DNA methylation profiling in the clinic: applications and challenges. Nat Rev Genet. 2012;13:679-692. [PMID: 22945394 DOI: 10.1038/nrg3270]

The impact of physical activity on promoter-specific methylation of genes involved in the redox-status and disease progression: A longitudinal study on post-surgery female breast cancer patients undergoing medical treatment

Most anticancer treatments act on oxidative-stress pathways by producing reactive oxygen species (ROS) to kill cancer cells, commonly resulting in consequential drug-induced systemic cytotoxicity. Physical activity (PA) has arisen as an integrative cancer therapy, having positive health effects, including in redox-homeostasis. Here, we investigated the impact of an online supervised PA program on promoter-specific DNA methylation, and corresponding gene expression/activity, in 3 antioxidants- (SOD1, SOD2, and CAT) and 3 breast cancer (BC)-related genes (BRCA1, L3MBTL1 and RASSF1A) in a population-based sample of women diagnosed with primary BC, undergoing medical treatment. We further examined mechanisms involved in methylating and demethylating pathways, predicted biological pathways and interactions of exercise-modulated molecules, and the functional relevance of modulated antioxidant markers on parameters related to aerobic capacity/endurance, physical fatigue and quality of life (QoL). PA maintained levels of SOD activity in blood plasma, and at the cellular level significantly increased SOD2 mRNA (≈+77 %), contrary to their depletion due to medical treatment. This change was inversely correlated with DNA methylation in SOD2 promoter (≈-20 %). Similarly, we found a significant effect of PA only on L3MBTL1 promoter methylation (≈-25 %), which was inversely correlated with its mRNA (≈+43 %). Finally, PA increased TET1 mRNA levels (≈+15 %) and decreased expression of DNMT3B mRNA (≈-28 %). Our results suggest that PA-modulated DNA methylation affects several signalling pathways/biological activities involved in the cellular oxidative stress response, chromatin organization/regulation, antioxidant activity and DNA/protein binding. These changes may positively impact clinical outcomes and improve the response to cancer treatment in post-surgery BC patients.

A label-free fluorescence sensing strategy based on GlaI-assisted EXPAR for rapid and accurate quantification of human methyltranferase activity

DNA methylation plays an important role in epigenetic modification. DNA methyltransferase (DNMT) is essential in the DNA methylation process, and its abnormal expression is closely related to cancer. In this study, we propose a novel biosensor platform (DS-GlaI-EXPAR) that combines hemi-methylated double-stranded DNA (dsDNA) as the substrate for DNMT1 with GlaI-assisted isothermal exponential amplification reaction (EXPAR) for rapid, simple, and sensitive detection of DNMT1 activity. The hemi-methylated dsDNA is fully methylated by DNMT1, and GlaI recognizes and cleaves the fully methylated sequence, generating terminal fragments that trigger EXPAR for efficient signal amplification. Whereas hemi-methylated dsDNA without DNMT1 will keep intact and cannot initiate EXPAR. DNMT1 activity can therefore be sensitively quantified by the real-time fluorescence signal of the DS-GlaI-EXPAR platform. The high-efficiency amplification of EXPAR and the recognition of GlaI enable the platform to overcome the inherent cumbersome and time-consuming shortcomings of traditional methods while meeting specificity and sensitivity. This DS-GlaI-EXPAR platform offers an impressively low limit of detection of 0.86 pg/μL and the entire detection process can be completed in a short time of 2.5 h in a single tube. Furthermore, DNMT1 activity detected by this platform in MCF-7 cells was significantly higher than that of HEK293 cells, and the inhibition of Apt. #9 was verified. This DNMT1 activity detection platform is very convenient and effective for the discovery of inhibitors and early cancer diagnosis.

Global scientific trends in research of epigenetic response to exercise: A bibliometric analysis

The purpose of this work is to comprehensively understand the adaptive response of multiple epigenetic modifications on gene expression changes driven by exercise. Here, we retrieved literatures from publications in the PubMed and Web of Science Core Collection databases up to and including October 15, 2023. After screening with the exclusion criteria, 1910 publications were selected in total, comprising 1399 articles and 511 reviews. Specifically, a total of 512, 224, and 772 publications is involved in DNA methylation, histone modification, and noncoding RNAs, respectively. The correlations between publication number, authors, institutions, countries, references, and the characteristics of hotspots were explored by CiteSpace. Here, the USA (621 publications) ranked the world's most-influential countries, the University of California System (68 publications) was the most productive, and Tiago Fernandes (14 publications) had the most-published publications. A comprehensive keyword analysis revealed that cardiovascular disease, cancer, skeletal muscle development, and metabolic syndrome, and are the research hotspots. The detailed impact of exercise was further discussed in different aspects of these three categories of epigenetic modifications. Detailed analysis of epigenetic modifications in response to exercise, including DNA methylation, histone modification, and changes in noncoding RNAs, will offer valuable information to help researchers understand hotspots and emerging trends.

