Novel ribosome biogenesis-related biomarkers and therapeutic targets identified in psoriasis

Abstract

Psoriasis is an immune-mediated chronic inflammatory disease. Increasing evidence suggests a close association between ribosome biogenesis (RiboSis) and the pathogenesis of psoriasis. However, the precise mechanisms remain unclear. We first obtained bulk transcriptome and single-cell RNA sequencing datasets from the GEO database. Subsequently, differential expression analysis (DEG) and weighted gene co-expression network analysis (WGCNA) were performed, preliminarily identifying 11 candidate biomarkers. Protein-protein interaction (PPI) analysis revealed that these biomarkers are primarily involved in protein synthesis, regulation of gene expression, and control of the cell cycle and growth. Consensus clustering analysis combined with immune infiltration analysis revealed that the candidate biomarkers were strongly associated with innate immune cells, such as NK cells, mast cells, and monocytes, and were more closely linked to signaling pathways related to cell proliferation, cell cycle, inflammation, and glycolysis. From the 11 candidate biomarkers, we selected MPHOSPH6 and ISG20 (exhibiting the highest fold-changes) for external dataset validation, scRNA-seq analysis, and in vivo expression verification. Subsequently, potential therapeutic compounds targeting these biomarkers were predicted and validated via molecular docking. Collectively, our findings not only substantiate the critical role of RiboSis in psoriasis pathogenesis but also provide a framework for developing targeted therapeutic strategies.

Keywords: Biomarkers; Immune infiltration; Molecular docking; Psoriasis; Ribosome biogenesis.

Fig. 1

A flowchart of this study. WGCNA: Weighted Gene Co-Expression Network Analysis; PPI: protein-protein interaction; GSVA: Gene Set Variation Analysis.

Fig. 2

Acquisition of candidate diagnostic biomarkers. (A) Analysis of scale-free fit indices and mean connectivity values for different soft-threshold powers (β) showed that β = 6 was an appropriate option. (B) The clustering dendrogram displays the modules formed by the co-expression network, with different colors representing different modules. (C) Module-trait relationship plot showing the correlation of different modules with LS and NL. (D) The Venn diagram shows that there are 11 common genes among RiboSis, DEGs, and core module genes. Red labels represent upregulated genes, while blue one indicates downregulated genes.

Fig. 3

Functional Enrichment Analysis. (A) Interactions and functions of the 11 candidate diagnostic biomarkers. The candidate biomarkers are in the inner ring, the predicted genes are in the outer ring, and the networks and functions formed between the genes are represented in different colors. (B) GO/KEGG enrichment analysis showing the top 5 items per category (enriched to 2 pathways in KEGG). (C) Network diagram of the candidate biomarkers with the top five items in BP. (D) Network diagram of the candidate biomarkers with the top five items in CC. (E) Network diagram of the candidate biomarkers with the top five items in MF.

Fig. 4

The results of consensus clustering and immune infiltration analysis. (A) The consensus matrix plot shows that 2 clusters are clearly separated. (B) Heatmap of the expression of candidate biomarkers in C1 and C2. (C) Violin Plot of the expression levels of 22 immune cell types in C1 and C2. (D) Heatmap of the correlations between immune cells. (E) GSVA analysis results in C1 and C2.

Fig. 5

Expression levels of MPHOSPH6 and ISG20 in the external validation dataset. (A) Violin plot of MPHOSPH6 and ISG20 expression levels in SL and NL in the GSE67853 dataset. (B) ROC curves of diagnostic efficacy for MPHOSPH6 and ISG20 in the GSE67853 dataset.

Fig. 6

scRNA-seq analysis. (A) Cell clustering and annotation based on UMAP. (B) Proportion of 9 cell types across different samples. (C) Boxplot comparing the abundance of 9 cell types between psoriasis and normal groups. (D) Violin plot showing differences in gene set (MPHOSPH6 and ISG20) scores across 9 cell types. (E) UMAP plot showing expression of MPHOSPH6 and ISG20 in psoriasis and control groups. (F) Communication frequency and strength among nine cell types. (G) Cell-cell communication of KCs and CD8 + T cells, respectively.

Fig. 7

Expression of key biomarkers in mouse models. Compared with the normal group (A), the skin histopathology of the model group (B) showed hyperkeratosis with hyperkeratosis, proliferation, and hypertrophy of the stratum spinosum, prolongation of the epidermal protuberance, dilation of capillaries in the superficial dermis, and many inflammatory cells were seen to be infiltrated (×200). The results of (C) WB and (D) qPCR showed that the expression of MPHOSPH6 and ISG20 was up-regulated in the model group (MPHOSPH6 and β-Actin were derived from different gels; however, the experimental samples and procedures were processed in parallel).

Fig. 8

Results of molecular docking. (A) Chelidonine with ISG20, (B) Chelidonine with MPHOSPH6, (C) 16,16-Dimethylprostaglandin with ISG20, (D) Anisomycin with ISG20, (E) Calcitrio1with ISG20, and (F) Calcitriol with MPHOSPH6.