Role of Chemokines in Thyroid Cancer Microenvironment: Is CXCL8 the Main Player?

Tumor-related inflammation does influence the biological behavior of neoplastic cells and ultimately the patient's outcome. With specific regard to thyroid cancer, the issue of tumor-associated inflammation has been extensively studied and recently reviewed. However, the role of chemokines, which play a crucial role in determining the immuno-phenotype of tumor-related inflammation, was not addressed in previous reviews on the topic. Experimental evidence shows that thyroid cancer cells actively secrete a wide spectrum of chemokines and, at least for some of them, solid scientific data support a role for these immune-active molecules in the aggressive behavior of the tumor. Our proposal for a review article on chemokines and thyroid cancer stems from the notion that chemokines, besides having the ability to attract and maintain immune cells at the tumor site, also produce several pro-tumorigenic actions, which include proangiogenetic, cytoproliferative, and pro-metastatic effects. Studies taking into account the role of CCL15, C-X-C motif ligand 12, CXCL16, CXCL1, CCL20, and CCL2 in the context of thyroid cancer will be reviewed with particular emphasis on CXCL8. The reason for focusing on CXCL8 is that this chemokine is the most studied one in human malignancies, displaying multifaceted pro-tumorigenic effects. These include enhancement of tumor cells growth, metastatization, and angiogenesis overall contributing to the progression of several cancers including thyroid cancer. We aim at reviewing current knowledge on the (i) ability of both normal and tumor thyroid cells to secrete CXCL8; (ii) direct/indirect pro-tumorigenic effects of CXCL8 demonstrated by in vitro and in vivo studies specifically performed on thyroid cancer cells; and (iii) pharmacologic strategies proven to be effective for lowering CXCL8 secretion and/or its effects on thyroid cancer cells

Effect of thyroglobulin autoantibodies on the metabolic clearance of serum thyroglobulin

Background: In order to establish whether thyroglobulin autoantibodies (TgAb) influence the metabolic clearance of thyroglobulin (Tg) in humans, serum Tg and TgAb were correlated shortly after radioiodine (131I) treatment. Methods: Samples were collected from 30 consecutive patients undergoing 131I activity for Graves' hyperthyroidism at the time of treatment and every 15 days thereafter, up to 90 days. Tg and TgAb were measured by immunometric assays (functional sensitivities: 0.1 ng/mL and 8 IU/mL). Results: Tg was detectable in all patients at day 0. Tg concentrations rose from a mean of 33.2 ng/mL [confidence interval (CI) 17.8–61.0 ng/mL] at day 0 to a mean of 214.6 ng/mL [CI 116.9–393.4 ng/mL] at day 30 and then steadily decreased, reaching the lowest concentration at day 90 (M = 10.9 ng/mL [CI 5.5–20.9 ng/mL]). Compared to their levels at day 0 (M = 23.6 IU/mL [CI 10.5–52.9 IU/mL]), TgAb remained stable through day 15 and then gradually increased up to a mean of 116.6 IU/mL [CI 51.9–262.2 IU/mL] at day 90. Patients were then split into two groups according to their TgAb status at day 0: undetectable (<8 IU/mL; 9 patients) or detectable (≥8 IU/mL; 21 patients) TgAb. Compared to the other cohort, patients with detectable TgAb showed significantly lower Tg concentrations at day 0 (M = 20.3 ng/mL [CI 10.1–40.2 ng/mL] vs. M = 101.8 ng/mL [CI 36.6–279.8 ng/mL]), similar at day 15, lower levels at day 30 (M = 146.5 ng/mL [CI 74.3–287.8 ng/mL] vs. M = 514.8 ng/mL [CI 187.8–1407.9 ng/mL]), at day 45 (M = 87.5 ng/mL [CI 43.1–176.6 ng/mL] vs. M = 337.9 ng/mL [CI 120.1–947.0 ng/mL]), at day 60 (M = 61.6 ng/mL [CI 31.0–121.4 ng/mL] vs. M = 255.8 ng/mL [CI 79.0–823.8 ng/mL]), and at day 75 (M = 24.5 ng/mL [CI 11.9–49.2 ng/mL] vs. M = 249.5 ng/mL [CI 63.5–971.1 ng/mL]), and similar levels at day 90. Patients with detectable TgAb showed a lower (M = 182.5 ng/mL [CI 92.0–361.0 ng/mL] vs. M = 514.8 ng/mL [CI 187.8–1407.9 ng/mL]) and an earlier (day 15 vs. day 30) peak of Tg. The mean Tg concentration was lower in patients with detectable TgAb than in those with undetectable TgAb (area under the curve: 17,340 ± 16,481 ng/mL vs. 36,883 ± 44,625 ng/mL; p = 0.02). Conclusions: TgAb influence the changes in Tg concentrations observed immediately after 131I treatment, inducing lower levels and an earlier peak of Tg. These observations indicate that TgAb significantly influence the metabolic clearance of Tg, supporting the concept that their interference in the measurement of Tg is mainly due to an in vivo effect

