AI Article Synopsis

  • Postmenopausal osteoporosis (PMO) is the most prevalent type of osteoporosis, but specific biomarkers for its early diagnosis are lacking.
  • A study used weighted gene co-expression network analysis to identify a clinically significant gene module associated with menopause, highlighting 12 key genes linked to PMO.
  • Of these, PPWD1 showed a strong correlation with bone mineral density in postmenopausal women, suggesting it could serve as a potential diagnostic biomarker for PMO.

Article Abstract

Postmenopausal osteoporosis (PMO) is the most common type of primary osteoporosis (OP), a systemic skeletal disease. Although many factors have been revealed to contribute to the occurrence of PMO, specific biomarkers for the early diagnosis and therapy of PMO are not available. In the present study, a weighted gene co‑expression network analysis (WGCNA) was performed to screen gene modules associated with menopausal status. The turquoise module was verified as the clinically significant module, and 12 genes (NUP133, PSMD12, PPWD1, RBM8A, CRNKL1, PPP2R5C, RBM22, PIK3CB, SKIV2L2, PAPOLA, SRSF1 and COPS2) were identified as 'real' hub genes in both the protein‑protein interaction (PPI) network and co‑expression network. Furthermore, gene expression analysis by microarray in blood monocytes from pre‑ and post‑menopausal women revealed an increase in the expression of these hub genes in postmenopausal women. However, only the expression of peptidylprolyl isomerase domain and WD repeat containing 1 (PPWD1) was correlated with bone mineral density (BMD) in postmenopausal women. In the validation set, a similar expression pattern of PPWD1 was revealed. Functional enrichment analysis revealed that the fatty acid metabolism pathway was significantly abundant in the samples that exhibited a higher expression of PPWD1. Collectively, PPWD1 is indicated as a potential diagnostic biomarker for the occurrence of PMO.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6755193PMC
http://dx.doi.org/10.3892/mmr.2019.10570DOI Listing

Publication Analysis

Top Keywords

co‑expression network
12
postmenopausal osteoporosis
8
weighted gene
8
gene co‑expression
8
network analysis
8
occurrence pmo
8
hub genes
8
postmenopausal women
8
ppwd1
6
expression
5

Similar Publications

Shaping the structural dynamics of motor learning through cueing during sleep.

Sleep

January 2025

UR2NF-Neuropsychology and Functional Neuroimaging Research Unit affiliated at CRCN - Centre for Research in Cognition and Neurosciences and UNI - ULB Neuroscience Institute, Université Libre de Bruxelles (ULB), Brussels, Belgium.

Enhancing the retention of recent memory traces through sleep reactivation is possible via Targeted Memory Reactivation (TMR), involving cueing learned material during post-training sleep. Evidence indicates detectable short-term microstructural changes in the brain within an hour after motor sequence learning, and post-training sleep is believed to contribute to the consolidation of these motor memories, potentially leading to enduring microstructural changes. In this study, we explored how TMR during post-training sleep affects performance gains and delayed microstructural remodeling, using both standard Diffusion Tensor Imaging (DTI) and advanced Neurite Orientation Dispersion & Density Imaging (NODDI).

View Article and Find Full Text PDF

Official development agencies are increasingly supporting civil society lobby and advocacy (L&A) to address poverty and human rights. However, there are challenges in evaluating L&A. As programme objectives are often to change policies or practices in a single institution like a Government Ministry, L&A programmes are often not amenable to large-n impact evaluation methods.

View Article and Find Full Text PDF

This study intents to detect graphical network features associated with seizure relapse following antiseizure medication (ASM) withdrawal. Twenty-four patients remaining seizure-free (SF-group) and 22 experiencing seizure relapse (SR-group) following ASM withdrawal as well as 46 matched healthy participants (Control) were included. Individualized morphological similarity network was constructed using T1-weighted images, and graphic metrics were compared between groups.

View Article and Find Full Text PDF

Objective: Segmentation of individual thigh muscles in MRI images is essential for monitoring neuromuscular diseases and quantifying relevant biomarkers such as fat fraction (FF). Deep learning approaches such as U-Net have demonstrated effectiveness in this field. However, the impact of reducing neural network complexity remains unexplored in the FF quantification in individual muscles.

View Article and Find Full Text PDF

Gymnostachyum febrifugum, a less-known ethnomedicinal plant from the Western Ghats of India, is used to treat various diseases and serves as an antioxidant and antibacterial herb. The present study aims to profile the cytotoxic phytochemicals in G. febrifugum roots using GC-MS/MS, in vitro confirmation of cytotoxic potential against breast cancer and an in silico study to understand the mechanism of action.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!