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Bioinformatics-integrated screening of systemic sclerosis-specific expressed markers to identify therapeutic targets

BACKGROUND: Systemic sclerosis (SSc) is a rare autoimmune disease characterized by extensive skin fibrosis. There are no effective treatments due to the severity, multiorgan presentation, and variable outcomes of the disease. Here, integrated bioinformatics was employed to discover tissue-specific e...

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Autores principales: Jin, Jiahui, Liu, Yifan, Tang, Qinyu, Yan, Xin, Jiang, Miao, Zhao, Xu, Chen, Jie, Jin, Caixia, Ou, Qingjian, Zhao, Jingjun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10098096/
https://www.ncbi.nlm.nih.gov/pubmed/37063926
http://dx.doi.org/10.3389/fimmu.2023.1125183
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author Jin, Jiahui
Liu, Yifan
Tang, Qinyu
Yan, Xin
Jiang, Miao
Zhao, Xu
Chen, Jie
Jin, Caixia
Ou, Qingjian
Zhao, Jingjun
author_facet Jin, Jiahui
Liu, Yifan
Tang, Qinyu
Yan, Xin
Jiang, Miao
Zhao, Xu
Chen, Jie
Jin, Caixia
Ou, Qingjian
Zhao, Jingjun
author_sort Jin, Jiahui
collection PubMed
description BACKGROUND: Systemic sclerosis (SSc) is a rare autoimmune disease characterized by extensive skin fibrosis. There are no effective treatments due to the severity, multiorgan presentation, and variable outcomes of the disease. Here, integrated bioinformatics was employed to discover tissue-specific expressed hub genes associated with SSc, determine potential competing endogenous RNAs (ceRNA) regulatory networks, and identify potential targeted drugs. METHODS: In this study, four datasets of SSc were acquired. To identify the genes specific to tissues or organs, the BioGPS web database was used. For differentially expressed genes (DEGs), functional and enrichment analyses were carried out, and hub genes were screened and shown in a network of protein-protein interactions (PPI). The potential lncRNA–miRNA–mRNA ceRNA network was constructed using the online databases. The specifically expressed hub genes and ceRNA network were validated in the SSc mouse and in normal mice. We also used the receiver operating characteristic (ROC) curve to determine the diagnostic values of effective biomarkers in SSc. Finally, the Drug-Gene Interaction Database (DGIdb) identified specific medicines linked to hub genes. RESULTS: The pooled datasets identified a total of 254 DEGs. The tissue/organ-specifically expressed genes involved in this analysis are commonly found in the hematologic/immune system and bone/muscle tissue. The enrichment analysis of DEGs revealed the significant terms such as regulation of actin cytoskeleton, immune-related processes, the VEGF signaling pathway, and metabolism. Cytoscape identified six gene cluster modules and 23 hub genes. And 4 hub genes were identified, including Serpine1, CCL2, IL6, and ISG15. Consistently, the expression of Serpine1, CCL2, IL6, and ISG15 was significantly higher in the SSc mouse model than in normal mice. Eventually, we found that MALAT1-miR-206-CCL2, let-7a-5p-IL6, and miR-196a-5p-SERPINE1 may be promising RNA regulatory pathways in SSc. Besides, ten potential therapeutic drugs associated with the hub gene were identified. CONCLUSIONS: This study revealed tissue-specific expressed genes, SERPINE1, CCL2, IL6, and ISG15, as effective biomarkers and provided new insight into the mechanisms of SSc. Potential RNA regulatory pathways, including MALAT1-miR-206-CCL2, let-7a-5p-IL6, and miR-196a-5p-SERPINE1, contribute to our knowledge of SSc. Furthermore, the analysis of drug-hub gene interactions predicted TIPLASININ, CARLUMAB and BINDARIT as candidate drugs for SSc.
