Prediction of a Competing Endogenous RNA Co-expression Network by Comprehensive Methods in Systemic Sclerosis-Related Interstitial Lung Disease.

Yue-Mei Yan,Qiao-Rong Yang,Qiang Wang,Ji-Na Zheng,Qian-Wen Rao,Di Gao,Li-Wei Wu

doi:10.3389/fgene.2021.633059

Abstract

Systemic sclerosis (SSc) is an immune-mediated connective tissue disease characterized by fibrosis of multi-organs, and SSc-related interstitial lung disease (SSc-ILD) is a leading cause of morbidity and mortality. To explore molecular biological mechanisms of SSc-ILD, we constructed a competing endogenous RNA (ceRNA) network for prediction. Expression profiling data were obtained from the Gene Expression Omnibus (GEO) database, and differential expressed mRNAs and miRNAs analysis was further conducted between normal lung tissue and SSc lung tissue. Also, the interactions of miRNA–lncRNA, miRNA–mRNA, and lncRNA–mRNA were predicted by online databases including starBase, LncBase, miRTarBase, and LncACTdb. The ceRNA network containing 11 lncRNAs, 7 miRNAs, and 20 mRNAs were constructed. Based on hub genes and miRNAs identified by weighted correlation network analysis (WGCNA) method, three core sub-networks—SNHG16, LIN01128, RP11-834C11.4(LINC02381)/hsa-let-7f-5p/IL6, LINC01128/has-miR-21-5p/PTX3, and LINC00665/hsa-miR-155-5p/PLS1—were obtained. Combined with previous studies and enrichment analyses, the lncRNA-mediated network affected LPS-induced inflammatory and immune processes, fibrosis development, and tumor microenvironment variations. The ceRNA network, especially three core sub-networks, may be served as early biomarkers and potential targets for SSc, which also provides further insights into the occurrence, progression, and accurate treatment of SSc at the molecular level.

Highlights

Systemic sclerosis (SSc), named scleroderma, is an immune-mediated connective tissue disease characterized by fibrosis of the skin and internal organs with unknown etiology (The Lancet, 2017)
Screening was performed in accordance with the following criteria: (1) at least 10 SSc and health control (HC) samples were included for each gene expression dataset; replication or drug-trail or cell lines samples should be excluded; (2) tissues originate from lung biopsy on Homo sapiens; and (3) datasets containing both non-coding RNA expression and mRNA expression were included
The biological processes (BP), cell components (CC), molecular function (MF), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were retrieved with a cutoff criterion of P < 0.05 and visualized by the R packages “enrichplot” and “GOplot.”

Summary

INTRODUCTION

Systemic sclerosis (SSc), named scleroderma, is an immune-mediated connective tissue disease characterized by fibrosis of the skin and internal organs with unknown etiology (The Lancet, 2017). The competing endogenous RNA (ceRNA) hypothesis posits that specific ncRNAs, such as lncRNAs and circular RNAs (circRNAs), can impair miRNA activity through occupying the binding sites on them, thereby upregulating mRNA gene expression (Thomson and Dinger, 2016). In another way, lncRNAs act as molecular sponges to attract miRNAs, contributing to various human disease processes. WGCNA was applied to detect key miRNAs and hub genes, which played significant roles in the pathological process of SSc. we identified 11 lncRNAs, 7 miRNAs, and 20 mRNAs to construct a lncRNA–miRNA–mRNA ceRNA network. To the best of our knowledge, this is the first study to focus on the prediction of ceRNA network in SSc and SSc-ILD, providing new perspectives on SSc pathogenesis, progression, and treatment

MATERIALS AND METHODS

RESULTS

DISCUSSION

DATA AVAILABILITY STATEMENT