ArticleMolecular biology reports2024
SIRT1 and ZNF350 as novel biomarkers for osteoporosis: a bioinformatics analysis and experimental validation.
Article in Molecular biology reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 7 citations in OpenAlex.
- Enhancing osteoporosis treatment: emerging roles of engineered exosomes in bone regeneration and repair.Journal of translational medicine · 2026Review
- Investigating the molecular mechanisms of glutamine metabolism and mitochondria-related biomarkers in Alzheimer's disease through transcriptomics and experimental validation.European journal of medical research · 2026Article
- Establishing an atherosclerosis diagnostic model based on WGCNA and machine learning algorithms with key genes in cholesterol metabolism and ferroptosis, and revealing the regulatory role of HMOX1 in cellular ferroptosis.Frontiers in cardiovascular medicine · 2026Article
- LRP1 as a potential diagnostic and immunomodulatory target in endometriosis: evidence from multi-omics and single-cell analyses.Frontiers in immunology · 2026Article
- Genetic variants reshape the mScientific reports · 2025Article
- Bioinformatics identification of key microRNA-correlated genes associated with hepatocellular carcinoma heterogeneity and prognosis.BMC gastroenterology · 2025Article
- Disulfidptosis‑related lncRNA prognosis model to predict survival therapeutic response prediction in lung adenocarcinoma.Oncology letters · 2024Article
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundOsteoporosis (OP) is characterized by bone mass decrease and bone tissue microarchitectural deterioration in bone tissue. This study identified potential biomarkers for early diagnosis of OP and elucidated the mechanism of OP.
methodsGene expression profiles were downloaded from Gene Expression Omnibus (GEO) for the GSE56814 dataset. A gene co-expression network was constructed using weighted gene co-expression network analysis (WGCNA) to identify key modules associated with healthy and OP samples. Functional enrichment analysis was conducted using the R clusterProfiler package for modules to construct the transcriptional regulatory factor networks. We used the "ggpubr" package in R to screen for differentially expressed genes between the two samples. Gene set variation analysis (GSVA) was employed to further validate hub gene expression levels between normal and OP samples using RT-PCR and immunofluorescence to evaluate the potential biological changes in various samples.
resultsThere was a distinction between the normal and OP conditions based on the preserved significant module. A total of 100 genes with the highest MM scores were considered key genes. Functional enrichment analysis suggested that the top 10 biological processes, cellular component and molecular functions were enriched. The Toll-like receptor signaling pathway, TNF signaling pathway, PI3K-Akt signaling pathway, osteoclast differentiation, JAK-STAT signaling pathway, and chemokine signaling pathway were identified by Kyoto Encyclopedia of Genes and Genomes pathway analysis. SIRT1 and ZNF350 were identified by Wilcoxon algorithm as hub differentially expressed transcriptional regulatory factors that promote OP progression by affecting oxidative phosphorylation, apoptosis, PI3K-Akt-mTOR signaling, and p53 pathway. According to RT-PCR and immunostaining results, SIRT1 and ZNF350 levels were significantly higher in OP samples than in normal samples.
conclusionSIRT1 and ZNF350 are important transcriptional regulatory factors for the pathogenesis of OP and may be novel biomarkers for OP treatment.
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