Serum biomarkers for thyroid cancer

Evaluation of the value of combined thyroid function-related indexes in the prognosis prediction of patients with differentiated thyroid cancer

This study aims to evaluate the prognostic significance of thyroid function-related indices in patients with differentiated thyroid cancer (DTC). This retrospective analysis included 90 patients diagnosed with DTC and treated at our hospital from January 2010 to January 2019. Patients were classified into 2 groups based on whole-body imaging results: 67 with a favorable prognosis and 23 with a poor prognosis. The study compared clinical data and thyroid function indices between these groups to assess their efficacy in prognostic prediction. Patients in the poor prognosis group had a higher occurrence of T3-4 stage cancer (P = .006) and ≥2 lymph node metastases (P = .019). Notably, levels of total thyroxine (TT4), thyroid-stimulating hormone (TSH), and thyroglobulin antibody (Tg-Ab) were significantly elevated in this group (P < .001 for each). Receiver operating characteristic analysis revealed substantial predictive accuracy for TT4, TSH, and Tg-Ab (area under curve of 0.747, 0.820, and 0.720, respectively). The columnar graphical model used for prediction demonstrated a high concordance index (C-index = 0.919), superior to single-indicator evaluations. Thyroid function indices, specifically TT4, TSH, and Tg-Ab, play a crucial role in the prognostic assessment of patients with DTC. The column-line diagram model effectively enhances prophetic prediction, aiding in clinical decision-making.

The Use of Tissue-on-Chip Technology to Focus the Search for Extracellular Vesicle miRNA Biomarkers in Thyroid Disease

Small extracellular vesicles (sEVs) contain microRNAs (miRNAs) which have potential to act as disease-specific biomarkers. The current study uses an established method to maintain human thyroid tissue ex vivo on a tissue-on-chip device, allowing the collection, isolation and interrogation of the sEVs released directly from thyroid tissue. sEVs were analysed for differences in miRNA levels released from benign thyroid tissue, Graves' disease tissue and papillary thyroid cancer (PTC), using miRNA sequencing and quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) to identify potential biomarkers of disease. Thyroid biopsies from patients with benign tissue (n = 5), Graves' disease (n = 5) and PTC (n = 5) were perfused with medium containing sEV-depleted serum for 6 days on the tissue-on-chip device. During incubation, the effluents were collected and ultracentrifuged to isolate sEVs; miRNA was extracted and sequenced (miRNASeq). Out of the 15 samples, 14 passed the quality control and miRNASeq analysis detected significantly higher expression of miR-375-3p, miR-7-5p, miR-382-5p and miR-127-3p in the sEVs isolated from Graves' tissue compared to those from benign tissue (false discovery rate; FDR p < 0.05). Similarly, miR-375-3p and miR-7-5p were also detected at a higher level in the Graves' tissue sEVs compared to the PTC tissue sEVs (FDR p < 0.05). No significant differences were observed between miRNA in sEVs from PTC vs. those from benign tissue. These results were supported by Quantitative Reverse Transcriptase Polymerase Chain Reaction (qRT-PCR). The novel findings demonstrate that the tissue-on-chip technology is a robust method for isolating sEVs directly from the tissue of interest, which has permitted the identification of four miRNAs, with which further investigation could be used as biomarkers or therapeutic targets within thyroid disease.

Evaluation of Mitochondrial Function in Blood Samples Shows Distinct Patterns in Subjects with Thyroid Carcinoma from Those with Hyperplasia

