Comprehensive DNA Methylation Profiling Identifies Novel Diagnostic Biomarkers for Thyroid Cancer

Tissue of origin prediction for cancer of unknown primary using a targeted methylation sequencing panel

RATIONALE

Cancer of unknown primary (CUP) is a group of rare malignancies with poor prognosis and unidentifiable tissue-of-origin. Distinct DNA methylation patterns in different tissues and cancer types enable the identification of the tissue of origin in CUP patients, which could help risk assessment and guide site-directed therapy.

METHODS

Using genome-wide DNA methylation profile datasets from The Cancer Genome Atlas (TCGA) and machine learning methods, we developed a 200-CpG methylation feature classifier for CUP tissue of origin prediction (MFCUP). MFCUP was further validated with public-available methylation array data of 2977 specimens and targeted methylation sequencing of 78 Formalin-fixed paraffin-embedded (FFPE) samples from a single center.

RESULTS

MFCUP achieved an accuracy of 97.2% in a validation cohort (n = 5923) representing 25 cancer types. When applied to an Infinium 450 K array dataset (n = 1052) and an Infinium EPIC (850 K) array dataset (n = 1925), MFCUP achieved an overall accuracy of 93.4% and 84.8%, respectively. Based on MFCUP, we established a targeted bisulfite sequencing panel and validated it with FFPE sections from 78 patients of 20 cancer types. This methylation sequencing panel correctly identified tissue of origin in 88.5% (69/78) of samples. We also found that the methylation levels of specific CpGs can distinguish one cancer type from others, indicating their potential as biomarkers for cancer diagnosis and screening.

CONCLUSION

Our methylation-based cancer classifier and targeted methylation sequencing panel can predict tissue of origin in diverse cancer types with high accuracy.

Advances and challenges in thyroid cancer: The interplay of genetic modulators, targeted therapies, and AI-driven approaches

Thyroid cancer continues to exhibit a rising incidence globally, predominantly affecting women. Despite stable mortality rates, the unique characteristics of thyroid carcinoma warrant a distinct approach. Differentiated thyroid cancer, comprising most cases, is effectively managed through standard treatments such as thyroidectomy and radioiodine therapy. However, rarer variants, including anaplastic thyroid carcinoma, necessitate specialized interventions, often employing targeted therapies. Although these drugs focus on symptom management, they are not curative. This review delves into the fundamental modulators of thyroid cancers, encompassing genetic, epigenetic, and non-coding RNA factors while exploring their intricate interplay and influence. Epigenetic modifications directly affect the expression of causal genes, while long non-coding RNAs impact the function and expression of micro-RNAs, culminating in tumorigenesis. Additionally, this article provides a concise overview of the advantages and disadvantages associated with pharmacological and non-pharmacological therapeutic interventions in thyroid cancer. Furthermore, with technological advancements, integrating modern software and computing into healthcare and medical practices has become increasingly prevalent. Artificial intelligence and machine learning techniques hold the potential to predict treatment outcomes, analyze data, and develop personalized therapeutic approaches catering to patient specificity. In thyroid cancer, cutting-edge machine learning and deep learning technologies analyze factors such as ultrasonography results for tumor textures and biopsy samples from fine needle aspirations, paving the way for a more accurate and effective therapeutic landscape in the near future.

