From molecular subgroups to molecular targeted therapy in rheumatoid arthritis: A bioinformatics approach

Abstract

1background: Rheumatoid Arthritis (RA) is a heterogeneous autoimmune disease with multiple unidentified pathogenic factors. The inconsistency between molecular subgroups poses challenges for early diagnosis and personalized treatment strategies. In this study, we aimed to accurately distinguish RA patients at the transcriptome level using bioinformatics methods.

2methods: We collected a total of 362 transcriptome datasets from RA patients in three independent samples from the GEO database. Consensus clustering was performed to identify molecular subgroups, and clinical features were assessed. Differential analysis was employed to annotate the biological functions of specifically upregulated genes between subgroups.

3results: Based on consensus clustering of RA samples, we identified three robust molecular subgroups, with Subgroup III representing the high-risk subgroup and Subgroup II exhibiting a milder phenotype, possibly associated with relatively higher levels of autophagic ability. Subgroup I showed biological functions mainly related to viral infections, cellular metabolism, protein synthesis, and inflammatory responses. Subgroup II involved autophagy of mitochondria and organelles, protein localization, and organelle disassembly pathways, suggesting heterogeneity in the autophagy process of mitochondria that may play a protective role in inflammatory diseases. Subgroup III represented a high-risk subgroup with pathological processes including abnormal amyloid precursor protein activation, promotion of inflammatory response, and cell proliferation.

4conclusion: The classification of the RA dataset revealed pathological heterogeneity among different subgroups, providing new insights and a basis for understanding the molecular mechanisms of RA, identifying potential therapeutic targets, and developing personalized treatment approaches.

Keywords: Diagnosis; Heterogeneity; Molecular subgroup; Rheumatoid arthritis; Transcriptomics.

Fig. 1

Illustrates the experimental design.

Fig. 2

Prisma flowchart for dataset filtering.

Fig. 3

Principal Component Analysis (PCA) scatter plots, where each color represents different datasets. (A) Visualization of samples before batch effect removal. (B) Visualization of samples after batch effect normalization.

Fig. 4

Consensus clustering analysis of RA samples. (A) Bar plot of consensus scores for clustering from k = 2 to k = 10, showing that three subgroups have higher consensus scores (>0.8). (B) Consensus matrix for clustering with k = 3.

Fig. 5

Core genes of subgroups. (A) PPI interactions network of subgroup I. (B) PPI interactions network of subgroup II. (C) PPI interactions network of subgroup III. (D) Hub genes of subpopulation I. (E) Hub genes of subpopulation II.(F) Hub genes of subpopulation III.

Fig. 6

Clinical features of subgroups. (A) Comparison of age among subgroups. (B) Comparison of ESR among subgroups. (C) Comparison of MMP3 among subgroups. (D) Comparison of CRP among subgroups. (E) Comparison of CDAI among subgroups. (F) Comparison of SDAI among subgroups. (G) Comparison of the proportion of females among subgroups. Pairwise t-test, nsP >0.05; *P < 0.05; **P < 0.01; ***P < 0.001.

Fig. 7

GSEA enrichment analysis results for subgroup-specific upregulated genes. (A) Significant upregulation of genes in Subgroup I (FDR <0.001), n = 295. (B) Non-significant upregulation of genes in Subgroup II (FDR >0.05), n = 58. (C) Significant upregulation of genes in Subgroup III (FDR <0.001), n = 251.

Fig. 8

(A) WGCNA gene module analysis. (B) Correlation heatmap for module analysis. Note: P < 0.05 is considered statistically significant. Red indicates a positive correlation, and blue indicates a negative correlation.

Fig. 9

(A) GO enrichment analysis of module genes. (B) KEGG enrichment analysis of module genes. (C) Heatmap showing the correlation between KEGG enrichment results and module genes.