Upper Aerodigestive Tract Squamous Cell Carcinomas Show Distinct Overall DNA Methylation Profiles and Different Molecular Mechanisms behind WNT Signaling Disruption

Universal penalized regression (Elastic-net) model with differentially methylated promoters for oral cancer prediction

BACKGROUND

DNA methylation showed notable potential to act as a diagnostic marker in many cancers. Many studies proposed DNA methylation biomarker in OSCC detection, while most of these studies are limited to specific cohorts or geographical location. However, the generalizability of DNA methylation as a diagnostic marker in oral cancer across different geographical locations is yet to be investigated.

METHODS

We used genome-wide methylation data from 384 oral cavity cancer and normal tissues from TCGA HNSCC and eastern India. The common differentially methylated CpGs in these two cohorts were used to develop an Elastic-net model that can be used for the diagnosis of OSCC. The model was validated using 812 HNSCC and normal samples from different anatomical sites of oral cavity from seven countries. Droplet Digital PCR of methyl-sensitive restriction enzyme digested DNA (ddMSRE) was used for quantification of methylation and validation of the model with 22 OSCC and 22 contralateral normal samples. Additionally, pyrosequencing was used to validate the model using 46 OSCC and 25 adjacent normal and 21 contralateral normal tissue samples.

RESULTS

With ddMSRE, our model showed 91% sensitivity, 100% specificity, and 95% accuracy in classifying OSCC from the contralateral normal tissues. Validation of the model with pyrosequencing also showed 96% sensitivity, 91% specificity, and 93% accuracy for classifying the OSCC from contralateral normal samples, while in case of adjacent normal samples we found similar sensitivity but with 20% specificity, suggesting the presence of early disease methylation signature at the adjacent normal samples. Methylation array data of HNSCC and normal tissues from different geographical locations and different anatomical sites showed comparable sensitivity, specificity, and accuracy in detecting oral cavity cancer with across. Similar results were also observed for different stages of oral cavity cancer.

CONCLUSIONS

Our model identified crucial genomic regions affected by DNA methylation in OSCC and showed similar accuracy in detecting oral cancer across different geographical locations. The high specificity of this model in classifying contralateral normal samples from the oral cancer compared to the adjacent normal samples suggested applicability of the model in early detection.

MYEOV with High Frequencies of Mutations in Head and Neck Cancers Facilitates Cancer Cell Malignant Behaviors

Cancer driver genes (CDGs) and the driver mutations disrupt the homeostasis of numerous critical cell activities, thereby playing a critical role in tumor initiation and progression. In this study, integrative bioinformatics analyses were performed based on a series of online databases, aiming to identify driver genes with high frequencies of mutations in head and neck cancers. Higher myeloma overexpressed (MYEOV) genetic variation frequency and expression level were connected to a poorer prognosis in head and neck cancer patients. MYEOV was dramatically upregulated within head and neck tumor samples and cells. Consistently, MYEOV overexpression remarkably enhanced the aggressiveness of head and neck cancer cells by promoting colony formation, cell invasion, and cell migration. Conversely, MYEOV knockdown attenuated cancer cell aggressiveness and inhibited tumor growth and metastasis in the oral orthotopic tumor model. In conclusion, MYEOV is overexpressed in head and neck cancer, with greater mutation frequencies correlating to a poorer prognosis in head and neck cancer patients. MYEOV serves as an oncogene in head and neck cancer through the promotion of tumor cell colony formation, invasion, and migration, as well as promoting tumor growth and metastasis in the oral orthotopic tumor model.

Oral tumor heterogeneity, its implications for patient monitoring and designing anti-cancer strategies

Oral cancer tumors occur in the mouth and are mainly derived from oral mucosa linings. It is one of the most common and fatal malignant diseases worldwide. The intratumor heterogeneity (ITH) of oral cancerous tumor is vast, so it is challenging to study and interpret. Due to environmental selection pressures, ITH arises through diverse genetic, epigenetic, and metabolic alterations. The ITH also talks about peri-tumoral vascular/ lymphatic growth, perineural permeation, tumor necrosis, invasion, and clonal expansion/ the coexistence of multiple subclones in a single tumor. The heterogeneity offers tumors the adaptability to survive, induce growth/ metastasis, and, most importantly, escape antitumor therapy. Unfortunately, the ITH is prioritized less in determining disease pathology than the traditional TNM classifications or tumor grade. Understanding ITH is challenging, but with the advancement of technology, this ITH can be decoded. Tumor genomics, proteomics, metabolomics, and other modern analyses can provide vast information. This information in clinics can assist in understanding a tumor's severity and be used for diagnostic, prognostic, and therapeutic decision-making. Lastly, the oral tumor ITH can lead to individualized, targeted therapy strategies fighting against OC.