Classification of Thyroid Tumors Based on DNA Methylation Patterns

Background: Alterations in DNA methylation are stable epigenetic events that can serve as clinical biomarkers. The aim of this study was to analyze methylation patterns among various follicular cell-derived thyroid neoplasms to identify disease subtypes and help understand and classify thyroid tumors. Methods: We employed an unsupervised machine learning method for class discovery to search for distinct methylation patterns among various thyroid neoplasms. Our algorithm was not provided with any clinical or pathological information, relying exclusively on DNA methylation data to classify samples. We analyzed 810 thyroid samples (n = 256 for discovery and n = 554 for validation), including benign and malignant tumors, as well as normal thyroid tissue. Results: Our unsupervised algorithm identified that samples could be classified into three subtypes based solely on their methylation profile. These methylation subtypes were strongly associated with histological diagnosis (p < 0.001) and were therefore named normal-like, follicular-like, and papillary thyroid carcinoma (PTC)-like. Follicular adenomas, follicular carcinomas, oncocytic adenomas, and oncocytic carcinomas clustered together forming the follicular-like methylation subtype. Conversely, classic papillary thyroid carcinomas (cPTC) and tall cell PTC clustered together forming the PTC-like subtype. These methylation subtypes were also strongly associated with genomic drivers: 98.7% BRAFV600E-driven cancers were PTC like, whereas 96.0% RAS-driven cancers had a follicular-like methylation pattern. Interestingly, unlike other diagnoses, follicular variant PTC (FVPTC) samples were split into two methylation clusters (follicular like and PTC like), indicating a heterogeneous group likely to be formed by two distinct diseases. FVPTC samples with a follicular-like methylation pattern were enriched for RAS mutations (36.4% vs. 8.0%; p < 0.001), whereas FVPTC- with PTC-like methylation patterns were enriched for BRAFV600E mutations (52.0% vs. 0%, Fisher exact p = 0.004) and RET fusions (16.0% vs. 0%, Fisher exact p = 0.003). Conclusions: Our data provide novel insights into the epigenetic alterations of thyroid tumors. Since our classification method relies on a fully unsupervised machine learning approach for subtype discovery, our results offer a robust background to support the classification of thyroid neoplasms based on methylation patterns.

erarchical tumor artificial intelligence classifier traces tissue of origin and tumor type in primary and metastasized tumors using DNA methylation

Human cancers are heterogenous by their cell composition and origination site. Cancer metastasis generates the conundrum of the unknown origin of migrated tumor cells. Tracing tissue of origin and tumor type in primary and metastasized cancer is vital for clinical significance. DNA methylation alterations play a crucial role in carcinogenesis and mark cell fate differentiation, thus can be used to trace tumor tissue of origin. In this study, we employed a novel tumor-type-specific hierarchical model using genome-scale DNA methylation data to develop a multilayer perceptron model, HiTAIC, to trace tissue of origin and tumor type in 27 cancers from 23 tissue sites in data from 7735 tumors with high resolution, accuracy, and specificity. In tracing primary cancer origin, HiTAIC accuracy was 99% in the test set and 93% in the external validation data set. Metastatic cancers were identified with a 96% accuracy in the external data set. HiTAIC is a user-friendly web-based application through https://sites.dartmouth.edu/salaslabhitaic/. In conclusion, we developed HiTAIC, a DNA methylation-based algorithm, to trace tumor tissue of origin in primary and metastasized cancers. The high accuracy and resolution of tumor tracing using HiTAIC holds promise for clinical assistance in identifying cancer of unknown origin.

Cell-free DNA methylation biomarker for the diagnosis of papillary thyroid carcinoma

BACKGROUND

Cell-free DNA (cfDNA) is being explored as biomarker for non-invasive diagnosis of cancer. We aimed to establish a cfDNA-based DNA methylation marker panel to differentially diagnose papillary thyroid carcinoma (PTC) from benign thyroid nodule (BTN).

METHODS

220 PTC- and 188 BTN patients were enrolled. Methylation markers of PTC were identified from patients' tissue and plasma by reduced representation bisulfite sequencing and methylation haplotype analyses. They were combined with PTC markers from literatures and were tested on additional PTC and BTN samples to verify PTC-detecting ability using targeted methylation sequencing. Top markers were developed into ThyMet and were tested in 113 PTC and 88 BTN cases to train and validate a PTC-plasma classifier. Integration of ThyMet and thyroid ultrasonography was explored to improve accuracy.

FINDINGS

From 859 potential PTC plasma-discriminating markers that include 81 markers identified by us, the top 98 most PTC plasma-discriminating markers were selected for ThyMet. A 6-marker ThyMet classifier for PTC plasma was trained. In validation it achieved an Area Under the Curve (AUC) of 0.828, similar to thyroid ultrasonography (0.833) but at higher specificity (0.722 and 0.625 for ThyMet and ultrasonography, respectively). A combinatorial classifier by them, ThyMet-US, improved AUC to 0.923 (sensitivity = 0.957, specificity = 0.708).

INTERPRETATION

The ThyMet classifier improved the specificity of differentiating PTC from BTN over ultrasonography. The combinatorial ThyMet-US classifier may be effective in preoperative diagnosis of PTC.

FUNDING

This work was supported by the grants from National Natural Science Foundation of China (82072956 and 81772850).

