Diagnosis of post-surgical fine-needle aspiration biopsies of thyroid lesions with indeterminate cytology using HRMAS NMR-based metabolomics

Metabolomic screening of radioiodine refractory thyroid cancer patients and the underlying chemical mechanism of iodine resistance

Radioiodine refractory (RAIR) patients do not benefit from iodine-131 therapy. Thus, timely identification of RAIR patients is critical for avoiding ineffective radioactive iodine therapy. In addition, determining the causes of iodine resistance will facilitate the development of novel treatment strategies. This study was comprised of 20 RAIR and 14 non-radioiodine refractory (non-RAIR) thyroid cancer patients. Liquid chromatography-mass spectrometry was used to identify differences in the serum metabolites of RAIR and non-RAIR patients. In addition, chemical assays were performed to determine the effects of the differential metabolites on iodine uptake. Metabolic pathway enrichment analysis of the differential metabolites revealed significant differences in the phenylalanine and tyrosine metabolic pathways. Notably, quinate and shikimic acid, metabolites of the tyrosine pathway, were significantly increased in the RAIR group. In contrast, the phenylalanine pathway metabolites, hippuric acid and 2-phenylacetamide, were markedly decreased in the RAIR group. Thyroid peroxidase plays an important role in catalyzing the iodination of tyrosine residues, while the ionic state of iodine promotes the iodination reaction. Quinate, shikimic acid, hippuric acid, and 2-phenylacetamide were found to be involved in the iodination of tyrosine, which is a key step in thyroid hormone synthesis. Specifically, quinate and shikimic acid were found to inhibit iodination, while hippuric acid and 2-phenylacetamide promoted iodination. Abnormalities in phenylalanine and tyrosine metabolic pathways are closely associated with iodine resistance. Tyrosine is required for thyroid hormone synthesis and could be a potential cause of iodine resistance.

Metabolite signature of human malignant thyroid tissue: A systematic review and meta-analysis

BACKGROUND

Thyroid cancer (TC) is the predominant malignancy within the endocrine system. However, the standard method for TC diagnosis lacks the capability to identify the pathological condition of all thyroid lesions. The metabolomics approach has the potential to manage this problem by identifying differential metabolites.

AIMS

This study conducted a systematic review and meta-analysis of the NMR-based metabolomics studies in order to identify significant altered metabolites associated with TC.

METHODS

A systematic search of published literature in any language in three databases including Embase, PubMed, and Scopus was conducted. Out of 353 primary articles, 12 studies met the criteria for inclusion in the systematic review. Among these, five reports belonging to three articles were eligible for meta-analysis. The correlation coefficient of the orthogonal partial least squares discriminant analysis, a popular model in the multivariate statistical analysis of metabolomic data, was chosen for meta-analysis. The altered metabolites were chosen based on the fact that they had been found in at least three studies.

RESULTS

In total, 49 compounds were identified, 40 of which were metabolites. The increased metabolites in thyroid lesions compared normal samples included lactate, taurine, alanine, glutamic acid, glutamine, leucine, lysine, phenylalanine, serine, tyrosine, valine, choline, glycine, and isoleucine. Lipids were the decreased compounds in thyroid lesions. Lactate and alanine were increased in malignant versus benign thyroid lesions, while, myo-inositol, scyllo-inositol, citrate, choline, and phosphocholine were found to be decreased. The meta-analysis yielded significant results for three metabolites of lactate, alanine, and citrate in malignant versus benign specimens.

DISCUSSION

In this study, we provided a concise summary of 12 included metabolomic studies, making it easier for future researchers to compare their results with the prior findings.

CONCLUSION

It appears that the field of TC metabolomics will experience notable advancement, leading to the discovery of trustworthy diagnostic and prognostic biomarkers.

