Combined metabolomic and lipidomic analysis uncovers metabolic profile and biomarkers for papillary thyroid carcinoma

Study on identification of diagnostic biomarkers in serum for papillary thyroid cancer in different iodine nutrition regions

BACKGROUND

At present, there is a lack of efficient biomarkers for the diagnosis of thyroid cancer, and the influence of natural factors such as high iodine exposure on the expression of biomarkers remains unclear.

METHODS

Serum samples from papillary thyroid cancer (PTC) and non-cancer controls matched 1:1 in different iodine nutritional regions were analyzed metabolomically using an ultra-high performance liquid chromatography-Orbitrap Exploris mass spectrometry (UHPLC-OE-MS) platform. Then the data were randomly divided into training and test sets in a 1:1 ratio according to the different iodine nutritional regions and different PTC status. In the training set, differential metabolites were selected by multivariate statistical analysis methods, and the prediction models were then built using Random forest (RF), Gradient boosting machine (GBM), and Support vector machine (SVM) models. At last, their diagnostic effects were examined in the test set.

RESULTS

PTCs were significantly separated from non-cancer samples, and a total of 37 differentially expressed metabolites were selected. The results of pathway analysis showed that the PTC-related differential metabolites were mainly involved in the sphingolipid metabolism and glycerophosphate metabolism. The prediction models constructed by the 6 screened potential biomarkers could all better identify PTCs in the test set. The metabolomic fingerprinting between PTC and non-cancer groups in different water iodine regions did not show significant disturbance. However, high iodine exposure would effect on the expression of six metabolites, reflecting in a significantly different diagnostic efficacy in different water iodine regions.

CONCLUSION

Serum metabolites have potential value as biomarkers of PTC, and iodine status affects the expression and even diagnostic levels of certain serum metabolites.

The influence of cancer on the reprogramming of lipid metabolism in healthy thyroid tissues of patients with papillary thyroid carcinoma

BACKGROUND AND OBJECTIVES

Over the years we observed changes in the metabolism of glucose, amino acids, fatty acids (FA) and nucleotides in cancer cells in order to maintain their viability and proliferate. Moreover, as the latest data show, cancer also forces a complete change in the behavior of other tissues. For instance, fat-filled adipocytes are often found in the vicinity of invasive solid human tumors. We investigated the effects of papillary thyroid carcinoma (PTC) on the lipid metabolism of healthy tissue distant from the tumor.

METHOD

Thyroid tissue was collected from female patients immediately after surgical removal of the entire thyroid gland. Blood samples were collected from PTC patients and healthy volunteers. Real-time PCR assays were performed to analyze the expression of lipogenic genes and a broad panel of FA was determined using the gas chromatography-mass spectrometry method.

RESULTS

The concentration of lipids was higher in paratumor tissue than in healthy thyroid tissue (p = 0.005). The lipogenic genes tested were significantly increased in paratumor tissue compared to healthy tissue, especially enzymes related to the synthesis of very long-chain saturated and polyunsaturated FAs (VLCSFAs and PUFAs, respectively) (p < 0.001). The FA profile also showed increased levels of C22-C26, VLCSFAs and almost all PUFAs in paratumor tissue (p < 0.05).

CONCLUSION

Our study suggests that a restructuring of lipid metabolism occurs in the adjacent healthy thyroid gland and that the metabolism of VLCSFAs and PUFAs is higher in the paratumor tissue than in the distant tissue of the healthy thyroid gland.

The Gut Microbiota and Its Metabolites and Their Association with the Risk of Autoimmune Thyroid Disease: A Mendelian Randomization Study

Objectives: Observational research shows associations of the gut microbiota and its metabolites with autoimmune thyroid disease (AITD), but the causality is undetermined. Methods: Two-sample Mendelian randomization (MR) was employed to analyze the association of the gut microbiota and its metabolites with AITD. A total of 119 gut microbiotas and nine fecal/circulating metabolites were the exposures. AITD, Graves' disease (GD), and Hashimoto's thyroiditis (HT) were the outcomes. Inverse-variance weighting (IVW) was primarily used to assess causality; Cochran's Q was used to assess heterogeneity. Sensitivity analyses (weighted median, MRPRESSO regression, MRPRESSO intercept, MRPRESSO global, Steiger filtering, leave-one-out) were conducted to assess causal estimate robustness. Multivariable MR (MVMR) was used to estimate the effects of body mass index (BMI) and alcohol consumption frequency on causality. Results: The outcomes were potentially causally associated with 22 gut microbiotas and three metabolites. After multiple-test correction, 3-indoleglyoxylic acid retained significant causality with AITD (IVW: odds ratio [OR] = 1.09, 95% confidence interval [CI] = 1.05-1.14, p = 2.43 × 10-5, FDR = 0.009). The sensitivity analyses were confirmatory (weighted median: OR = 1.06, 95% CI = 1.01-1.12, p = 0.025; MRPRESSO: OR = 1.09, 95% CI = 1.15-1.14, p = 0.001). MVMR revealed no confounding effects on this association (BMI: OR = 1.21, 95% CI =1.08-1.35, p = 0.001; drinks/week: OR = 1.22, 95% CI = 1.04-1.43, p = 0.014). Conclusions: MR revealed no significant causal effects of the gut microbiota on the outcomes. However, MR revealed the causal effects of 3-indoleglyoxylic acid on the risk of AITD.

In Search of Relevant Urinary Biomarkers for Thyroid Papillary Carcinoma and Benign Thyroid Nodule Differentiation, Targeting Metabolic Profiles and Pathways via UHPLC-QTOF-ESI+-MS Analysis

BACKGROUND

Identification of specific urine metabolic profiles for patients diagnosed with papillary thyroid carcinoma (TC) vs. benign nodules (B) to identify specific biomarkers and altered pathways compared to those of healthy controls (C).

