Identification of hub genes, pathways, and related transcription factors in systemic lupus erythematosus: A preliminary bioinformatics analysis

TET protein inhibitors: Potential and limitations

TET proteins (methylcytosine dioxygenases) play an important role in the regulation of gene expression. Dysregulation of their activity is associated with many serious pathogenic states such as oncological diseases. Regulation of their activity by specific inhibitors could represent a promising therapeutic strategy. Therefore, this review describes various types of TET protein inhibitors in terms of their inhibitory mechanism and possible applicability. The potential and possible limitations of this approach are thoroughly discussed in the context of TET protein functionality in living systems. Furthermore, possible therapeutic strategies based on the inhibition of TET proteins are presented and evaluated, especially in the field of oncological diseases.

Identification of CKS2 and RRM2 as potential markers of vitiligo using bioinformatics analysis

Previous studies have attempted to elucidate the molecular mechanism of vitiligo; however, its pathogenesis remains unclear. This study aimed to explore biomarkers related to vitiligo through bioinformatic analysis. The microarray datasets GSE53146 and GSE65127 were downloaded from the Gene Expression Omnibus database. Firstly, differentially expressed genes (DEGs) in GSE53146 were screened, and then an enrichment analysis was performed. Secondly, the protein-protein interaction (PPI) network of DEGs was constructed using the STRING database, and the key genes were screened using the MCODE plugin in Cytoscape and verified using GSE65127. Finally, quantiseq was used to evaluate immune cell infiltration in vitiligo, then to observe the correlation between biomarkers and immune cells. In total, 544 DEGs were identified, including 342 upregulated and 202 downregulated genes. Gene Ontology (GO) enrichment showed that DEGs were related to inflammatory and immune responses, and Kyoto Encyclopedia of Genes and Genomes enrichment showed that DEGs were involved in many autoimmune diseases. In the PPI network, 7 key genes, CENPN, CKS2, PLK4, RRM2, TPX2, CCNA2, and CDC45 were identified by MCODE cluster and verified using the GSE65127 dataset. With an area under the curve (AUC) > 0.8 as the standard, 2 genes were screened, namely CKS2 and RRM2. Further immune infiltration analysis showed that M2 macrophages were involved in the pathogenesis of vitiligo, whereas CKS2 and RRM2 were both related to M2 macrophages. This study shows that CKS2 and RRM2 have potential as biomarkers of vitiligo and provides a theoretical basis for a better understanding of the pathogenesis of vitiligo.

Predicted Immune-Related Genes and Subtypes in Systemic Lupus Erythematosus Based on Immune Infiltration Analysis

Objective

The present investigation is aimed at identifying key immune-related genes linked with SLE and their roles using integrative analysis of publically available gene expression datasets.

Methods

Four gene expression datasets pertaining to SLE, 2 from whole blood and 2 experimental PMBC, were sourced from GEO. Shared differentially expressed genes (DEG) were determined as SLE-related genes. Immune cell infiltration analysis was performed using CIBERSORT, and case samples were subjected to k-means cluster analysis using high-abundance immune cells. Key immune-related SLE genes were identified using correlation analysis with high-abundance immune cells and subjected to functional enrichment analysis for enriched Gene Ontology Biological Processes and KEGG pathways. A PPI network of genes interacting with the key immune-related SLE genes was constructed. LASSO regression analysis was performed to identify the most significant key immune-related SLE genes, and correlation with clinicopathological features was examined.

Results

309 SLE-related genes were identified and found functionally enriched in several pathways related to regulation of viral defenses and T cell functions. k-means cluster analysis identified 2 sample clusters which significantly differed in monocytes, dendritic cell resting, and neutrophil abundance. 65 immune-related SLE genes were identified, functionally enriched in immune response-related signaling, antigen receptor-mediated signaling, and T cell receptor signaling, along with Th17, Th1, and Th2 cell differentiation, IL-17, NF-kappa B, and VEGF signaling pathways. LASSO regression identified 9 key immune-related genes: DUSP7, DYSF, KCNA3, P2RY10, S100A12, SLC38A1, TLR2, TSR2, and TXN. Imputed neutrophil percentage was consistent with their expression pattern, whereas anti-Ro showed the inverse pattern as gene expression.

Conclusions

Comprehensive bioinformatics analyses revealed 9 key immune-related genes and their associated functions highly pertinent to SLE pathogenesis, subtypes, and identified valuable candidates for experimental research.

