Plasma Metabolic Profiling of Human Thyroid Nodules by Gas Chromatography-Mass Spectrometry (GC-MS)-Based Untargeted Metabolomics

Phosphoproteomics analysis of serum from dogs affected with pulmonary hypertension secondary to degenerative mitral valve disease

Pulmonary hypertension (PH), a common complication in dogs affected by degenerative mitral valve disease (DMVD), is a progressive disorder characterized by increased pulmonary arterial pressure (PAP) and pulmonary vascular remodeling. Phosphorylation of proteins, impacting vascular function and cell proliferation, might play a role in the development and progression of PH. Unlike gene or protein studies, phosphoproteomic focuses on active proteins that function as end-target proteins within signaling cascades. Studying phosphorylated proteins can reveal active contributors to PH development. Early diagnosis of PH is crucial for effective management and improved clinical outcomes. This study aimed to identify potential serum biomarkers for diagnosing PH in dogs affected with DMVD using a phosphoproteomic approach. Serum samples were collected from healthy control dogs (n = 28), dogs with DMVD (n = 24), and dogs with DMVD and PH (n = 29). Phosphoproteins were enriched from the serum samples and analyzed using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Data analysis was performed to identify uniquely expressed phosphoproteins in each group and differentially expressed phosphoproteins among groups. Phosphoproteomic analysis revealed nine uniquely expressed phosphoproteins in the serum of dogs in the DMVD+PH group and 15 differentially upregulated phosphoproteins in the DMVD+PH group compared to the DMVD group. The phosphoproteins previously implicated in PH and associated with pulmonary arterial remodeling, including small nuclear ribonucleoprotein G (SNRPG), alpha-2-macroglobulin (A2M), zinc finger and BTB domain containing 42 (ZBTB42), hemopexin (HPX), serotransferrin (TRF) and complement C3 (C3), were focused on. Their unique expression and differential upregulation in the serum of DMVD dogs with PH suggest their potential as biomarkers for PH diagnosis. In conclusion, this phosphoproteomic study identified uniquely expressed and differentially upregulated phosphoproteins in the serum of DMVD dogs with PH. Further studies are warranted to validate the diagnostic utility of these phosphoproteins.

Plasma metabolites analysis of patients with papillary thyroid cancer: A preliminary untargeted 1H NMR-based metabolomics

Metabolomics plays a crucial role in identifying molecular biomarkers that can differentiate pathological conditions. In the case of thyroid cancer, it is essential to accurately diagnose malignancy from benignity to avoid unnecessary surgeries. The objective of this research was to apply untargeted NMR-based metabolomics in order to identify metabolic biomarkers that can distinguish between plasma samples of patients with papillary thyroid cancer (PTC) and multinodular goiter (MNG), as well as PTC and healthy individuals. The study included a cohort of 55 patients who were divided into three groups: PTC (n=20), MNG (n=16), and healthy (n=19). Plasma samples were collected from all participants and subjected to 1H NMR spectroscopy. Differential metabolites were identified using chemometric pattern recognition algorithms. The obtained metabolic profile had the potential to differentiate PTC from healthy plasma, but not from MNG. In patients diagnosed with PTC, a total of 18 compounds were discovered, revealing elevated levels of leucine, lysine, and 4-acetamidobutyric acid, while acetate, proline, acetoacetate, 3-hydroxybutyrate, glutamate, pyruvate, cystine, glutathione, asparagine, ethanolamine, histidine, tyrosine, myo-inositol, and glycerol along with a lipid compound were found to be lower in comparison to those of healthy individuals. According to the area under the curve (AUC) of the receiver operating characteristic curve, this particular profile exhibited an impressive capability of 85% to discern PTC from healthy subjects (AUC=0.853, sensitivity=78.95, specificity=84.21). The utilization of the 1H NMR-based metabolomics approach revealed considerable promise in the identification of PTC from healthy plasma specimens. The modifications noticed in the plasma metabolites have the potential to act as practical biomarkers that are non-invasive and could suggest transformations in the metabolic profile of thyroid tumors.

Hypoxia expedites the progression of papillary thyroid carcinoma by promoting the CPT1A-mediated fatty acid oxidative pathway

Hypoxia has been reported to promote the proliferation and migration of thyroid cancer, while the special mechanism was still unclear. HIF-1α/carnitine palmitoyl-transferase 1A (CPT1A) was found to be associated with papillary thyroid carcinoma (PTC) but the biological role of CPT1A in PTC was not explored. The effects of hypoxia and carnitine palmitoyl-transferase 1A (CPT1A) expression on PTC cells were determined by cell counting kit-8 assay, detection of oxidative stress, inflammation response and mitochondrial membrane motential (MMP). Oil Red O staining and the detection of free fatty acids were performed to assess the status of lipid metabolism. Flow cytometric analysis was performed to assess cell apoptosis. Quantitative polymerase chain reaction (qPCR) and western blot analysis were applied to investigate the expressions of CPT1A and HIF-1α and the molecules involved cell function. The expressions of CPT1A and HIF-1α were significantly increased in PTC cells with or without hypoxia treatment. CPT1A overexpression or silencing promoted or inhibited cell viability, and hypoxia further repressed cell viability. In addition, CPT1A overexpression alleviates hypoxia-induced increased oxidative stress, inflammation response and elevated MMP. CPT1A overexpression enhanced palmitic acid-induced decreased cell growth, enhanced the metabolic capacity of free fatty acid and suppressed cell apoptosis. Animal experiments showed that CPT1A overexpression promoted PTC tumor growth, reduced lipid deposition, oxidative stress and inflammation, as well as enhancing cell function indicators. However, CPT1A silencing showed the opposite effects both in vitro and in vivo. Hypoxia induces the high expression of HIF-1α/CPT1A, thereby reprogramming the lipid metabolism of PTC cells for adapting the hypoxia environment, meanwhile inhibiting the cell damage and apoptosis caused by oxidative stress.

