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Screening of key pathogenic genes in advanced knee osteoarthritis based on bioinformatics analysis

BACKGROUND: At present, the progression mechanism of knee osteoarthritis (KOA) has not been fully elucidated, and there is a clinical need for late KOA-specific diagnostic markers to provide reference for preventive treatment. This study aimed to analyze the sequencing results of early- and late-sta...

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Autores principales: Yang, Yongju, Zhang, Yuqian, Min, Dongyu, Yu, Heshan, Guan, Xuefeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9577766/
https://www.ncbi.nlm.nih.gov/pubmed/36267772
http://dx.doi.org/10.21037/atm-22-3863
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author Yang, Yongju
Zhang, Yuqian
Min, Dongyu
Yu, Heshan
Guan, Xuefeng
author_facet Yang, Yongju
Zhang, Yuqian
Min, Dongyu
Yu, Heshan
Guan, Xuefeng
author_sort Yang, Yongju
collection PubMed
description BACKGROUND: At present, the progression mechanism of knee osteoarthritis (KOA) has not been fully elucidated, and there is a clinical need for late KOA-specific diagnostic markers to provide reference for preventive treatment. This study aimed to analyze the sequencing results of early- and late-stage KOA synovial tissue based on the key genes of late-stage KOA in combination with a machine learning algorithm. METHODS: The whole transcriptome sequencing results of synovial tissue from KOA patients (GSE176223 and GSE32317) were downloaded from the gene expression omnibus (GEO) database. Thirty-nine early KOA synovial tissue samples and 31 late KOA synovial tissue samples were included in this study. The diagnostic criteria and baseline data balance of early and late KOA were referred to the data source literature, and the two groups of data had good baseline data balance. R software (V3.5.1) and R packages were used for screening and enrichment analysis of differentially expressed genes (DEGs). The key genes were screened by weighted correlation network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) regression analysis. A receiver operating characteristic curve (ROC) curve was used to evaluate the diagnostic efficacy of key genes for advanced KOA. RESULTS: A total of 211 DEGs related to knee arthritis were screened out. Compared with synovial tissue of early knee arthritis, 111 genes were upregulated and 100 genes were downregulated in the synovial tissue of late knee arthritis. Sixty-six key genes were screened out through WGCNA and 34 key genes were screened out in the LASSO analysis. The genes obtained by the two algorithms combined with three overlapping genes, namely interleukin- 6 (IL-6), C-X-C chemokine ligand 12 (CXCL12), and macrophage migration inhibitor factor (MIF). The areas under the ROC curves of CXCL12, IL-6, and MIF were 0.96, 0.944, and 0.961, respectively (P<0.001). CONCLUSIONS: IL-6, CXCL12, and MIF are the key pathogenic genes of KOA, which have good diagnostic efficacy for advanced KOA.
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spelling pubmed-95777662022-10-19 Screening of key pathogenic genes in advanced knee osteoarthritis based on bioinformatics analysis Yang, Yongju Zhang, Yuqian Min, Dongyu Yu, Heshan Guan, Xuefeng Ann Transl Med Original Article BACKGROUND: At present, the progression mechanism of knee osteoarthritis (KOA) has not been fully elucidated, and there is a clinical need for late KOA-specific diagnostic markers to provide reference for preventive treatment. This study aimed to analyze the sequencing results of early- and late-stage KOA synovial tissue based on the key genes of late-stage KOA in combination with a machine learning algorithm. METHODS: The whole transcriptome sequencing results of synovial tissue from KOA patients (GSE176223 and GSE32317) were downloaded from the gene expression omnibus (GEO) database. Thirty-nine early KOA synovial tissue samples and 31 late KOA synovial tissue samples were included in this study. The diagnostic criteria and baseline data balance of early and late KOA were referred to the data source literature, and the two groups of data had good baseline data balance. R software (V3.5.1) and R packages were used for screening and enrichment analysis of differentially expressed genes (DEGs). The key genes were screened by weighted correlation network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) regression analysis. A receiver operating characteristic curve (ROC) curve was used to evaluate the diagnostic efficacy of key genes for advanced KOA. RESULTS: A total of 211 DEGs related to knee arthritis were screened out. Compared with synovial tissue of early knee arthritis, 111 genes were upregulated and 100 genes were downregulated in the synovial tissue of late knee arthritis. Sixty-six key genes were screened out through WGCNA and 34 key genes were screened out in the LASSO analysis. The genes obtained by the two algorithms combined with three overlapping genes, namely interleukin- 6 (IL-6), C-X-C chemokine ligand 12 (CXCL12), and macrophage migration inhibitor factor (MIF). The areas under the ROC curves of CXCL12, IL-6, and MIF were 0.96, 0.944, and 0.961, respectively (P<0.001). CONCLUSIONS: IL-6, CXCL12, and MIF are the key pathogenic genes of KOA, which have good diagnostic efficacy for advanced KOA. AME Publishing Company 2022-09 /pmc/articles/PMC9577766/ /pubmed/36267772 http://dx.doi.org/10.21037/atm-22-3863 Text en 2022 Annals of Translational Medicine. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Original Article
Yang, Yongju
Zhang, Yuqian
Min, Dongyu
Yu, Heshan
Guan, Xuefeng
Screening of key pathogenic genes in advanced knee osteoarthritis based on bioinformatics analysis
title Screening of key pathogenic genes in advanced knee osteoarthritis based on bioinformatics analysis
title_full Screening of key pathogenic genes in advanced knee osteoarthritis based on bioinformatics analysis
title_fullStr Screening of key pathogenic genes in advanced knee osteoarthritis based on bioinformatics analysis
title_full_unstemmed Screening of key pathogenic genes in advanced knee osteoarthritis based on bioinformatics analysis
title_short Screening of key pathogenic genes in advanced knee osteoarthritis based on bioinformatics analysis
title_sort screening of key pathogenic genes in advanced knee osteoarthritis based on bioinformatics analysis
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9577766/
https://www.ncbi.nlm.nih.gov/pubmed/36267772
http://dx.doi.org/10.21037/atm-22-3863
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