Identification of serum exosomal lncRNAs and their potential regulation of characteristic genes of fibroblast-like synoviocytes in rheumatoid arthritis

Abstract

Rheumatoid arthritis (RA) is a common autoimmune disease whose pathogenesis is poorly understand. Gaps in laboratory biomarkers cause a lack of clinically available strategies for the early diagnosis and treatment of RA. This study aims to identify serum exosomal lncRNAs as promising biomarkers and to unravel potential mechanisms by which they affect characteristic genes of fibroblast-like synoviocytes (FLSs) to induce RA malignant properties. RNA sequencing datasets of serum exosomes (GSE271161 and PRJNA911001) and FLSs (GSE103578, GSE122616, GSE128813, GSE181614 and GSE83147) were purposively mined. Visualization and functional enrichment of differentially expressed (DE) lncRNAs/protein-coding genes, screening of significant lncRNAs, and construction of competing endogenous RNAs (ceRNAs) and protein–protein interaction (PPI) network were carried out. Quantitative real-time PCR, receiver operating characteristic curve (ROC) and correlation analysis were conducted on the validation cohort. As a result, we screened a total of 131 serum exosomal DElncRNAs and 125 FLSs DEmRNAs, which were predominantly enriched in the proliferative, inflammatory and metabolic pathways. In-depth learning of DElncRNAs expression profiles was performed to identify models with better performance and lncRNAs with higher importance scores using 4 machine learning algorithms (SVM, KNN, RF, Logit), which led to the establishment of ceRNAs network linking serum exosomal lncRNAs and characteristic genes of FLSs. In short, we proposed that 4 RA-representative serum exosomal lncRNAs (DLEU2, FAM13A-AS1, MEG3 and SNHG15) may be applied as valuable indicators for laboratory tests, and their-mediated intercellular communication and ceRNAs network may regulate the characteristic genes of FLSs, thereby generating malignant phenotypes and adaptive synovial microenvironment in RA.