Identification and Validation of a Prognostic Signature Based on Methylation Profiles and Methylation-Driven Gene DAB2 as a Prognostic Biomarker in Differentiated Thyroid Carcinoma

Recurrence is the major death cause of differentiated thyroid carcinoma (DTC), and a better understanding of recurrence risk at early stage may lead to make the optimal medical decision to improve patients’ prognosis. The 2015 American Thyroid Association (ATA) risk stratification system primary based on clinic-pathologic features is the most commonly used to describe the initial risk of persistent/recurrent disease. Besides, multiple prognostics models based on multigenes expression profiles have been developed to predict the recurrence risk of DTC patients. Recent evidences indicated that aberrant DNA methylation is involved in the initiation and progression of DTC and can be useful biomarkers for clinical diagnosis and prognosis prediction of DTC. Therefore, there is a need for integrating gene methylation feature to assess the recurrence risk of DTC. Gene methylation profile from The Cancer Genome Atlas (TCGA) was used to construct a recurrence risk model of DTC by successively performed univariate Cox regression, LASSO regression, and multivariate Cox regression. Two Gene Expression Omnibus (GEO) methylation cohorts of DTC were utilized to validate the predictive value of the methylation profiles model as external cohort by receiver operating characteristic (ROC) curve and survival analysis. Besides, CCK-8, colony-formation assay, transwell, and scratch-wound assay were used to investigate the biological significance of critical gene in the model. In our study, we constructed and validated a prognostic signature based on methylation profiles of SPTA1, APCS, and DAB2 and constructed a nomogram based on the methylation-related model, age, and AJCC_T stage that could provide evidence for the long-term treatment and management of DTC patients. Besides, in vitro experiments showed that DAB2 inhibited proliferation, colony-formation, and migration of BCPAP cells and the gene set enrichment analysis and immune infiltration analysis showed that DAB2 may promote antitumor immunity in DTC. In conclusion, promoter hypermethylation and loss expression of DAB2 in DTC may be a biomarker of unfavorable prognosis and poor response to immune therapy.

Classification of follicular-patterned thyroid lesions using a minimal set of epigenetic biomarkers

OBJECTIVE

The minimally invasive fine-needle aspiration cytology (FNAC) is the current gold standard for the diagnosis of thyroid nodule malignancy. However, the correct discrimination of follicular neoplasia often requires more invasive diagnostic techniques. The lack of suitable immunohistochemical markers to distinguish between follicular thyroid carcinoma and other types of follicular-derived lesions complicates diagnosis, and despite most of these tumours being surgically resected, only a small number will test positive for malignancy. As such, the development of new orthogonal diagnostic approaches may improve the accuracy of diagnosing thyroid nodules.

DESIGN

This study includes a retrospective, multi-centre training cohort including 54 fresh-frozen follicular-patterned thyroid samples and two independent, multi-centre validation cohorts of 103 snap-frozen biopsies and 33 FNAC samples, respectively.

METHODS

We performed a genome-wide genetic and epigenetic profiling of 54 fresh-frozen follicular-patterned thyroid samples using exome sequencing and the Illumina Human DNA Methylation EPIC platform. An extensive validation was performed using the bisulfite pyrosequencing technique.

RESULTS

Using a random forest approach, we developed a three-CpG marker-based diagnostic model that was subsequently validated using bisulfite pyrosequencing experiments. According to the validation cohort, this cost-effective method discriminates between benign and malignant nodules with a sensitivity and specificity of 97 and 88%, respectively (positive predictive value (PPV): 0.85, negative predictive value (NPV): 0.98).

CONCLUSIONS

Our classification system based on a minimal set of epigenetic biomarkers can complement the potential of the diagnostic techniques currently available and would prioritize a considerable number of surgical interventions that are often performed due to uncertain cytology.

SIGNIFICANCE STATEMENT

In recent years, there has been a significant increase in the number of people diagnosed with thyroid nodules. The current challenge is their etiological diagnosis to discount malignancy without resorting to thyroidectomy. The method proposed here, based on DNA pyrosequencing assays, has high sensitivity (0.97) and specificity (0.88) for the identification of malignant thyroid nodules. This simple and cost-effective approach can complement expert pathologist evaluation to prioritize the classification of difficult-to-diagnose follicular-patterned thyroid lesions and track tumor evolution, including real-time monitoring of treatment efficacy, thereby stimulating adherence to health promotion programs.

