ArticleAging2023
Identification of ROCK1 as a novel biomarker for postmenopausal osteoporosis and pan-cancer analysis.
Article in Aging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed, 10 citations in OpenAlex.
- Extracellular vesicle-mediated crosstalk in bone: miR-150-5p as a mechanosensitive regulator of osteoclastogenesis.Molecular therapy : the journal of the American Society of Gene Therapy · 2026Article
- TTC26 scaffolds MINDY3-mediated RACK1 deubiquitination to drive osteoclastogenesis and pathological bone resorption.Journal of orthopaedic translation · 2026Article
- Exploring the role of mitochondrial dysfunction-related genes in osteoporosis using weighted gene co-expression network analysis and machine learning.European journal of medical research · 2025Article
- Role and Validation of Lactylation-Related Gene Markers in Postmenopausal Osteoporosis.Applied biochemistry and biotechnology · 2025Article
- Identification of Endoplasmic Reticulum Stress-Related Genes in Osteoporosis Pathogenesis.Mediators of inflammation · 2025Article
- Integrated Single-cell RNA-seq and Bulk RNA-seq Identify Diagnostic Biomarkers for Postmenopausal Osteoporosis.Current medicinal chemistry · 2025Article
- Re-evaluating basophil count as a hematological indicator for bone density: a subgroup analysis from an East Asian population.Frontiers in endocrinology · 2025Article
- Application of artificial intelligence in osteoporosis: a review.Frontiers in medicine · 2025Review
- Integrated single-cell and bulk RNA sequencing analysis reveal immune-related biomarkers in postmenopausal osteoporosis.Heliyon · 2024Article
- PGK1: A Common Biomarker and Therapeutic Target Linking Sarcopenia and Osteoporosis Through Fibroblast-Mediated Pathways.IET systems biologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPostmenopausal osteoporosis (PMOP) is a prevalent bone disorder with significant global impact. The elevated risk of osteoporotic fracture in elderly women poses a substantial burden on individuals and society. Unfortunately, the current lack of dependable diagnostic markers and precise therapeutic targets for PMOP remains a major challenge.
methodsPMOP-related datasets GSE7429, GSE56814, GSE56815, and GSE147287, were downloaded from the GEO database. The DEGs were identified by "limma" packages. WGCNA and Machine Learning were used to choose key module genes highly related to PMOP. GSEA, DO, GO, and KEGG enrichment analysis was performed on all DEGs and the selected key hub genes. The PPI network was constructed through the GeneMANIA database. ROC curves and AUC values validated the diagnostic values of the hub genes in both training and validation datasets. xCell immune infiltration and single-cell analysis identified the hub genes' function on immune reaction in PMOP. Pan-cancer analysis revealed the role of the hub genes in cancers.
resultsA total of 1278 DEGs were identified between PMOP patients and the healthy controls. The purple module and cyan module were selected as the key modules and 112 common genes were selected after combining the DEGs and module genes. Five Machine Learning algorithms screened three hub genes (KCNJ2, HIPK1, and ROCK1), and a PPI network was constructed for the hub genes. ROC curves validate the diagnostic values of ROCK1 in both the training (AUC = 0.73) and validation datasets of PMOP (AUC = 0.81). GSEA was performed for the low-ROCK1 patients, and the top enriched field included protein binding and immune reaction. DCs and NKT cells were highly expressed in PMOP. Pan-cancer analysis showed a correlation between low ROCK1 expression and SKCM as well as renal tumors (KIRP, KICH, and KIRC).
conclusionsROCK1 was significantly associated with the pathogenesis and immune infiltration of PMOP, and influenced cancer development, progression, and prognosis, which provided a potential therapy target for PMOP and tumors. However, further laboratory and clinical evidence is required before the clinical application of ROCK1 as a therapeutic target.
Indexed as
Identifiers
What 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.