Table 1.
Shared biomarkers between psoriatic arthritis and rheumatoid arthritis from clinical trials.
Fig 1.
The workflow of the present study.
Table 2.
The information of datasets used in this study.
Fig 2.
Differentially expressed genes and their enrichment analysis in PsA and RA training cohort.
(A) Volcano plot providing visual representation of the significance and fold-change of differentially expressed genes in GSE61281 (PsA). (B) Heatmap displaying expression levels of DEGs in GSE61281 (PsA). (C) Volcano plot displaying DEGs in GSE93272 (RA). (D) Heatmap of DEGs in GSE93272 (RA). (E) Venn plot showing the intersection of DEGs of two diseases. F) Bubble diagram shows the significant GO terms for 7 DEGs. (G) Bar graph shows the significant pathways for 7 DEGs.
Fig 3.
Screening process for marker genes from 7 DEGs and the diagnostic value of marker genes.
(A-D) The LASSO regression algorithm to screen candidate marker genes in PsA dataset GSE61281 (A, B) and RA dataset GSE93272 (C, D). (E, F) The SVM-RFE algorithm to screen candidate marker genes in PsA dataset GSE61281 (C) and RA dataset GSE93272 (D). (G) The Venn diagram highlights the intersection of candidate marker genes in two diseases and two algorithms. (H, J) ROC curves assess the diagnostic ability of marker genes in the training cohort. (I, K) DCA curves evaluate the practical value of marker genes in the training cohort.
Fig 4.
(A, B, C) Box plots and ROC curves assess the differential expression and diagnostic ability of marker genes in psoriatic skin (A), RA peripheral blood mononuclear cells (B), and RA synovial fluid (C). (D) ROC curves assess the diagnostic ability of marker genes in whole blood experimental data. ns p > 0.05, * p < 0.05, *** p < 0.001, **** p < 0.0001.
Fig 5.
(A) Top ten significantly enriched pathways of RPL22L1 in PsA dataset. (B) Top ten significantly enriched pathways of LY96 in PsA dataset. (C) Top ten significantly enriched pathways of RPL22L1 in RA dataset. (D) Top ten significantly enriched pathways of LY96 in RA dataset. (E) The results of GSEA demonstrate significant enrichment in the gap junction pathway.
Fig 6.
The size of the circle is proportional to its betweenness centrality. Brown circles represent 2 marker genes and 10 core enrichment genes in gap junction pathway. The bottle-green shows mutual TFs of marker genes and core enrichment genes. The pale blue denotes TFs unique to marker genes or core enrichment genes.
Fig 7.
Immune infiltration analysis of PsA and RA.
(A, E) Boxplot of the proportions of 22 infiltrating immune cells in PsA(A) and RA (E). (B, F) Correlation of 22 immune cell type compositions in PsA (B) and RA (F). (C) Correlation between RPL22L1 and infiltrating immune cells in PsA. (D) Correlation between LY96 and infiltrating immune cells in PsA. (G) Correlation between RPL22L1 and infiltrating immune cells in RA. (H) Correlation between RPL22L1 and infiltrating immune cells in RA.
Fig 8.
Single-cell sequencing analysis.
(A) Harmony plot colored by one RA and three normal samples. (B) UMAP plot colored by various cell clusters. (C) UMAP plot colored by cells after annotation. (D) Heat map shows the expression of hallmark genes in different cell clusters from RA PBMCs. The scaled average expression levels of marker genes and the percentage of cells expressing marker genes are expressed by color and size of each dot corresponding to cell clusters, respectively. (E, F) The violin plots illustrate the expression of RPL22L1 (E) and LY96 (F) in each of the cell clusters within the RA and control groups. (G) Stacked bar plot to demonstrate cell percentage between control and RA samples.
Fig 9.
Trajectory and cell-cell communication analysis.
(A) Differentiation trajectory results for T cells. (B) Heatmap of marker genes expression along the pseudotime trajectory. (C, E) The number and weight of interaction in cell-cell communication network. (D, F) Heatmap visualizing the possible incoming or outgoing signaling pathways among cell.
Table 3.