METHODS

Patient urine samples were collected, before surgery and after a histological confirmation of TC (n = 30) and B (n = 30), in parallel with sample collection from healthy controls (n = 20). The untargeted and semi-targeted metabolomic protocols were applied using UPLC-QTOF-ESI+-MS analysis, and the statistical analysis was performed using the Metaboanalyst 6.0 platform. The results for the blood biomarkers, previously published, were compared with the data obtained from urine sampling using the Venny algorithm and multivariate statistics.

RESULTS

Partial least squares discrimination, including VIP values, random forest graphs, and heatmaps (p < 0.05), together with biomarker analysis (AUROC ranking) and pathway analysis, suggested a specific model for the urinary metabolic profile and pathway alterations in TC and B vs. C, based on 190 identified metabolites in urine that were compared with the serum metabolites. By semi-targeted metabolomics, 10 classes of metabolites, considered putative biomarkers, were found to be responsible for specific alterations in the metabolic pathways, from polar molecules to lipids. Specific biomarkers for discrimination were identified in each class of metabolites that were either upregulated or downregulated when compared to those of the controls.

CONCLUSIONS

The lipidomic window was the most relevant for identifying biomarkers related to thyroid cancer and benign conditions, since this study detected a stronger involvement of lipids and selenium-related molecules for metabolic discrimination.

The Role of Lipid Metabolism Disorders in the Development of Thyroid Cancer

Thyroid cancer (TC) is a neoplasm with an increasing incidence worldwide. Its etiology is complex and based on a multi-layered interplay of factors. Among these, disorders of lipid metabolism have emerged as an important area of investigation. Cancer cells are metabolically reprogrammed to promote their rapid growth, proliferation, and survival. This reprogramming is associated with significant changes at the level of lipids, mainly fatty acids (FA), as they play a critical role in maintaining cell structure, facilitating signaling pathways, and providing energy. These lipid-related changes help cancer cells meet the increased demands of continued growth and division while adapting to the tumor microenvironment. In this review, we examine lipid metabolism at different stages, including synthesis, transport, and oxidation, in the context of TC and the effects of obesity and hormones on TC development. Recent scientific efforts have revealed disturbances in lipid homeostasis that are specific to thyroid cancer, opening up potential avenues for early detection and targeted therapeutic interventions. Understanding the intricate metabolic pathways involved in FA metabolism may provide insights into potential interventions to prevent cancer progression and mitigate its effects on surrounding tissues.

Deciphering two decades of cellular reprogramming in cancer: A bibliometric analysis of evolving trends and research frontiers

Recent research has reevaluated the traditional view of cancer's linear progression and recurrence by introducing cellular reprogramming a process in which cancer cells can their state under certain conditions. This change is driven by a combination of genetic and epigenetic factors, with pivotal roles played by key genes, and pathways, notably Wnt and Notch. The complexity of cancer's behavior is further influenced by factors such as the epithelial-mesenchymal transition (EMT) and therapy-induced stress, both of which are significant contributors to cancer recurrence. In this context bibliometric analysis emerges as a crucial tool for evaluating the impacts and trends within scientific literature. Our study utilized bibliometrics to analysis the role of cellular reprogramming oncology over the past two decades, highlighting its potential to improve cancer treatment outcomes. In conducting this analysis, we searched for literature search on cellular reprogramming (CR) in the Web of Science database, covering the years 2002-2022. We employed visualization tools like Citespace, VOSviewer, and Bibliometrix to analyze the collected data resulting in a dataset of 3102 articles. The United States and China emerged as leading contributors to this field, with the University of Texas MD Anderson Cancer Center being the most prolific institution. Menendez was the most influential scholar in this research domain. Cancers was the journal with the most publications on this subject. The most local-cited document was the article titled "Hallmarks of Cancer: The Next Generation". A comprehensive analysis has been conducted based on keywords and cited references. In recent years, the research emphasis has shifted to "extracellular vesicles," "cancer therapy," and "cellular plasticity". Therefore, this analysis uses bibliometrics to chart cutting-edge progress in cancer's cellular reprogramming, aiding experts to quickly understand and innovate in this crucial area.

An Overview on Lipid Droplets Accumulation as Novel Target for Acute Myeloid Leukemia Therapy

Metabolic reprogramming is a key alteration in tumorigenesis. In cancer cells, changes in metabolic fluxes are required to cope with large demands on ATP, NADPH, and NADH, as well as carbon skeletons. In particular, dysregulation in lipid metabolism ensures a great energy source for the cells and sustains cell membrane biogenesis and signaling molecules, which are necessary for tumor progression. Increased lipid uptake and synthesis results in intracellular lipid accumulation as lipid droplets (LDs), which in recent years have been considered hallmarks of malignancies. Here, we review current evidence implicating the biogenesis, composition, and functions of lipid droplets in acute myeloid leukemia (AML). This is an aggressive hematological neoplasm originating from the abnormal expansion of myeloid progenitor cells in bone marrow and blood and can be fatal within a few months without treatment. LD accumulation positively correlates with a poor prognosis in AML since it involves the activation of oncogenic signaling pathways and cross-talk between the tumor microenvironment and leukemic cells. Targeting altered LD production could represent a potential therapeutic strategy in AML. From this perspective, we discuss the main inhibitors tested in in vitro AML cell models to block LD formation, which is often associated with leukemia aggressiveness and which may find clinical application in the future.