ArticleEvolutionary bioinformatics online2022
Screening of Important Markers in Peripheral Blood Mononuclear Cells to Predict Female Osteoporosis Risk Using LASSO Regression Algorithm and SVM Method.
Article in Evolutionary bioinformatics online, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 9 citations in OpenAlex.
- E3 ubiquitin ligase RNF40: Structure, function and its context‑dependent roles in tumorigenesis (Review).Oncology reports · 2026Review
- An intelligent auxiliary diagnostic system for early osteoporosis screening using stacking ensemble learning.BMC medical informatics and decision making · 2026Article
- ZNF473 promotes colorectal cancer progression and chemoresistance by destabilizing p53 protein to upregulate Survivin.Cell death discovery · 2026Article
- ZNF473 as a biomarker and potential therapeutic target in cancer: integrated bioinformatics and experimental evidence with a focus on hepatocellular carcinoma.Frontiers in oncology · 2026Article
- Identification and validation of the cellular senescence-associated molecular pattern and diagnostic markers for osteoporosis.BMC medical genomics · 2025Article
- Comparing machine learning models for osteoporosis prediction in Tibetan middle aged and elderly women.Scientific reports · 2025Article
- NMF typing and machine learning algorithm-based exploration of preeclampsia-related mechanisms on ferroptosis signature genes.Cell biology and toxicology · 2024Article
- A study on the anti-osteoporosis mechanism of isopsoralen based on network pharmacology and molecular experiments.Journal of orthopaedic surgery and research · 2023Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOsteoporosis is a bone disease that increases the patient's risk of fracture. We aimed to identify robust marker genes related to osteoporosis based on different bioinformatic methods and multiple datasets.
methodsThree datasets from Gene Expression Omnibus (GEO) were utilized for analysis separately. Significantly differentially expressed genes (DEGs) from comparing high hip and low hip low bone mineral density (BMD) groups in the first dataset were identified for Gene Ontology (GO), Gene set enrichment analysis (GSEA) and Kyoto encyclopedia of genes and genomes (KEGG) to investigate the discrepantly enriched biological processes between high hip and low hip group. Last absolute shrinkage and selection operator (LASSO), SVM model and protein-protein interaction (PPI) regulatory network were performed and generated robust marker genes for downstream TF-target and miRNA-target prediction.
resultsSeveral DEGs between high hip BMD group and low hip BMD group were obtained. And the metabolism-related pathways such as metabolic pathways, carbon metabolism, glyoxylate and dicarboxylate metabolism shown enrichment in these DEGs. Integration with LASSO regression analysis, 8 differential expression genes (
conclusionThe optimal differential genes combination (
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