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

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.