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Identification of combined biomarkers for predicting the risk of osteoporosis using machine learning

Authors
 Zhenlong Zheng  ;  Xianglan Zhang  ;  Bong-Kyeong Oh  ;  Ki-Yeol Kim 
Citation
 Aging, Vol.14(10) : 4270-4280, 2022-05 
Journal Title
Aging
Issue Date
2022-05
MeSH
Biomarkers ; Bone Diseases, Metabolic* ; Female ; Humans ; Machine Learning ; Osteoporosis* / genetics ; Risk Factors
Keywords
combined biomarker ; gene expression ; machine learning ; osteoporosis ; risk prediction
Abstract
Osteoporosis is a severe chronic skeletal disorder that affects older individuals, especially postmenopausal women. However, molecular biomarkers for predicting the risk of osteoporosis are not well characterized. The aim of this study was to identify combined biomarkers for predicting the risk of osteoporosis using machine learning methods. We merged three publicly available gene expression datasets (GSE56815, GSE13850, and GSE2208) to obtain expression data for 6354 unique genes in postmenopausal women (45 with high bone mineral density and 45 with low bone mineral density). All machine learning methods were implemented in R, with the GEOquery and limma packages, for dataset download and differentially expressed gene identification, and a nomogram for predicting the risk of osteoporosis was constructed. We detected 378 significant differentially expressed genes using the limma package, representing 15 major biological pathways. The performance of the predictive models based on combined biomarkers (two or three genes) was superior to that of models based on a single gene. The best predictive gene set among two-gene sets included PLA2G2A and WRAP73. The best predictive gene set among three-gene sets included LPN1, PFDN6, and DOHH. Overall, we demonstrated the advantages of using combined versus single biomarkers for predicting the risk of osteoporosis. Further, the predictive nomogram constructed using combined biomarkers could be used by clinicians to identify high-risk individuals and in the design of efficient clinical trials to reduce the incidence of osteoporosis.
Files in This Item:
T202202080.pdf Download
DOI
10.18632/aging.204084
Appears in Collections:
2. College of Dentistry (치과대학) > Others (기타) > 1. Journal Papers
Yonsei Authors
Kim, Ki Yeol(김기열) ORCID logo https://orcid.org/0000-0001-5357-1067
Zhang, Xiang Lan(장향란)
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/188615
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