Profiling of endogenous metabolites and changes in intestinal microbiota distribution after GEN-001 (Lactococcus lactis) administration

This study aimed to identify metabolic biomarkers and investigate changes in intestinal microbiota in the feces of healthy participants following administration of Lactococcus lactis GEN-001. GEN-001 is a single-strain L. lactis strain isolated from the gut of a healthy human volunteer. The study was conducted as a parallel, randomized, phase 1, open design trial. Twenty healthy Korean males were divided into five groups according to the GEN-001 dosage and dietary control. Groups A, B, C, and D1 received 1, 3, 6, and 9 GEN-001 capsules (1 × 1011 colony forming units), respectively, without dietary adjustment, whereas group D2 received 9 GEN-001 capsules with dietary adjustment. All groups received a single dose. Fecal samples were collected 2 days before GEN-001 administration to 7 days after for untargeted metabolomics and gut microbial metagenomic analyses; blood samples were collected simultaneously for immunogenicity analysis. Levels of phenylalanine, tyrosine, cholic acid, deoxycholic acid, and tryptophan were significantly increased at 5-6 days after GEN-001 administration when compared with predose levels. Compared with predose, the relative abundance (%) of Parabacteroides and Alistipes significantly decreased, whereas that of Lactobacillus and Lactococcus increased; Lactobacillus and tryptophan levels were negatively correlated. A single administration of GEN-001 shifted the gut microbiota in healthy volunteers to a more balanced state as evidenced by an increased abundance of beneficial bacteria, including Lactobacillus, and higher levels of the metabolites that have immunogenic properties.

Non-Targeted Metabolomics Investigation of a Sub-Chronic Variable Stress Model Unveils Sex-Dependent Metabolic Differences Induced by Stress

Depression is twice as prevalent in women as in men, however, most preclinical studies of depression have used male rodent models. This study aimed to examine how stress affects metabolic profiles depending on sex using a rodent depression model: sub-chronic variable stress (SCVS). The SCVS model of male and female mice was established in discovery and validation sets. The stress-induced behavioral phenotypic changes were similar in both sexes, however, the metabolic profiles of female plasma and brain became substantially different after stress, whereas those of males did not. Four stress-differential plasma metabolites-β-hydroxybutyric acid (BHB), L-serine, glycerol, and myo-inositol-could yield biomarker panels with excellent performance to discern the stressed individuals only for females. Disturbances in BHB, glucose, 1,5-anhydrosorbitol, lactic acid, and several fatty acids in the plasma of stressed females implied a systemic metabolic shift to β-oxidation in females. The plasma levels of BHB and corticosterone only in stressed females were observed not only in SCVS but also in an acute stress model. These results collectively suggest a sex difference in the metabolic responses by stress, possibly involving the energy metabolism shift to β-oxidation and the HPA axis dysregulation in females.

Thyroid cancer cell metabolism: A glance into cell culture system-based metabolomics approaches

Thyroid cancer is the most common malignancy of the endocrine system and the seventh most prevalent cancer in women worldwide. It is a complex and diverse disease characterized by heterogeneity, underscoring the importance of understanding the underlying metabolic alterations within tumor cells. Metabolomics technologies offer a powerful toolset to explore and identify endogenous and exogenous biochemical reaction products, providing crucial insights into the intricate metabolic pathways and processes within living cells. Metabolism plays a central role in cell function, making metabolomics a valuable reflection of a cell's phenotype. In the OMICs era, metabolomics analysis of cells brings numerous advantages over existing methods, propelling cell metabolomics as an emerging field with vast potential for investigating metabolic pathways and their perturbation in pathophysiological conditions. This review article aims to look into recent developments in applying metabolomics for characterizing and interpreting the cellular metabolome in thyroid cancer cell lines, exploring their unique metabolic characteristics. Understanding the metabolic alterations in tumor cells can lead to the identification of critical nodes in the metabolic network that could be targeted for therapeutic intervention.

Prediction of positive pulmonary nodules based on machine learning algorithm combined with central carbon metabolism data

BACKGROUND

Lung cancer causes a huge disease burden, and early detection of positive pulmonary nodules (PPNs) as an early sign of lung cancer is extremely important for effective intervention. It is necessary to develop PPNs risk recognizer based on machine learning algorithm combined with central carbon metabolomics.

METHODS

The study included 2248 participants at high risk for lung cancer from the Ma'anshan Community Lung Cancer Screening cohort. The Least Absolute Shrinkage and Selection Operator (LASSO) was used to screen 18 central carbon-related metabolites in plasma, recursive feature elimination (RFE) was used to select all 42 features, followed by five machine learning algorithms for model development. The performance of the model was evaluated using area under the receiver operator characteristic curve (AUC), accuracy, precision, recall, and F1 scores. In addition, SHapley Additive exPlanations (SHAP) was performed to assess the interpretability of the final selected model and to gain insight into the impact of features on the predicted results.