Paving the path toward multi-omics approaches in the diagnostic challenges faced in thyroid pathology

INTRODUCTION

Despite advancements in diagnostic methods, the classification of indeterminate thyroid nodules still poses diagnostic challenges not only in pre-surgical evaluation but even after histological evaluation of surgical specimens. Proteomics, aided by mass spectrometry and integrated with artificial intelligence and machine learning algorithms, shows great promise in identifying diagnostic markers for thyroid lesions.

AREAS COVERED

This review provides in-depth exploration of how proteomics has contributed to the understanding of thyroid pathology. It discusses the technical advancements related to immunohistochemistry, genetic and proteomic techniques, such as mass spectrometry, which have greatly improved sensitivity and spatial resolution up to single-cell level. These improvements allowed the identification of specific protein signatures associated with different types of thyroid lesions.

EXPERT COMMENTARY

Among all the proteomics approaches, spatial proteomics stands out due to its unique ability to capture the spatial context of proteins in both cytological and tissue thyroid samples. The integration of multi-layers of molecular information combining spatial proteomics, genomics, immunohistochemistry or metabolomics and the implementation of artificial intelligence and machine learning approaches, represent hugely promising steps forward toward the possibility to uncover intricate relationships and interactions among various molecular components, providing a complete picture of the biological landscape whilst fostering thyroid nodule diagnosis.

Pilot Study on the Use of Untargeted Metabolomic Fingerprinting of Liquid-Cytology Fluids as a Diagnostic Tool of Malignancy for Thyroid Nodules

Although it is the gold standard for assessing the malignancy of thyroid nodules (TNs) preoperatively, the cytological analysis of fine-needle aspiration cytology (FNAC) samples results in 20-30% of cases in indeterminate lesions (ITNs). As two-thirds of these lesions will appear benign after diagnostic surgery, improved preoperative diagnostic methods need to be developed. In this pilot study, we evaluate if the metabolomic profiles of liquid-based (CytoRich^®) FNAC samples of benign and malignant nodules can allow the molecular diagnosis of TNs. We performed untargeted metabolomic analyses with CytoRich^® FNAC in a monocentric retrospective study. The cohort was composed of cytologically benign TNs, histologically benign or papillary thyroid carcinomas (PTCs) cytologically ITNs, and suspicious/malignant TNs histologically confirmed as PTCs. The diagnostic performance of the identified metabolomic signature was assessed using several supervised classification methods. Seventy-eight patients were enrolled in the study. We identified 7690 peaks, of which 2697 ions were included for further analysis. We selected a metabolomic signature composed of the top 15 metabolites. Among all the supervised classification methods, the supervised autoencoder deep neural network exhibited the best performance, with an accuracy of 0.957 (0.842-1), an AUC of 0.945 (0.833-1), and an F1 score of 0.947 (0.842-1). Here, we report a promising new ancillary molecular technique to differentiate PTCs from benign TNs (including among ITNs) based on the metabolomic signature of FNAC sample fluids. Further studies with larger cohorts are now needed to identify a larger number of biomarkers and obtain more robust signatures.

High-resolution magic angle spinning NMR for intact biological specimen analysis: Initial discovery, recent developments, and future directions

High-resolution magic angle spinning (HRMAS) NMR, an approach for intact biological material analysis discovered more than 25 years ago, has been advanced by many technical developments and applied to many biomedical uses. This article provides a history of its discovery, first by explaining the key scientific advances that paved the way for HRMAS NMR's invention, and then by turning to recent developments that have profited from applying and advancing the technique during the last 5 years. Developments aimed at directly impacting healthcare include HRMAS NMR metabolomics applications within studies of human disease states such as cancers, brain diseases, metabolic diseases, transplantation medicine, and adiposity. Here, the discussion describes recent HRMAS NMR metabolomics studies of breast cancer and prostate cancer, as well as of matching tissues with biofluids, multimodality studies, and mechanistic investigations, all conducted to better understand disease metabolic characteristics for diagnosis, opportune windows for treatment, and prognostication. In addition, HRMAS NMR metabolomics studies of plants, foods, and cell structures, along with longitudinal cell studies, are reviewed and discussed. Finally, inspired by the technique's history of discoveries and recent successes, future biomedical arenas that stand to benefit from HRMAS NMR-initiated scientific investigations are presented.

