HORMAD2 methylation-mediated epigenetic regulation of gene expression in thyroid cancer

Bioinformatics identify the role of chordin-like 1 in thyroid cancer

The abnormal expression of chordin-like 1 (CHRDL1) is identified in many cancers, while the effect of CHRDL1 in thyroid cancer (THCA) remains unclear. The University of California Santa Cruz, Gene Expression Profiling Interactive Analysis, University of Alabama at Birmingham Cancer, and Gene Expression Omnibus database (GSE33570, GSE33630, and GSE60542) were used for determining the mRNA and methylation expression of CHRDL1 in tumor and normal tissues. Human Protein Atlas was used for exploring the protein expression level of CHRDL1. The genes correlated to CHRDL1 were assessed by cBioPortal database. The prognostic value of CHRDL1 was evaluated through Kaplan-Meier method, cox regression, and nomogram analysis. Kyoto Encyclopedia of Genes and Genomes, Gene Ontology, and gene set enrichment analysis were used for predicting potential function of CHRDL1. The relationship between CHRDL1 and immune cell infiltration was determined by Pearson method. The downregulated mRNA and protein expressions of CHRDL1 were identified in THCA through the analysis of data from The Cancer Genome Atlas, Gene Expression Omnibus, and Human Protein Atlas database. The survival analysis showed that the CHRDL1 expression significantly affected disease-free interval (DFI) and progression-free interval, and CHRDL1 was an independent predictor of DFI. Besides, we found that C-C motif chemokine ligand 21 could significantly affect DFI time when it was co-expressed with CHRDL1. Additionally, the function of CHRDL1 was enriched in cell migration, apoptosis, and immune cell receptor. The downregulated expression of CHRDL1 was observed in THCA and caused poor prognosis. CHRDL1 may be involved in signal pathway related to cancer development and immune response, which suggested it could be a potential biomarker.

Identification of biomarkers predictive of metastasis development in early-stage colorectal cancer using network-based regularization

Colorectal cancer (CRC) is the third most common cancer and the second most deathly worldwide. It is a very heterogeneous disease that can develop via distinct pathways where metastasis is the primary cause of death. Therefore, it is crucial to understand the molecular mechanisms underlying metastasis. RNA-sequencing is an essential tool used for studying the transcriptional landscape. However, the high-dimensionality of gene expression data makes selecting novel metastatic biomarkers problematic. To distinguish early-stage CRC patients at risk of developing metastasis from those that are not, three types of binary classification approaches were used: (1) classification methods (decision trees, linear and radial kernel support vector machines, logistic regression, and random forest) using differentially expressed genes (DEGs) as input features; (2) regularized logistic regression based on the Elastic Net penalty and the proposed iTwiner-a network-based regularizer accounting for gene correlation information; and (3) classification methods based on the genes pre-selected using regularized logistic regression. Classifiers using the DEGs as features showed similar results, with random forest showing the highest accuracy. Using regularized logistic regression on the full dataset yielded no improvement in the methods' accuracy. Further classification using the pre-selected genes found by different penalty factors, instead of the DEGs, significantly improved the accuracy of the binary classifiers. Moreover, the use of network-based correlation information (iTwiner) for gene selection produced the best classification results and the identification of more stable and robust gene sets. Some are known to be tumor suppressor genes (OPCML-IT2), to be related to resistance to cancer therapies (RAC1P3), or to be involved in several cancer processes such as genome stability (XRCC6P2), tumor growth and metastasis (MIR602) and regulation of gene transcription (NME2P2). We show that the classification of CRC patients based on pre-selected features by regularized logistic regression is a valuable alternative to using DEGs, significantly increasing the models' predictive performance. Moreover, the use of correlation-based penalization for biomarker selection stands as a promising strategy for predicting patients' groups based on RNA-seq data.

Meiotic Genes and DNA Double Strand Break Repair in Cancer

Tumor cells show widespread genetic alterations that change the expression of genes driving tumor progression, including genes that maintain genomic integrity. In recent years, it has become clear that tumors frequently reactivate genes whose expression is typically restricted to germ cells. As germ cells have specialized pathways to facilitate the exchange of genetic information between homologous chromosomes, their aberrant regulation influences how cancer cells repair DNA double strand breaks (DSB). This drives genomic instability and affects the response of tumor cells to anticancer therapies. Since meiotic genes are usually transcriptionally repressed in somatic cells of healthy tissues, targeting aberrantly expressed meiotic genes may provide a unique opportunity to specifically kill cancer cells whilst sparing the non-transformed somatic cells. In this review, we highlight meiotic genes that have been reported to affect DSB repair in cancers derived from somatic cells. A better understanding of their mechanistic role in the context of homology-directed DNA repair in somatic cancers may provide useful insights to find novel vulnerabilities that can be targeted.