RESULTS

Finally, the two prediction models based on the random forest (RF) algorithm performed best, with AUC values of 0.87 and 0.83, respectively, better than other models. We found that homogentisic acid, fumaric acid, maleic acid, hippuric acid, gluconic acid, and succinic acid played a significant role in both PPNs prediction model and NPNs vs PPNs model, while 2-oxadipic acid only played a role in the former model and phosphopyruvate only played a role in the NPNs vs PPNs model. This model demonstrates the potential of central carbon metabolism for PPNs risk prediction and identification.

CONCLUSION

We developed a series of predictive models for PPNs, which can help in the early detection of PPNs and thus reduce the risk of lung cancer.

Targeted metabolomics reveals the association between central carbon metabolism and pulmonary nodules

With the widespread application of low-dose computed tomography (LDCT) technology, pulmonary nodules have aroused more attention. Significant alteration in plasma metabolite levels, mainly amino acid and lipid, have been observed in patients of PNs. However, evidence on the association between central carbon metabolism and PNs are largely unknown. The aim of this study was to investigate the underlying association of PNs and plasma central carbon metabolites. We measured the levels of 16 plasma central carbon metabolites in 1954 participants who gained LDCT screening in MALSC cohort. The inverse probability weighting (IPW) technique was used to control for bias due to self-selection for LDCT in the assessed high-risk population. The least absolute shrinkage and selection operator (LASSO) penalized regression was used to deal with the problem of multicollinearity among metabolites and the combined association of central carbon metabolites with PNs was estimated by using quantile g-computation (QgC) models. A quartile increase in 3-hydroxybutyric acid, gluconic acid, succinic acid and hippuric acid was positively associated with the PNs risk, whereas a quartile increase in 2-oxadipic acid and fumaric acid was negatively associated with the risk of PNs in multiple-metabolite models. A positive but insignificant joint associations of the mixture of 16 metabolites with PNs was observed by using QgC models analyses. Further studies are warranted to clarify the association between circulating metabolites and PNs and the biological mechanisms.

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

Papillary thyroid carcinoma (PTC) is the most common endocrine malignancy with a rapidly increasing incidence. The pathogenesis of PTC is unclear, but metabolic and lipidomic reprogramming may play a role in tumor growth. We applied ultra-performance liquid chromatography-tandem mass spectrometry to perform widely targeted metabolomics and lipidomics on plasma samples from 94 patients with PTC and 100 healthy controls. We identified 113 differential metabolites and 236 differential lipids, mainly involved in branched-chain amino acid metabolism, glutamate and glutamine metabolism, tricarboxylic acid cycle, and lipid metabolism. We also screened three potential metabolite biomarkers: sebacic acid, L-glutamine, and indole-3-carboxaldehyde. These biomarkers showed excellent diagnostic performance for PTC in both discovery and validation cohorts, with areas under the receiver operating characteristic curves of 0.994 and 0.925, respectively. Our findings reveal distinct metabolic and lipidomic features of PTC and provide novel targets for diagnosis and treatment.

Auxiliary Diagnosis of Papillary Thyroid Carcinoma Based on Spectral Phenotype

Thyroid cancer, a common endocrine malignancy, is one of the leading death causes among endocrine tumors. The diagnosis of pathological section analysis suffers from diagnostic delay and cumbersome operating procedures. Therefore, we intend to construct the models based on spectral data that can be potentially used for rapid intraoperative papillary thyroid carcinoma (PTC) diagnosis and characterize PTC characteristics. To alleviate any concerns pathologists may have about using the model, we conducted an analysis of the used bands that can be interpreted pathologically. A spectra acquisition system was first built to acquire spectra of pathological section images from 91 patients. The obtained spectral dataset contains 217 spectra of normal thyroid tissue and 217 spectra of PTC tissue. Clinical data of the corresponding patients were collected for subsequent model interpretability analysis. The experiment has been approved by the Ethics Review Committee of the Wuhu Hospital of East China Normal University. The spectral preprocessing method was used to process the spectra, and the preprocessed signal respectively optimized by the first and secondary informative wavelengths selection was used to develop the PTC detection models. The PTC detection model using mean centering (MC) and multiple scattering correction (MSC) has optimal performance, and the reasons for the good performance were analyzed in combination with the spectral acquisition process and composition of the test slide. For model interpretable analysis, the near-ultraviolet band selected for modeling corresponds to the location of amino acid absorption peak, and this is consistent with the clinical phenomenon of significantly lower amino acid concentrations in PTC patients. Moreover, the absorption peak of hemoglobin selected for modeling is consistent with the low hemoglobin index in PTC patients. In addition, the correlation analysis was performed between the selected wavelengths and the clinical data, and the results show: the reflection intensity of selected wavelengths in normal cells has a moderate correlation with cell arrangement structure, nucleus size and free thyroxine (FT4), and has a strong correlation with triiodothyronine (T3); the reflection intensity of selected bands in PTC cells has a moderate correlation with free triiodothyronine (FT3).

Experimental study on changes in metabolic mechanism of papillary thyroid carcinoma complicated with Hashimoto's thyroiditis

Background

Whether the mechanism of thyroid papillary carcinoma (PTC) is the same in patients with a Hashimoto's thyroiditis (HT) background as compared with patients with a normal background remains a highly debated and controversial issue. In this study, we aimed to analyze the differences and similarities of the metabolic mechanism of PTC in normal and HT background, and to explore the relationship between HT and PTC.