Thyroid Nodules with Indeterminate FNAC According to the Italian Classification System: Prevalence, Rate of Operation, and Impact on Risk of Malignancy. An Updated Systematic Review and Meta-analysis

A thyroid nodule classified as indeterminate on fine-needle aspiration cytology (FNAC), hereafter referred to as an indeterminate thyroid nodule (ITN), represents a clinical dilemma. The Italian Consensus for the Classification and Reporting of Thyroid Cytology (ICCRTC) divides ITNs into low- and high-risk categories (i.e., TIR3A and TIR3B, respectively) to better manage patients. This study aimed to achieve high-evidence estimates of the prevalence, rate of operation, and risk of malignancy of ITNs, including TIR3A and TIR3B ITNs. This systematic review was conducted according to MOOSE to retrieve all original studies citing ICCRTC. The last search was performed in February 2022. The risk of bias of the included studies was assessed. Separate proportion meta-analyses were performed with a random-effect model using OpenMeta[Analyst]. The online search processed 271 studies, and 33 were finally considered. First, the cancer prevalence among ITNs was 32.4%. Second, the cancer prevalence among TIR3As was 12.4%, with heterogeneity (I₂ 90%) explained by a linear correlation between sample size and cancer rate (p = 0.009). Third, the cancer prevalence among TIR3Bs was 44.4%, with heterogeneity (I₂ 75%) explained by the inverse correlation between sample size and cancer rate (p = 0.031). Fourth, the prevalence of ITNs, TIR3A, and TIR3B among FNACs was 29.6%, 12.6%, and 12.9%, respectively, with sample size and TIR3B prevalence being inversely correlated (p = 0.04). Fifth, the operation rates of ITNs, TIR3A, and TIR3B were 54.3%, 48.3%, and 75.2%, respectively, and the sample size and TIR3A operation rate were inversely correlated (p = 0.010). These data strongly support the division of ITNs into low- and high-risk subcategories. Importantly for clinical practice, the cancer rate among ITNs is significantly influenced by the study sample size.

ACLY inhibitors induce apoptosis and potentiate cytotoxic effects of sorafenib in thyroid cancer cells

PURPOSE

ATP-citrate lyase (ACLY) is a critical enzyme at the intersection of glucose and lipid metabolism. ACLY is often upregulated or activated in cancer cells to accelerate lipid synthesis and promote tumor progression. In this study, we aimed to explore the possibility of utilizing ACLY inhibition as a new strategy in the treatment of thyroid cancer.

METHODS

Bioinformatics analysis of the public datasets was performed. Thyroid cancer cells were treated with two different ACLY inhibitors, SB-204990 and NDI-091143.

RESULTS

Bioinformatics analysis revealed that ACLY expression was increased in anaplastic thyroid cancer. In thyroid cancer cell lines FTC-133 and 8505C, ACLY inhibitors suppressed monolayer cell growth and clonogenic ability in a dose-dependent and time-dependent manner. Flow cytometry analysis showed that ACLY inhibitors increased the proportion of sub-G1 cells in the cell cycle and the number of annexin V-positive cells. Immunoblotting confirmed caspase-3 activation and PARP1 cleavage following treatment with ACLY inhibitors. Compromised cell viability could be partially rescued by co-treatment with the pan-caspase inhibitor Z-VAD-FMK. Additionally, we showed that ACLY inhibitors impeded three-dimensional growth and cell invasion in thyroid cancer cells. Isobolograms and combination index analysis indicated that ACLY inhibitors synergistically potentiated the cytotoxicity rendered by sorafenib.

CONCLUSIONS

Targeting ACLY holds the potential for being a novel therapeutic strategy for thyroid cancer.

