Purine metabolism-associated key genes depict the immune landscape in gout patients

Abstract

Gout is the most common form of inflammatory arthritis that affects approximately 1% to 6.8% of the global population. Less than half of gout patients received urate-lowering therapy and compliance to the therapy, along with the climbing prevalence, adds a tight burden to public health, not to mention the potential tumor risk incurred by persistent inflammation in the patients. Thus, the study aimed to explore the links between gout, immune responses, and tumor development in terms of genetic alterations. Using RNA-seq data of peripheral blood mononuclear cells (PBMCs) from gout patients, we screened the differentially expressed genes (DEGs) of gout patients and found that they were closely involved in purine metabolism. We then focused on purine metabolism-related DEGs, and machine learning algorithms validated that these genes can help to discriminate gout from healthy conditions. ssGSEA revealed that these DEGs were significantly linked to immune reprogramming concerning fluctuation in the proportion and activity of various immunocytes. Most importantly, they were also partially dysregulated in a wide range of malignancies and exerted a notable influence on the survival of tumor patients, especially LIHC (Liver hepatocellular carcinoma). Therefore, our study made an urgent appeal to due attention to the underlying crosstalk among purine metabolism, immune responses, and tumor development in gout patients.

Keywords: Cellular immunities; Gout; Metabolic diseases; Tumors.

Fig. 1

DEGs between gout patients and health controls. A, B Sample distribution before (A) and after (B) homogenization dataset. The data per se were homogenous as the distribution was identical before and after homogenization. C Volcano plot of DEGs between gout patients and healthy controls. Blue bubbles represented down-regulated genes and red represented up-regulated ones. D A heat map of the DEGs between patients and healthy controls, with red indicating higher expression and blue indicating lower expression. DEGs were ranked by their fold change and only the top 20 upregulated and downregulated candidates were displayed

Fig. 2

Enrichment analyses of DEGs. A–C GO analyses of DEGs in aspects of (A) biological process (BP), (B) cellular components (CC), and (C) molecular function (MF). D Results of KEGG analyses concerning DEGs between gout patients and control cases. The color shades represented the P-value, and the size of the circle represented the number of enriched genes

Fig. 3

Screening of purine metabolism-related DEGs and pathway analyses. A Intersection of upregulated DEGs with purine metabolism genes list. B Intersection of downregulated DEGs with purine metabolism genes list. C–E GO annotation of genes from (A) and (B) in aspects of (C) biological process (BP), (D) cellular components (CC), and (E) molecular function (MF). F Results of KEGG analyses concerning intersected DEGs

Fig. 4

DEGs participating in purine metabolism in gout patients. A The differences in expression of the 33 DEGs obtained from the intersection were shown in a volcano plot. B The differences in expression of the 33 DEGs obtained from the intersection were shown in a heatmap. C The differences in expression of the 33 DEGs obtained from the intersection were shown in a box plot. Ns, p > 0.05; *p < 0.05, **p < 0.01, ***p < 0.001

Fig. 5

Selection of key DEGs using machine learning. A LASSO analysis of the 33 candidates. B Five key DGEs were selected by LASSO analysis. C The internal correlation of 5 key DEGs. Red bands represented a positive correlation and green bands represented a negative correlation. D ROC curve of 5 genes predicting disease occurrence. The X-axis depicted “1-specificity” and the Y-axis “sensitivity”

Fig. 6

Immune proportion fluctuation of gout patients and its correlation with core DEGs. The internal correlation of immunocytes. Red bubbles presented a positive correlation, blue bubbles presented a negative correlation, and asterisks indicated the significance of the correlation. B Integrated box plots demonstrating differences in immune cell landscape between gout patients and healthy controls. Ns, p > 0.05; *p < 0.05, **p < 0.01, ***p < 0.001. C The correlations between the five core genes and immunocyte signature were shown respectively (immune cells with p < 0.05 were shown). The size of the circle presented a correlation coefficient, and the color represented the P value

Fig. 7

Correlation analyses of core DEGs with genomic alteration. The association of the 5 core DEGs with all the other genes was calculated and the top 50 genes correlated to each core gene were depicted in the heatmap, with red presented gout group and blue presented control group

Fig. 8

Enrichment analyses concerning satellite genes. Based on the results of the correlation analysis in Fig. 7, GSEA results of the top 20 genes related to each core DEG were shown. The values at the bottom represent the enrichment score. A value larger than 0 indicates a positive correlation between the gene and the pathway, and a value smaller than 0 indicates a negative correlation

Fig. 9

Upstream regulatory network of the core DEGs. miRNAs and transcription factors of 4 core genes were predicted using the regnetwork database. NME5 was not recorded in the database. The regulatory network was built using Cytoscape software, and red bubbles presented the core genes

Fig. 10

Pan-cancer expression of the core genes. Pan-cancer expression of 5 core genes was analyzed using the TIMER2 database (http://timer.comp-genomics.org/timer/)

Fig. 11

Pan-cancer survival analyses concerning the core genes. Red indicates that patients with high expression of the indicated gene experienced a poor prognosis, blue indicates that patients with high expression enjoyed a favorable prognosis, and X indicates that the association of gene expression and tumor survival failed to reach statistical significance