NR3C1 rs6198 Variant May Be Involved in the Relationship of Graves' Disease with Stressful Events

Causal effects of post-traumatic stress disorder on autoimmune thyroid disease: insights from mendelian randomization

Objective

The relationship between post-traumatic stress disorder (PTSD) and autoimmune thyroid disease (AITD) needs further evaluation. This study employs Mendelian randomization (MR) to investigate the causal correlations of PTSD with autoimmune thyroiditis (AIT) and Graves' disease (GD).

Methods

Datasets for PTSD, AIT, and GD were obtained from FinnGen. The exposure-outcome causal relationship was assessed using inverse variance weighted, MR-Egger, and weighted median. Horizontal pleiotropy was evaluated through the MR-Egger intercept, heterogeneity was examined using Cochran's Q test, and robustness was assessed via leave-one-out sensitivity analysis.

Results

MR analysis indicated no significant causal relationship between PTSD and AIT (OR 0.920, 95% CI 0.832 to 1.017, p = 0.103), but a potential increase in the risk of GD associated with PTSD (OR 1.056, 95% CI 1.008 to 1.105, p = 0.021). MR-Egger intercept showed no horizontal pleiotropy (p > 0.05), and Cochran's Q showed no heterogeneity (p > 0.05). Sensitivity analysis suggested the MR results were robust.

Conclusions

Evidence of an MR association between genetic liability to PTSD and an increased risk of GD were provided, but no evidence of association between PTSD and AIT. The findings indicate that individuals with PTSD may have an increased likelihood of developing GD, underscoring the importance of further research to comprehend the intricate interplay between PTSD and thyroid disorders.

Potential new cancer biomarkers revealed by quantum chemistry associated with bioinformatics in the study of selectin polymorphisms

Understanding the complex mechanisms involved in diseases caused by or related to important genetic variants has led to the development of clinically useful biomarkers. However, the increasing number of described variants makes it difficult to identify variants worthy of investigation, and poses challenges to their validation. We combined publicly available datasets and open source robust bioinformatics tools with molecular quantum chemistry methods to investigate the involvement of selectins, important molecules in the cell adhesion process that play a fundamental role in the cancer metastasis process. We applied this strategy to investigate single nucleotide variants (SNPs) in the intronic and UTR regions and missense SNPs with amino acid changes in the SELL, SELP, SELE, and SELPLG genes. We then focused on thyroid cancer, seeking these SNPs potential to identify biomarkers for susceptibility, diagnosis, prognosis, and therapeutic targets. We demonstrated that SELL gene polymorphisms rs2229569, rs1131498, rs4987360, rs4987301 and rs2205849; SELE gene polymorphisms rs1534904 and rs5368; rs3917777, rs2205894 and rs2205893 of SELP gene; and rs7138370, rs7300972 and rs2228315 variants of SELPLG gene may produce important alterations in the DNA structure and consequent changes in the morphology and function of the corresponding proteins. In conclusion, we developed a strategy that may save valuable time and resources in future investigations, as we were able to provide a solid foundation for the selection of selectin gene variants that may become important biomarkers and deserve further investigation in cancer patients. Large-scale clinical studies in different ethnic populations and laboratory experiments are needed to validate our results.