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Identification of shared gene signatures and pathways for diagnosing osteoporosis with sarcopenia through integrated bioinformatics analysis and machine learning.
Zhou, Xiaoli; Zhao, Lina; Zhang, Zepei; Chen, Yang; Chen, Guangdong; Miao, Jun; Li, Xiaohui.
Afiliação
  • Zhou X; Department of Spine Surgery, Tianjin Hospital, Tianjin University, Tianjin, 300211, China.
  • Zhao L; Department of Toxicology, Tianjin Centers for Disease Control and Prevention, Tianjin, 300011, China.
  • Zhang Z; The Third Central, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Clinical College of Tianjin Medical University, Nankai University Affinity the Third Central Hospital, Artificial Cell Engineering Technology Research Center, Tianjin Institute of Hepatobiliary Disease, T
  • Chen Y; Department of Anaesthesiology, Tianjin Hospital, Tianjin, 300211, China.
  • Chen G; Department of Spine Surgery, Tianjin Hospital, Tianjin University, Tianjin, 300211, China.
  • Miao J; Department of Spine Surgery, Tianjin Hospital, Tianjin University, Tianjin, 300211, China.
  • Li X; Department of Orthopaedics, Cangzhou Central Hospital, Hebei, 061001, China.
BMC Musculoskelet Disord ; 25(1): 435, 2024 Jun 03.
Article em En | MEDLINE | ID: mdl-38831425
ABSTRACT

BACKGROUND:

Prior studies have suggested a potential relationship between osteoporosis and sarcopenia, both of which can present symptoms of compromised mobility. Additionally, fractures among the elderly are often considered a common outcome of both conditions. There is a strong correlation between fractures in the elderly population, decreased muscle mass, weakened muscle strength, heightened risk of falls, and diminished bone density. This study aimed to pinpoint crucial diagnostic candidate genes for osteoporosis patients with concomitant sarcopenia.

METHODS:

Two osteoporosis datasets and one sarcopenia dataset were obtained from the Gene Expression Omnibus (GEO). Differential expression genes (DEGs) and module genes were identified using Limma and Weighted Gene Co-expression Network Analysis (WGCNA), followed by functional enrichment analysis, construction of protein-protein interaction (PPI) networks, and application of a machine learning algorithm (least absolute shrinkage and selection operator (LASSO) regression) to determine candidate hub genes for diagnosing osteoporosis combined with sarcopenia. Receiver operating characteristic (ROC) curves and column line plots were generated.

RESULTS:

The merged osteoporosis dataset comprised 2067 DEGs, with 424 module genes filtered in sarcopenia. The intersection of DEGs between osteoporosis and sarcopenia module genes consisted of 60 genes, primarily enriched in viral infection. Through construction of the PPI network, 30 node genes were filtered, and after machine learning, 7 candidate hub genes were selected for column line plot construction and diagnostic value assessment. Both the column line plots and all 7 candidate hub genes exhibited high diagnostic value (area under the curve ranging from 1.00 to 0.93).

CONCLUSION:

We identified 7 candidate hub genes (PDP1, ALS2CL, VLDLR, PLEKHA6, PPP1CB, MOSPD2, METTL9) and constructed column line plots for osteoporosis combined with sarcopenia. This study provides reference for potential peripheral blood diagnostic candidate genes for sarcopenia in osteoporosis patients.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Osteoporose / Biologia Computacional / Sarcopenia / Aprendizado de Máquina Limite: Aged / Female / Humans Idioma: En Revista: BMC Musculoskelet Disord Assunto da revista: FISIOLOGIA / ORTOPEDIA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Osteoporose / Biologia Computacional / Sarcopenia / Aprendizado de Máquina Limite: Aged / Female / Humans Idioma: En Revista: BMC Musculoskelet Disord Assunto da revista: FISIOLOGIA / ORTOPEDIA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: China