Abnormal microRNAs (miRNAs) were reported to be involved in the mechanism of Graves’ disease (GD). Dysregulated miRNAs may be overlapping in different cells and can be secreted to circulation. We chose miRNAs which were previously reported to be differentially expressed in peripheral blood mononuclear cells (PBMCs) in patients with GD with different disease stage, detected the expression of those miRNAs in serum, corroborated the findings in thyroid tissue, and validated the target gene in vitro to investigate the possible role of circulating miRNAs in GD. A total of 54 individuals with untreated GD, 12 individuals with GD in remission and 14 disease-free controls were enrolled. The expression of miR-142-3p, miR-154-3p, miR-431-3p, miR-590-5p, and let-7b was detected in the serum. Ten thyroid tissue samples from patients with GD and six disease-free thyroid samples were used for further validation. The potential target genes were identified and validated in vitro. miR-142-3p, miR-154-3p, miR-431-3p, miR-590-5p, and let-7b were present in serum and two of them (miR-142-3p and let-7b) were significantly increased in serum of patients with untreated GD (for serum miR-142-3p, P = 0.033, for serum let-7b, P = 0.026) and gradually decreased to normal levels in patients with GD in remission. Correlation analysis showed that let-7b level was strongly correlated with TRAb level (r = 0.305, P = 0.001). let-7b directly inhibited promyelocytic leukemia zinc finger (PLZF) expression and increased the expression of TSHR in thyroid cells in vitro. Furthermore, let-7b levels in GD thyroid tissue were found to be inversely correlated with PLZF levels (r = − 0.849, P = 0.033). Decreased PLZF and increased TSHR was validated in thyroid tissue in patients with GD. The present study confirmed that a portion of miRNAs in PBMCs were also presented and differentially expressed in serum and thyroid tissue. Upregulated in all these three compartments, let-7b may be used as a disease biomarker and therapeutic targets in patients with GD. Circulating let-7b had a strong correlation with disease severity and let-7b may participate in the production of TRAb via targeting PLZF in patients with GD.

Graves’ disease (GD) is a common autoimmune disease that affects the thyroid gland. As a new class of modulators of gene expression, long non-coding RNAs (lncRNAs) have been reported to play a vital role in immune functions and in the development of autoimmunity and autoimmune disease. The aim of this study is to identify lncRNAs in CD4+ T cells as potential biomarkers of GD. lncRNA and mRNA microarrays were performed to identify differentially expressed lncRNAs and mRNAs in GD CD4+ T cells compared with healthy control CD4+ T cells. Quantitative polymerase chain reaction (qPCR) was used to validate the results, and correlation analysis was used to analyze the relationship between these aberrantly expressed lncRNAs and clinical parameters. The microarray identified 164 lncRNAs and 93 mRNAs in GD CD4+ T cells differentially expressed compared to healthy control CD4+ T cells (fold change >2.0 and a P<0.05). Further analysis consistently showed that the expression of HMlincRNA1474 (P<0.01) and TCONS 00012608 (P<0.01) was suppressed, while the expression of AK021954 (P<0.01) and AB075506 (P<0.01) was upregulated from initial GD patients. In addition, their expression levels were recovered in euthyroid GD patients and GD patients in remission. Moreover, these four aberrantly expressed lncRNAs were correlated with GD clinical parameters. Moreover, the areas under the ROC curve were 0.8046, 0.7579, 0.8115 for AK021954, AB075506, HMlincRNA1474, respectively. The present work revealed that differentially expressed lncRNAs were associated with GD, which might serve as novel biomarkers of GD and potential targets for GD treatment.

Thyroid carcinoma (THCA) ranks among the most prevalent cancers globally. Integrating advanced genomic and proteomic analyses to construct a protein-based prognostic model promises to identify effective biomarkers and explore new therapeutic avenues. In this study, proteomic data from The Cancer Proteomics Atlas (TCPA) and clinical data from The Cancer Genome Atlas (TCGA) were utilized. Using Kaplan–Meier, Cox regression, and LASSO penalized Cox analyses, we developed a prognostic risk model comprising 13 proteins (S100A4, PAI1, IGFBP2, RICTOR, B7-H3, COLLAGENVI, PAR, SNAIL, FAK, Connexin-43, Rheb, EVI1, and P90RSK_pT359S363). The protein prognostic model was validated as an independent predictor of survival time in THCA patients, based on risk curves, survival analysis, receiver operating characteristic curves and independent prognostic analysis. Additionally, we explored the immune cell infiltration and tumor mutational burden (TMB) related to these features. Notably, our study proved a novel approach for predicting treatment responses in THCA patients, including those undergoing chemotherapy and targeted therapy.