Treatment of lung cancer by acupuncture combined with medicine based on pathophysiological mechanism: A review

Lung cancer is one of the most frequently diagnosed cancers in the world. There are an estimated 2.2 million new cases and 1.79 million deaths each year. Over the past 2 decades, our understanding of disease biology, the use of predictive biomarkers, and improvements in therapeutic approaches have made significant progress and transformed the outcomes of many patients. Treatment is determined by the subtype and stage of the cancer; however, the effect of personalized treatment remains unsatisfactory. The use of Chinese medicines has attracted increasing attention worldwide. Chinese medicine treatment of lung cancer has few side effects, which can effectively prolong the survival expectation of patients and improve their quality of life, and has attracted increasing attention. Based on the pathophysiological mechanism of lung cancer reported in modern medical research, this article explores the efficacy and safety of acupuncture combined with medicine in the treatment of lung cancer.

Liquid biopsy in colorectal cancer: Onward and upward

Colorectal cancer (CRC) remains a leading cause of cancer-related deaths worldwide. In recent years, liquid biopsy has emerged as one of the most interesting areas of research in oncology, leading to innovative trials and practical changes in all aspects of CRC management. RNAs and cell free DNA (cfDNA) methylation are emerging as promising biomarkers for early diagnosis. Post-surgical circulating tumour DNA (ctDNA) can aid in evaluating minimal residual disease and personalising adjuvant treatment. In rectal cancer, ctDNA could improve response assessment to neoadjuvant therapy and risk stratification, especially in the era of organ-preservation trials. In the advanced setting, ctDNA analysis offers the opportunity to monitor treatment response and identify driver and resistance mutations more comprehensively than traditional tissue analysis, providing prognostic and predictive information. The aim of this review is to provide a detailed overview of the clinical applications and future perspectives of liquid biopsy in CRC.

Digital methylation-specific PCR: New applications for liquid biopsy

Epigenetic analysis is a fundamental part of understanding pathophysiological processes with potential applications in diagnosis, prognosis, and assessment of disease susceptibility. Epigenetic changes have been widely studied in chronic obstructive pulmonary disease (COPD), but currently, there is no molecular marker used to improve the treatment of patients. Furthermore, this progressive disease is a risk factor for the development of more severe COVID-19. Methylation-specific polymerase chain reaction (MSP-PCR) plays an important role in the analysis of DNA methylation profiles, and it is one of the most widely used techniques. In this context, the combination of MSP-PCR with emerging PCR technologies, such as digital PCR (dPCR), results in more accurate analyses of the DNA methylation profile of the genes under study. In this study, we propose the application of the MSP-dPCR technique to evaluate the methylation profile of the ADAM33 gene from saliva samples and lung tissue biopsies of patients with COPD and COVID-19. MSP-dPCR generated a measurable prediction of gene methylation rate, with the potential application of this combined technology for diagnostic and prognostic purposes. It has also proven to be a powerful tool for liquid biopsy applications.

External quality assessment for detection of colorectal cancer by Septin9 DNA methylation in clinical laboratories

BACKGROUND AND AIMS

The incidence and mortality rate of colorectal cancer (CRC) are increasing worldwide. Septin9 methylated (mSEPT9) DNA in circulation can be used as a non-invasive detection method to assist in the early diagnosis of CRC; however, the detection methods and procedures are complicated. This study aimed to evaluate the ability of clinical laboratories to detect Septin9 methylation in plasma cell-free DNA (cfDNA).

MATERIALS AND METHODS

We prepared a sample panel consisting of positive and negative Septin9 methylation cells and CRC cells. Three positive samples with different methylation levels, one negative sample and one duplicate sample, two samples containing interference, three different CRC cell samples, and a fictitious case report were included. The panel was distributed to 59 laboratories for mSEPT9 analysis, result comparison, and scoring.

RESULTS

The sample panel, validated by National Medical Products Administration (NMPA)-approved tests and targeted bisulfite sequencing, met expectations and could be used for external quality assessment (EQA). Among the 59 laboratories, 55 (93.22%) correctly reported the mSEPT9 results for all samples, while four (6.79%) reported 15 false negatives and were considered improvable. All false negatives originated from four laboratories using laboratory-developed tests (LDTs), with three failing to detect weakly positive samples, samples containing interference, and samples from different CRC cells, and one reported erroneous results on all positive samples.

CONCLUSION

Our results illustrated that the detection of mSEPT9 in cfDNA is satisfactory in China. EQA is indispensable because it can help improve the diagnostic capability and quality management of the laboratories, and provide suggestions for the problems existing in mSEPT9 detection.

Advances in the Delivery and Development of Epigenetic Therapeutics for the Treatment of Cancer

Gene expression at the transcriptional level is altered by epigenetic modifications such as DNA methylation, histone methylation, and acetylation, which can upregulate, downregulate, or entirely silence genes. Pathological dysregulation of epigenetic processes can result in the development of cancer, neurological problems, metabolic disorders, and cardiovascular diseases. It is of promising therapeutic interest to find medications that target these epigenetic alterations. Despite the enormous amount of work that has been done in this area, very few molecules have been approved for clinical purposes. This article provides a comprehensive review of recent advances in epigenetic therapeutics for cancer, with a specific focus on emerging delivery and development strategies. Various delivery systems, including pro-drugs, conjugated molecules, nanoparticles (NPs), and liposomes, as well as remedial strategies such as combination therapies, and epigenetic editing, are being investigated to improve the efficacy and specificity of epigenetic drugs (epi-drugs). Furthermore, the challenges associated with available epi-drugs and the limitations of their translation into clinics have been discussed. Target selection, isoform selectivity, physiochemical properties of synthesized molecules, drug screening, and scalability of epi-drugs from preclinical to clinical fields are the major shortcomings that are addressed. This Review discusses novel strategies for the identification of new biomarkers, exploration of the medicinal chemistry of epigenetic modifiers, optimization of the dosage regimen, and design of proper clinical trials that will lead to better utilization of epigenetic modifiers over conventional therapies. The integration of these approaches holds great potential for improving the efficacy and precision of epigenetic treatments, ultimately benefiting cancer patients.

