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Copyright (c) 2023 Shuye Qu, Hui Huang, Yan Diao, Bowei Liu, Baozhu Tang, Shijiao Huo, Yu Lei, Xiuchen Xuan, Wenling Mou, Ping Liu, Jiye Zhang, Ying Liu, Yanze Li
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
The undersigned hereby assign all rights, included but not limited to copyright, for this manuscript to CMB Association upon its submission for consideration to publication on Cellular and Molecular Biology. The rights assigned include, but are not limited to, the sole and exclusive rights to license, sell, subsequently assign, derive, distribute, display and reproduce this manuscript, in whole or in part, in any format, electronic or otherwise, including those in existence at the time this agreement was signed. The authors hereby warrant that they have not granted or assigned, and shall not grant or assign, the aforementioned rights to any other person, firm, organization, or other entity. All rights are automatically restored to authors if this manuscript is not accepted for publication.Identification of biomarkers associated with immune-propionate metabolism in nonalcoholic fatty liver disease
Corresponding Author(s) : Ying Liu
Cellular and Molecular Biology,
Vol. 69 No. 10: Issue 10
Abstract
The mechanisms of the effect of propionate metabolism and immunity on nonalcoholic fatty liver disease (NAFLD) have not been adequately studied. Firstly, differentially expressed-propionate metabolism-related genes (DE-PMRGs) were selected by overlapping PMRGs and differentially expressed genes (DEGs) between the simple steatosis (SS) and health control (HC) groups. Then, common genes were selected by overlapping DE-PMRGs and key module genes obtained from weighted gene co-expression network analysis (WGCNA). Subsequently, the biomarkers were screened out by machine learning algorithms. The expression of the biomarkers was validated by quantitative Real-time PCR. In total, 5 biomarkers (JUN, LDLR, CXCR4, NNMT, and ANXA1) were acquired. The nomogram constructed based on 5 biomarkers had good predictive power for the risk of SS. Next, 5 biomarkers, 11 miRNAs, and 149 lncRNAs were encompassed in the ceRNA regulatory network. The expression of biomarkers was significantly higher in the HC group than in the SS group, which was consistent with the results in the GSE89632 and GSE126848 datasets. In this study, 5 immune and propionate metabolism-related biomarkers (JUN, LDLR, CXCR4, NNMT, and ANXA1) were screened out to provide a basis for exploring the prediction of diagnosis of NAFLD.
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