Methods

The ultra performance liquid chromatography-quadrupole-time of flight-mass spectrometry (UPLC-Q-TOF/MS) technology was used to analyze 61 PTC patient tissues (31 HT background and 30 normal tissue (NC) background). Potential biomarkers were screened from principal component analysis (PCA) to orthogonal partial least square (OPLS) discriminant analysis. HMDB was searched to identify potential differential metabolites and final metabolic pathway analysis was performed by MetaboAnalyst 5.0. We analyzed the differential metabolites diagnostic accuracy through receiver operating characteristic (ROC) curves analysis.

Results

Seven different metabolites were screened from HT group and NC group, including arginine, glutamic acid, cysteine, citric acid, malic acid, uracil and taurine. Logistic regression model combined with ROC analysis of these 7 biomarkers had good discriminability for PTC (area under operating characteristic curve of HT group and NC group were 0.867 and 0.973, respectively). The HT group had specific metabolic pathways, including aminoacyl-tRNA biosynthesis, glycine, serine and threonine metabolism.

Conclusions

The metabolic profiles of the NC and HT groups had important similarities and differences in PTC. The correlation of PTC with HT may be related to aminoacyl-tRNA biosynthesis, serine and threonine metabolism.

Alterations in the amino acid profile in patients with papillary thyroid carcinoma with and without Hashimoto's thyroiditis

Purpose

Amino acids (AAs) play important physiological roles in living cells. Some amino acid changes in blood are specific for autoimmune disorders, and some are specific for thyroid cancer. The aims of this study were to profile AA metabolites in the serum of patients with papillary thyroid carcinoma (PTC0) without Hashimoto's thyroiditis (HT) and patients with PTC with HT (PTC1) and predict whether AA metabolites are associated with thyroid disease, thyroid hormone and thyroid autoantibodies.

Methods

A total of 95 serum samples were collected, including 28 healthy controls (HCs), 28 PTC0 patients and 39 PTC1 patients. Serum samples were analyzed by high-performance liquid chromatography-triple stage quadrupole-mass spectrometry (HPLC-TSQ-MS), and twenty-one amino acids (AAs) were detected.

Results

The serum concentration of glutamic acid was significantly elevated in PTC1 patients compared with PTC0 patients. Lysine was the second amino acid that differentiated these two groups of PTC patients. In addition, the serum concentrations of glycine, alanine and tyrosine were significantly reduced in both PTC patient groups compared to the HC group. These AAs were also correlated with thyroid hormones and antibodies. Five amino acid markers, namely, glycine, tyrosine, glutamic acid, glutamine and arginine, separated/distinguished PTC0 patients from healthy subjects, and eight AA markers, the same AAs as above without arginine but with alanine, leucine, valine and histidine, separated/distinguished PTC1 patients from healthy subjects based on ROC analysis.

Conclusion

Compared with the HCs, changes in AAs in PTC0 and PTC1 patients showed similar patterns, suggesting the possibility of a common pathophysiological basis, which confirms preliminary research that PTC is significantly associated with pathologically confirmed HT. We found two AAs, lysine and alanine, that can perform diagnostic functions in distinguishing PTC1 from PTC0.

Diagnostic Value of Salivary Amino Acid Levels in Cancer

This review analyzed 21 scientific papers on the determination of amino acids in various types of cancer in saliva. Most of the studies are on oral cancer (8/21), breast cancer (4/21), gastric cancer (3/21), lung cancer (2/21), glioblastoma (2/21) and one study on colorectal, pancreatic, thyroid and liver cancer. The amino acids alanine, valine, phenylalanine, leucine and isoleucine play a leading role in the diagnosis of cancer via the saliva. In an independent version, amino acids are rarely used; the authors combine either amino acids with each other or with other metabolites, which makes it possible to obtain high values of sensitivity and specificity. Nevertheless, a logical and complete substantiation of the changes in saliva occurring in cancer, including changes in salivary amino acid levels, has not yet been formed, which makes it important to continue research in this direction.

SPK1/S1P axis confers gastrointestinal stromal tumors (GISTs) resistance of imatinib

BACKGROUND

Imatinib mesylate (IM) is highly effective in the treatment of gastrointestinal stromal tumors (GISTs). However, the most of GISTs patients develop secondary drug resistance after 1-3 years of IM treatment. The aim of this study was to explore the IM-resistance mechanism via the multi-scope combined with plasma concentration of IM, genetic polymorphisms and plasma sensitive metabolites.

METHODS

This study included a total of 40 GISTs patients who had been regularly treated and not treated with IM. The plasma samples were divided into three experiments, containing therapeutic drug monitoring (TDM), OCT1 genetic polymorphisms and non-targeted metabolomics. According to the data of above three experiments, the IM-resistant cell line, GIST-T1/IMR cells, was constructed for verification the IM-resistance mechanism.

RESULTS

The results of non-targeted metabolomics analysis suggested that the sphingophospholipid metabolic pathway including the SPK1/S1P axis was inferred in IM-insensitive patients with GISTs. A GIST cell line (GIST-T1) was immediately induced as an IM resistance cell model (GIST-T1/IMR) and we found that blocking the signal pathway of SPK1/S1P in the GIST-T1/IMR could sensitize treatment of IM and reverse the IM-resistance.

CONCLUSIONS

Our findings suggest that IM secondary resistance is associated with the elevation of S1P, and blockage the signaling pathway of SPK1/S1P warrants evaluation as a potential therapeutic strategy in IM-resistant GISTs. The design of this study from blood management, group information collection, IM plasma concentration with different elements, identification of sphingolipid metabolism and lastly verification the function of SPK1/S1P in the IM-resistance GISTs cells.

