ArticleComputational and mathematical methods in medicine2017
Construction of an miRNA-Regulated Pathway Network Reveals Candidate Biomarkers for Postmenopausal Osteoporosis.
Article in Computational and mathematical methods in medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 17 citations in OpenAlex.
- Tumour necrosis factor α regulates the miR-27a-3p-Sfrp1 axis in a mouse model of osteoporosis.Experimental physiology · 2024Article
- Long non-coding RNA Homeobox D gene cluster antisense growth-associated long noncoding RNA/microRNA-182-5p/Homeobox protein A10 alleviates postmenopausal osteoporosis via accelerating osteoblast differentiation of bone marrow mesenchymal stem cells.Journal of orthopaedic surgery and research · 2023Article
- Rising to the challenge: Value based research for Orthopaedic ailments.Journal of clinical orthopaedics and trauma · 2022Article
- miR-21 mimic blocks obesity in mice: A novel therapeutic option.Molecular therapy. Nucleic acids · 2021Article
- Short-range UV-LED irradiation in postmenopausal osteoporosis using ovariectomized mice.Scientific reports · 2021Article
- The Osteoporosis/Microbiota Linkage: The Role of miRNA.International journal of molecular sciences · 2020Review
- Identification of circRNA-associated ceRNA network in BMSCs of OVX models for postmenopausal osteoporosis.Scientific reports · 2020Article
- Integrative Analysis of Genomics and Transcriptome Data to Identify Regulation Networks in Female Osteoporosis.Frontiers in genetics · 2020Article
- Identification of differentially expressed microRNAs in the bone marrow of osteoporosis patients.American journal of translational research · 2019Article
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Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We aimed to identify risk pathways for postmenopausal osteoporosis (PMOP) via establishing an microRNAs- (miRNA-) regulated pathway network (MRPN). Firstly, we identified differential pathways through calculating gene- and pathway-level statistics based on the accumulated normal samples using the individual pathway aberrance score (iPAS). Significant pathways based on differentially expressed genes (DEGs) using DAVID were extracted, followed by identifying the common pathways between iPAS and DAVID methods. Next, miRNAs prediction was implemented via calculating TargetScore values with precomputed input (log fold change (FC), TargetScan context score (TSCS), and probabilities of conserved targeting (PCT)). An MRPN construction was constructed using the common genes in the common pathways and the predicted miRNAs. Using false discovery rate (FDR) < 0.05, 279 differential pathways were identified. Using the criteria of FDR < 0.05 and |logFC| ≥ 2, 39 DEGs were retrieved, and these DEGs were enriched in 64 significant pathways identified by DAVID. Overall, 27 pathways were the common ones between two methods. Importantly, MAPK signaling pathway and PI3K-Akt signaling pathway were the first and second significantly enriched ones, respectively. These 27 common pathways separated PMOP from controls with the accuracy of 0.912. MAPK signaling pathway and PI3K/Akt signaling pathway might play crucial roles in PMOP.
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