Thyroid ultrasonography reporting: consensus of Italian Thyroid Association (AIT), Italian Society of Endocrinology (SIE), Italian Society of Ultrasonography in Medicine and Biology (SIUMB) and Ultrasound Chapter of Italian Society of Medical Radiology (SIRM)

Thyroid ultrasonography (US) is the gold standard for thyroid imaging and its widespread use is due to an optimal spatial resolution for superficial anatomic structures, a low cost and the lack of health risks. Thyroid US is a pivotal tool for the diagnosis and follow-up of autoimmune thyroid diseases, for assessing nodule size and echostructure and defining the risk of malignancy in thyroid nodules. The main limitation of US is the poor reproducibility, due to the variable experience of the operators and the different performance and settings of the equipments. Aim of this consensus statement is to standardize the report of thyroid US through the definition of common minimum requirements and a correct terminology. US patterns of autoimmune thyroid diseases are defined. US signs of malignancy in thyroid nodules are classified and scored in each nodule. We also propose a simplified nodule risk stratification, based on the predictive value of each US sign, classified and scored according to the strength of association with malignancy, but also to the estimated reproducibility among different operators

Prevalence of psychiatric disorders in thyroid diseased patients.

Several studies have underlined the high prevalence of psychiatric symptoms and disorders in thyroid diseases. The aim of this study was to evaluate the prevalence of psychiatric disorders in 93 inpatients affected by different thyroid diseases during their lifetimes, by means of a standardized instrument, i.e., the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders-III-Revised, Upjohn Version (SCID-UP-R). The results showed higher rates of panic disorder, simple phobia, obsessive-compulsive disorder, major depressive disorder, bipolar disorder and cyclothymia in thyroid patients than in the general population. These findings would suggest that the co-occurrence of psychiatric and thyroid diseases may be the result of common biochemical abnormalities

Type I and type II interferons inhibit both basal and tumor necrosis factor-α-induced CXCL8 secretion in primary cultures of human thyrocytes.

Interferons (IFNs) and tumor necrosis factor-α (TNF-α) cooperate in activating several inflammation-related genes, which sustain chronic inflammation in autoimmune thyroid disease (AITD). Much is known about the positive signaling of IFNs to activate gene expression in AITD, while the mechanisms by which IFNs negatively regulate genes remain less studied. While IFNs inhibit CXCL8 secretion in several human cell types, their effects on thyroid cells were not evaluated. Our aim was to study the interplay between TNF-α and type I or type II IFNs on CXCL8 secretion by human thyroid cells. CXCL8 was measured in supernatants of primary cultures of thyroid cells basally and after a 24-h incubation with TNF-α. CXCL8 was detected in thyroid cell supernatants in basal conditions (96.2±23.5 pg/mL) being significantly increased (784.7±217.3 pg/mL; PIFN-β>IFN-α. This study demonstrates that type I and type II IFNs downregulate both basal and TNF-α-induced CXCL8 secretion by human thyrocytes, IFN-γ being the most powerful inhibitor. Future studies aimed at a better comprehension of the interplay between CXCL8 and thyroid diseases appear worthwhile