Preliminary Study on the Sequencing of Whole Genomic Methylation and Transcriptome-Related Genes in Thyroid Carcinoma

Simple Summary

Epigenetic alterations are critical for tumor onset and development. DNA methylation is one of the most studied pathways concerning various types of cancer. A promising and exciting avenue of research is the discovery of biomarkers of early-stage malignancies for disease prevention and prognostic indicators following cancer treatment by examining the DNA methylation modification of relevant genes implicated in cancer development. We have made significant advances in the study of DNA methylation and thyroid cancer. This study is novel in that it distinguished methylation changes that occurred primarily in the gene body region of the aforementioned hypermethylated or hypomethylated thyroid cancer genes. Our findings imply that exposing whole-genome DNA methylation patterns and gene expression profiles in thyroid cancer provides new insight into the carcinogenesis of thyroid cancer, demonstrating that gene expression mediated by DNA methylation modifications may play a significant role in tumor growth.

Abstract

Thyroid carcinoma is the most prevalent endocrine cancer globally and the primary cause of cancer-related mortality. Epigenetic modifications are progressively being linked to metastasis. This study aimed to examine whole-genome DNA methylation patterns and the gene expression profiles in thyroid cancer tissue samples using a MethylationEPIC BeadChip (850K), RNA sequencing, and a targeted bisulfite sequencing assay. The results of the Illumina Infinium human methylation kit (850K) analyses identified differentially methylated CpG locations (DMPs) and differentially methylated CpG regions (DMRs) encompassing nearly the entire genome with high resolution and depth. Gene ontology and KEGG pathway analyses revealed that the genes associated with DMRs belonged to various domain-specific ontologies, including cell adhesion, molecule binding, and proliferation. The RNA-Seq study found 1627 differentially expressed genes, 1174 of which that were up-regulated and 453 of which that were down-regulated. The targeted bisulfite sequencing assay revealed that CHST2, DPP4, DUSP6, ITGA2, SLC1A5, TIAM1, TNIK, and ABTB2 methylation levels were dramatically lowered in thyroid cancer patients when compared to the controls, but GALNTL6, HTR7, SPOCD1, and GRM5 methylation levels were significantly raised. Our study revealed that the whole-genome DNA methylation patterns and gene expression profiles in thyroid cancer shed new light on the tumorigenesis of thyroid cancer.

Emerging Biomarkers in Thyroid Practice and Research

Simple Summary

Tumor biomarkers are molecules at genetic or protein level, or certain evaluable characteristics. These help in perfecting patient management. Over the past decade, advanced and more sensitive techniques have led to the identification of many new biomarkers in the field of oncology. A knowledge of the recent developments is essential for their application to clinical practice, and furthering research. This review provides a comprehensive account of such various markers identified in thyroid carcinoma, the most common endocrine malignancy. While some of these have been brought into use in routine patient management, others are novel and need more research before clinical application.

Abstract

Thyroid cancer is the most common endocrine malignancy. Recent developments in molecular biological techniques have led to a better understanding of the pathogenesis and clinical behavior of thyroid neoplasms. This has culminated in the updating of thyroid tumor classification, including the re-categorization of existing and introduction of new entities. In this review, we discuss various molecular biomarkers possessing diagnostic, prognostic, predictive and therapeutic roles in thyroid cancer. A comprehensive account of epigenetic dysregulation, including DNA methylation, the function of various microRNAs and long non-coding RNAs, germline mutations determining familial occurrence of medullary and non-medullary thyroid carcinoma, and single nucleotide polymorphisms predisposed to thyroid tumorigenesis has been provided. In addition to novel immunohistochemical markers, including those for neuroendocrine differentiation, and next-generation immunohistochemistry (BRAF V600E, RAS, TRK, and ALK), the relevance of well-established markers, such as Ki-67, in current clinical practice has also been discussed. A tumor microenvironment (PD-L1, CD markers) and its influence in predicting responses to immunotherapy in thyroid cancer and the expanding arena of techniques, including liquid biopsy based on circulating nucleic acids and plasma-derived exosomes as a non-invasive technique for patient management, are also summarized.