Epigenetic age in African American adolescents with type 2 diabetes: A cross-sectional case-control study protocol

Background and Aims

Type 2 diabetes (T2D) is a disease caused by a relative insulin deficiency compared to the significant insulin requirement needed by the body to achieve glycemic control. T2D in adolescence appears to be increasing in prevalence over the past several decades, necessitating studies to understand for the onset of the disease to occur early in the lifespan. Given the high burden of disease, specifically in young African American adolescents, our study chose to focus initially on feasibility of recruitment of this population.

Methods

Data was collected at a single study center at Children's of Alabama. The protocol was completed as part of routine care or at a study visit. The study team was able to leverage the Electronic Medical Record to prescreen eligible patients to discuss the study. A variety of times of day were utilized to improve likely of success with reaching potential participants. Inclusion criteria for patients with T2D was focused on the adolescent population (ages 12-18 years), with no history of an obesity syndrome. DNA methylation age will be calculated using the EPIC 850K array. Statistical analysis will be done using linear regression analysis, adjusting for covariates.

Conclusions

This study's aim was to screen and enroll young African American adolescents for a study investigating epigenetic aging and T2D. Our study found that more direct contact (face-to-face- or phone call) improve success of recruitment. Leveraging the electronic medical record also helped improve success with pre-screening participants. Challenges included recruiting participants who might come from long distances to a tertiary care center. Consolidating appointments helped improve the success of reaching these participants. Other challenges included frequent address changes and changed phone numbers. Close attention to the barriers as well as the successes will aid in understanding effective strategies for this important population.

Early screening of nasopharyngeal carcinoma

The low positive predictive value (PPV) of early screening of nasopharyngeal carcinoma (NPC) is the problems that need to be solved urgently. The combination of cell-free DNA (cfDNA) methylation testing and Epstein-Barr virus (EBV) serological testing is the key to solve this problem. This paper reviews recent advances in early screening for NPC and cfDNA methylation, with future perspectives. Pubmed was searched for the literature related to early screening of NPC and cfDNA methylation in the past 5 years. The results of these studies were summarized. Despite these efforts, the PPV is still low (10%). Previous studies have shown that cfDNA methylation analysis has good specificity and accuracy across a variety of tumors. The combination of cfDNA methylation and EBV detection helps to improve the PPV for early screening of NPC. The combination of cfDNA methylation and EBV serological testing is key to addressing the low PPV of NPC early screening.

Evaluating Stacked Methylation Markers for Blood-Based Multicancer Detection

The ability to detect several types of cancer using a non-invasive, blood-based test holds the potential to revolutionize oncology screening. We mined tumor methylation array data from the Cancer Genome Atlas (TCGA) covering 14 cancer types and identified two novel, broadly-occurring methylation markers at TLX1 and GALR1. To evaluate their performance as a generalized blood-based screening approach, along with our previously reported methylation biomarker, ZNF154, we rigorously assessed each marker individually or combined. Utilizing TCGA methylation data and applying logistic regression models within each individual cancer type, we found that the three-marker combination significantly increased the average area under the ROC curve (AUC) across the 14 tumor types compared to single markers (p = 1.158 × 10-10; Friedman test). Furthermore, we simulated dilutions of tumor DNA into healthy blood cell DNA and demonstrated increased AUC of combined markers across all dilution levels. Finally, we evaluated assay performance in bisulfite sequenced DNA from patient tumors and plasma, including early-stage samples. When combining all three markers, the assay correctly identified nine out of nine lung cancer plasma samples. In patient plasma from hepatocellular carcinoma, ZNF154 alone yielded the highest combined sensitivity and specificity values averaging 68% and 72%, whereas multiple markers could achieve higher sensitivity or specificity, but not both. Altogether, this study presents a comprehensive pipeline for the identification, testing, and validation of multi-cancer methylation biomarkers with a considerable potential for detecting a broad range of cancer types in patient blood samples.

Genome-Wide Identification of lncRNA and mRNA for Diagnosing Type 2 Diabetes in Saudi Arabia

Purpose

According to the World Health Organization, Saudi Arabia ranks seventh worldwide in the number of patients with diabetes mellitus. To our knowledge, no research has addressed the potential of noncoding RNA as a diagnostic and/or management biomarker for patients with type 2 diabetes mellitus (T2DM) living in high-altitude areas. This study aimed to identify molecular biomarkers influencing patients with T2DM living in high-altitude areas by analyzing lncRNA and mRNA.

Patients and Methods

RNA sequencing and bioinformatics analyses were used to identify significantly expressed lncRNAs and mRNAs in T2DM and healthy control groups. Coding potential was analyzed using coding-noncoding indices, the coding potential calculator, and PFAM, and the lncRNA function was predicted using Pearson's correlation. Differentially expressed transcripts between the groups were identified, and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed to identify the biological functions of both lncRNAs and mRNAs.

