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. 2022 Jun 29:13:925695.
doi: 10.3389/fimmu.2022.925695. eCollection 2022.

Identification of Critical Biomarkers and Immune Infiltration in Rheumatoid Arthritis Based on WGCNA and LASSO Algorithm

Fan Jiang  1   2 , Hongyi Zhou  3 , Haili Shen  4
Affiliations

Identification of Critical Biomarkers and Immune Infiltration in Rheumatoid Arthritis Based on WGCNA and LASSO Algorithm

Fan Jiang et al. Front Immunol. .

Abstract

Rheumatoid arthritis(RA) is the most common inflammatory arthritis, and a significant cause of morbidity and mortality. RA patients' synovial inflammation contains a variety of genes and signalling pathways that are poorly understood. It was the goal of this research to discover the major biomarkers related to the course of RA and how they connect to immune cell infiltration. The Gene Expression Omnibus was used to download gene microarray data. Differential expression analysis, weighted gene co-expression network analysis (WGCNA), and least absolute shrinkage and selection operator (LASSO) regression were used to identify hub markers for RA. Single-sample GSEA was used to examine the infiltration levels of 28 immune cells and their connection to hub gene markers. The hub genes' expression in RA-HFLS and HFLS cells was verified by RT-PCR. The CCK-8 assay was applied to determine the roles of hub genes in RA. In this study, we identified 21 differentially expressed genes (DEGs) in RA. WGCNA yielded two co-expression modules, one of which exhibited the strongest connection with RA. Using a combination of differential genes, a total of 6 intersecting genes was discovered. Six hub genes were identified as possible biomarkers for RA after a lasso analysis was performed on the data. Three hub genes, CKS2, CSTA, and LY96, were found to have high diagnostic value using ROC curve analysis. They were shown to be closely related to the concentrations of several immune cells. RT-PCR confirmed that the expressions of CKS2, CSTA and LY96 were distinctly upregulated in RA-HFLS cells compared with HFLS cells. More importantly, knockdown of CKS2 suppressed the proliferation of RA-HFLS cells. Overall, to help diagnose and treat RA, it's expected that CKS2, CSTA, and LY96 will be available, and the aforementioned infiltration of immune cells may have a significant impact on the onset and progression of the disease.

Keywords: GEO datasets; diagnostic marker; immune cells infiltration; machinelearning; rheumatoid arthritis.

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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
The dysregulated genes in RA from GSE17755 was shown in (A) Volcanic map and (B) Heat map.
Figure 2
Figure 2
(A, B) GO term analysis of DEGs.
Figure 3
Figure 3
(A, B) KEGG term analysis of DEGs.
Figure 4
Figure 4
Enrichment analyses via gene set enrichment analysis. (A) Enriched in control group. (B) Enriched in treat group.
Figure 5
Figure 5
Construction of WGCNA modules. (A) He module-trait relationship heat map. RA was strongly linked to the turquoise module. (B) Distribution of average gene significance in the modules related to RA. (C, D) Associations between module membership and gene importance is depicted in a scatter plot. (E) The Overlapping genes between DEGs and the MEturquoise module.
Figure 6
Figure 6
Establishment of diagnostic biomarkers by LASSO regression analysis. (A) LASSO coefficient profiles of the six genes in RA. (B) The log (lambda) sequence was used to construct a coefficient profile diagram. The LASSO model’s optimal parameter (lambda) was chosen.
Figure 7
Figure 7
The expressing pattern of six genes in RA samples and normal samples from GSE17755. ***p < 0.001.
Figure 8
Figure 8
(A–C) The expressing pattern of six genes in RA samples and normal samples from GSE93272. *p<0.05, ***p<0.001. ns represents no significance.
Figure 9
Figure 9
ROC assays for six genes based on GSE17755.
Figure 10
Figure 10
(A, B) ROC assays for six genes based on GSE93272.
Figure 11
Figure 11
Assays of immune landscape related to RA. Heatmap (A) and violin plot (B) exhibiting the distribution of 28 immune cells in normal samples and RA samples. (C) The associations between immune cell infiltration and six hub genes. *p < 0.05, **p < 0.01, ***p < 0.001.
Figure 12
Figure 12
The expression of CKS2, CSTA and LY96 in RA cells and the potential functions. (A) CKS2, (B) STA, and (C) LY96 was highly expressed in RA-HFLS cells compared with normal HFLS cells. (D) RT-PCR confirmed the distinct down-regulation of CKS2 in RA-HFLS cells after the transfection of si-CKS2. (E) CCK-8 assays revealed that knockdown of CKS2 suppressed the proliferation of RA-HFLS cells.**p < 0.01.
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