Blood leukocytes as a non-invasive diagnostic tool for thyroid nodules: a prospective cohort study.

BACKGROUND: Thyroid nodule (TN) patients in China are subject to overdiagnosis and overtreatment. The implementation of existing technologies such as thyroid ultrasonography has indeed contributed to the improved diagnostic accuracy of TNs. However, a significant issue persists, where many patients undergo unnecessary biopsies, and patients with malignant thyroid nodules (MTNs) are advised to undergo surgery therapy. METHODS: This study included a total of 293 patients diagnosed with TNs. Differential methylation haplotype blocks (MHBs) in blood leukocytes between MTNs and benign thyroid nodules (BTNs) were detected using reduced representation bisulfite sequencing (RRBS). Subsequently, an artificial intelligence blood leukocyte DNA methylation (BLDM) model was designed to optimize the management and treatment of patients with TNs for more effective outcomes. RESULTS: The DNA methylation profiles of peripheral blood leukocytes exhibited distinctions between MTNs and BTNs. The BLDM model we developed for diagnosing TNs achieved an area under the curve (AUC) of 0.858 in the validation cohort and 0.863 in the independent test cohort. Its specificity reached 90.91% and 88.68% in the validation and independent test cohorts, respectively, outperforming the specificity of ultrasonography (43.64% in the validation cohort and 47.17% in the independent test cohort), albeit with a slightly lower sensitivity (83.33% in the validation cohort and 82.86% in the independent test cohort) compared to ultrasonography (97.62% in the validation cohort and 100.00% in the independent test cohort). The BLDM model could correctly identify 89.83% patients whose nodules were suspected malignant by ultrasonography but finally histological benign. In micronodules, the model displayed higher specificity (93.33% in the validation cohort and 92.00% in the independent test cohort) and accuracy (88.24% in the validation cohort and 87.50% in the independent test cohort) for diagnosing TNs. This performance surpassed the specificity and accuracy observed with ultrasonography. A TN diagnostic and treatment framework that prioritizes patients is provided, with fine-needle aspiration (FNA) biopsy performed only on patients with indications of MTNs in both BLDM and ultrasonography results, thus avoiding unnecessary biopsies. CONCLUSIONS: This is the first study to demonstrate the potential of non-invasive blood leukocytes in diagnosing TNs, thereby making TN diagnosis and treatment more efficient in China.

MeIS: DNA methylation-based immune response signatures for thyroid nodule diagnostics.

CONTEXT: Accurately distinguishing between benign thyroid nodules (BTNs) and papillary thyroid cancers (PTCs) with current conventional methods poses a significant challenge. OBJECTIVE: We identify DNA methylation markers of immune response-related genes for distinguishing BTNs and PTCs. METHODS: In this study, we analyzed a public reduced representative bisulfite sequencing (RRBS) dataset and revealed distinct methylation patterns associated with immune signals in PTCs and BTNs. Based on these findings, we developed a diagnostic classifier named as the Methylation-based Immune Response Signature (MeIS), which was composed of fifteen DNA methylation markers associated with immune response-related genes. We validated the MeIS's performance in two independent cohorts: ZS's retrospective cohort (50 PTC and 18 BTN surgery-leftover samples) and ZS's preoperative cohort (31 PTC and 30 BTN fine-needle aspiration (FNA) samples). RESULTS: The MeIS classifier demonstrated significant clinical promise, achieving AUCs of 0.96, 0.98, 0.89 and 0.90 in the training set, validation set, ZS's retrospective cohort, and ZS's preoperative cohort, respectively. For the cytologically indeterminate thyroid nodules, in the ZS's retrospective cohort, MeIS exhibited a sensitivity of 91% and a specificity of 82%; in the ZS's preoperative cohort, MeIS achieved a sensitivity of 84% and a specificity of 74%. Additionally, combining MeIS and BRAFV600E detection improved the detecting performance of cytologically indeterminate thyroid nodules, yielding sensitivities of 98% and 87%, and specificities of 82% and 74% in the ZS's retrospective cohort and ZS's preoperative cohort, respectively. CONCLUSIONS: The fifteen markers we identified can be employed to improve the diagnostic of cytologically indeterminate thyroid nodules.

A diagnostic model based on DNA methylation haplotype block characteristics for identifying papillary thyroid carcinoma from thyroid adenoma.

Papillary thyroid carcinoma (PTC) is the most prevalent form of thyroid cancer. Methylation of some genes plays a crucial role in the tendency to malignancy as well as poor prognosis of thyroid cancer, suggesting that methylation features can serve as complementary markers for molecular diagnosis. In this study, we aimed to develop and validate a diagnostic model for PTC based on DNA methylation markers. A total of 142 thyroid nodule tissue samples containing 84 cases of PTC and 58 cases of thyroid adenoma (TA) were collected for reduced representation bisulfite sequencing (RRBS) and subsequent analysis. The diagnostic model was constructed by the logistic regression (LR) method followed by 5-cross validation and based on 94 tissue methylation haplotype block (MHB) markers. The model achieved an area under the receiver operating characteristic curve (AUROC) of 0.974 (95% CI, 0.964-0.981) on 108 training samples and 0.917 (95% CI, 0.864-0.973) on 27 independent testing samples. The diagnostic model scores showed significantly high in males (P = 0.0016), age ≤ 45 years (P = 0.026), high body mass index (BMI) (P = 0.040), lymph node metastasis (P = 0.00052) and larger nodules (P = 0.0017) in the PTC group, and the risk score of this diagnostic model showed significantly high in recurrent PTC group (P = 0.0005). These results suggest that the diagnostic model can be expected to be a powerful tool for PTC diagnosis and there are more potential clinical applications of methylation markers to be excavated.

OsSPL13 controls grain size in cultivated rice.

Although genetic diversity has a cardinal role in domestication, abundant natural allelic variations across the rice genome that cause agronomically important differences between diverse varieties have not been fully explored. Here we implement an approach integrating genome-wide association testing with functional analysis on grain size in a diverse rice population. We report that a major quantitative trait locus, GLW7, encoding the plant-specific transcription factor OsSPL13, positively regulates cell size in the grain hull, resulting in enhanced rice grain length and yield. We determine that a tandem-repeat sequence in the 5' UTR of OsSPL13 alters its expression by affecting transcription and translation and that high expression of OsSPL13 is associated with large grains in tropical japonica rice. Further analysis indicates that the large-grain allele of GLW7 in tropical japonica rice was introgressed from indica varieties under artificial selection. Our study demonstrates that new genes can be effectively identified on the basis of genome-wide association data.