Results

We assembled 1766 lncRNAs in the T2DM group, of which 582 were novel. This study identified three lncRNA target genes (KLF2, CREBBP, and REL) and seven mRNAs (PIK3CD, PIK3R5, IL6R, TYK2, ZAP70, LAMTOR4, and SSH2) significantly enriched in important pathways, playing a role in the progression of T2DM.

Conclusion

To the best of our knowledge, this comprehensive study is the first to explore the applicability of certain lncRNAs as diagnostic or management biomarkers for T2DM in females in Taif City, Saudi Arabia through the genome-wide identification of lncRNA and mRNA profiling using RNA seq and bioinformatics analysis. Our findings could help in the early diagnosis of T2DM and in designing effective therapeutic targets.

DNA Methylation in Cancer: Epigenetic View of Dietary and Lifestyle Factors

Background

Alterations in DNA methylation play an important role in cancer development and progression. Dietary nutrients and lifestyle behaviors can influence DNA methylation patterns and thereby modulate cancer risk.

Introduction

To comprehensively review available evidence on how dietary and lifestyle factors impact DNA methylation and contribute to carcinogenesis through epigenetic mechanisms.

Materials and methods

A literature search was conducted using PubMed to identify relevant studies published between 2005 and 2022 that examined relationships between dietary/lifestyle factors and DNA methylation in cancer. Studies investigating the effects of dietary components (eg, micronutrients, phytochemicals), physical activity, smoking, and obesity on global and gene-specific DNA methylation changes in animal and human cancer models were included. Data on specific dietary/lifestyle exposures, cancer types, DNA methylation targets and underlying mechanisms were extracted.

Results

Multiple dietary and lifestyle factors were found to influence DNA methylation patterns through effects on DNA methyltransferase activity, methyl donor availability, and generation of oxidative stress. Altered methylation of specific genes regulating cell proliferation, apoptosis, and inflammation were linked to cancer development and progression.

Conclusion

Dietary and lifestyle interventions aimed at modulating DNA methylation have potential for both cancer prevention and treatment through epigenetic mechanisms. Further research is needed to identify actionable targets for nutrition and lifestyle-based epigenetic therapies.

A systematic review on the molecular and clinical association between Human Papillomavirus and Human Immunodeficiency Virus co-infection in Head, Neck and Oral squamous cell carcinoma

Head and neck cancer, one of the most commonly prevalent malignancies globally is a complex category of tumours that comprises cancers of the oral cavity, pharynx, and larynx. A specific subgroup of such cancers has been found with some unique chromosomal, therapeutic, and epidemiologic traits with the possibility of affecting via co-infection. About 25% of all head and neck cancers in the population are human papillomavirus infection (HPV)-associated, typically developing in the oropharynx, which comprises the tonsils. In the period of efficient combined antiviral treatment, HPV-positive oral cancers are also becoming a significant contributor to illness and fatality for Human Immunodeficiency Virus (HIV)-infected persons. Although the prevalence and historical background of oral HPV transmission are not thoroughly understood, it seems likely that oral HPV transmission is relatively frequent in HIV-infected people when compared to the overall population. Therefore, there is a need to understand the mechanisms leading to this co-infection, as there is very little research related to that. Hence, this study mainly focus on the therapeutical and biomedical analysis of HPV and HIV co-infection in the above-mentioned cancer, including oral squamous cell carcinoma.

Human Neuralized is a novel tumour suppressor targeting Wnt/β-catenin signalling in colon cancer

While there is growing evidence that many epigenetically silenced genes in cancer are tumour suppressor candidates, their significance in cancer biology remains unclear. Here, we identify human Neuralized (NEURL), which acts as a novel tumour suppressor targeting oncogenic Wnt/β-catenin signalling in human cancers. The expression of NEURL is epigenetically regulated and markedly suppressed in human colorectal cancer. We, therefore, considered NEURL to be a bona fide tumour suppressor in colorectal cancer and demonstrate that this tumour suppressive function depends on NEURL-mediated oncogenic β-catenin degradation. We find that NEURL acts as an E3 ubiquitin ligase, interacting directly with oncogenic β-catenin, and reducing its cytoplasmic levels in a GSK3β- and β-TrCP-independent manner, indicating that NEURL-β-catenin interactions can lead to a disruption of the canonical Wnt/β-catenin pathway. This study suggests that NEURL is a therapeutic target against human cancers and that it acts by regulating oncogenic Wnt/β-catenin signalling.

