Shared Biomarkers and Potential Mechanisms Between Hashimoto’s Thyroiditis and Recurrent Miscarriage Revealed by Transcriptomics Analysis

Zengmei Cheng,1,* Shuyun Zhao,2,* Lu Yang,1 Yaqiong Xu,3 Peiyu Zhang,1 Sha Chen,1 Hua Zhou2 1Department of Obstetrics and Gynecology, Guizhou Medical University, Guiyang, Guizhou, People’s Republic of China; 2Reproductive Medicine Center, Department of Obstetrics and Gynecology of the Affiliated Hospital of Guizhou Medical University, Gulyang, Guizhou, People’s Republic of China; 3Department of Plastic and Burn Surgery, Xingyi People’s Hospital, Xingyi, Guizhou, People’s Republic of China*These authors contributed equally to this workCorrespondence: Hua Zhou, Reproductive Medicine Center, Department of Obstetrics and Gynecology of the Affiliated Hospital of Guizhou Medical University, Gulyang, Guizhou, People’s Republic of China, Email [email protected]: More and more Research has shown that Hashimoto’s thyroiditis (HT) is significantly associated with recurrent miscarriage (RM), but the specific mechanism is not yet clear. This study aimed to identify the key shared biomarkers between HT and RM using bioinformatics methods, reveal the potential molecular mechanisms they were involved in and the characteristics of the immune microenvironment, and provide new theoretical basis and potential diagnostic and therapeutic targets for the association between these two diseases.Methods: This study adopted an integrated transcriptomic analysis strategy. First, the HT thyroid tissue dataset (GSE138198) and RM endometrial tissue datasets (GSE165004 and GSE26787) were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were screened, and the intersection was taken to identify key genes. Further verification was conducted through the protein-protein interaction (PPI) network to screen candidate biomarkers. Subsequently, the final biomarkers were determined through consistency verification of expression levels. Gene Set Enrichment Analysis (GSEA) was used to reveal biomarker-related pathways, and the ssGSEA algorithm was applied to quantify immune cell infiltration for analyzing its association with the immune microenvironment. Finally, targeted drugs were predicted via molecular docking, and experimental verification was performed using an HT animal model.Results: CFL1 and TRAPPC1 were identified as biomarkers, and their expression levels were up-regulated in disease groups. A nomogram with superior diagnostic performance was constructed to predict the occurrence of RM. In the GSE138198 dataset, biomarkers CFL1 and TRAPPC1 were found to be enriched in multiple pathways, like “graft-versus-host disease”, “autoimmune thyroid disease”, and “antigen processing and presentation”. In the GSE165004 dataset, biomarkers were enriched in multiple pathways, like “ribosome”, “Huntington’s disease”, and “cell adhesion molecules (CAMs)”. Additionally, the abundance of infiltration of monocytes and eosinophils infiltration showed significant differences between HT and RM patients (p < 0.05). Biomarkers showed significant positive correlations with monocytes and eosinophils in HT and RM, respectively. Moreover, artenimol and S-palmitoyl-L-cysteine might be potential therapeutic drugs for HT and RM.Conclusion: CFL1 and TRAPPC1 were found to be common biomarkers for HT and RM in this study. These genes were thoroughly investigated and analyzed, yielding novel insights for both fundamental experimental research and early clinical diagnosis and treatment of disease. Keywords: hashimoto’s thyroiditis, recurrent miscarriage, nomogram, CFL1, TRAPPC1