The potential value of LC-MS non-targeted metabonomics in the diagnosis of follicular thyroid carcinoma

Background

To explore the metabolic differences of follicular thyroid carcinoma (FTC) by metabonomics, to find potential biomarkers for the diagnosis of FTC, and to explore the pathogenesis and diagnosis and treatment strategies of FTC.

Method

The metabonomics of 15 patients with FTC and 15 patients with follicular thyroid nodules(FTN) treated in Henan Cancer Hospital were analyzed by liquid chromatography-mass spectrometry (LC-MS).

Results

The analysis showed that the metabolite profiles of FTC tissues could be well distinguished from those of control tissues, and 6 kinds of lipids were identified respectively, including lysophosphatidic acid(LysoPA) [LysoPA(0:0/18:0),LysoPA(0:0/18:2(9Z,12Z)],LysoPA[20:4(8Z,11Z,14Z,17Z)/0:0)]; phosphatidic acid(PA) [PA(20:3(8Z,11Z,14Z)/0:0),PA(20:4(5Z,8Z,11Z,14Z)/0:0),PA(20:5(5Z,8Z,11Z,14Z,17Z)/0:0)]; lysophosphatidylcholine(LPC) [LPC(18:1),LPC(16:0),LPC[16:1(9Z)/0:0],LPC(17:0),LPC[22:4(7Z,10Z,13Z,16Z),LPC(20:2(11Z,14Z); phosphatidylcholine(PC)(PC(14:0/0:0),PC(16:0/0:0); sphingomyelin(SM) (d18:0/12:0); fatty acid(FA)(18:1(OH3)]. There are 2 kinds of amino acids, including L-glutamate,L-glutamine.There are 3 other metabolites, including retinol,flavin adenine dinucleotide,androsterone glucuronide.Lipid metabolites are the main metabolites in these metabolites.The metabolic pathways related to FTC were analyzed by KEGG and HMDB, and 9 metabolic pathways were found, including 4 amino acid related metabolic pathways, 1 lipid metabolic pathways and 4 other related pathways.

Conclusion

There are significant differences in many metabonomic characteristics between FTC and FTN, suggesting that these metabolites can be used as potential biomarkers. Further study found that LysoPA and its analogues can be used as biomarkers in the early diagnosis of FTC.It may be related to the abnormal metabolism of phospholipase D (PLD), the key enzyme of LysoPA synthesis caused by RAS pathway. At the same time, it was found that the metabolic pathway of amino acids and lipids was the main metabolic pathway of FTC. The abnormality of LysoPA may be the cause of follicular tumor carcinogenesis caused by lipid metabolic pathway.

Metabolomics-A Tool to Find Metabolism of Endocrine Cancer

Clinical endocrinology entails an understanding of the mechanisms involved in the regulation of tumors that occur in the endocrine system. The exact cause of endocrine cancers remains an enigma, especially when discriminating malignant lesions from benign ones and early diagnosis. In the past few years, the concepts of personalized medicine and metabolomics have gained great popularity in cancer research. In this systematic review, we discussed the clinical metabolomics studies in the diagnosis of endocrine cancers within the last 12 years. Cancer metabolomic studies were largely conducted using nuclear magnetic resonance (NMR) and mass spectrometry (MS) combined with separation techniques such as gas chromatography (GC) and liquid chromatography (LC). Our findings revealed that the majority of the metabolomics studies were conducted on tissue, serum/plasma, and urine samples. Studies most frequently emphasized thyroid cancer, adrenal cancer, and pituitary cancer. Altogether, analytical hyphenated techniques and chemometrics are promising tools in unveiling biomarkers in endocrine cancer and its metabolism disorders.

Gas chromatography-mass spectrometry-based untargeted metabolomics reveals metabolic perturbations in medullary thyroid carcinoma

Medullary thyroid cancer (MTC) is a rare tumor that arises from parafollicular cells within the thyroid gland. The molecular mechanism underlying MTC has not yet been fully understood. Here, we aimed to perform plasma metabolomics profiling of MTC patients to explore the perturbation of metabolic pathways contributing to MTC tumorigenesis. Plasma samples from 20 MTC patients and 20 healthy subjects were obtained to carry out an untargeted metabolomics by gas chromatography–mass spectrometry. Multivariate and univariate analyses were employed as diagnostic tools via MetaboAnalyst and SIMCA software. A total of 76 features were structurally annotated; among them, 13 metabolites were selected to be differentially expressed in MTC patients compared to controls (P < 0.05). These metabolites were mainly associated with the biosynthesis of unsaturated fatty acids and amino acid metabolisms, mostly leucine, glutamine, and glutamate, tightly responsible for tumor cells' energy production. Moreover, according to the receiver operating characteristic curve analysis, metabolites with the area under the curve (AUC) value up to 0.90, including linoleic acid (AUC = 0.935), linolenic acid (AUC = 0.92), and leucine (AUC = 0.948) could discriminate MTC from healthy individuals. This preliminary work contributes to existing knowledge of MTC metabolism by providing evidence of a distinctive metabolic profile in MTC patients relying on the metabolomics approach.