Metabolic adaptations are a hallmark of cancer and may be exploited to develop novel diagnostic and therapeutic tools. Only about 50% of the patients who undergo thyroidectomy due to suspicion of thyroid cancer actually have the disease, highlighting the diagnostic limitations of current tools. We explored the possibility of using non-invasive blood tests to accurately diagnose thyroid cancer. We analyzed blood and thyroid tissue samples from two independent cohorts of patients undergoing thyroidectomy at the Hospital Universitario 12 de Octubre (Madrid, Spain). As expected, histological comparisons of thyroid cancer and hyperplasia revealed higher proliferation and apoptotic rates and enhanced vascular alterations in the former. Notably, they also revealed increased levels of membrane-bound phosphorylated AKT, suggestive of enhanced glycolysis, and alterations in mitochondrial sub-cellular distribution. Both characteristics are common metabolic adaptations in primary tumors. These data together with reduced mtDNA copy number and elevated levels of the mitochondrial antioxidant PRX3 in cancer tissue samples suggest the presence of mitochondrial oxidative stress. In plasma, cancer patients showed higher levels of cfDNA and mtDNA. Of note, mtDNA plasma levels inversely correlated with those in the tissue, suggesting that higher death rates were linked to lower mtDNA copy number. In PBMCs, cancer patients showed higher levels of PGC-1α, a positive regulator of mitochondrial function, but this increase was not associated with a corresponding induction of its target genes, suggesting a reduced activity in cancer patients. We also observed a significant difference in the PRDX3/PFKFB3 correlation at the gene expression level, between carcinoma and hyperplasia patients, also indicative of increased systemic metabolic stress in cancer patients. The correlation of mtDNA levels in tissue and PBMCs further stressed the interconnection between systemic and tumor metabolism. Evaluation of the mitochondrial gene ND1 in plasma, PBMCs and tissue samples, suggested that it could be a good biomarker for systemic oxidative metabolism, with ND1/mtDNA ratio positively correlating in PBMCs and tissue samples. In contrast, ND4 evaluation would be informative of tumor development, with ND4/mtDNA ratio specifically altered in the tumor context. Taken together, our data suggest that metabolic dysregulation in thyroid cancer can be monitored accurately in blood samples and might be exploited for the accurate discrimination of cancer from hyperplasia.

Blood Profiles in the Prediction of Radioiodine Refractory Papillary Thyroid Cancer: A Case-Control Study

Purpose

Although most patients with papillary thyroid cancer can be cured by surgery and I-131 ablation, a small proportion will progress to radioactive iodine refractory (RAIR) thyroid cancer. The prediction of RAIR in its early stages can improve patient prognosis. The aim of this article is to evaluate the blood biomarkers in patients with RAIR and to establish a prediction model.

Patients and Methods

Data collected from patients with thyroid cancer that were enrolled from Jan. 2017 to Dec. 2021 were screened. RAIR was defined based on the criteria in the 2015 American Thyroid Association guidelines. The blood biomarkers from the study participants at three admissions timepoints (surgery and first and secondary I-131 ablations) were compared using both parametric and nonparametric tests to identify predictive factors for RAIR. Binary logistic regression analysis was used to construct a prediction model using parameters associated with surgical procedure decision. The model was then assessed with receiver operating characteristic curves.

Results

Thirty-six patients were included in the data analysis. Sixteen blood variables, including the low density lipoprotein-cholesterol-total cholesterol ratio, neutrophils, thyroglobulins, thyroglobulin antibody, thyroid peroxidase antibody, anion gap, etc., were revealed to be predictors for RAIR. The prediction model, which incorporated two parameters, reached an area under the curve of 0.861 (p<0.001).

Conclusion

Conventional blood biomarkers can be used in the prediction of early-stage RAIR. In addition, a prediction model incorporating multiple biomarkers can improve the predictive accuracy.

Machine learning for identifying benign and malignant of thyroid tumors: A retrospective study of 2,423 patients