Genome-wide DNA methylation profiling of HPV-negative leukoplakia and gingivobuccal complex cancers

BACKGROUND

Gingivobuccal complex oral squamous cell carcinoma (GBC-OSCC) is an aggressive malignancy with high mortality often preceded by premalignant lesions, including leukoplakia. Previous studies have reported genomic drivers in OSCC, but much remains to be elucidated about DNA methylation patterns across different stages of oral carcinogenesis.

RESULTS

There is a serious lack of biomarkers and clinical application of biomarkers for early detection and prognosis of gingivobuccal complex cancers. Hence, in search of novel biomarkers, we measured genome-wide DNA methylation in 22 normal oral tissues, 22 leukoplakia, and 74 GBC-OSCC tissue samples. Both leukoplakia and GBC-OSCC had distinct methylation profiles as compared to normal oral tissue samples. Aberrant DNA methylation increases during the different stages of oral carcinogenesis, from premalignant lesions to carcinoma. We identified 846 and 5111 differentially methylated promoters in leukoplakia and GBC-OSCC, respectively, with a sizable fraction shared between the two sets. Further, we identified potential biomarkers from integrative analysis in gingivobuccal complex cancers and validated them in an independent cohort. Integration of genome, epigenome, and transcriptome data revealed candidate genes with gene expression synergistically regulated by copy number and DNA methylation changes. Regularised Cox regression identified 32 genes associated with patient survival. In an independent set of samples, we validated eight genes (FAT1, GLDC, HOXB13, CST7, CYB5A, MLLT11, GHR, LY75) from the integrative analysis and 30 genes from previously published reports. Bisulfite pyrosequencing validated GLDC (P = 0.036), HOXB13 (P < 0.0001) promoter hypermethylation, and FAT1 (P < 0.0001) hypomethylation in GBC-OSCC compared to normal controls.

CONCLUSIONS

Our findings identified methylation signatures associated with leukoplakia and gingivobuccal complex cancers. The integrative analysis in GBC-OSCC identified putative biomarkers that enhance existing knowledge of oral carcinogenesis and may potentially help in risk stratification and prognosis of GBC-OSCC.

Assessing the Differential Methylation Analysis Quality for Microarray and NGS Platforms

Differential methylation (DM) is actively recruited in different types of fundamental and translational studies. Currently, microarray- and NGS-based approaches for methylation analysis are the most widely used with multiple statistical models designed to extract differential methylation signatures. The benchmarking of DM models is challenging due to the absence of gold standard data. In this study, we analyze an extensive number of publicly available NGS and microarray datasets with divergent and widely utilized statistical models and apply the recently suggested and validated rank-statistic-based approach Hobotnica to evaluate the quality of their results. Overall, microarray-based methods demonstrate more robust and convergent results, while NGS-based models are highly dissimilar. Tests on the simulated NGS data tend to overestimate the quality of the DM methods and therefore are recommended for use with caution. Evaluation of the top 10 DMC and top 100 DMC in addition to the not-subset signature also shows more stable results for microarray data. Summing up, given the observed heterogeneity in NGS methylation data, the evaluation of newly generated methylation signatures is a crucial step in DM analysis. The Hobotnica metric is coordinated with previously developed quality metrics and provides a robust, sensitive, and informative estimation of methods' performance and DM signatures' quality in the absence of gold standard data solving a long-existing problem in DM analysis.

Hypermethylation-Mediated lncRNA MAGI2-AS3 Downregulation Facilitates Malignant Progression of Laryngeal Squamous Cell Carcinoma via Interacting With SPT6