Operation rate and cancer prevalence among thyroid nodules with FNAC report of suspicious for malignancy (TIR4) or malignant (TIR5) according to Italian classification system: a systematic review and meta-analysis

BACKGROUND

In the Italian system for reporting thyroid cytology (ICCRTC), nodules suspicious for (TIR4) and consistent with (TIR5) malignancy are thought being 5% and 4-8% of all biopsies and having risk of malignancy of 60-80% and >95%, respectively. However, no evidence-based data exist about these figures. The present systematic review aimed at achieving solid estimates about TIR4 and TIR5 also considering potential influencing factors.

METHODS

The review was conducted according to MOOSE. Databases of Google Scholar and Cochrane were searched. No language restriction was used. The last search was performed on February 26th 2022. Quality assessment was performed. Proportion meta-analyses were performed using random-effect model. Statistical analyses were performed using OpenMeta [Analyst].

RESULTS

The online search retrieved 271 articles and 16 were finally included for quantitative analysis. The risk of bias was generally low. The pooled cancer prevalence in TIR4 was 92.5% (95%CI 89.4-95.6%) with unexplained moderate heterogeneity. The pooled cancer rate among TIR5 was 99.7% (95%CI 99.3-100%) without heterogeneity. The resection rate in TIR4 and TIR5 showed heterogeneity, being the latter explained when using their prevalence among biopsies: the higher the prevalence, the higher the operation rate. The pooled risk difference between TIR5 and TIR4 was significant (OR 11.153).

CONCLUSIONS

These figures can form the basis for the next updated version of ICCRTC. Any institution using ICCRTC should revise its series of TIR4/TIR5 to calculate the cancer rate, and, importantly, consider the modifiers of the risk of malignancy. A cross check among institutions is advised.

Uncontrolled Thyroid during Pregnancy Alters the Circulative and Exerted Metabolome

Normal levels of thyroid hormones (THs) are essential for a normal pregnancy outcome, fetal growth and the normal function of the central nervous system. Hypothyroidism, a common endocrine disorder during pregnancy, is a significant metabolic factor leading to cognitive impairments. It is essential to investigate whether patients with thyroid dysfunction may present an altered circulative and excreted metabolic profile, even after receiving treatment with thyroxine supplements. NMR metabolomics was employed to analyze 90 serum and corresponding colostrum samples. Parallel analyses of the two biological specimens provided a snapshot of the maternal metabolism through the excretive and circulating characteristics of mothers. The metabolomics data were analyzed by performing multivariate statistical, biomarker and pathway analyses. Our results highlight the impact of hypothyroidism on metabolites’ composition during pregnancy and lactation. Thyroid disorder causing metabolite fluctuations may lead to impaired lipid and glucose metabolic pathways as well as aberrant prenatal neurodevelopment, thus posing a background for the occurrence of metabolic syndrome or neurogenerative diseases later in life. This risk applies to not only untreated but also hypothyroid women under replacement therapy since our findings in both biofluids framed a different metabolic phenotype for the latter group, thus emphasizing the need to monitor women adequately after treatment initiation.

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.

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.

Diagnostic and Prognostic Performance of Liquid Biopsy-Derived Exosomal MicroRNAs in Thyroid Cancer Patients: A Systematic Review and Meta-Analysis