Integrative metabolomic characterization identifies plasma metabolomic signature in the diagnosis of papillary thyroid cancer

Discrimination of malignancy from thyroid nodules poses challenges in clinical practice. We aimed to identify the plasma metabolomic biomarkers in discriminating papillary thyroid cancer (PTC) from benign thyroid nodule (BTN). Metabolomics profiling of plasma was performed in two independent cohorts of 651 subjects of PTC (n = 215), BTN (n = 230), and healthy controls (n = 206). In addition, 132 patients with thyroid micronodules (<1 cm) and 44 patients with BTN suspected malignancy by ultrasound were used for biomarker validation. Recursive feature elimination algorithm was used for metabolic biomarkers selecting. Significant differential metabolites were demonstrated in patients with thyroid nodules (PTC and BTN) from healthy controls (P = 0.0001). A metabolic biomarker panel (17 differential metabolites) was identified to discriminate PTC from BTN with an AUC of 97.03% (95% CI: 95.28-98.79%), 91.89% sensitivity, and 92.63% specificity in discovery cohort. The panel had an AUC of 92.72% (95% CI: 87.46-97.99%), 86.57% sensitivity, and 92.50% specificity in validation cohort. The metabolic biomarker signature could correctly identify 84.09% patients whose nodules were suspected malignant by ultrasonography but finally histological benign. Moreover, high accuracy of 87.88% for diagnosis of papillary thyroid microcarcinoma was displayed by this panel and showed significant improvement in accuracy, AUC and specificity when compared with ultrasound. We identified a novel metabolic biomarker signature to discriminate PTC from BTN. The clinical use of this biomarker panel would have improved diagnosis stratification of thyroid microcarcinoma in comparison to ultrasound.

Detection of the Disorders of Glycerophospholipids and Amino Acids Metabolism in Lung Tissue From Male COPD Patients

Background: At present, few studies have reported the metabolic profiles of lung tissue in patients with COPD. Our study attempted to analyze the lung metabolome in male COPD patients and to screen the overlapping biomarkers of the lung and plasma metabolomes.

Methods: We performed untargeted metabolomic analysis of normal lung tissue from two independent sets (the discovery set: 20 male COPD patients and 20 controls and the replication set: 47 male COPD patients and 27 controls) and of plasma samples from 80 male subjects containing 40 COPD patients and 40 controls.

Results: We found glycerophospholipids (GPs) and Amino acids were the primary classes of differential metabolites between male COPD patients and controls. The disorders of GPs metabolism and the valine, leucine and isoleucine biosynthesis metabolism pathways were identified in lung discovery set and then also validated in the lung replication set. Combining lung tissue and plasma metabolome, Phytosphingosine and l-tryptophan were two overlapping metabolites biomarkers. Binary logistic regression suggested that phytosphingosine together with l-tryptophan was closely associated with male COPD and showed strong diagnostic power with an AUC of 0.911 (95% CI: 0.8460-0.9765).

Conclusion: Our study revealed the metabolic perturbations of lung tissues from male COPD patients. The detected disorders of GPs and amino acids may provide an insight into the pathological mechanism of COPD. Phytosphingosine and l-tryptophan were two novel metabolic biomarkers for differentiating COPD patients and controls.

Metabolic Profile Characterization of Different Thyroid Nodules Using FTIR Spectroscopy: A Review

Thyroid cancer’s incidence has increased in the last decades, and its diagnosis can be a challenge. Further and complementary testing based in biochemical alterations may be important to correctly identify thyroid cancer and prevent unnecessary surgery. Fourier-transform infrared (FTIR) spectroscopy is a metabolomic technique that has already shown promising results in cancer metabolome analysis of neoplastic thyroid tissue, in the identification and classification of prostate tumor tissues and of breast carcinoma, among others. This work aims to gather and discuss published information on the ability of FTIR spectroscopy to be used in metabolomic studies of the thyroid, including discriminating between benign and malignant thyroid samples and grading and classifying different types of thyroid tumors.

Multi-Omics Analysis of Fatty Acid Metabolism in Thyroid Carcinoma

Objective

Papillary thyroid carcinoma (PTC) accounts for the majority of thyroid cancer and affects a large number of individuals. The pathogenesis of PTC has not been completely elucidated thus far. Metabolic reprogramming is a common feature in tumours. Our previous research revealed the reprogramming of lipid metabolism in PTC. Further studies on lipid metabolism reprogramming may help elucidate the pathogenesis of PTC.

Methods

Clinical samples of PTC and para-tumour tissue were analysed using lipidomic, proteomic, and metabolomic approaches. A multi-omics integrative strategy was adopted to identify the important pathways in PTC. The findings were further confirmed using western blotting, tissue microarray, bioinformatics, and cell migration assays.

Results

Multi-omics data and the results of integrated analysis revealed that the three steps of fatty acid metabolism (hydrolysis, transportation, and oxidation) were significantly enhanced in PTC. Especially, the expression levels of LPL, FATP2, and CPT1A, three key enzymes in the respective steps, were elevated in PTC. Moreover, LPL, FATP2 and CPT1A expression was associated with the TNM stage, lymph node metastasis of PTC. Moreover, high levels of FATP2 and CPT1A contributed to poor prognosis of PTC. In addition, ectopic overexpression of LPL, FATP2 and CPT1A can each promote the migration of thyroid cancer cells.

Conclusions

Our data suggested that enhanced fatty acid metabolism supplied additional energy and substrates for PTC progression. This may help elucidating the underlying mechanism of PTC pathogenesis and identifying the potential therapeutic targets for PTC.

