Comprehensive bioinformatics analysis reveals the crosstalk genes and immune relationship between the systemic lupus erythematosus and venous thromboembolism

Machine learning and weighted gene co-expression network analysis identify a three-gene signature to diagnose rheumatoid arthritis

Background

Rheumatoid arthritis (RA) is a systemic immune-related disease characterized by synovial inflammation and destruction of joint cartilage. The pathogenesis of RA remains unclear, and diagnostic markers with high sensitivity and specificity are needed urgently. This study aims to identify potential biomarkers in the synovium for diagnosing RA and to investigate their association with immune infiltration.

Methods

We downloaded four datasets containing 51 RA and 36 healthy synovium samples from the Gene Expression Omnibus database. Differentially expressed genes were identified using R. Then, various enrichment analyses were conducted. Subsequently, weighted gene co-expression network analysis (WGCNA), random forest (RF), support vector machine-recursive feature elimination (SVM-RFE), and least absolute shrinkage and selection operator (LASSO) were used to identify the hub genes for RA diagnosis. Receiver operating characteristic curves and nomogram models were used to validate the specificity and sensitivity of hub genes. Additionally, we analyzed the infiltration levels of 28 immune cells in the expression profile and their relationship with the hub genes using single-sample gene set enrichment analysis.

Results

Three hub genes, namely, ribonucleotide reductase regulatory subunit M2 (RRM2), DLG-associated protein 5 (DLGAP5), and kinesin family member 11 (KIF11), were identified through WGCNA, LASSO, SVM-RFE, and RF algorithms. These hub genes correlated strongly with T cells, natural killer cells, and macrophage cells as indicated by immune cell infiltration analysis.

Conclusion

RRM2, DLGAP5, and KIF11 could serve as potential diagnostic indicators and treatment targets for RA. The infiltration of immune cells offers additional insights into the underlying mechanisms involved in the progression of RA.

Causal relationship between systemic lupus erythematosus and primary liver cirrhosis based on two-sample bidirectional Mendelian randomization and transcriptome overlap analysis

BACKGROUND

Overlapping cases of systemic lupus erythematosus (SLE) and primary biliary cirrhosis (PBC) are rare and have not yet been fully proven to be accidental or have a common genetic basis.

METHODS

Two-sample bidirectional Mendelian randomization (MR) analysis was applied to explore the potential causal relationship between SLE and PBC. The heterogeneity and reliability of MR analysis were evaluated through Cochran's Q-test and sensitivity test, respectively. Next, transcriptome overlap analysis of SLE and PBC was performed using the Gene Expression Omnibus database to identify the potential mechanism of hub genes. Finally, based on MR analysis, the potential causal relationship between hub genes and SLE or PBC was validated again.

RESULTS

The MR analysis results indicated that SLE and PBC were both high-risk factors for the occurrence and development of the other party. On the one hand, MR analysis had heterogeneity, and on the other hand, it also had robustness. Nine hub genes were identified through transcriptome overlap analysis, and machine learning algorithms were used to verify their high recognition efficiency for SLE patients. Finally, based on MR analysis, it was verified that there was no potential causal relationship between the central gene SOCS3 and SLE, but it was a high-risk factor for the potential risk of PBC.

CONCLUSION

The two-sample bidirectional MR analysis revealed that SLE and PBC were high-risk factors for each other, indicating that they had similar genetic bases, which could to some extent overcome the limitation of insufficient overlap in case samples of SLE and PBC. The analysis of transcriptome overlapping hub genes provided a theoretical basis for the potential mechanisms and therapeutic targets of SLE with PBC overlapping cases.