Thyroid tumors, one of the common tumors in the endocrine system, while the discrimination between benign and malignant thyroid tumors remains insufficient. The aim of this study is to construct a diagnostic model of benign and malignant thyroid tumors, in order to provide an emerging auxiliary diagnostic method for patients with thyroid tumors. The patients were selected from the Chongqing General Hospital (Chongqing, China) from July 2020 to September 2021. And peripheral blood, BRAFV600E gene, and demographic indicators were selected, including sex, age, BRAFV600E gene, lymphocyte count (Lymph#), neutrophil count (Neu#), neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), red blood cell distribution width (RDW), platelets count (PLT), red blood cell distribution width-coefficient of variation (RDW-CV), alkaline phosphatase (ALP), and parathyroid hormone (PTH). First, feature selection was executed by univariate analysis combined with least absolute shrinkage and selection operator (LASSO) analysis. Afterward, we used machine learning algorithms to establish three types of models. The first model contains all predictors, the second model contains indicators after feature selection, and the third model contains patient peripheral blood indicators. The four machine learning algorithms include extreme gradient boosting (XGBoost), random forest (RF), light gradient boosting machine (LightGBM), and adaptive boosting (AdaBoost) which were used to build predictive models. A grid search algorithm was used to find the optimal parameters of the machine learning algorithms. A series of indicators, such as the area under the curve (AUC), were intended to determine the model performance. A total of 2,042 patients met the criteria and were enrolled in this study, and 12 variables were included. Sex, age, Lymph#, PLR, RDW, and BRAFV600E were identified as statistically significant indicators by univariate and LASSO analysis. Among the model we constructed, RF, XGBoost, LightGBM and AdaBoost with the AUC of 0.874 (95% CI, 0.841-0.906), 0.868 (95% CI, 0.834-0.901), 0.861 (95% CI, 0.826-0.895), and 0.837 (95% CI, 0.802-0.873) in the first model. With the AUC of 0.853 (95% CI, 0.818-0.888), 0.853 (95% CI, 0.818-0.889), 0.837 (95% CI, 0.800-0.873), and 0.832 (95% CI, 0.797-0.867) in the second model. With the AUC of 0.698 (95% CI, 0.651-0.745), 0.688 (95% CI, 0.639-0.736), 0.693 (95% CI, 0.645-0.741), and 0.666 (95% CI, 0.618-0.714) in the third model. Compared with the existing models, our study proposes a model incorporating novel biomarkers which could be a powerful and promising tool for predicting benign and malignant thyroid tumors.

Overexpression of serum lncRNA-ABHD11-AS1 as poor prognosis of patients with papillary thyroid carcinoma

This paper was aimed at exploring the correlation of long non-coding RNA (lncRNA)-ABHD11 Antisense RNA1 (ABHD11-AS1) with the poor prognosis of patients with papillary thyroid carcinoma (PTC) and at investigating its effects on the survival of PTC cells. Serum was respectively collected from 64 PTC patients who were admitted to our hospital (PTC group) and from 50 healthy controls who underwent physical examinations (HC group) both from April 2011 to April 2015. The expression levels of ABHD11-AS1 in the serum were detected, and the values of it for diagnosis and prognosis (5-year follow-ups) were analyzed. The knockdown and overexpression models of ABHD11-AS1 in were constructed to explore the effects of the models on their proliferation, cycles and apoptosis. According to the data, the expression levels of serum ABHD11-AS1 in the PTC patients were remarkably higher than those in the healthy controls, and the area under the curve (AUC) for distinguishing the patients from the controls was 0.920. In the analysis of prognosis, the levels in patients with a poor prognosis were remarkably higher than those in patients with a good prognosis. According to the curves of overall survival rates (OSRs), the high levels of ABHD11-AS1 were remarkably correlated with the poor prognosis (a lower 5-year OSR). COX analysis showed that TNM staging, lymph node metastasis and ABHD11-AS1 were the independent prognostic factors of PTC patients. In the cell experiments, knocking down ABHD11-AS1 remarkably inhibited PTC cells from proliferation, arrested them in G0/G1 phase, and induced their apoptosis, negatively affecting their survival indices. Overexpressing this RNA had positive effects on the survival indices. Taken together, high levels of serum ABHD11-AS1 are related to the poor prognosis of PTC patients, and knocking down its expression can inhibit the survival of PTC cells.

Carbon Based Nanodots in Early Diagnosis of Cancer

Detection of cancer at an early stage is one of the principal factors associated with successful treatment outcome. However, current diagnostic methods are not capable of making sensitive and robust cancer diagnosis. Nanotechnology based products exhibit unique physical, optical and electrical properties that can be useful in diagnosis. These nanotech-enabled diagnostic representatives have proved to be generally more capable and consistent; as they selectively accumulated in the tumor site due to their miniscule size. This article rotates around the conventional imaging techniques, the use of carbon based nanodots viz Carbon Quantum Dots (CQDs), Graphene Quantum Dots (GQDs), Nanodiamonds, Fullerene, and Carbon Nanotubes that have been synthesized in recent years, along with the discovery of a wide range of biomarkers to identify cancer at early stage. Early detection of cancer using nanoconstructs is anticipated to be a distinct reality in the coming years.