Diagnostic approach to thyroid cancer based on amino acid metabolomics in saliva by ultra-performance liquid chromatography with high resolution mass spectrometry

Thyroid cancer is a malignant disease with dramatically low advanced-stage 10-year survival. Meanwhile, the metabolites in saliva are becoming a wealthy source of disease biomarkers. However, there is a lack of non-invasive analytical methods for the identification of biomarkers in saliva for the preoperative diagnosis of thyroid cancer. Therefore, we developed an ultra-high performance liquid chromatography-high resolution mass spectrometry (UPLC-HRMS) method to simultaneously determine the metabolic levels of 10 amino acids in saliva, aiming to study the amino acid metabolism profile to promote early diagnosis of thyroid cancer. We tested unstimulated whole saliva from patients with papillary thyroid carcinoma (PTC; n = 61) and healthy controls (HC; n = 61), and used receiver operating characteristic (ROC) curves to establish the diagnostic value of potential markers. The method validation results showed good precision, linearity (R² > 0.99), recovery (92.2 %-110.3 %), intra- and inter-day precision (RSD < 7 % and RSD < 9 %, respectively). The concentration of 10 amino acids was significantly different between PTC and HC in human salivary analysis (P < 0.05), the area under the curve (AUC) values of a single marker for the diagnosis of PTC were ranging from 0.678 to 0.833. A panel of alanine, valine, proline, phenylalanine was selected in combination yielded the AUC of 0.936, which will improve the accuracy of early diagnosis of thyroid cancer (sensitivity: 91.2 %; specificity: 85.2 %). This study proved the possibility of salivary amino acid biomarkers for PTC early diagnosis, providing a simple auxiliary way for the non-invasive diagnosis of thyroid cancer.

Metabolic Reprogramming in Thyroid Cancer: Role of the Epithelial-Mesenchymal Transition

Thyroid cancer (TC) represents the most common endocrine malignancy, with an increasing incidence all over the world. Papillary TC (PTC), a differentiated TC subtype, is the most common and, even though it has an excellent prognosis following radioiodine (RAI) ablation, it shows an aggressive behavior in 20–30% of cases, becoming RAI-resistant and/or metastatic. On the other side, anaplastic thyroid carcinoma (ATC), the most undifferentiated TC, is a rare but devastating disease, indicating that progression of differentiated to undifferentiated forms of TC could be responsible for RAI-resistance and increased mortality. The epithelial-to-mesenchymal transition (EMT) plays a pivotal role in both tumor progression and resistance to therapy. Moreover, during tumor progression, cancer cells modify their metabolism to meet changed requirements for cellular proliferation. Through these metabolic changes, cancer cells may adopt cancer stem cell-like properties and express an EMT phenotype. EMT, in turn, can induce metabolic changes to which cancer cells become addicted. Here we review metabolic reprogramming in TC highlighting the role of EMT with the aim to explore a potential field to find out new therapeutic strategies for advanced-stage PTC. Accordingly, we discuss the identification of the metabolic enzymes and metabolites, critical to TC progression, which can be employed either as predicting biomarkers of tumor response to RAI therapy or possible targets in precision medicine.

Aberrant expression of PROS1 correlates with human papillary thyroid cancer progression

Background

Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer (TC). Considering the important association between cellular immunity and PTC progression, it is worth exploring the biological significance of immune-related signaling in PTC.

Methods

Several bioinformatics tools, such as R software, WEB-based Gene SeT AnaLysis Toolkit (WebGestalt), Database for Annotation, Visualization and Integrated Discovery (DAVID), Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape were used to identify the immune-related hub genes in PTC. Furthermore, in vitro experiments were adopted to identify the proliferation and migration ability of PROS1 knockdown groups and control groups in PTC cells.

Results

The differentially expressed genes (DEGs) of five datasets from Gene Expression Omnibus (GEO) contained 154 upregulated genes and 193 downregulated genes, with Protein S (PROS1) being the only immune-related hub gene. Quantitative real-time polymerase chain reaction (RT-qPCR) and immunohistochemistry (IHC) have been conducted to prove the high expression of PROS1 in PTC. Moreover, PROS1 expression was significantly correlated with lymph nodes classification. Furthermore, knockdown of PROS1 by shRNAs inhibited the cell proliferation and cell migration in PTC cells.

Conclusions

The findings unveiled the clinical relevance and significance of PROS1 in PTC and provided potential immune-related biomarkers for PTC development and prognosis.

A glance at the actual role of glutamine metabolism in thyroid tumorigenesis

Thyroid cancers (TCs) are the most prevalent malignancy of the endocrine system and the seventh most common cancer in women. According to estimates from the Global Cancer Observatory (GCO) in 2020, the incidence of thyroid cancer globally was 586,000 cases. As thyroid cancer incidences have dramatically increased, identifying the most important metabolic pathways and biochemical markers involved in thyroid tumorigenesis can be critical strategies for controlling the prevalence and ultimately treatment of this disease. Cancer cells undergo cellular metabolism and energy alteration in order to promote cell proliferation and invasion. Glutamine is one of the most abundant free amino acids in the human body that contributes to cancer metabolic remodeling as a carbon and nitrogen source to sustain cell growth and proliferation. In the present review, glutamine metabolism and its regulation in cancer cells are highlighted. Thereafter, emphasis is given to the perturbation of glutamine metabolism in thyroid cancer, focusing on metabolomics studies.