Diagnostic and prognostic value of tumor-infiltrating B cells in lymph node metastases of papillary thyroid carcinoma

Lymph node metastases are strongly associated with unfavorable prognosis in papillary thyroid carcinoma (PTC) patients. However, there are few sensitive or specific indicators that can diagnose or predict lymph node metastases in PTC. The objective of our study was to identify reliable indicators for the diagnosis and prediction of lymph node metastases of PTC. The PTC data set was obtained from The Cancer Genome Atlas (TCGA) cohort. Information on tumor-infiltrating immune cells in PTC was acquired using single-sample gene set enrichment analysis (ssGSEA). Then, the progression-free survival (PFS) rates of PTC patients were evaluated by Kaplan-Meier curves. A tissue microarray including 58 normal thyroid tissues and 57 PTC tissues was processed for CD19 immunohistochemistry staining. Finally, evaluation of phenotype permutations was performed using gene set enrichment analysis (GSEA). There was an appreciable association between immune infiltration and lymph node metastases in PTC. Among those immune cells, B cells and cytotoxic cells showed significant predictive accuracy for lymph node metastases in PTC. Tumor-infiltrating B cells and NK cells were associated with favorable prognosis, while tumor-associated NK CD56^bright cells were correlated with poor prognosis in PTC patients. IHC analyses of PTC further confirmed a notably negative correlation between B cell infiltration and lymph node metastases in PTC. Additionally, mutations in BRAF, a dominant cause of tumor mutation burden (TMB), were positively correlated with reduced B cell infiltration and lymph node metastases in PTC. GSEA revealed that epithelial-mesenchymal transition, IL-6/JAK/STAT3 signaling, the inflammatory response, and TNF-α signaling via the NFκB pathway were remarkably suppressed pathways in patients with BRAF mutations. Tumor-associated lymphocytic infiltration, especially B cell infiltration, provides diagnostic and prognostic value for lymph node metastases in PTC.

Identification of TNIK as a novel potential drug target in thyroid cancer based on protein druggability prediction

Thyroid cancer is a common endocrine malignancy; however, surgery remains its primary treatment option. A novel targeted drug for the development and application of targeted therapy in thyroid cancer treatment remain underexplored.We obtained RNA sequence data of thyroid cancer from The Cancer Genome Atlas database and identified differentially expressed genes (DEGs). Then, we constructed co-expression network with DEGs and combined it with differentially methylation analysis to screen the key genes in thyroid cancer. PockDrug-Server, an online tool, was applied to predict the druggability of the key genes. Finally, we constructed protein-protein interaction (PPI) network to observe potential targeted drugs for thyroid cancer.We identified 3 genes correlated with altered DNA methylation level and oncogenesis of thyroid cancer. According to the druggable analysis and PPI network, we predicted TRAF2 and NCK-interacting protein kinase (TNIK) sever as the drug targeted for thyroid cancer. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis indicated that genes in protein-protein interaction network of TNIK enriched in mitogen-activated protein kinase signaling pathway. For drug repositioning, we identified a targeted drug of genes in PPI network.Our study provides a bioinformatics method for screening drug targets and provides a theoretical basis for thyroid cancer targeted therapy.

Genome-wide DNA methylome and whole-transcriptome landscapes of spontaneous intraductal papilloma in tree shrews

Background

Breast intraductal papilloma (IP) is mainly caused by the abnormal proliferation of ductal epithelial cells. Tree shrews have potential as an animal model for the study of breast tumours; however, little is known regarding the transcriptome and DNA methylome landscapes of breast IP in tree shrews. In this research, we conducted whole-genome DNA methylation and transcriptome analyses of breast IP and normal mammary glands in tree shrews.

Methods

DNA methylation profiles were generated from the whole-genome bisulfite sequencing and whole-transcriptome landscapes of IP and control groups of tree shrews through strand-specific library construction and RNA sequencing. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment analyses and gene set enrichment analysis were performed. Spearman’s correlation analysis was used to identify statistical relationships between gene expression and DNA methylation.

