New evidence on tumor suppressor activity of PTEN and KLLN in papillary thyroid carcinoma

From balance to imbalance: disruption of plasma glutathione concentration in micropapillary thyroid carcinoma

BACKGROUND

Despite the presence of evidence that establishes a strong correlation between oxidative stress and thyroid cancer, there exists a scarcity of research that investigates the specific role of glutathione as an important antioxidant in this particular context. The objective of this study was to assess the altered balance of oxidative stress in cases of thyroid cancer, which includes both papillary thyroid carcinoma (PTC) and micro PTC (mPTC), by examining and comparing the total antioxidant capacity (TAC), total oxidant status (TOS), oxidative stress index (OSI), reduced glutathione (GSH), oxidized glutathione (GSSG), and GSSG/GSH ratio with those of individuals diagnosed with multinodular goiter (MNG) as well as Healthy subjects.

MATERIALS AND METHODS

Plasma samples were collected from 92 patients (23 mPTC, 23 PTC, 23 MNG, 23 Healthy). The levels of TAC, TOS, GSH, and GSSG were measured using a commercial assay kits, and the OSI and GSSG/GSH ratio were calculated for each sample. Statistical analyses were performed to compare the oxidative stress between the groups.

RESULTS

The plasma levels of TOS were significantly higher in the mPTC, PTC, and MNG groups compared to the Healthy individuals (p < 0.05). The OSI in the mPTC and PTC groups showed a significant increase compared to the Healthy group (p < 0.05). The levels of GSH in mPTC and PTC were markedly lower compared to the Healthy subjects (p < 0.01). Interestingly, the concentration of GSH in mPTC was found to be considerably lower than in PTC and MNG patients (p < 0.01).

CONCLUSION

These findings indicate that GSH may be a useful biomarker for evaluating oxidative stress and antioxidant system status in patients with PTC, especially mPTC. Low levels of GSH may indicate increased levels of oxidative stress, which may contribute to the development and progression of mPTC to PTC.

A computational approach to assessing the prognostic implications of BRAF and RAS mutations in patients with papillary thyroid carcinoma

Papillary thyroid carcinoma (PTC) is the most common thyroid cancer, posing a growing clinical challenge. PTC exhibits two age-related peaks, with established risk factors including family history and radiation exposure. Managing even low-risk, localized PTC cases remain complex, with growing interest in active surveillance as an alternative to immediate surgery. This study employed single-cell RNA sequencing (scRNA-Seq) to explore the predictive value of BRAF and RAS mutations in PTC, shedding light on their impact on disease progression and outcomes. The analyses emphasized the significance of BRAF and RAS mutations in tumor advancement, particularly the unique BRAF V600E mutation associated with aggressive features. The methodology involved scRNA-Seq analysis of PTC and normal samples, unveiling distinct cell clusters and indicating upregulated BRAF and RAS genes. Pathway enrichment analysis highlighted altered biological processes and immune-related pathways in PTC. The study consolidated previous research showing the prevalence of BRAF and RAS mutations in PTC, subtypes with distinct molecular profiles, and the impact of TERT promoter mutations on disease severity. In summary, this study unveils the complex interplay of genetic mutations and the cellular microenvironment in PTC through scRNA-Seq. The upregulated BRAF and RAS genes suggest their roles as PTC drivers, and pathway enrichment reveals alterations in immune-related processes. This synthesis of prior research enhances our understanding of PTC's molecular foundations, informing better prognosis and personalized treatment approaches. These insights advance the landscape of PTC management and provide directions for further research.

The role of rs2236242 at SERPINA12 gene and vaspin level on papillary thyroid carcinoma

Several studies showed that adipokines are associated with types of cancer which are documented to be effective in cancer biology. This study aimed to determine the relationship between vaspin rs2236242 polymorphism and the vaspin level with papillary thyroid carcinoma (PTC), and multinodular goiter (MNG). In this cross-sectional study, we recruited 170 candidates. Ninety patients with newly diagnosed (PTC 60 patients and MNG 30 patients), and 80 participants as a control group referred to Shariati Hospital, Tehran, Iran, were enrolled in the study. The vaspin hormone measurements were conducted utilizing the Elisa Kit. Using Tetra amplification resistant-mutation system polymerase chain reaction (T-ARMS-PCR), the genotype of single nucleotide polymorphism (SNP) rs2236242 was determined. The statistical analysis was performed using SPSS software version 20. Our findings showed significant age and genotype frequency differences in three groups (p-value < 0.05). There was no significant difference in vaspin levels between PTC, and control groups. The level of vaspin in MNG compared to the control group had significantly different, but there were no differences after adjustment for age. Results showed the genotypes of vaspin rs2236242 polymorphism are not associated with the level of vaspin. The genotypes and allele frequencies of vaspin rs2236242 in the PTC and MNG groups were significant compared to the control group. We have found vaspin rs2236242 gene polymorphism as a potential marker of papillary thyroid cancer. The A allele of the vaspin SNP rs2236242 plays a protective role against PTC and MNG. SNP at rs2236242 was not significantly associated with vaspin levels.

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.

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.

Genomic variant benchmark: if you cannot measure it, you cannot improve it

Genomic benchmark datasets are essential to driving the field of genomics and bioinformatics. They provide a snapshot of the performances of sequencing technologies and analytical methods and highlight future challenges. However, they depend on sequencing technology, reference genome, and available benchmarking methods. Thus, creating a genomic benchmark dataset is laborious and highly challenging, often involving multiple sequencing technologies, different variant calling tools, and laborious manual curation. In this review, we discuss the available benchmark datasets and their utility. Additionally, we focus on the most recent benchmark of genes with medical relevance and challenging genomic complexity.