Circulatory tumor-derived exosomal microRNAs (miRNAs) play key roles in cancer development/progression. We aimed to assess the diagnostic/prognostic value of circulating exosomal miRNA in thyroid cancer (TC). A search in PubMed, Scopus, Web of Science, and Science Direct up to 22 May 2021 was performed. The true/false positive (TP/FP) and true/false negative (TN/FN) rates were extracted from each eligible study to obtain the pooled sensitivity, specificity, positive/negative likelihood ratios (PLR/NLR), diagnostic odds ratio (DOR), and their 95% confidence intervals (95%CIs). The meta-analysis included 12 articles consisting of 1164 Asian patients and 540 controls. All miRNAs were quantified using qRT-PCR assays. The pooled sensitivity was 82% (95%CI = 77-86%), pooled specificity was 76% (95%CI = 71-80%), and pooled DOR was 13.6 (95%CI = 8.8-21.8). The best biomarkers with high sensitivity were miR-16-2-3p (94%), miR-223-5p (91%), miR-130a-3p (90%), and miR182-5p (94%). Similarly, they showed high specificity, in addition to miR-34c-5p. Six panels of two to four exosomal miRNAs showed higher diagnostic values with an area under the curve (AUC) ranging from 0.906 to 0.981. The best discriminative ability to differentiate between cancer and non-cancer individuals was observed for miR-146b-5p + miR-223-5p + miR-182-5p (AUC = 0.981, sensitivity = 93.8% (84.9-98.3), specificity = 92.9% (76.5-99.1)). In conclusion, the expression levels of exosomal miRNAs could predict TC.

Metabolomics of Oral/Head and Neck Cancer

Oral/head and neck cancer is the sixth most common human malignancies in the world. Despite the treatment advances in surgery, chemotherapy, and radiotherapy, the patient survival has not been significantly improved in the past several decades. As a new methodological approach, metabolomics may help reveal the metabolic reprogramming mechanisms underlying head and neck cancer cell proliferation, invasion, and metastasis and may be used to identify metabolite biomarkers for clinical applications of the disease. In this chapter, we briefly review recent metabolomic applications in head and neck cancer.

Integrated analysis of metabolome in a EUS-FNA sample with transcriptome in the TCGA cohort of pancreatic head and body/tail adenocarcinoma

Metabolome profiles are largely unknown for pancreatic head cancers, in which the predominant anatomical feature is the exosure of bile, pancreatic juice, and duodenal juice. In this research, 30 head and 30 body/tail cytological samples acquired by endoscopic ultrasound-guided fine needle aspiration (EUS-FNA) of pancreatic adenocarcinoma were delivered for liquid chromatography coupled with mass spectrometry (LC-MS). Transcriptome analysis was performed using the sequencing data from The Cancer Genome Atlas (TCGA) cohort. LC-MS obtained 4,857 features in EUS-FNA cytological samples, and 586 metabolites were certified. Among them, 30 differential metabolites were identified. In the TCGA cohort, 247 differential metabolism genes were selected from 1,583 differential genes. The integrated analysis identified the top three enriched metabolic pathways as follows: branched chain amino acid (BCAA) biosynthesis; glycerophospholipid metabolism; and phenylalanine metabolism. In cell line, BCAA promoted pancreatic cancer proliferation and inhibited Oxaliplatin-induced apoptosis. In conclusion, metabolomic analysis with the EUS-FNA sample is feasible for pancreatic cancer. The integrated analysis can identify key metabolites and enzyme-coded genes between pancreatic head and body/tail adenocarcinoma. Anti-BCAA metabolism therapy may exert promising effect, especially for the body/tail cancer.

Predictive Modeling for Metabolomics Data

In recent years, mass spectrometry (MS)-based metabolomics has been extensively applied to characterize biochemical mechanisms, and study physiological processes and phenotypic changes associated with disease. Metabolomics has also been important for identifying biomarkers of interest suitable for clinical diagnosis. For the purpose of predictive modeling, in this chapter, we will review various supervised learning algorithms such as random forest (RF), support vector machine (SVM), and partial least squares-discriminant analysis (PLS-DA). In addition, we will also review feature selection methods for identifying the best combination of metabolites for an accurate predictive model. We conclude with best practices for reproducibility by including internal and external replication, reporting metrics to assess performance, and providing guidelines to avoid overfitting and to deal with imbalanced classes. An analysis of an example data will illustrate the use of different machine learning methods and performance metrics.