Whole-genome CpG-resolution DNA Methylation Profiling of HNSCC Reveals Distinct Mechanisms of Carcinogenesis for Fine-scale HPV+ Cancer Subtypes

DNA methylation is a vital early step in carcinogenesis. Most findings of aberrant DNA methylation in head and neck squamous cell carcinomas (HNSCC) are array based with limited coverage and resolution, and mainly explored by human papillomavirus (HPV) status, ignoring the high heterogeneity of this disease. In this study, we performed whole-genome bisulfite sequencing on a well-studied HNSCC cohort (n = 36) and investigated the methylation changes between fine-scaled HNSCC subtypes in relation to genomic instability, repetitive elements, gene expression, and key carcinogenic pathways. The previously observed hypermethylation phenotype in HPV-positive (HPV+) tumors compared with HPV-negative tumors was robustly present in the immune-strong (IMU) HPV+ subtype but absent in the highly keratinized (KRT) HPV+ subtype. Methylation levels of IMU tumors were significantly higher in repetitive elements, and methylation showed a significant correlation with genomic stability, consistent with the IMU subtype having more genomic stability and better prognosis. Expression quantitative trait methylation (cis-eQTM) analysis revealed extensive functionally-relevant differences, and differential methylation pathway analysis recapitulated gene expression pathway differences between subtypes. Consistent with their characteristics, KRT and HPV-negative tumors had high regulatory potential for multiple regulators of keratinocyte differentiation, which positively correlated with an expression-based keratinization score. Together, our findings revealed distinct mechanisms of carcinogenesis between subtypes in HPV+ HNSCC and uncovered previously ignored epigenomic differences and clinical implications, illustrating the importance of fine-scale subtype analysis in cancer.

Significance

This study revealed that the previously observed hypermethylation of HPV(+) HNSCC is due solely to the IMU subtype, illustrating the importance of fine-scale subtype analysis in such a heterogeneous disease. Particularly, IMU has significantly higher methylation of transposable elements, which can be tested as a prognosis biomarker in future translational studies.

Next-Generation Sequencing Technology: Current Trends and Advancements

The advent of next-generation sequencing (NGS) has brought about a paradigm shift in genomics research, offering unparalleled capabilities for analyzing DNA and RNA molecules in a high-throughput and cost-effective manner. This transformative technology has swiftly propelled genomics advancements across diverse domains. NGS allows for the rapid sequencing of millions of DNA fragments simultaneously, providing comprehensive insights into genome structure, genetic variations, gene expression profiles, and epigenetic modifications. The versatility of NGS platforms has expanded the scope of genomics research, facilitating studies on rare genetic diseases, cancer genomics, microbiome analysis, infectious diseases, and population genetics. Moreover, NGS has enabled the development of targeted therapies, precision medicine approaches, and improved diagnostic methods. This review provides an insightful overview of the current trends and recent advancements in NGS technology, highlighting its potential impact on diverse areas of genomic research. Moreover, the review delves into the challenges encountered and future directions of NGS technology, including endeavors to enhance the accuracy and sensitivity of sequencing data, the development of novel algorithms for data analysis, and the pursuit of more efficient, scalable, and cost-effective solutions that lie ahead.

A Porcine DNMT1 Variant: Molecular Cloning and Generation of Specific Polyclonal Antibody

DNA methyltransferase 1 (DNMT1), the first-identified DNA methyltransferase in mammals, has been well studied in the control of embryo development and somatic homeostasis in mice and humans. Accumulating reports have demonstrated that DNMT1 plays an important role in the regulation of differentiation and the activation of immune cells. However, little is known about the effects of porcine DNMT1 on such functional regulation, especially the regulation of the biological functions of immune cells. In this study, we report the cloning of DNMT1 (4833 bp in length) from porcine alveolar macrophages (PAMs). According to the sequence of the cloned DNMT1 gene, the deduced protein sequence contains a total of 1611 amino acids with a 2 amino acid insertion, a 1 amino acid deletion, and 12 single amino acid mutations in comparison to the reported DNMT1 protein. A polyclonal antibody based on a synthetic peptide was generated to study the expression of the porcine DNMT1. The polyclonal antibody only recognized the cloned porcine DNMT1 and not the previously reported protein due to a single amino acid difference in the antigenic peptide region. However, the polyclonal antibody recognized the endogenous DNMT1 in several porcine cells (PAM, PK15, ST, and PIEC) and the cells of other species (HEK-293T, Marc-145, MDBK, and MDCK cells). Moreover, our results demonstrated that all the detected tissues of piglet express DNMT1, which is the same as that in porcine alveolar macrophages. In summary, we have identified a porcine DNMT1 variant with sequence and expression analyses.

Comprehensive analysis of candidate signatures of long non-coding RNA LINC01116 and related protein-coding genes in patients with hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is a long-term malignancy that causes high morbidities and mortalities worldwide. Notably, long non-coding RNAs (LncRNAs) have been identified as candidate targets for malignancy treatments.

METHODS

LncRNA LINC01116 and its Pearson-correlated genes (PCGs) were identified and analyzed in HCC patients. The diagnostic and prognostic value of the lncRNA was evaluated using data from The Cancer Genome Atlas (TCGA). Further, we explored the target drugs of LINC01116 for clinical application. Relationships between immune infiltration and PCGs, methylation and PCGs were explored. The diagnostic potentials were then validated by Oncomine cohorts.

RESULTS

LINC01116 and the PCG OLFML2B are differentially and highly expressed in tumor tissues (both P ≤ 0.050). We found that LINC01116, TMSB15A, PLAU, OLFML2B, and MRC2 have diagnostic potentials (all AUC ≥ 0.700, all P ≤ 0.050) while LINC01116 and TMSB15A have prognostic significance (both adjusted P ≤ 0.050). LINC01116 was enriched in the vascular endothelial growth factor (VEGF) receptor signaling pathway, mesenchyme morphogenesis, etc. After that, candidate target drugs with potential clinical significance were identified: Thiamine, Cromolyn, Rilmenidine, Chlorhexidine, Sulindac_sulfone, Chloropyrazine, and Meprylcaine. Analysis of immune infiltration revealed that MRC2, OLFML2B, PLAU, and TMSB15A are negatively associated with the purity but positively associated with the specific cell types (all P < 0.050). Analysis of promoter methylation demonstrated that MRC2, OLFML2B, and PLAU have differential and high methylation levels in primary tumors (all P < 0.050). Validation results of the differential expressions and diagnostic potential of OLFML2B (Oncomine) were consistent with those obtained in the TCGA cohort (P < 0.050, AUC > 0.700).