Evolutionary Dynamics and Molecular Mechanisms of HORMA Domain Protein Signaling

Controlled assembly and disassembly of multi-protein complexes is central to cellular signaling. Proteins of the widespread and functionally diverse HORMA family nucleate assembly of signaling complexes by binding short peptide motifs through a distinctive safety-belt mechanism. HORMA proteins are now understood as key signaling proteins across kingdoms, serving as infection sensors in a bacterial immune system and playing central roles in eukaryotic cell cycle, genome stability, sexual reproduction, and cellular homeostasis pathways. Here, we describe how HORMA proteins' unique ability to adopt multiple conformational states underlies their functions in these diverse contexts. We also outline how a dedicated AAA+ ATPase regulator, Pch2/TRIP13, manipulates HORMA proteins' conformational states to activate or inactivate signaling in different cellular contexts. The emergence of Pch2/TRIP13 as a lynchpin for HORMA protein action in multiple genome-maintenance pathways accounts for its frequent misregulation in human cancers and highlights TRIP13 as a novel therapeutic target. Expected final online publication date for the Annual Review of Biochemistry, Volume 91 is June 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

The REASON score: an epigenetic and clinicopathologic score to predict risk of poor survival in patients with early stage oral squamous cell carcinoma

BACKGROUND

Oral squamous cell carcinoma (OSCC) is a capricious cancer with poor survival rates, even for early-stage patients. There is a pressing need to develop more precise risk assessment methods to appropriately tailor clinical treatment. Genome-wide association studies have not produced a viable biomarker. However, these studies are limited by using heterogeneous cohorts, not focusing on methylation although OSCC is a heavily epigenetically-regulated cancer, and not combining molecular data with clinicopathologic data for risk prediction. In this study we focused on early-stage (I/II) OSCC and created a risk score called the REASON score, which combines clinicopathologic characteristics with a 12-gene methylation signature, to predict the risk of 5-year mortality.

METHODS

We combined data from an internal cohort (n = 515) and The Cancer Genome Atlas (TCGA) cohort (n = 58). We collected clinicopathologic data from both cohorts to derive the non-molecular portion of the REASON score. We then analyzed the TCGA cohort DNA methylation data to derive the molecular portion of the risk score.

RESULTS

5-year disease specific survival was 63% for the internal cohort and 86% for the TCGA cohort. The clinicopathologic features with the highest predictive ability among the two the cohorts were age, race, sex, tobacco use, alcohol use, histologic grade, stage, perineural invasion (PNI), lymphovascular invasion (LVI), and margin status. This panel of 10 non-molecular features predicted 5-year mortality risk with a concordance (c)-index = 0.67. Our molecular panel consisted of a 12-gene methylation signature (i.e., HORMAD2, MYLK, GPR133, SOX8, TRPA1, ABCA2, HGFAC, MCPH1, WDR86, CACNA1H, RNF216, CCNJL), which had the most significant differential methylation between patients who survived vs. died by 5 years. All 12 genes have already been linked to survival in other cancers. Of the genes, only SOX8 was previously associated with OSCC; our study was the first to link the remaining 11 genes to OSCC survival. The combined molecular and non-molecular panel formed the REASON score, which predicted risk of death with a c-index = 0.915.

CONCLUSIONS

The REASON score is a promising biomarker to predict risk of mortality in early-stage OSCC patients. Validation of the REASON score in a larger independent cohort is warranted.

The ectopic expression of meiCT genes promotes meiomitosis and may facilitate carcinogenesis

Cancer meiomitosis is defined as the concurrent activation of both mitotic and meiotic machineries in neoplastic cells that confer a selective advantage together with increased genomic instability. MeiCT (meiosis-specific cancer/testis) genes that perform specialized functions in the germline events required for the first meiotic division are ectopically expressed in several cancers. Here we describe the expression profiles of meiCT genes and proteins across a number of cancers and review the proposed mechanisms that increase aneuploidy and elicit reduction division in polyploid cells. These mechanisms are centered on the overexpression and function of meiCT proteins in cancers under various conditions that includes a response to genotoxic stress. Since meiCT genes are transcriptionally repressed in somatic cells, their target offers a promising therapeutic approach with limited toxicity to healthy tissues. Throughout the review, we provide a detailed description of the roles for each gene in the context of meiosis and we discuss proposed functions and outcomes resulting from their ectopic reactivation in cancer.