Results

A genome-wide perspective of the epigenetic regulation of protein-coding genes in breast IP in tree shrews was obtained. The methylation levels at CG sites were considerably higher than those at CHG or CHH sites, and were highest in gene body regions. In total, 3,486, 82 and 361 differentially methylated regions (DMRs) were identified in the context of CG, CHG, and CHH, respectively, and 701 differentially methylated genes (DMGs) were found. Further, through transcriptomic analysis, 62 differentially expressed genes, 50 long noncoding RNAs, and 32 circular RNAs were identified in breast IP compared to normal mammary glands. Correlation analysis between the DNA methylation and transcriptome data revealed that 25 DMGs were also differentially expressed genes, among which the expression levels of 9 genes were negatively correlated with methylation levels in gene body regions. Importantly, integrated analysis identified 3 genes (PDZ domain-containing 1, ATPase plasma membrane Ca²⁺ transporting 4 and Lymphocyte cytosolic protein 1) that could serve as candidates for further study of breast IP in tree shrews.

Conclusions

This research has unearthed the comprehensive landscape of the transcriptome and DNA methylome of spontaneous IP in tree shrews, as well as candidate tumorigenesis related genes in IP. These results will contribute to the use of tree shrews in animal models of breast tumours.

DNA Methylation Haplotype Block Markers Efficiently Discriminate Follicular Thyroid Carcinoma from Follicular Adenoma

CONTEXT

Follicular thyroid carcinoma (FTC) is the second most common type of thyroid carcinoma and must be pathologically distinguished from benign follicular adenoma (FA). Additionally, the clinical assessment of thyroid tumors with uncertain malignant potential (TT-UMP) demands effective indicators.

OBJECTIVE

We aimed to identify discriminating DNA methylation markers between FA and FTC.

METHODS

DNA methylation patterns were investigated in 33 FTC and 33 FA samples using reduced representation bisulfite sequencing and methylation haplotype block-based analysis. A prediction model was constructed and validated in an independent cohort of 13 FTC and 13 FA samples. Moreover, 36 TT-UMP samples were assessed using this model.

RESULTS

A total of 70 DNA methylation markers, approximately half of which were located within promoters, were identified to be significantly different between the FTC and FA samples. All the Gene Ontology terms enriched among the marker-associated genes were related to "DNA binding," implying that the inactivation of DNA binding played a role in FTC development. A random forest model with an area under the curve of 0.994 was constructed using those markers for discriminating FTC from FA in the validation cohort. When the TT-UMP samples were scored using this model, those with fewer driver mutations also exhibited lower scores.

CONCLUSION

An FTC-predicting model was constructed using DNA methylation markers, which distinguished between FA and FTC tissues with a high degree of accuracy. This model can also be used to help determine the potential of malignancy in TT-UMP.

MicroRNA Profile for Diagnostic and Prognostic Biomarkers in Thyroid Cancer

The challenge in managing thyroid nodules is to accurately diagnose the minority of those with malignancy. We aimed to identify diagnostic and prognostic miRNA markers for thyroid nodules. In a discovery cohort, we identified 20 candidate miRNAs to differentiate between noninvasive follicular thyroid neoplasms with papillary-like nuclear features (NIFTP) and papillary thyroid carcinomas (PTC) by using the high-throughput small RNA sequencing method. We then selected three miRNAs (miR-136, miR-21, and miR-127) that were differentially expressed between the PTC follicular variant and other variants in The Cancer Genome Atlas data. High expression of three miRNAs differentiated thyroid cancer from nonmalignant tumors, with an area under curve (AUC) of 0.76-0.81 in an independent cohort. In patients with differentiated thyroid cancer, the high-level expression of the three miRNAs was an independent indicator for both distant metastases and recurrent or persistent disease. In patients with PTC, a high expression of miRNAs was associated with an aggressive histologic variant, extrathyroidal extension, distant metastasis, or recurrent or persistent disease. Three miRNAs may be used as diagnostic markers for differentiating thyroid cancers from benign tumors and tumors with extremely low malignant potential (NIFTP), as well as prognostic markers for predicting the risk of recurrent/persistent disease for differentiated thyroid cancer.

Overexpression of PAX8-AS1 Inhibits Malignant Phenotypes of Papillary Thyroid Carcinoma Cells via miR-96-5p/PKN2 Axis

BACKGROUND

Thyroid carcinoma (THCA) is the most frequent endocrine malignancy. Papillary thyroid carcinoma (PTC) is the major subtype of THCA, accounting for over 80% of all THCA cases. LncRNA PAX8-AS1, a tumor suppressor associated with various human cancers, has been reported to be relevant to the regulation of all sorts of cellular processes. The purpose of this study was to verify the role of PAX8-AS1 in PTC.