CONCLUSIONS

Differentially expressed LINC01116 could be a candidate diagnostic and an independent prognostic signature in HCC. Besides, its target drugs may work for HCC therapy via the VEGF receptor signaling pathway. Differentially expressed OLFML2B could be a diagnostic signature involved in HCC via immune infiltrates.

Phenomic Studies on Diseases: Potential and Challenges

The rapid development of such research field as multi-omics and artificial intelligence (AI) has made it possible to acquire and analyze the multi-dimensional big data of human phenomes. Increasing evidence has indicated that phenomics can provide a revolutionary strategy and approach for discovering new risk factors, diagnostic biomarkers and precision therapies of diseases, which holds profound advantages over conventional approaches for realizing precision medicine: first, the big data of patients' phenomes can provide remarkably richer information than that of the genomes; second, phenomic studies on diseases may expose the correlations among cross-scale and multi-dimensional phenomic parameters as well as the mechanisms underlying the correlations; and third, phenomics-based studies are big data-driven studies, which can significantly enhance the possibility and efficiency for generating novel discoveries. However, phenomic studies on human diseases are still in early developmental stage, which are facing multiple major challenges and tasks: first, there is significant deficiency in analytical and modeling approaches for analyzing the multi-dimensional data of human phenomes; second, it is crucial to establish universal standards for acquirement and management of phenomic data of patients; third, new methods and devices for acquirement of phenomic data of patients under clinical settings should be developed; fourth, it is of significance to establish the regulatory and ethical guidelines for phenomic studies on diseases; and fifth, it is important to develop effective international cooperation. It is expected that phenomic studies on diseases would profoundly and comprehensively enhance our capacity in prevention, diagnosis and treatment of diseases.

A Self-attention Graph Convolutional Network for Precision Multi-tumor Early Diagnostics with DNA Methylation Data

DNA methylation-based precision tumor early diagnostics is emerging as state-of-the-art technology that could capture early cancer signs 3 ~ 5 years in advance, even for clinically homogenous groups. Presently, the sensitivity of early detection for many tumors is ~ 30%, which needs significant improvement. Nevertheless, based on the genome-wide DNA methylation data, one could comprehensively characterize tumors' entire molecular genetic landscape and their subtle differences. Therefore, novel high-performance methods must be modeled by considering unbiased information using excessively available DNA methylation data. To fill this gap, we have designed a computational model involving a self-attention graph convolutional network and multi-class classification support vector machine to identify the 11 most common cancers using DNA methylation data. The self-attention graph convolutional network automatically learns key methylation sites in a data-driven way. Then, multi-tumor early diagnostics is realized by training a multi-class classification support vector machine based on the selected methylation sites. We evaluated our model's performance through several data sets of experiments, and our results demonstrate the effectiveness of the selected key methylation sites, which are highly relevant for blood diagnosis. The pipeline of the self-attention graph convolutional network based computational framework.

TRIM29 acts as a potential senescence suppressor with epigenetic activation in nasopharyngeal carcinoma

Epigenetic alterations marked by DNA methylation are frequent events during the early development of nasopharyngeal carcinoma (NPC). We identified that TRIM29 is hypomethylated and overexpressed in NPC cell lines and tissues. TRIM29 silencing not only limited the growth of NPC cells in vitro and in vivo, but also induced cellular senescence, along with reactive oxygen species (ROS) accumulation. Mechanistically, we found that TRIM29 interacted with voltage-dependent anion-selective channel 1 (VDAC1) to activate mitophagy clearing up damaged mitochondria, which are the major source of ROS. In patients with NPC, high levels of TRIM29 expression are associated with an advanced clinical stage. Moreover, we detected hypomethylation of TRIM29 in patient nasopharyngeal swab DNA. Our findings indicate that TRIM29 depends on VDAC1 to induce mitophagy and prevents cellular senescence by decreasing ROS. Detection of aberrantly methylated TRIM29 in the nasopharyngeal swab DNA could be a promising strategy for the early detection of NPC.

