ArticleCurrent medicinal chemistry2025
Integrated Single-cell RNA-seq and Bulk RNA-seq Identify Diagnostic Biomarkers for Postmenopausal Osteoporosis.
Article in Current medicinal chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- A Novel SIRT1 Activator Hydroxygenkwanin Alleviates Osteoporosis by Inhibiting Ferroptosis and Lactylation in Skeletal Stem/Progenitor Cells.Antioxidants (Basel, Switzerland) · 2026Article
- Engineering osteoporosis-related bone organoids: a mechanism-module-functional readout framework for disease modelling and biomarker translation.Frontiers in cell and developmental biology · 2026Review
- Using Disease Models for Mechanistic Studies: Special Focus on Gene Editing.Current medicinal chemistry · 2026Article
- Comprehensive Analysis of the Role of Metabolic Features in Osteoporosis: A Multi-Omics Analysis.International journal of general medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
aimWe aimed to explore diagnostic biomarkers of postmenopausal osteoporosis (PMOP).
backgroundPMOP brings enormous physical and economic burden to elderly women.
objectivesThis study aims to screen new biomarkers for osteoporosis, providing insights for early diagnosis and therapeutic targets of osteoporosis.
methodsWeighted gene co-expression network analysis (WGCNA) was applied to identify osteoporosis-related hub genes. Single-cell transcriptomic atlas of osteoporosis was depicted and the heterogeneity of monocytes was analyzed, based on which the biomarkers for osteoporosis were screened. Gene set enrichment analysis (GSEA) was conducted on the biomarkers. The diagnostic model (nomogram) was established and evaluated based on the expression levels of biomarkers. Additionally, the transcription factor (TF) regulatory network was constructed to predict the potential TF and targeted miRNA of biomarkers. The drugs with significant correlation with biomarkers were identified by Spearman correlation analysis.
resultsWe obtained 30 osteoporosis-associated hub genes. 9 cell types were identified, and the monocytes were subdivided to 4 subtypes. Three biomarkers, DHX29, LSM5, and UBE2V2, were screened. DHX29 and UBE2V2 were highly expressed in non-classical monocytes, while LSM5 exhibited the highest expression in other monocytes, followed by non-classical monocytes. GSEA indicated that osteoporosis may be correlated with vascular calcification and the biomarkers may be involved in the formation of immune cells. Then, nomogram was constructed and exhibited good robustness. In addition, MYC and SETDB1 were the shared IF in three biomarkers, which may play critical regulatory roles in the progression of osteoporosis. Moreover, 41, 49, and 68 drugs appeared significant correlations with DHX29, LSM5, and UBE2V2, respectively.
conclusionThis study provided a basis for early diagnosis and targeted treatment of osteoporosis.
Indexed as
Identifiers
39364870What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.