Transcriptome Analysis of lncRNA–mRNA Interactions in Chronic Atrophic Gastritis

The aim of this study was to identify prognosis-related differentially expressed lncRNAs and mRNAs in chronic atrophic gastritis (CAG). By analysis of high-throughput whole-transcriptome sequencing data, the levels of lncRNAs and mRNAs between CAG and chronic non-atrophic gastritis were compared pai...

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Bibliographic Details
Main Authors: Yang Chao, Jingpeng Jin, Liqiang Wang, Xiya Jin, Lei Yang, Bin Zhang
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-01-01
Series:Frontiers in Genetics
Subjects:
Online Access:https://www.frontiersin.org/articles/10.3389/fgene.2020.612951/full
Description
Summary:The aim of this study was to identify prognosis-related differentially expressed lncRNAs and mRNAs in chronic atrophic gastritis (CAG). By analysis of high-throughput whole-transcriptome sequencing data, the levels of lncRNAs and mRNAs between CAG and chronic non-atrophic gastritis were compared pairwisely. In total, 97,282 lncRNA transcripts and 20,307 mRNA transcripts were acquired, including 50 upregulated and 66 downregulated lncRNAs and 377 upregulated and 763 downregulated mRNAs in CAG (p < 0.05, fold change ≥ 2). Moreover, the interactions of the differentially expressed genes in CAG were investigated by gene ontology enrichment analysis, showing that the enriched genes are involved in many biological processes, such as MAP kinase activity, heat generation, and protein modification processes. Through the construction of co-expression networks of the differentially expressed genes in CAG, three critical lncRNAs nodes were identified as potential key factors in CAG. Eight mRNAs common in both the co-expression network and the protein–protein interaction network were selected via Venn analysis, including DGKA, EIF6, HKDC1, DHRS11, 1, KRT15, TESPA1, and CDHR2. Finally, the expression levels of five differentially expressed lncRNAs in CAG were confirmed by quantitative real-time polymerase chain reaction. In conclusion, this study presents novel promising biomarkers for the diagnosis of CAG.
ISSN:1664-8021