An electrochemical biosensor for detecting DNA methylation based on AuNPs/rGO/g-C3N4 nanocomposite

DNA methylation as a ubiquitously regulation is closely associated with cell proliferation and differentiation. Growing data shows that aberrant methylation contributes to disease incidence, especially in tumorigenesis. The approach for identifying DNA methylation usually depends on treatment of sodium bisulfite, which is time-consuming and conversion-insufficient. Here, with a special biosensor, we establish an alternative approach for detecting DNA methylation. The biosensor is consisted of two parts, which are gold electrode and nanocomposite (AuNPs/rGO/g-C₃N₄). Nanocomposite was fabricated by three components, which are gold nanoparticles (AuNPs), reduced graphene oxide (rGO) and graphite carbon nitride (g-C₃N₄). For methylated DNA detection, the target DNA was captured by probe DNA immobilized on the gold electrode surface through thiolating process and subjected to hybrid with anti-methylated cytosine conjugated to nanocomposite. When the methylated cytosines in target DNA were recognized by anti-methylated cytosine, a change of electrochemical signals will be observed. With different size of target DNAs, the concentration and methylation level were tested. It is shown that in short size methylated DNA fragment, the linear range and LOD of concentration is 10^-7M-10^-15M and 0.74 fM respectively; in longer size methylated DNA, the linear range of methylation proportion and LOD of copy number is 3%-84% and 10³ respectively. Also, this approach has a high sensitivity and specificity as well as anti-disturbing ability.

A DNA methylation signature for the prediction of tumour recurrence in stage II colorectal cancer

BACKGROUND

A major challenge in stage II colorectal carcinoma is to identify patients with increased risk of recurrence. Biomarkers that distinguish patients with poor prognosis from patients without recurrence are currently lacking. This study aims to develop a robust DNA methylation classifier that allows the prediction of recurrence and chemotherapy benefit in patients with stage II colorectal cancer. We performed a genome-wide DNA methylation capture sequencing in 243 stage II colorectal carcinoma samples and identified a relapse-specific DNA methylation signature consisting of eight CpG sites.

METHODS

Two hundred and forty-three patients with stage II CRC were enrolled in this study. In order to select differential methylation sites among recurrence and non-recurrence stage II CRC samples, DNA methylation profiles of 62 tumour samples including 31 recurrence and 31 nonrecurrence samples were analysed using the Agilent SureSelectXT Human Methyl-Seq, a comprehensive target enrichment system to analyse CpG methylation. Pyrosequencing was applied to quantify the methylation level of candidate DNA methylation sites in 243 patients. Least absolute shrinkage and selection operator (LASSO) method was employed to build the disease recurrence prediction classifier.

RESULTS

We identified a relapse-related DNA methylation signature consisting of eight CpG sites in stage II CRC by DNA methylation capture sequencing. The classifier showed significantly higher prognostic accuracy than any clinicopathological risk factors. The Kaplan-Meier survival curve showed an association of high-risk score with poor prognosis. In multivariate analysis, the signature was the most significant prognosis factor, with an HR of 2.80 (95% CI, 1.71-4.58, P < 0.001). The signature could identify patients who are suitable candidates for adjuvant chemotherapy.

CONCLUSIONS

An eight-CpG DNA methylation signature is a reliable prognostic and predictive tool for disease recurrence in patients with stage II CRC.

Enhancing clinical potential of liquid biopsy through a multi-omic approach: A systematic review

In the last years, liquid biopsy gained increasing clinical relevance for detecting and monitoring several cancer types, being minimally invasive, highly informative and replicable over time. This revolutionary approach can be complementary and may, in the future, replace tissue biopsy, which is still considered the gold standard for cancer diagnosis. “Classical” tissue biopsy is invasive, often cannot provide sufficient bioptic material for advanced screening, and can provide isolated information about disease evolution and heterogeneity. Recent literature highlighted how liquid biopsy is informative of proteomic, genomic, epigenetic, and metabolic alterations. These biomarkers can be detected and investigated using single-omic and, recently, in combination through multi-omic approaches. This review will provide an overview of the most suitable techniques to thoroughly characterize tumor biomarkers and their potential clinical applications, highlighting the importance of an integrated multi-omic, multi-analyte approach. Personalized medical investigations will soon allow patients to receive predictable prognostic evaluations, early disease diagnosis, and subsequent ad hoc treatments.

Recurrence risk assessment for stage III colorectal cancer based on five methylation biomarkers in plasma cell-free DNA

For stage III colorectal cancer (CRC) patients with a high risk of recurrence, intensified adjuvant chemotherapy can improve overall survival. We aimed to develop a circulating tumor DNA (ctDNA) methylation marker model for predicting the relapse risk of stage III CRC patients. Differentially methylated markers identified between 53 normal mucosa samples and 165 CRC tissue samples, as well as between plasma samples from 75 stage I/II (early-stage) CRC patients and 55 stage IV (late-stage) CRC patients, were analyzed using Student's t-tests. The overlapping methylation markers shared by plasma and tissue samples were used to establish a methylation marker model to evaluate the tumor burden in the peripheral blood of CRC patients using the random forest method. This model was verified in the validation cohort (n = 44) and then applied to predict recurrence risk in 50 stage III CRC patients and monitor the clinical disease course in serial samples from four CRC patients. We built a five-marker-based ctDNA methylation model that had high sensitivity (84.21%) and specificity (84%) in identifying late-stage CRC in a validation cohort containing 24 stage I/II CRC patients and 20 stage IV CRC patients. The model achieved high sensitivity (87.5%) and specificity (94.12%) in predicting tumor relapse in an independent cohort of 50 stage III CRC patients and could be an independent recurrence risk factor for stage III patients [Hazard ratio (HR), 60.4; 95% confidence interval (CI): 7.68-397; p = 9.73e-5]. Analysis of serial blood samples of CRC showed that the model could monitor disease relapse earlier than imaging examination and serum carcinoembryonic antigen (CEA) and so may provide an opportunity for the early adjustment of therapeutic strategies. Moreover, the model could potentially monitor the clinical course and treatment response dynamically. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Group-shrinkage feature selection with a spatial network for mining DNA methylation data

