ArticlePloS one2021
Analysis of potential genetic biomarkers and molecular mechanism of smoking-related postmenopausal osteoporosis using weighted gene co-expression network analysis and machine learning.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 13 citations in OpenAlex.
- Artificial Intelligence for Osteoporosis Diagnosis, Risk Prediction and Therapy: Current Advances, Clinical Challenges, and Future Perspectives.Clinical interventions in aging · 2026Review
- Identification and validation ofBiomolecules & biomedicine · 2025Article
- Harnessing machine learning in contemporary tobacco research.Toxicology reports · 2025Review
- Can some algorithms of machine learning identify osteoporosis patients after training and testing some clinical information about patients?BMC medical informatics and decision making · 2025Article
- Integrated Single-cell RNA-seq and Bulk RNA-seq Identify Diagnostic Biomarkers for Postmenopausal Osteoporosis.Current medicinal chemistry · 2025Article
- Identifying Key Genes and Functionally Enriched Pathways in Osteoporotic Patients by Weighted Gene Co-Expression Network Analysis.Biochemical genetics · 2024Article
- Using machine learning to identify patients at high risk of developing low bone density or osteoporosis after gastrectomy: a 10-year multicenter retrospective analysis.Journal of cancer research and clinical oncology · 2023Article
- Exploring the shared gene signatures of smoking-related osteoporosis and chronic obstructive pulmonary disease using machine learning algorithms.Frontiers in molecular biosciences · 2023Article
- The Hub Genes Related to Osteoporosis Were Identified by Bioinformatics Analysis.BioMed research international · 2023Article
- Identification of key ferroptosis genes in diabetic retinopathy based on bioinformatics analysis.PloS one · 2023Article
- Consensus gene modules related to levels of bone mineral density (BMD) among smokers and nonsmokers.Bioengineered · 2021Article
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesSmoking is a significant independent risk factor for postmenopausal osteoporosis, leading to genome variations in postmenopausal smokers. This study investigates potential biomarkers and molecular mechanisms of smoking-related postmenopausal osteoporosis (SRPO). MATERIALS AND
methodsThe GSE13850 microarray dataset was downloaded from Gene Expression Omnibus (GEO). Gene modules associated with SRPO were identified using weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) analysis, and pathway and functional enrichment analyses. Feature genes were selected using two machine learning methods: support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF). The diagnostic efficiency of the selected genes was assessed by gene expression analysis and receiver operating characteristic curve.
resultsEight highly conserved modules were detected in the WGCNA network, and the genes in the module that was strongly correlated with SRPO were used for constructing the PPI network. A total of 113 hub genes were identified in the core network using topological network analysis. Enrichment analysis results showed that hub genes were closely associated with the regulation of RNA transcription and translation, ATPase activity, and immune-related signaling. Six genes (HNRNPC, PFDN2, PSMC5, RPS16, TCEB2, and UBE2V2) were selected as genetic biomarkers for SRPO by integrating the feature selection of SVM-RFE and RF.
conclusionThe present study identified potential genetic biomarkers and provided a novel insight into the underlying molecular mechanism of SRPO.
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