Identification of potential key mRNAs and LncRNAs for psoriasis by bioinformatic analysis using weighted gene co-expression network analysis.

Abstract

Psoriasis is a common chronic autoimmune inflammatory skin disease that involves genetic and environmental factors. To date, psoriasis is still incurable. Thus, detection of its underlying molecular mechanisms is urgent. Weighted gene co-expression network analysis (WGCNA) was performed on the basis of the RNA-Seq data of psoriatic and normal (NN) skin tissues to detect the key mRNAs and long non-coding RNAs (LncRNAs) implicated in psoriasis and to identify psoriasis-related gene modules. Subsequently, 23 independent modules were obtained, and the pink module that contained differentially expressed 212 mRNAs and 100 LncRNAs was the most remarkable. Differentially expressed genes (DEGs) between psoriasis and healthy control in other RNA-Seq and microarray datasets were integrated to identify convinced psoriasis-associated genes. A total of 312 genes in the pink module and 613 DEGs were scanned. Eleven overlapped key mRNAs were identified, including two known genes (e.g., KRT15 and CCL27) and nine novel ones (e.g., ARSF, CLDN1, DACH1, LONRF1, PAMR1, RORC, SLC26A2, STS, UNC93A). A total of 11 key mRNAs were selected to construct a co-expression network to investigate potential candidate LncRNAs. Seventy-six pairs of LncRNA-mRNA co-expression relationships were found. To validate the findings, CCL27 and LncRNA-AL162231.4 expressions were detected in psoriatic and NN skin tissues. Result of RT-qPCR showed that CCL27 and LncRNA-AL162231.4 decreased in psoriatic lesions with statistical significance (P ≤ 0.05). Our study provides a new direction for elucidating the pathogenesis of psoriasis, but further experiments are still required.