Novel polymorphism of IFI44L associated with the susceptibility and clinical characteristics of systemic lupus erythematosus in a Chinese population

Identification of driver genes in lupus nephritis based on comprehensive bioinformatics and machine learning

Background

Lupus nephritis (LN) is a common and severe glomerulonephritis that often occurs as an organ manifestation of systemic lupus erythematosus (SLE). However, the complex pathological mechanisms associated with LN have hindered the progress of targeted therapies.

Methods

We analyzed glomerular tissues from 133 patients with LN and 51 normal controls using data obtained from the GEO database. Differentially expressed genes (DEGs) were identified and subjected to enrichment analysis. Weighted gene co-expression network analysis (WGCNA) was utilized to identify key gene modules. The least absolute shrinkage and selection operator (LASSO) and random forest were used to identify hub genes. We also analyzed immune cell infiltration using CIBERSORT. Additionally, we investigated the relationships between hub genes and clinicopathological features, as well as examined the distribution and expression of hub genes in the kidney.

Results

A total of 270 DEGs were identified in LN. Using weighted gene co-expression network analysis (WGCNA), we clustered these DEGs into 14 modules. Among them, the turquoise module displayed a significant correlation with LN (cor=0.88, p<0.0001). Machine learning techniques identified four hub genes, namely CD53 (AUC=0.995), TGFBI (AUC=0.997), MS4A6A (AUC=0.994), and HERC6 (AUC=0.999), which are involved in inflammation response and immune activation. CIBERSORT analysis suggested that these hub genes may contribute to immune cell infiltration. Furthermore, these hub genes exhibited strong correlations with the classification, renal function, and proteinuria of LN. Interestingly, the highest hub gene expression score was observed in macrophages.

Conclusion

CD53, TGFBI, MS4A6A, and HERC6 have emerged as promising candidate driver genes for LN. These hub genes hold the potential to offer valuable insights into the molecular diagnosis and treatment of LN.

Genetic Correlation of miRNA Polymorphisms and STAT3 Signaling Pathway with Recurrent Implantation Failure in the Korean Population

The growing prevalence of in vitro fertilization-embryo transfer procedures has resulted in an increased incidence of recurrent implantation failure (RIF), necessitating focused research in this area. STAT3, a key factor in maternal endometrial remodeling and stromal proliferation, is crucial for successful embryo implantation. While the relationship between STAT3 and RIF has been studied, the impact of single nucleotide polymorphisms (SNPs) in miRNAs, well-characterized gene expression modulators, on STAT3 in RIF cases remains uncharacterized. Here, we investigated 161 RIF patients and 268 healthy control subjects in the Korean population, analyzing the statistical association between miRNA genetic variants and RIF risk. We aimed to determine whether SNPs in specific miRNAs, namely miR-218-2 rs11134527 G>A, miR-34a rs2666433 G>A, miR-34a rs6577555 C>A, and miR-130a rs731384 G>A, were significantly associated with RIF risk. We identified a significant association between miR-34a rs6577555 C>A and RIF prevalence (implantation failure [IF] ≥ 2: adjusted odds ratio [AOR] = 2.264, 95% CI = 1.007-5.092, p = 0.048). These findings suggest that miR-34a rs6577555 C>A may contribute to an increased susceptibility to RIF. However, further investigations are necessary to elucidate the precise mechanisms underlying the role of miR-34a rs6577555 C>A in RIF. This study sheds light on the genetic and molecular factors underlying RIF, offering new avenues for research and potential advancements in the diagnosis and treatment of this complex condition.

Identification and Clinical Correlation Analysis of IFI44 in Systemic Lupus Erythematosus Combined with Bioinformatics and Immune Infiltration Analysis

Purpose

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease that can cause systemic damage to multiple organs. This study aims to analyze the value and function of IFI44 in the diagnosis and pathology of SLE by bioinformatics and immune infiltration analysis.

Patients and Methods

GSE49454 and GSE65391 of SLE were obtained from the GEO dataset, and R software was employed to identify DEGs and investigate their functions. The PPI network was utilized to identify hub genes associated with SLE. CIBERSORT was used to assess differences in immune cell infiltration in SLE patients and controls. ROC curve analysis was performed to evaluate the diagnostic value of IFI44 in SLE. The expression of IFI44 in PBMCs was detected by RT-qPCR, and the correlation between IFI44 expression and SLE-related clinical indicators was analyzed.

Results

A total of 65 DEGs were identified from the GSE49454 and GSE65391 databases. Through PPI analysis, IFI44 and RSAD2 were identified as significantly aberrantly expressed in SLE patients. SLE patients and controls showed a significant difference in the proportion of immune cell infiltration. IFI44 expression was positively correlated with activated DCs, monocytes, PCs, neutrophils, and activated memory CD4+T cells, while negatively correlated with M0 and CD8+T cells. The expression of IFI44 was significantly higher in SLE patients (P<0.01), especially in male patients (P=0.0376). ROC curve analysis demonstrated that IFI44 had a high diagnostic value for SLE. Correlation analysis indicated that IFI44 expression was correlated with levels of RBC, HGB, HCT, IgA, ESR, UPRO, C3, C4, and ENA in SLE patients.

Conclusion

IFI44 may play a role in the pathogenesis of SLE by influencing the immune microenvironment of SLE patients, and thus has the potential to serve as a diagnostic marker and therapeutic target for SLE.