Shared and unique metabolic features of the malignant and benign thyroid lesions determined with use of 1H HR MAS NMR spectroscopy

The purpose of this work was to investigate the distinct and common metabolic features of the malignant and benign thyroid lesions in reference to the non-transformed tissue from the contralateral gland (chronic thyroiditis and colloid goiter). 1H HR MAS NMR spectra of 38 malignant lesions, 32 benign lesions and 112 samples from the non-tumoral tissue (32 from chronic thyroiditis and 80 samples from colloid goiter) were subjected both to multivariate and univariate analysis. The increased succinate, glutamine, glutathione, serine/cysteine, ascorbate, lactate, taurine, threonine, glycine, phosphocholine/glycerophosphocholine and decreased lipids were found in both lesion types in comparison to either colloid goiter or chronic thyroiditis. The elevated glutamate and choline, and reduced citrate and glucose were additionally evident in these lesions in reference to goiter, while the increased myo-inositol-in comparison to thyroiditis. The malignant lesions were characterized by the higher alanine and lysine levels than colloid goiter and thyroiditis, while scyllo-inositol was uniquely increased in the benign lesions (not in cancer) in comparison to both non-tumoral tissue types. Moreover, the benign lesions presented with the unique increase of choline in reference to thyroiditis (not observed in the cancerous tissue). The metabolic heterogeneity of the non-tumoral tissue should be considered in the analysis of metabolic reprogramming in the thyroid lesions.

The related mechanism of complete Freund's adjuvant-induced chronic inflammation pain based on metabolomics analysis

Chronic inflammation pain is a debilitating disease, and its mechanism still remains poorly understood. This study attempted to illuminate the metabolic mechanism of chronic inflammation pain induced by complete Freund’s adjuvant (CFA) injection, especially at spinal level. The chronic inflammation pain model was established by CFA administration. Behavioral testing including mechanical allodynia and thermal hyperalgesia was performed. Meanwhile, a liquid chromatography–mass spectrometry‐based metabolomics approach was applied to analyze potential metabolic biomarkers. The orthogonal partial least squares discrimination analysis mode was employed for determining metabolic changes, and a western blot was performed to detect the protein expression change. The results showed that 27 metabolites showed obviously abnormal expression and seven metabolic pathways were significantly enriched, comprising aminoacyl‐tRNA biosynthesis, arginine and proline metabolism, histidine metabolism, purine metabolism, phenylalanine, tyrosine and tryptophan biosynthesis, glutathione metabolism, and phenylalanine metabolism. Meanwhile, the results showed that the expression of arginase I and nitric oxide levels were elevated in the CFA group compared with the control group, while the argininosuccinate synthetase and argininosuccinatelyase proteins were not significantly different between the groups. These findings demonstrate that metabolic changes of the spinal cord may be implicated in neurotransmitter release and pain conductivity following CFA administration.

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.

Undetectable Free Aromatic Amino Acids in Nails of Breast Carcinoma: Biomarker Discovery by a Novel Metabolite Purification VTGE System

