 interacting killer, adrenoceptor β1 (ADRB1), neogenin 1 (NEO1), RB binding protein 4 (RBBP4), GPR87, cylicin 2, EF‑hand calcium binding domain 1 and DEAH‑box helicase 35. RBBP4 (degree=12) was revealed to be the hub gene of this PMOP‑specific PPI network. Among these 11 genes, three genes (OSTF1, ADRB1 and NEO1) were speculated to serve roles in PMOP by regulating the balance between bone formation and bone resorption, while two genes (GPR87 and GPR116) may be involved in PMOP by regulating the nuclear factor‑κB signaling pathway. Furthermore, DHTKD1 and RBBP4 may be involved in PMOP by regulating mitochondrial dysfunction and interacting with ESR1, respectively. In conclusion, the findings of the current study provided an insight for exploring the mechanism and developing novel biomarkers for PMOP. 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