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spelling pubmed-100980962023-04-14 Bioinformatics-integrated screening of systemic sclerosis-specific expressed markers to identify therapeutic targets Jin, Jiahui Liu, Yifan Tang, Qinyu Yan, Xin Jiang, Miao Zhao, Xu Chen, Jie Jin, Caixia Ou, Qingjian Zhao, Jingjun Front Immunol Immunology BACKGROUND: Systemic sclerosis (SSc) is a rare autoimmune disease characterized by extensive skin fibrosis. There are no effective treatments due to the severity, multiorgan presentation, and variable outcomes of the disease. Here, integrated bioinformatics was employed to discover tissue-specific expressed hub genes associated with SSc, determine potential competing endogenous RNAs (ceRNA) regulatory networks, and identify potential targeted drugs. METHODS: In this study, four datasets of SSc were acquired. To identify the genes specific to tissues or organs, the BioGPS web database was used. For differentially expressed genes (DEGs), functional and enrichment analyses were carried out, and hub genes were screened and shown in a network of protein-protein interactions (PPI). The potential lncRNA–miRNA–mRNA ceRNA network was constructed using the online databases. The specifically expressed hub genes and ceRNA network were validated in the SSc mouse and in normal mice. We also used the receiver operating characteristic (ROC) curve to determine the diagnostic values of effective biomarkers in SSc. Finally, the Drug-Gene Interaction Database (DGIdb) identified specific medicines linked to hub genes. RESULTS: The pooled datasets identified a total of 254 DEGs. The tissue/organ-specifically expressed genes involved in this analysis are commonly found in the hematologic/immune system and bone/muscle tissue. The enrichment analysis of DEGs revealed the significant terms such as regulation of actin cytoskeleton, immune-related processes, the VEGF signaling pathway, and metabolism. Cytoscape identified six gene cluster modules and 23 hub genes. And 4 hub genes were identified, including Serpine1, CCL2, IL6, and ISG15. Consistently, the expression of Serpine1, CCL2, IL6, and ISG15 was significantly higher in the SSc mouse model than in normal mice. Eventually, we found that MALAT1-miR-206-CCL2, let-7a-5p-IL6, and miR-196a-5p-SERPINE1 may be promising RNA regulatory pathways in SSc. Besides, ten potential therapeutic drugs associated with the hub gene were identified. CONCLUSIONS: This study revealed tissue-specific expressed genes, SERPINE1, CCL2, IL6, and ISG15, as effective biomarkers and provided new insight into the mechanisms of SSc. Potential RNA regulatory pathways, including MALAT1-miR-206-CCL2, let-7a-5p-IL6, and miR-196a-5p-SERPINE1, contribute to our knowledge of SSc. Furthermore, the analysis of drug-hub gene interactions predicted TIPLASININ, CARLUMAB and BINDARIT as candidate drugs for SSc. Frontiers Media S.A. 2023-03-30 /pmc/articles/PMC10098096/ /pubmed/37063926 http://dx.doi.org/10.3389/fimmu.2023.1125183 Text en Copyright © 2023 Jin, Liu, Tang, Yan, Jiang, Zhao, Chen, Jin, Ou and Zhao https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Immunology
Jin, Jiahui
Liu, Yifan
Tang, Qinyu
Yan, Xin
Jiang, Miao
Zhao, Xu
Chen, Jie
Jin, Caixia
Ou, Qingjian
Zhao, Jingjun
Bioinformatics-integrated screening of systemic sclerosis-specific expressed markers to identify therapeutic targets
title Bioinformatics-integrated screening of systemic sclerosis-specific expressed markers to identify therapeutic targets
title_full Bioinformatics-integrated screening of systemic sclerosis-specific expressed markers to identify therapeutic targets
title_fullStr Bioinformatics-integrated screening of systemic sclerosis-specific expressed markers to identify therapeutic targets
title_full_unstemmed Bioinformatics-integrated screening of systemic sclerosis-specific expressed markers to identify therapeutic targets
title_short Bioinformatics-integrated screening of systemic sclerosis-specific expressed markers to identify therapeutic targets
title_sort bioinformatics-integrated screening of systemic sclerosis-specific expressed markers to identify therapeutic targets
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10098096/
https://www.ncbi.nlm.nih.gov/pubmed/37063926
http://dx.doi.org/10.3389/fimmu.2023.1125183
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