Background: Metabolic reprogramming in breast cancer is depicted as a crucial change in the tumor microenvironment. Besides the molecular understanding of metabolic heterogeneity, appreciable attention is drawn to characterizing metabolite profiles in tumor tissue and derived biological fluids and tissue materials. Several findings reported on the metabolic alterations of free aromatic amino acids (FAAAs) and other metabolites in biological fluids. Furthermore, there is a significant gap in the development of a suitable method for the purification and analysis of metabolite biomarkers in nails of cancer patients. Methods: To address the metabolite alterations, specifically FAAA levels in nails, fingernail clippings of breast cancer patients (N = 10) and healthy subjects (N-12) were used for extraction and purification of metabolites. Here, we reported a novel and specifically designed vertical tube gel electrophoresis (VTGE) system that helped in the purification of metabolites in the range of 100-1,000 Da from nail materials. Here, the VTGE system uses 15% polyacrylamide under non-denaturing and non-reducing conditions, which makes eluted metabolites directly compatible with LC-HRMS and other analytical techniques. Qualitative and quantitative determination of FAAAs in nail lysates was done in positive ESI mode of the Agilent LC-HRMS platform. Results: The analysis on collected data of nail metabolites clearly suggested that FAAAs including tryptophan, tyrosine, phenylalanine, and histidine were undetectable in nail lysates of breast cancer over healthy subjects. This is a first report that showed highly reduced levels of FAAAs in nails of breast cancer patients. Furthermore, the present observation is in consonance with previous findings that showed cancer cachexia and high amino acid catabolism in breast cancer patients that drive metabolite-led cancer growth and proliferation. Conclusion: This paper provides a proof of concept for a novel and specifically developed VTGE process that showed first evidence on the undetectable level of FAAAs in nails of breast cancer patients as metabolite biomarkers. Here, the authors propose the potential use of a VTGE-assisted process to achieve metabolomic discovery in nails of breast cancer and other tumor types.

Metabolomic Reprogramming Detected by 1H-NMR Spectroscopy in Human Thyroid Cancer Tissues

Thyroid cancer cells demonstrate an increase in oxidative stress and decreased antioxidant action, but the effects of this increased oxidative stress on cell function remain unknown. We aimed to identify changes in the metabolism of thyroid cancer cells caused by oxidative stress, using proton nuclear magnetic resonance (¹H-NMR) spectroscopy. Samples of thyroid cancer and healthy thyroid tissue were collected from patients undergoing thyroidectomy and analyzed with ¹H-NMR spectroscopy for a wide array of metabolites. We found a significant increase in lactate content in thyroid cancer tissue compared to healthy tissue. Metabolomic analysis demonstrated significant differences between cancer tissue and healthy tissue, including an increase in aromatic amino acids, and an average decrease in citrate in thyroid cancer tissue. We hypothesize that these changes in metabolism may be due to an oxidative stress-related decrease in activity of the Krebs cycle, and a shift towards glycolysis in cancer tissue. Thus, thyroid cancer cells are able to reprogram their metabolic activity to survive in conditions of high oxidative stress and with a compromised antioxidant system. Our findings, for the first time, suggested a connection between oxidative stress and the alteration of the metabolic profile in thyroid tumors.

Rationales for the Use of Cancer Stem Cells Markers in the Staging of Papillary Thyroid Carcinoma

Fine needle aspiration biopsy (FNAB) is a standard procedure for the detection of thyroid nodules malignancy, yet 10-25% of the sample diagnosed may go undetermined or suspicious. The utility of cancer stem cell markers (CSCM) as a differential diagnosis molecular marker in nodules of suspicious decision in FNAB was hypothesized. Papillary thyroid carcinoma (PTC) and thyroid fibroadenoma (TFA) samples were selected to test the hypothesis. The samples employed in this study were from patients who had thyroid hyperplasia and a suspicious or undetermined diagnosis by FNAB. The patient underwent a successful thyroidectomy at Al-Yarmouk Teaching Hospital in Baghdad between January 2015 and December 2017. All nodule samples underwent a systematic histopathological examination after resection. Tumors diagnosed as PTC and those diagnosed as fibroadenoma (TFA) were selected for this study. Collectively 39 PTC and 11 TFA nodules were included. Quantitative reverse transcriptase real-time PCR (qRT-PCR) and immunohistochemistry (IHC) were used to determine levels of mRNA and proteins of CSCM ALDH1A1, CD44, ABCG2, and Oct3/4 in both types of tumors were used. This study revealed that the expression levels of CSCM were significantly increased in PTC tissues when compared to benign tissues and the positive correlation was found between the CSCM expression levels and tumor stage, size, and gender. In conclusion, for a more precise diagnosis, we suggest these markers be included in what is currently available to characterize malignancy from what is not in thyroid cancer, as well as for the staging process of PTC.