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. 2023 Jan 6:13:1095058.
doi: 10.3389/fgene.2022.1095058. eCollection 2022.

Exploration of comorbidity mechanisms and potential therapeutic targets of rheumatoid arthritis and pigmented villonodular synovitis using machine learning and bioinformatics analysis

Hongquan Heng  1 , Dazhuang Li  2 , Wenxing Su  3 , Xinyue Liu  4 , Daojiang Yu  3 , Zhengjun Bian  2 , Jian Li  1
Affiliations

Exploration of comorbidity mechanisms and potential therapeutic targets of rheumatoid arthritis and pigmented villonodular synovitis using machine learning and bioinformatics analysis

Hongquan Heng et al. Front Genet. .

Abstract

Background: Rheumatoid arthritis (RA) is a chronic autoimmune disease. Pigmented villonodular synovitis (PVNS) is a tenosynovial giant cell tumor that can involve joints. The mechanisms of co-morbidity between the two diseases have not been thoroughly explored. Therefore, this study focused on investigating the functions, immunological differences, and potential therapeutic targets of common genes between RA and PVNS. Methods: Through the dataset GSE3698 obtained from the Gene Expression Omnibus (GEO) database, the differentially expressed genes (DEGs) were screened by R software, and weighted gene coexpression network analysis (WGCNA) was performed to discover the modules most relevant to the clinical features. The common genes between the two diseases were identified. The molecular functions and biological processes of the common genes were analyzed. The protein-protein interaction (PPI) network was constructed using the STRING database, and the results were visualized in Cytoscape software. Two machine learning algorithms, least absolute shrinkage and selection operator (LASSO) logistic regression and random forest (RF) were utilized to identify hub genes and predict the diagnostic efficiency of hub genes as well as the correlation between immune infiltrating cells. Results: We obtained a total of 107 DEGs, a module (containing 250 genes) with the highest correlation with clinical characteristics, and 36 common genes after taking the intersection. Moreover, using two machine learning algorithms, we identified three hub genes (PLIN, PPAP2A, and TYROBP) between RA and PVNS and demonstrated good diagnostic performance using ROC curve and nomogram plots. Single sample Gene Set Enrichment Analysis (ssGSEA) was used to analyze the biological functions in which three genes were mostly engaged. Finally, three hub genes showed a substantial association with 28 immune infiltrating cells. Conclusion: PLIN, PPAP2A, and TYROBP may influence RA and PVNS by modulating immunity and contribute to the diagnosis and therapy of the two diseases.

Keywords: hub gene; immune cell infiltration; machine learning; pigmented villonodular synovitis; rheumatoid arthritis; weighted gene co-expression network analysis.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Research process flow diagram.
FIGURE 2
FIGURE 2
(A) The volcano map of GSE3698. (B) The heatmap of GSE3698.
FIGURE 3
FIGURE 3
(A) WGCNA provides the definition for soft threshold power. For different soft threshold powers (β), scale-free indices and mean connectedness are examined (B) The method of hierarchical clustering is used to find gene co-expression clusters. Each branch of the tree diagram represented a gene, and genes that belong to the same module have the same coloring. (C) Four modules with different colors are obtained by linking the clinical characteristics of PVNS and RA, combining modules with a feature factor greater than 0.45 and setting the minimum number of module genes to 40 for identification. (D) Venn diagram demonstrates the intersection of common genes obtained by WGCNA and DEGs.
FIGURE 4
FIGURE 4
(A) Results of GO analysis of the top 10 common genes, including BP, MF and CC (B) Analysis of KEEG enrichment revealed signaling pathways strongly related with PVNS and RA. (C) PPI network constructed using the STRING database and Cytoscape. The wider the circle, the greater its significance, and the redder the color, the greater its significance.
FIGURE 5
FIGURE 5
(A,B) LASSO logistic regression algorithm is used to retain the most predictive features and tuning parameter selection in the LASSO model (C,D) Identification of the relative importance via PVNS and RA by calculating RF. (E) Intersection of two machine learning genes to obtain three machine learning.
FIGURE 6
FIGURE 6
(A) A developed nomogram for the prognostic prediction of PVNS and RA hub genes. (B) This graph shows the predicted scores after aggregation of three hub genes’ proportions.
FIGURE 7
FIGURE 7
(A–D) ROC curve of PLIN, PPAP2A, TYROBP and nomoscore in PVNS and RA samples.
FIGURE 8
FIGURE 8
(A) UpGSEA results of PLIN (B) UpGSEA results of PPAP2A. (C) UpGSEA results of TYROBP.
FIGURE 9
FIGURE 9
(A) Expression differences of 28 immune infiltrating cells in samples of PVNS and RA (B) Correlation between hub genes and infiltrating immune cells. Low p-values are green, whereas high ones are red. (nsP < 1, #p < 0.2, *p < 0.05, **p < 0.01, ***p < 0.001).
FIGURE 10
FIGURE 10
(A) Correlation of the three hub genes (B) The expression levels of upgrade hub gene in PVNS. (C,D) The expression levels of upgrade hub genes in RA.
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