Implication of VDR Rs7975232 and FCGR2A Rs1801274 Gene Polymorphisms in the Risk and the Prognosis of autoimmune Thyroid Diseases in the Tunisian Population

Unraveling susceptibility genes: A contemporary overview of autoimmune thyroid diseases

Autoimmune thyroid diseases (AITDs), including Graves' disease and Hashimoto's thyroiditis, are organ-specific autoimmune disorders characterized by conditions including goiter, autoimmune thyroiditis, hyperthyroidism, and hypothyroidism, which represent the most severe clinical manifestations of AITDs. The prevalence of autoimmune thyroid disorders is on the rise, influenced by increased environmental factors and changes in modern lifestyles. Understanding the pathophysiology of AITDs is crucial for identifying key factors that affect the disease's onset, progression, and recurrence, thereby laying a solid foundation for precise diagnosis and treatment. The development of AITDs involves a complex interplay of environmental influences, immune dysfunctions, and genetic predispositions. Genetic predispositions, in particular, are significant, with numerous genes identified as being linked to AITDs. This article focuses on examining the genes vulnerable to AITDs to deepen our understanding of the relevant genetic contributors, ultimately facilitating the development of effective prevention and treatment methods.

5Apal, Taql, Fokl, and Bsml polymorphisms and the susceptibility of Behcet's disease: an updated meta-analysis

OBJECTIVE

The aim of this study was to investigate whether the gene polymorphisms of vitamin D receptor (VDR) had a genetic effect on the susceptibility of Behcet's disease (BD).

MATERIAL AND METHODS

We conducted a meta-analysis emphasizing the association between the VDR gene polymorphisms and the risk of BD. The strength of the association in five genetic models was assessed by pooled odds ratios (OR) with a corresponding 95%confidence interval (CI).

RESULTS

A total of seven independent comparisons with 478 cases and 666 healthy controls were included in this meta-analysis. The overall results suggested that a significant association between ApaI polymorphism and BD risk was found in allele comparison, recessive model, and homozygote model among total populations. Subgroup analysis indicated that a significant association of ApaI polymorphism in the development of BD existed under the allelic model among Africans, while for Caucasians, a similar link was identified in the recessive model and homozygote model. Regarding Bsml polymorphism, an obvious relationship was detected to be significant in allele comparison and recessive model in the Caucasian population. Interestingly, the Fokl variant decreased the risk of BD in Africans under five genetic models, while it increased the risk in Caucasians across the recessive model and homozygote model.

CONCLUSION

The results of this meta-analysis provide evidence of the link between the four widely studied polymorphisms in the VDR gene and BD, indicating a robust estimate of the genetic risk.

Bioinformatics Analysis Identifies Molecular Markers Regulating Development and Progression of Endometriosis and Potential Therapeutic Drugs

Endometriosis, a common disease that presents as polymorphism, invasiveness, and extensiveness, with clinical manifestations including dysmenorrhea, infertility, and menstrual abnormalities, seriously affects quality of life in women. To date, its underlying etiological mechanism of action and the associated regulatory genes remain unclear. This study aimed to identify molecular markers and elucidate mechanisms underlying the development and progression of endometriosis. Specifically, we downloaded five microarray expression datasets, namely, GSE11691, GSE23339, GSE25628, GSE7305, and GSE105764, from the Gene Expression Omnibus (GEO) database. These datasets, obtained from endometriosis tissues, alongside normal controls, were subjected to in-depth bioinformatics analysis for identification of differentially expressed genes (DEGs), followed by analysis of their function and pathways via gene ontology (GO) and KEGG pathway enrichment analyses. Moreover, we constructed a protein–protein interaction (PPI) network to explore the hub genes and modules, and then applied machine learning algorithms support vector machine-recursive feature elimination and least absolute shrinkage and selection operator (LASSO) analysis to identify key genes. Furthermore, we adopted the CIBERSORTx algorithm to estimate levels of immune cell infiltration while the connective map (CMAP) database was used to identify potential therapeutic drugs in endometriosis. As a result, a total of 423 DEGs, namely, 233 and 190 upregulated and downregulated, were identified. On the other hand, a total of 1,733 PPIs were obtained from the PPI network. The DEGs were mainly enriched in immune-related mechanisms. Furthermore, machine learning and LASSO algorithms identified three key genes, namely, apelin receptor (APLNR), C–C motif chemokine ligand 21 (CCL21), and Fc fragment of IgG receptor IIa (FCGR2A). Furthermore, 16 small molecular compounds associated with endometriosis treatment were identified, and their mechanism of action was also revealed. Taken together, the findings of this study provide new insights into the molecular factors regulating occurrence and progression of endometriosis and its underlying mechanism of action. The identified therapeutic drugs and molecular markers may have clinical significance in early diagnosis of endometriosis.