Identification of Urine Metabolic Biomarkers for Vogt-Koyanagi-Harada Disease

The diagnosis of Vogt-Koyanagi-Harada (VKH) disease is mainly based on a complex clinical manifestation while it lacks objective laboratory biomarkers. To explore the potential molecular biomarkers for diagnosis and disease activity in VKH, we performed an untargeted urine metabolomics analysis by ultra-high-performance liquid chromatography equipped with quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF/MS). Through univariate and multivariate statistical analysis, we found 9 differential metabolites when comparing VKH patients with healthy controls, and 26 differential metabolites were identified when comparing active VKH patients with inactive VKH patients. Pathway enrichment analysis showed that glycine, serine and threonine metabolism, and arginine and proline metabolism were significantly altered in VKH versus healthy controls. Lysine degradation and biotin metabolism pathways were significantly altered in active VKH versus inactive VKH. Furthermore, the receiver operating characteristic (ROC) curve analysis revealed that the combination of acetylglycine and gamma-glutamylalanine could differentiate VKH from healthy controls with an area under the curve (AUC) of 0.808. A combination of ureidopropionic acid and 5′-phosphoribosyl-5-amino-4-imidazolecarboxamide (AICAR) had an excellent AUC of 0.958 for distinguishing active VKH from inactive VKH. In summary, this study identified abnormal metabolites in urine of patients with VKH disease. Further studies are needed to confirm whether these metabolites are specific for this disease.

Serum Metabolomics Study of Papillary Thyroid Carcinoma Based on HPLC-Q-TOF-MS/MS

This study examined metabolite profile differences between serum samples of thyroid papillary carcinoma (PTC) patients and healthy controls, aiming to identify candidate biomarkers and pathogenesis pathways in this cancer type. Serum samples were collected from PTC patients (n = 80) and healthy controls (n = 80). Using principal component analysis (PCA), partial least squares discrimination analysis(PLS-DA), orthogonal partial least square discriminant analysis (OPLS-DA), t-tests, and the volcano plot, a model of abnormal metabolic pathways in PTC was constructed. PCA, PLS-DA, and OPLS-DA analysis revealed differences in serum metabolic profiles between the PTC and control group. OPLS-Loading plot analysis, combined with Variable importance in the projection (VIP)>1, Fold change (FC) > 1.5, and p < 0.05 were used to screen 64 candidate metabolites. Among them, 22 metabolites, including proline betaine, taurocholic acid, L-phenylalanine, retinyl beta-glucuronide, alpha-tocotrienol, and threonine acid were upregulated in the PTC group; meanwhile, L-tyrosine, L-tryptophan, 2-arachidonylglycerol, citric acid, and other 42 metabolites were downregulated in this group. There were eight abnormal metabolic pathways related to the differential metabolites, which may be involved in the pathophysiology of PTC. Six metabolites yielded an area under the receiver operating curve of >0.75, specifically, 3-hydroxy-cis-5-tetradecenoylcarnitine, aspartylphenylalanine, l-kynurenine, methylmalonic acid, phenylalanylphenylalanine, and l-glutamic acid. The Warburg effect was observed in PTC. The levels of 3-hydroxy-cis-5-tetradecenoylcarnitine, aspartylphenylalanine, l-kynurenine, methylmalonic acid, phenylalanine, and L-glutamic acid may help distinguish PTC patients from healthy controls. Aspartic acid metabolism, glutamic acid metabolism, urea cycle, and tricarboxylic acid cycle are involved in the mechanism of PTC.

Low level of plasma fibulin-1 in patients with thyroid lesions: a case-control study and literature review

Investigating novel biomarkers discriminating thyroid nodules is a matter of great importance for differential diagnosis. The current study was planned to investigate the diagnostic value of fibulin-1 in plasma specimens of patients with thyroid nodules. A literature review was also performed to gain an understanding of the existing research relevant to the main role of fibulin-1 in carcinogenesis. In this case-control study, the levels of plasma fibulin-1 were compared in 82 subjects including papillary thyroid cancer (PTC; n = 30), multinodular goiter (MNG; n = 30), and healthy subjects (n = 22) using enzyme-linked immunosorbent assay (ELISA). Fibulin-1 levels of patients with PTC and MNG were documented to be significantly lower than those of healthy subjects (PTC vs. Healthy; P = 0.000, MNG vs. Healthy; P = 0.000). No statistically significant differences were found between PTC and MNG groups when fibulin-1 levels were compared (P > 0.05). Low level of plasma fibulin-1 was associated with an increased risk of PTC tumorigenesis (odds ratio = 0.810; 95% CI: 0.704-0.933; P = 0.003). Further, fibulin-1 had an appropriate diagnostic value for detecting PTC patients with a sensitivity of 73.33%, and specificity of 100% at the cutoff value > 4.9 (ng/ml). According to the results of the present research which are tied well with previous studies, the abnormal downregulation of fibulin-1 may play a role in the PTC and MNG tumorigenesis. In addition, fibulin-1 probably promotes the development and progression of other human cancer; however, further studies are needed to improve current understandings.

The Potential of Metabolomics in the Diagnosis of Thyroid Cancer

Thyroid cancer is the most common endocrine system malignancy. However, there is still a lack of reliable and specific markers for the detection and staging of this disease. Fine needle aspiration biopsy is the current gold standard for diagnosis of thyroid cancer, but drawbacks to this technique include indeterminate results or an inability to discriminate different carcinomas, thereby requiring additional surgical procedures to obtain a final diagnosis. It is, therefore, necessary to seek more reliable markers to complement and improve current methods. "Omics" approaches have gained much attention in the last decade in the field of biomarker discovery for diagnostic and prognostic characterisation of various pathophysiological conditions. Metabolomics, in particular, has the potential to identify molecular markers of thyroid cancer and identify novel metabolic profiles of the disease, which can, in turn, help in the classification of pathological conditions and lead to a more personalised therapy, assisting in the diagnosis and in the prediction of cancer behaviour. This review considers the current results in thyroid cancer biomarker research with a focus on metabolomics.