Opening the black box: Personalizing type 2 diabetes patients based on their latent phenotype and temporal associated complication rules

© 2020 The Authors. It is widely considered that approximately 10% of the population suffers from type 2 diabetes. Unfortunately, the impact of this disease is underestimated. Patient's mortality often occurs due to complications caused by the disease and not the disease itself. Many techniques utilized in modeling diseases are often in the form of a “black box” where the internal workings and complexities are extremely difficult to understand, both from practitioners' and patients' perspective. In this work, we address this issue and present an informative model/pattern, known as a “latent phenotype,” with an aim to capture the complexities of the associated complications' over time. We further extend this idea by using a combination of temporal association rule mining and unsupervised learning in order to find explainable subgroups of patients with more personalized prediction. Our extensive findings show how uncovering the latent phenotype aids in distinguishing the disparities among subgroups of patients based on their complications patterns. We gain insight into how best to enhance the prediction performance and reduce bias in the models applied using uncertainty in the patients' data

The effect of pregnancy on subsequent relapse from Graves' disease after a successful course of antithyroid drug therapy.

OBJECTIVE: Pregnancy and the postpartum (PP) period are associated with profound changes of the immune system, which largely influence the clinical activity of autoimmune diseases. The aim of this study was to evaluate the effect of pregnancy and/or the PP period in driving a clinical relapse of hyperthyroidism in patients with Graves' disease (GD) who are in remission after antithyroid drug (ATD) treatment. Data were retrospectively collected from 150 female patients with GD, who were assigned to two groups according to the occurrence of a successful pregnancy after ATD withdrawal. RESULTS: Relapsing Graves' hyperthyroidism was observed in 70 of 125 patients in group I (no pregnancy after ATD withdrawal) (56.0%) and 21 of 25 patients in group II (pregnancy after ATD withdrawal) (84.0%) (P < 0.05). Logistic regression analysis (dependent variable: relapse/nonrelapse; covariates: age, positive family history for autoimmune thyroid disease, duration of treatment with ATD, number pregnancies at diagnosis, number of pregnancies after ATD withdrawal) showed a significant effect only for the number of pregnancies after ATD withdrawal [4.257 (1.315-13.782)]. The effect was ascribed to the PP period rather than to pregnancy itself because in 20 of 21 patients of group II (95.2%), the relapse of Graves' hyperthyroidism occurred between 4 and 8 months after delivery. CONCLUSIONS: The PP period is significantly associated with a relapse of hyperthyroidism in GD patients being in remission after ATD. We therefore recommend that patients with GD in remission after a course of ATD should have their thyroid function tested at 3 and 6 months after delivery

Opening the Black Box: Discovering and Explaining Hidden Variables in Type 2 Diabetic Patient Modelling

Clinicians predict disease and related complications based on prior knowledge and each individual patient's clinical history. The prediction process is complex due to the existence of unmeasured risk factors, the unexpected development of complications and varying responses of patients to disease over time. Exploiting these unmeasured risk factors (hidden variables) can improve the modeling of disease progression and thus enables clinicians to focus on early diagnosis and treatment of unexpected conditions. However, the overuse of hidden variables can lead to complex models that can overfit and are not well understood (being 'black box' in nature). Identifying and understanding groups of patients with similar disease profiles (based on discovered hidden variables) makes it possible to better understand disease progression in different patients while improving prediction. We explore the use of a stepwise method for incrementally identifying hidden variables based on the Induction Causation (IC*) algorithm. We exploit Dynamic Time Warping and hierarchical clustering to cluster patients based upon these hidden variables to uncover their meaning with respect to the complications of Type 2 Diabetes Mellitus patients. Our results reveal that inferring a small number of targeted hidden variables and using them to cluster patients not only leads to an improvement in the prediction accuracy but also assists the explanation of different discovered sub-groups