Academic Journal
Bioinformatics identification and experimental validation of m6A-related diagnostic biomarkers in the subtype classification of blood monocytes from postmenopausal osteoporosis patients
| العنوان: | Bioinformatics identification and experimental validation of m6A-related diagnostic biomarkers in the subtype classification of blood monocytes from postmenopausal osteoporosis patients |
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| المؤلفون: | Zhang, Peng, Chen, Honglin, Xie, Bin, Zhao, Wenhua, Shang, Qi, He, Jiahui, Shen, Gengyang, Yu, Xiang, Zhang, Zhida, Zhu, Guangye, Chen, Guifeng, Yu, Fuyong, Liang, De, Tang, Jingjing, Cui, Jianchao, Liu, Zhixiang, Ren, Hui, Jiang, Xiaobing |
| المساهمون: | National Natural Science Foundation of China |
| المصدر: | Frontiers in Endocrinology ; volume 14 ; ISSN 1664-2392 |
| بيانات النشر: | Frontiers Media SA |
| سنة النشر: | 2023 |
| المجموعة: | Frontiers (Publisher - via CrossRef) |
| الوصف: | Background Postmenopausal osteoporosis (PMOP) is a common bone disorder. Existing study has confirmed the role of exosome in regulating RNA N6-methyladenosine (m6A) methylation as therapies in osteoporosis. However, it still stays unclear on the roles of m6A modulators derived from serum exosome in PMOP. A comprehensive evaluation on the roles of m6A modulators in the diagnostic biomarkers and subtype identification of PMOP on the basis of GSE56815 and GSE2208 datasets was carried out to investigate the molecular mechanisms of m6A modulators in PMOP. Methods We carried out a series of bioinformatics analyses including difference analysis to identify significant m6A modulators, m6A model construction of random forest, support vector machine and nomogram, m6A subtype consensus clustering, GO and KEGG enrichment analysis of differentially expressed genes (DEGs) between different m6A patterns, principal component analysis, and single sample gene set enrichment analysis (ssGSEA) for evaluation of immune cell infiltration, experimental validation of significant m6A modulators by real-time quantitative polymerase chain reaction (RT-qPCR), etc. Results In the current study, we authenticated 7 significant m6A modulators via difference analysis between normal and PMOP patients from GSE56815 and GSE2208 datasets. In order to predict the risk of PMOP, we adopted random forest model to identify 7 diagnostic m6A modulators, including FTO, FMR1, YTHDC2, HNRNPC, RBM15, RBM15B and WTAP. Then we selected the 7 diagnostic m6A modulators to construct a nomogram model, which could provide benefit with patients according to our subsequent decision curve analysis. We classified PMOP patients into 2 m6A subtypes (clusterA and clusterB) on the basis of the significant m6A modulators via a consensus clustering approach. In addition, principal component analysis was utilized to evaluate the m6A score of each sample for quantification of the m6A subgroups. The m6A scores of patients in clusterB were higher than those of patients in ... |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | unknown |
| DOI: | 10.3389/fendo.2023.990078 |
| DOI: | 10.3389/fendo.2023.990078/full |
| الاتاحة: | http://dx.doi.org/10.3389/fendo.2023.990078 https://www.frontiersin.org/articles/10.3389/fendo.2023.990078/full |
| Rights: | https://creativecommons.org/licenses/by/4.0/ |
| رقم الانضمام: | edsbas.A536699F |
| قاعدة البيانات: | BASE |
| DOI: | 10.3389/fendo.2023.990078 |
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