Fig 1.
(A) PCA of the datasets GSE55235, GSE55457, and GSE77298 after batch effect correction;(B) PCA of the integrated RA dataset after batch effect correction;(C) PCA of the datasets GSE3365 and GSE87466 after batch effect correction;(D) PCA of the integrated UC dataset after batch effect correction.
Fig 2.
(A) Volcano plot of DEGs between RA and healthy controls;(B) Volcano plot of DEGs between UC and healthy controls;(C) Heatmap of DEGs between RA and healthy controls;(D) Heatmap of DEGs between UC and healthy controls;(E) The 19 shared DEGs between UC and RA.
Fig 3.
PPI network analysis and enrichment analysis.
(A) PPI network of key genes constructed by STRING;(B) Bar plot of GO pathway enrichment analysis results;(C) KEGG pathway enrichment analysis results;(D) DO pathway enrichment analysis results.
Fig 4.
(A) Boxplot of immune cell abundance between RA and controls;(B) Boxplot of immune cell abundance between UC and controls;(C) Heatmap of immune cell infiltration in RA;(D) Heatmap of immune cell infiltration in UC. ***P < 0.001; **P < 0.01; *P < 0.05; ns, not significant.
Fig 5.
(A) Machine learning model for RA;(B) Machine learning model for UC; (C) Four genes at the intersection of RA and UC models.
Fig 6.
Disease subtype based on key genes.
(A) Consistency clustering heatmap for RA; (B) CDF plot of RA consistency clustering results; (C) Delta Area plot of RA consistency clustering results; (D) Boxplot of differentially expressed gene-related expression in RA subtypes; (E) Heatmap of differentially expressed gene-related expression in RA subtypes; (F) Consistency clustering heatmap for UC; (G) CDF plot of UC consistency clustering results; (H) Delta Area plot of UC consistency clustering results; (I) Boxplot of differentially expressed gene-related expression in UC subtypes; (J) Heatmap of differentially expressed gene-related expression in UC subtypes. ***P < 0.001; **P < 0.01; *P < 0.05; ns, not significant.
Fig 7.
Evaluation of the diagnostic performance of key genes and expression detection of key genes in MH7A cell lines and LPS-induced MH7A cell lines.
(A) Nomogram model for RA based on four key genes; (B) Nomogram model for UC based on four key genes; (C-D) ROC curve analysis of four key genes in the RA training set and test set; (E-F) ROC curve analysis of four key genes in the UC training set and test set; (G-H) The mRNA expression levels of IDO1 and NPY1R validated by qRT-PCR in MH7A cell lines and LPS-induced MH7A cell lines. Data are presented as mean ± SD. Statistical significance was determined using a two-tailed Student’s t-test, with * p < 0.05, ** p < 0.01, and *** p < 0.001 compared to control MH7A cell lines (n = 3).
Fig 8.
UMAP Plot for Cell Clustering and trajectory analysis.
(A) UMAP Plot for Cell Clustering. (B) Expression levels for selected marker genes of each celltype. (C) Violin Plot for hubmarker Expression in each cell type. (D/E) Pseudo-time and trajectory analyses of macrophages.
Fig 9.
Cellchat results presentation and expression detections of key gene in RAW264. cell lines and co-cultured RAW264.7 cell lines.
(A) Number or strength of cell population interactions. (B) Heatmap of cellular communication. Heatmap showing afferent and efferent signal intensities of all cell interactions. (C) Outcoming contribution bubble plots showing the expression of cellular communication patterns. (D) incoming contribution bubble plots showing the expression of cellular communication patterns. (E) Interaction of cells in the VEGF signaling pathway shown by heatmap. (F)Macrophage as a receiver interaction ligand diagram. Hierarchical diagram of macrophage celltype interacting with other cells in the VEGF signaling pathway. (G-H) qRT-PCR analysis showing the mRNA expression levels of IDO1 and NPY1R in RAW264.7 cell lines and co-cultured RAW264.7 cell lines. Data are presented as mean ± SD. Statistical significance was determined using a two-tailed Student’s t-test, with * p < 0.05, ** p < 0.01, and *** p < 0.001 compared to control RAW264.7 cell lines (n = 3).