Clinical Value of Serum Neuron-Specific Enolase Combined with Serum S100B Protein in the Diagnosis of Systemic Lupus Erythematosus

Objective

To investigate the clinical value of serum neuron-specific enolase (NSE) combined with serum S100B protein in the diagnosis of systemic lupus erythematosus (SLE).

Methods

Sixty patients with SLE treated in our hospital from January 2019 to April 2021 were enrolled as the study group. According to the degree of activity, the study group was assigned into three groups: mild activity group (n = 20), moderate activity group (n = 20), and severe activity group (n = 20). A total of 60 healthy people who underwent physical examination in our hospital in the same period were enrolled as the control group. The NSE and serum S100B protein were detected in the two groups, and the correlation between serum nerve-specific enolase and serum S100B protein and the clinical value in the diagnosis of SLE were analyzed.

Results

First of all, we compared the general data of the two groups. There was no significant difference in sex, age, marital status, and education level, and no significant difference was exhibited (p > 0.05). There was no significant difference in sex, age, marital status, and education level among mild activity group, moderate activity group, and severe activity group, and no significant difference in data was exhibited (p > 0.05). Secondly, we compared the levels of serum S100B protein and NSE. The levels of serum S100B protein and NSE in the study group were higher compared to the control group (p < 0.05). The levels of serum S100B protein and NSE in patients with different activity levels of SLE were compared. The levels of serum S100B protein and NSE in mild activity group < moderate activity group < severe activity group were significantly different (p < 0.05). Correlation analysis between serum S100B, NSE levels, and SLE activity indicated that serum S100B and NSE levels were positively correlated with SLE activity. With the increase of SLE activity, serum S100B and NSE levels gradually increased, and the data difference was statistically significant (r = 0.855, 0.844, p < 0.05). Finally, we established the logistic prediction model, take the probability of generating prediction as the analysis index, and draw the ROC curve to evaluate the diagnostic value of different combinations to SLE. The highest AUC and sensitivity of the two indexes in the diagnosis of SLE were 0.773 and 0.836, respectively. The levels of serum S100B protein and NSE have a certain value in the diagnosis of SLE, while the combined diagnosis is of higher value, sensitivity, and specificity in the diagnosis of SLE.

Conclusion

Serum S100B protein and NSE are very sensitive indexes to judge the damage of central nervous system. However, due to the small number of cases in this study, there were as many as 19 kinds of NPSLE classification, so the relationship between serum S100B protein, NSE levels, and various NPSLE and their exact application value in diagnosing the disease and judging the prognosis needs to be confirmed by expanding the number of cases.

Determination of estrogen receptor alpha gene (ESR1) polymorphism and its relation to systemic lupus erythematosus disease status

Background

Systemic lupus erythematosus (SLE) is a chronic inflammatory disease with variable clinical manifestations that can affect various organs and tissues. Estrogen is an important element that performs a vital role in the pathology of SLE. It acts on target cells through binding to estrogen receptors (ERs). This study aimed to assess the effect of ER alpha gene polymorphism on SLE disease activity and clinical manifestations. This study included 30 SLE female patients and 20 healthy subjects as controls. ERα gene (pvull and xbal) polymorphisms were genotyped using the real-time polymerase chain reaction (RT-PCR) and correlated with clinical and laboratory manifestations of SLE as well as the activity and severity scores.

Results

Regarding ERα (rs1 2234693 Pvull) polymorphism, the TC and CC genotypes were mainly associated with SLE patients, with a high frequency of the mutant C allele. The TT genotype was found mainly in the control group. Concerning rs2 9340799 Xbal polymorphisms, the AG, AA, and GG genotypes frequencies were not significantly different between patient and controls. The TC/AA, CC/GG, and CC/GG genotypes were the most prevalent combinations among SLE patients, while the later combination is completely absent from the control group. There was a significant statistical association with the AA genotype with the neurological disorders and/or hematological affection in SLE patients. The TC genotype was more related to serositis, leucopenia and pyuria, while the AA polymorphism was associated only with leucopenia.

Conclusions

We conclude that the study offers a clue to the associations of ERα gene polymorphisms in SLE disease, and the combinations relevant to certain clinical manifestations. Estrogen level itself does not affect SLE susceptibility or activity but the mutations in its receptors are the main pathogenic factor.