Long noncoding RNAs (lncRNAs) have an effect on the occurrence and progression of a considerable number of diseases, especially cancer. Existing research has suggested that MAGI2 antisense RNA 3 (MAGI2-AS3) takes on a critical significance in the development of hepatocellular carcinoma and lung cancer. However, the functions of MAGI2-AS3 in laryngeal squamous cell carcinoma (LSCC) remain unclear. In this study, MAGI2-AS3 expression level in LSCC tissue and cell lines was detected, and the effect of MAGI2-AS3 overexpressed on LSCC phenotypes and the possible influence mechanisms were examined. MAGI2-AS3 was downregulated in the tissues of LSCC patients versus non-tumor tissues, and it was correlated with advanced TNM (tumor, node, metastasis) stage and lymph node metastases, as indicated by the results of this study. MAGI2-AS3 inhibited the proliferation, migration, and invasion of LSCC cells in vitro and in vivo. Furthermore, the hypermethylation level of the MAGI2-AS3 promoter region was indicated by bisulfite genomic sequencing and methylation-specific polymerase chain reaction, such that MAGI2-AS3 expression was downregulated. Besides, MAGI2-AS3 promoter hypermethylation was regulated by DNA methyltransferase 1 (DNMT1), and MAGI2-AS3 expression was reversed by 5-Aza-2'-deoxycytidine (5-Aza). Moreover, the result of the RNA pull-down experiment suggested that 38 proteins were enriched in the MAGI2-AS3 group versus the control group in TU177 cells. To be specific, SPT6 (ie, a conserved protein) was enriched by fold change >10. SPT6 knockdown reduced the antitumor effect of MAGI2-AS3 in TU177 and AMC-HN-8 cells. Meanwhile, SPT6 overexpression inhibited the proliferation, metastasis, and invasion of TU177 and AMC-HN-8 cells. As revealed by the above findings, DNMT1-regulated MAGI2-AS3 promoter hypermethylation led to downregulated MAGI2-AS3 expression, such that the presence and progression of LSCC were inhibited in an SPT6 binding-dependent manner.

FBXL7 Body Hypomethylation Is Frequent in Tumors from the Digestive and Respiratory Tracts and Is Associated with Risk-Factor Exposure

Squamous cell carcinoma is the main histological tumor type in the upper aerodigestive tract (UADT), including the esophagus (ESCC) and the head and neck sites, as well as the oral cavity (OCSCC), larynx (LSCC) and oropharynx (OPSCC). These tumors are induced by alcohol and tobacco exposure, with the exception of a subgroup of OPSCC linked to human papillomavirus (HPV) infection. Few genes are frequently mutated in UADT tumors, pointing to other molecular mechanisms being involved during carcinogenesis. The F-box and leucine-rich repeat protein 7 (FBXL7) is a potential tumor-suppressing gene, one that is frequently hypermethylated in pancreatic cancer and where the encoded protein promotes the degradation of AURKA, BIRC5 and c-SRC. Thus, the aim of this study was to evaluate the methylation and expression profile of FBXL7 in the UADT and the gene’s association with the clinical, etiological and pathological characteristics of patients, as well as the expression of its degradation targets. Here we show that the FBXL7 gene’s body is hypomethylated in the UADT, independently of histology, but not in virus-associated tumors. FBXL7 body methylation and gene expression levels were correlated in the ESCC, LSCC, OCSCC and OPSCC. Immunohistochemistry analysis showed that FBXL7 protein levels are not correlated with the levels of its degradation targets, AURKA and BIRC5, in the UADT. The high discriminatory potential of FBXL7 body hypomethylation between non-tumor and tumor tissues makes it a promising biomarker.

Head and Neck Cancers Are Not Alike When Tarred with the Same Brush: An Epigenetic Perspective from the Cancerization Field to Prognosis

Simple Summary

Squamous cell carcinomas affect different head and neck subsites and, although these tumors arise from the same epithelial lining and share risk factors, they differ in terms of clinical behavior and molecular carcinogenesis mechanisms. Differences between HPV-negative and HPV-positive tumors are those most frequently explored, but further data suggest that the molecular heterogeneity observed among head and neck subsites may go beyond HPV infection. In this review, we explore how alterations of DNA methylation and microRNA expression contribute to head and neck squamous cell carcinoma (HNSCC) development and progression. The association of these epigenetic alterations with risk factor exposure, early carcinogenesis steps, transformation risk, and prognosis are described. Finally, we discuss the potential application of the use of epigenetic biomarkers in HNSCC.

Abstract

Head and neck squamous cell carcinomas (HNSCC) are among the ten most frequent types of cancer worldwide and, despite all efforts, are still diagnosed at late stages and show poor overall survival. Furthermore, HNSCC patients often experience relapses and the development of second primary tumors, as a consequence of the field cancerization process. Therefore, a better comprehension of the molecular mechanisms involved in HNSCC development and progression may enable diagnosis anticipation and provide valuable tools for prediction of prognosis and response to therapy. However, the different biological behavior of these tumors depending on the affected anatomical site and risk factor exposure, as well as the high genetic heterogeneity observed in HNSCC are major obstacles in this pursue. In this context, epigenetic alterations have been shown to be common in HNSCC, to discriminate the tumor anatomical subsites, to be responsive to risk factor exposure, and show promising results in biomarker development. Based on this, this review brings together the current knowledge on alterations of DNA methylation and microRNA expression in HNSCC natural history, focusing on how they contribute to each step of the process and on their applicability as biomarkers of exposure, HNSCC development, progression, and response to therapy.