Multi-omics Signatures and Translational Potential to Improve Thyroid Cancer Patient Outcome

Recent advances in high-throughput molecular and multi-omics technologies have improved our understanding of the molecular changes associated with thyroid cancer initiation and progression. The translation into clinical use based on molecular profiling of thyroid tumors has allowed a significant improvement in patient risk stratification and in the identification of targeted therapies, and thereby better personalized disease management and outcome. This review compiles the following: (1) the major molecular alterations of the genome, epigenome, transcriptome, proteome, and metabolome found in all subtypes of thyroid cancer, thus demonstrating the complexity of these tumors and (2) the great translational potential of multi-omics studies to improve patient outcome.

Adaptive Elman Model of Gene Regulation Network Based on Time Series Data

Background:

Time series expression data of genes contain relations among different genes, which are difficult to model precisely. Slime-forming bacteria is one of the three major harmful bacteria types in industrial circulating cooling water systems.

Objective:

This study aimed at constructing gene regulation network(GRN) for slime-forming bacteria to understand the microbial fouling mechanism.

Methods:

For this purpose, an Adaptive Elman Neural Network (AENN) to reveal the relationships among genes using gene expression time series is proposed. The parameters of Elman neural network were optimized adaptively by a Genetic Algorithm (GA). And a Pearson correlation analysis is applied to discover the relationships among genes. In addition, the gene expression data of slime-forming bacteria by transcriptome gene sequencing was presented.

Results:

To evaluate our proposed method, we compared several alternative data-driven approaches, including a Neural Fuzzy Recurrent Network (NFRN), a basic Elman Neural Network (ENN), and an ensemble network. The experimental results of simulated and real datasets demonstrate that the proposed approach has a promising performance for modeling Gene Regulation Networks (GRNs). We also applied the proposed method for the GRN construction of slime-forming bacteria and at last a GRN for 6 genes was constructed.

Conclusion:

The proposed GRN construction method can effectively extract the regulations among genes. This is also the first report to construct the GRN for slime-forming bacteria.

DNA methylation in thyroid cancer

In recent years, cancer genomics has provided new insights into genetic alterations and signaling pathways involved in thyroid cancer. However, the picture of the molecular landscape is not yet complete. DNA methylation, the most widely studied epigenetic mechanism, is altered in thyroid cancer. Recent technological advances have allowed the identification of novel differentially methylated regions, methylation signatures and potential biomarkers. However, despite recent progress in cataloging methylation alterations in thyroid cancer, many questions remain unanswered. The aim of this review is to comprehensively examine the current knowledge on DNA methylation in thyroid cancer and discuss its potential clinical applications. After providing a general overview of DNA methylation and its dysregulation in cancer, we carefully describe the aberrant methylation changes in thyroid cancer and relate them to methylation patterns, global hypomethylation and gene-specific alterations. We hope this review helps to accelerate the use of the diagnostic, prognostic and therapeutic potential of DNA methylation for the benefit of thyroid cancer patients.

HORMAD2 methylation-mediated epigenetic regulation of gene expression in thyroid cancer

This study is aimed to investigate the methylation level of candidate genes and its impact on thyroid carcinoma (THCA) development. Infinium Human Methylation 450 BeadChip Arrays by Illumina (Illumina HM450K) was the most popular CpG microarray platform widely used in biological and medical research. The methylation level of differentially expressed genes and their corresponding CpG sites were analysed by R programme. The expression of HORMAD2 was evaluated by qRT-PCR and Western blot, while the methylation level was examined via methylation-specific PCR. Cell viability, metastasis, cell cycle and apoptosis were detected by MTT assay, transwell and wound healing assay and flow cytometry, respectively, after treatment with 5-aza-2'-deoxycytidine (5-Aza). Tumour formation assay was used to analyse thyroid tumour growth in nude mice in vivo. The methylation levels of all 116 differentially expressed genes were analysed. HORMAD2 was significantly hypermethylated and its mRNA expression was inhibited in THCA cells. After treatment with 5-Aza, HORMAD2 expression was up-regulated in THCA cells and its overexpression can suppress thyroid cancer cell viability, mobility and invasiveness remarkably. Up-regulation of HORMAD2 in THCA cells could prolong G0/G1 phase and shorten S phase to impede cell mitosis as well as promote thyroid cancer cells apoptosis. Furthermore, tumour formation assay showed that increased HORMAD2 level impeded tumour growth in vivo. Hypermethylation of HORMAD2 could induce THCA progression, while hypomethylation of HORMAD2 retard cell growth and mobility and facilitate apoptosis through increasing its mRNA expression.