Identification of circulating lncRNA in chronic kidney disease based on bioinformatics analysis

Exp Biol Med (Maywood). 2022 Jun 25;15353702221104035. doi: 10.1177/15353702221104035. Online ahead of print.

Abstract

Chronic kidney disease (CKD) is a high mortality disease and generally remains asymptomatic in the early stages. Long non-coding RNA (lncRNA) is defined as a non-protein-coding transcript more than 200 nucleotides which participate in numerous biological processes and have been identified as novel diagnostic markers for many diseases. Detection of circulating lncRNAs is a rapidly evolving, new area of molecular diagnosis. The purpose of our research was to identify circulating lncRNA expression profiles and possible molecular mechanisms involved in CKD. Blood samples were obtained from patients with CKD and healthy volunteers, and high-throughput sequencing was performed to identify differentially expressed (DE) lncRNAs and mRNAs. DE lncRNAs and mRNAs in peripheral blood mononuclear cells (PBMCs) were confirmed by quantitative reverse transcription polymerase chain reaction (qRT-PCR) to ensure the reliability and validity of RNA-seq data. Bioinformatics analysis was used to obtain biological functions and key pathways related to the pathogenesis of CKD. The interaction and co-expression functional networks for DE lncRNAs and mRNAs were also constructed. Our data showed that of the 425 DE lncRNAs detected, 196 lncRNAs were upregulated, while that of 229 lncRNAs were downregulated. A total of 433 DE mRNAs were identified in patients with CKD compared to healthy individuals. GO analysis revealed that DE lncRNAs were highly correlated with binding and pathway regulation. KEGG analysis suggested that DE lncRNAs were obviously enriched in regulatory pathways, such as antigen processing and presentation. We successfully constructed a potential DE lncRNA-mRNA co-expression network and analyzed the target genes of DE lncRNAs to predict cis- and trans-regulation in CKD. 100 lncRNAs that corresponded to 14 transcription factors (TFs) were identified in the TF-lncRNA binary network. Our findings on the lncRNA expression profiles and functional networks may help to interpret the possible molecular mechanisms implied in the pathogenesis of CKD; the results demonstrated that lncRNAs could potentially to be used as diagnostic biomarkers in CKD.

Keywords: bioinformatics analysis; chronic kidney disease; co-expression network; high-throughput sequencing; lncRNA.