Identification of potential key molecules and signaling pathways for psoriasis based on weighted gene co-expression network analysis

Abstract

Background: Psoriasis is a chronic inflammatory skin disease, the pathogenesis of which is more complicated and often requires long-term treatment. In particular, moderate to severe psoriasis usually requires systemic treatment. Psoriasis is also associated with many diseases, such as cardiometabolic diseases, malignant tumors, infections, and mood disorders. Psoriasis can appear at any age, and lead to a substantial burden for individuals and society. At present, psoriasis is still a treatable, but incurable, disease. Previous studies have found that microRNAs (miRNAs) play an important regulatory role in the progression of various diseases. Currently, miRNAs studies in psoriasis and dermatology are relatively new. Therefore, the identification of key miRNAs in psoriasis is helpful to elucidate the molecular mechanism of psoriasis.

Aim: To identify key molecular markers and signaling pathways to provide potential basis for the treatment and management of psoriasis.

Methods: The miRNA and mRNA data were obtained from the Gene Expression Omnibus database. Then, differentially expressed mRNAs (DEmRNAs) and differentially expressed miRNAs (DEmiRNAs) were screened out by limma R package. Subsequently, DEmRNAs were analyzed for Gene Ontology and Kyoto Encyclopedia of Genes and Genomics functional enrichment. The "WGCNA" R package was used to analyze the co-expression network of all miRNAs. In addition, we constructed miRNA-mRNA regulatory networks based on identified hub miRNAs. Finally, in vitro validation was performed. All experimental procedures were approved by the ethics committee of Chinese PLA General Hospital (S2021-012-01).

Results: A total of 639 DEmRNAs and 84 DEmiRNAs were identified. DEmRNAs screening criteria were adjusted P (adj. P) value < 0.01 and |logFoldChange| (|logFC|) > 1. DEmiRNAs screening criteria were adj. P value < 0.01 and |logFC| > 1.5. KEGG functional analysis demonstrated that DEmRNAs were significantly enriched in immune-related biological functions, for example, toll-like receptor signaling pathway, cytokine-cytokine receptor interaction, and chemokine signaling pathway. In weighted gene co-expression network analysis, turquoise module was the hub module. Moreover, 10 hub miRNAs were identified. Among these 10 hub miRNAs, only 8 hub miRNAs predicted the corresponding target mRNAs. 97 negatively regulated miRNA-mRNA pairs were involved in the miRNA-mRNA regulatory network, for example, hsa-miR-21-5p-claudin 8 (CLDN8), hsa-miR-30a-3p-interleukin-1B (IL-1B), and hsa-miR-181a-5p/hsa-miR-30c-2-3p-C-X-C motif chemokine ligand 9 (CXCL9). Real-time polymerase chain reaction results showed that IL-1B and CXCL9 were up-regulated and CLDN8 was down-regulated in psoriasis with statistically significant differences.

Conclusion: The identification of potential key molecular markers and signaling pathways provides potential research directions for further understanding the molecular mechanisms of psoriasis. This may also provide new research ideas for the prevention and treatment of psoriasis in the future.

Keywords: Functional enrichment; MicroRNA-mRNA regulatory network; MicroRNAs; Psoriasis; Weighted gene co-expression network analysis.

Figure 1

Removal of data batch effect. A: Ven diagram of batch effect of mRNA; B: Boxplot of batch effect of mRNA; C: Ven diagram of batch effect of miRNA; D: Boxplot of batch effect of miRNA.

Figure 2

Identification of differentially expressed mRNAs and differentially expressed miRNAs. A: Heat map of top 100 differentially expressed mRNAs (DEmRNAs). Complete‑linkage method combined with Euclidean distance was used to construct clustering; B: Heat map of differentially expressed miRNAs (DEmiRNAs); C: Volcano map of DEmRNAs. Blue, red, and gray points represent down-expressed, up-expressed, and not DEmRNAs, respectively; D: Volcano map of DEmiRNAs.

Figure 3

Gene Ontology and Kyoto Encyclopedia of Genes and Genomics analysis of differentially expressed mRNAs. A: Biological processes terms were enriched of differentially expressed mRNAs (DEmRNAs) by Gene Ontology (GO) function analysis; B: Cellular components terms were enriched of DEmRNAs by GO function analysis; C: Molecular functions terms were enriched of DEmRNAs by GO function analysis; D: Kyoto Encyclopedia of Genes and Genomics enrichment of DEmRNAs.

Figure 4

Construction of weighted gene co-expression network. A: Sample screening dendrogram; B: Sample dendrogram and trait heat map; C: Scale-free fit index of different soft threshold power (β) and mean connectivity of various soft threshold power; D: The cluster dendrogram of the miRNAs. Each branch represents one gene, and every color below represents one module.

Figure 5

Hub module and hub miRNAs. A: Heat map of the correlation between module eigengene and psoriasis. The correlation between turquoise module and psoriasis was highest; B: Scatter plot of miRNAs in the turquoise module. MiRNAs with module connectivity > 0.8 and clinical trait relationship > 0.5 were selected as candidate hub miRNAs; C: The 10 intersecting miRNAs were identified as real hub miRNAs.

Figure 6

Regulatory network of miRNA-mRNA. Red, yellow, oval, and V-shaped represent up-regulated, down-regulated, differentially expressed mRNAs, and hub differentially expressed miRNAs, respectively.

Figure 7

Gene Ontology and Kyoto Encyclopedia of Genes and Genomics analysis of target differentially expressed mRNAs. A: biological processes terms were enriched of target differentially expressed mRNAs (DEmRNAs) by Gene Ontology (GO) function analysis; B: Cellular components terms were enriched of target DEmRNAs by GO function analysis; C: Molecular functions terms were enriched of target DEmRNAs by GO function analysis; D: Kyoto Encyclopedia of Genes and Genomics enrichment of target DEmRNAs.

Figure 8

Real-time polymerase chain reaction validation. A: Interleukin-1β; B: C-X-C motif chemokine ligand 9; C: C-C motif chemokine ligand 8; D: Claudin 8; E: hsa-miR-181a-5p; F: hsa-miR-21-5p; G: hsa-miR-30a-3p. ^aP < 0.05, P < 0.05 was considered significant.