METHODS

Three human PTC cell lines (K1, TPC-1, and IHH4) and one normal human thyroid cell line, Nthy-ori3-1, were used in our study. The expression of genes was detected by qRT-PCR. The bioinformatic analysis and luciferase reporter assay were used to confirm the binding relationship of PAX8-AS1 to miR-96-5p, and the targeting relationship of miR-96-5p to PKN2 was also predicted. Cell proliferation and apoptosis capacities were assessed by MTT and flow cytometry, respectively. EdU assay was used to detect cell proliferation. Western blot assay was employed to examine protein expression.

RESULTS

The expression of PAX8-AS1 was decreased in PTC tissues and cells. PAX8-AS1 overexpression inhibited the proliferation of PTC cells and promoted cell apoptosis. In addition, PAX8-AS1 bonds with miR-96-5p, whose downregulation elevated the expression of PKN2 in PTC cells. Importantly, according to the rescue experiments, PKN2 silencing partially reversed the inhibitory effects of PAX8-AS1 expression on PTC cell proliferation and apoptosis.

CONCLUSIONS

We found that the PAX8-AS1/miR-96-5p/PKN2 axis was closely related to the progression of PTC, which could be a potential target for treating PTC patients.

Epigenomics in Hurthle Cell Neoplasms: Filling in the Gaps Towards Clinical Application

It has been widely described that cancer genomes have frequent alterations to the epigenome, including epigenetic silencing of various tumor suppressor genes with functions in almost all cancer-relevant signalling pathways, such as apoptosis, cell proliferation, cell migration and DNA repair. Epigenetic alterations comprise DNA methylation, histone modification, and microRNAs dysregulated expression and they play a significant role in the differentiation and proliferation properties of TC. In this review, our group assessed the published evidence on the tumorigenic role of epigenomics in Hurthle cell neoplasms (HCN), highlighting the yet limited, heteregeneous and non-validated data preventing its current use in clinical practice, despite the well developed assessment techniques available. The identified evidence gaps call for a joint endeavour by the medical community towards a deeper and more systematic study of HCN, aiming at defining epigenetic markers in early diagnose, allowing for accurate stratification of maligancy and disease risk and for effective systemic treatment.

Correlation among VEGFR3 gene promoter methylation, protein overexpression, and clinical pathology in early gastric cancer

BACKGROUND

The occurrence and development of gastric cancer is a multi-factor, multi-stage, multi-gene abnormal accumulation process. Both genetic and epigenetic mechanisms play an important role in the molecular mechanism of gastric cancer. DNA methylation is one of the most studied epigenetic expression mechanisms. To study the correlation between gene promoter methylation status and protein expression of vascular endothelial growth factor receptor 3 (VEGFR3), as well as their association with clinicopathological features in early gastric cancer (EGC) cases.

METHODS

Immunohistochemical analysis and methylation-specific PCR (MSP) were used to detect the expression of VEGFR3 protein and methylation status of the VEGFR3 promoter in 50 cases of EGC and their paired normal gastric mucosa tissues. The level of DNA methylation of the VEGFR3 promoter, in situ VEGFR3 protein expression, and the clinicopathological characteristics of EGC patients were statistically analyzed.

RESULTS

The positive rate of VEGFR3 protein expression in EGC tumor tissue (60%) was significantly higher than that in the normal gastric mucosa (10%). The detectable methylation frequency of VEGFR3 promoter in EGC tumor tissue (48%) was significantly lower than that in the normal gastric mucosa (85%). As anticipated, the methylation level of the VEGFR3 gene promoter was negatively associated with the overexpression of VEGFR3 protein. In addition, methylation status of the VEGFR3 gene promoter was positively correlated with lymph node metastasis in EGC patients (P<0.05), but was not linked to patients' gender, age, tumor size, degree of differentiation, or tumor invasion depth (P>0.05).

CONCLUSIONS

Hypomethylation of the VEGFR3 gene promoter is one of the major mechanisms underlying VEGFR3 gene overexpression in EGC tumor tissues and is related to lymph node metastasis in EGC patients. DNA methylation of VEGFR3 is expected to become a molecular diagnostic and prognostic biomarker for EGC.