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Development and Verification of a Combined Diagnostic Model for Sarcopenia with Random Forest and Artificial Neural Network

BACKGROUND: Sarcopenia is a chronic disease characterized by an age-related decline in skeletal muscle mass and function, and diagnosis is challenging owing to the lack of a clear “gold standard” assessment method. OBJECTIVE: This study is aimed at combining random forest (RF) and artificial neural...

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Autores principales: Lin, Shangjin, Chen, Cong, Cai, Xiaoxi, Yang, Fengjian, Fan, Yongqian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9427323/
https://www.ncbi.nlm.nih.gov/pubmed/36050999
http://dx.doi.org/10.1155/2022/2957731
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author Lin, Shangjin
Chen, Cong
Cai, Xiaoxi
Yang, Fengjian
Fan, Yongqian
author_facet Lin, Shangjin
Chen, Cong
Cai, Xiaoxi
Yang, Fengjian
Fan, Yongqian
author_sort Lin, Shangjin
collection PubMed
description BACKGROUND: Sarcopenia is a chronic disease characterized by an age-related decline in skeletal muscle mass and function, and diagnosis is challenging owing to the lack of a clear “gold standard” assessment method. OBJECTIVE: This study is aimed at combining random forest (RF) and artificial neural network (ANN) methods to screen key potential biomarkers and establish an early sarcopenia diagnostic model. METHODS: Three gene expression datasets were downloaded and merged by searching the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in the merged dataset were identified by R software and subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Afterward, the STRING database was employed for interaction analysis of the differentially encoded proteins. Then, RF was used to identify key genes from the DEGs, and a sarcopenia diagnostic model was constructed by ANN. Finally, the diagnostic model was assessed using a validation dataset, while its diagnostic performance was evaluated by the area under curve (AUC) value. RESULTS: 107 sarcopenia-related DEGs were identified, and they were mainly enriched in the FoxO and AMPK signaling pathways involved in the molecular pathogenesis of sarcopenia. Thereafter, seven key genes (MT1X, FAM171A1, ZNF415, ARHGAP36, CISD1, ETNPPL, and WISP2) were identified by the RF classifier. The proteins encoded by three of these genes (CISD1, ETNPPL, and WISP2) may be potential biomarkers for sarcopenia. Finally, a diagnostic model for sarcopenia was successfully designed by ANN, achieving an AUC of 0.999 and 0.85 in the training and testing datasets, respectively. CONCLUSION: We identified several potential genetic biomarkers and successfully developed an early predictive model with high diagnostic performance for sarcopenia. Moreover, our results provide a valuable reference for the early diagnosis and screening of sarcopenia in the future.
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spelling pubmed-94273232022-08-31 Development and Verification of a Combined Diagnostic Model for Sarcopenia with Random Forest and Artificial Neural Network Lin, Shangjin Chen, Cong Cai, Xiaoxi Yang, Fengjian Fan, Yongqian Comput Math Methods Med Research Article BACKGROUND: Sarcopenia is a chronic disease characterized by an age-related decline in skeletal muscle mass and function, and diagnosis is challenging owing to the lack of a clear “gold standard” assessment method. OBJECTIVE: This study is aimed at combining random forest (RF) and artificial neural network (ANN) methods to screen key potential biomarkers and establish an early sarcopenia diagnostic model. METHODS: Three gene expression datasets were downloaded and merged by searching the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in the merged dataset were identified by R software and subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Afterward, the STRING database was employed for interaction analysis of the differentially encoded proteins. Then, RF was used to identify key genes from the DEGs, and a sarcopenia diagnostic model was constructed by ANN. Finally, the diagnostic model was assessed using a validation dataset, while its diagnostic performance was evaluated by the area under curve (AUC) value. RESULTS: 107 sarcopenia-related DEGs were identified, and they were mainly enriched in the FoxO and AMPK signaling pathways involved in the molecular pathogenesis of sarcopenia. Thereafter, seven key genes (MT1X, FAM171A1, ZNF415, ARHGAP36, CISD1, ETNPPL, and WISP2) were identified by the RF classifier. The proteins encoded by three of these genes (CISD1, ETNPPL, and WISP2) may be potential biomarkers for sarcopenia. Finally, a diagnostic model for sarcopenia was successfully designed by ANN, achieving an AUC of 0.999 and 0.85 in the training and testing datasets, respectively. CONCLUSION: We identified several potential genetic biomarkers and successfully developed an early predictive model with high diagnostic performance for sarcopenia. Moreover, our results provide a valuable reference for the early diagnosis and screening of sarcopenia in the future. Hindawi 2022-08-23 /pmc/articles/PMC9427323/ /pubmed/36050999 http://dx.doi.org/10.1155/2022/2957731 Text en Copyright © 2022 Shangjin Lin et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Lin, Shangjin
Chen, Cong
Cai, Xiaoxi
Yang, Fengjian
Fan, Yongqian
Development and Verification of a Combined Diagnostic Model for Sarcopenia with Random Forest and Artificial Neural Network
title Development and Verification of a Combined Diagnostic Model for Sarcopenia with Random Forest and Artificial Neural Network
title_full Development and Verification of a Combined Diagnostic Model for Sarcopenia with Random Forest and Artificial Neural Network
title_fullStr Development and Verification of a Combined Diagnostic Model for Sarcopenia with Random Forest and Artificial Neural Network
title_full_unstemmed Development and Verification of a Combined Diagnostic Model for Sarcopenia with Random Forest and Artificial Neural Network
title_short Development and Verification of a Combined Diagnostic Model for Sarcopenia with Random Forest and Artificial Neural Network
title_sort development and verification of a combined diagnostic model for sarcopenia with random forest and artificial neural network
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9427323/
https://www.ncbi.nlm.nih.gov/pubmed/36050999
http://dx.doi.org/10.1155/2022/2957731
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