Identifying disease-related biomarkers from high-dimensional DNA methylation data helps in reducing early screening costs and inferring pathogenesis mechanisms. Good discovery results have been achieved through spatial correlation methods of methylation sites, group-based regularization, and network constraints. However, these methods still have some key limitations as they cannot exclude isolated differential sites and only consider adjacent site ordering. Therefore, we propose a group-shrinkage feature selection algorithm to encourage the selection of clustered sites and discourage the selection of isolated differential sites. Specifically, a network-guided group-shrinkage strategy is developed to penalize weakly-correlated isolated methylation sites through a network structure constraint. The spatial network is constructed based on spatial correlation information of DNA methylation sites, where this information accounts for the uneven site distribution. The experimental simulations and applications demonstrated that the proposed method outperforms the advanced regularization methods, especially in rejecting isolated methylation sites; hence this study provides an efficient and clinical-valuable method for biomarker candidate discovery in DNA methylation data. Additionally, the proposed method exhibits enhanced reliability due to introducing biological prior knowledge into a regularization-based feature selection framework and could promote more research in the integration between biological prior knowledge and classical feature selection methods, thus facilitating their clinical application. Our source codes will be released at https://github.com/SJTUBME-QianLab/Group-shrinkage-Spatial-Network once this manuscript is accepted for publication.

Harnessing Genomic Analysis to Explore the Role of Telomeres in the Pathogenesis and Progression of Diabetic Kidney Disease

The prevalence of diabetes is increasing globally, and this trend is predicted to continue for future decades. Research is needed to uncover new ways to manage diabetes and its co-morbidities. A significant secondary complication of diabetes is kidney disease, which can ultimately result in the need for renal replacement therapy, via dialysis or transplantation. Diabetic kidney disease presents a substantial burden to patients, their families and global healthcare services. This review highlights studies that have harnessed genomic, epigenomic and functional prediction tools to uncover novel genes and pathways associated with DKD that are useful for the identification of therapeutic targets or novel biomarkers for risk stratification. Telomere length regulation is a specific pathway gaining attention recently because of its association with DKD. Researchers are employing both observational and genetics-based studies to identify telomere-related genes associated with kidney function decline in diabetes. Studies have also uncovered novel functions for telomere-related genes beyond the immediate regulation of telomere length, such as transcriptional regulation and inflammation. This review summarises studies that have revealed the potential to harness therapeutics that modulate telomere length, or the associated epigenetic modifications, for the treatment of DKD, to potentially slow renal function decline and reduce the global burden of this disease.

WDR74 rs11231247 contributes to the susceptibility and prognosis of non-small cell lung cancer

OBJECTIVES

WD repeat-containing protein 74 (WDR74) has been linked with the development of lung cancer. This study aims to investigate the relationship between WDR74 rs11231247 and non-small cell lung cancer (NSCLC) susceptibility and the prognosis of NSCLC patients.

METHODS

UALCAN, MethPrimer, ensembl and Pancan meQTL databases were used for bioinformatics analysis. The case-control study included 462 NSCLC patients and 462 health controls. WDR74 rs11231247 genotype was determined by PCR-RFLP. Logistic regression model was used to calculate odds ratio (OR) and 95% confidence interval (95% CI) for analyzing the association of WDR74 SNP with the risk of NSCLC. Log-rank test and Cox regression analysis were used to evaluate the effect of WDR74 genetic variation on the prognosis of NSCLC.

RESULTS

Compared with normal tissues, WDR74 expression level was higher and methylation level was lower in LUAD tissues. There were two CpG islands presented in the promoter of WDR74. And rs11231247 was in the second CpG island. We then discovered that rs11231247 CC and CT were more likely modified by methylation. LUAD case-control study demonstrated that rs11231247 CC genotype was associated with NSCLC risk with OR (95%CI) of 5.29 (2.59-10.79). Stratified analysis showed that rs11231247 T > C polymorphism could increase NSCLC risk in younger subjects (age≤58) (OR = 1.64, 95%CI = 1.06-2.54, P = 0.027). Survival analysis and Cox regression analysis showed rs11231247 CC genotype contributed to a poor prognosis of NSCLC patients (MST=21, HR=2.09, 95%CI=1.17-3.75).

CONCLUSION

WDR74 rs11231247 polymorphism affected the risk and prognosis of NSCLC.

Circulating Tumor DNA Methylation Biomarkers for Characterization and Determination of the Cancer Origin in Malignant Liver Tumors

Malignant liver tumors include primary malignant liver tumors and liver metastases. They are among the most common malignancies worldwide. The disease has a poor prognosis and poor overall survival, especially with liver metastases. Therefore, early detection and differentiation between malignant liver tumors are critical for patient treatment selection. The detection of cancer and the prediction of its origin is possible with a DNA methylation profile of the tumor DNA compared to that of normal cells, which reflects tissue differentiation and malignant transformation. New technologies enable the characterization of the tumor methylome in circulating tumor DNA (ctDNA), providing a variety of new ctDNA methylation biomarkers, which can provide additional information to clinical decision-making. Our review of the literature provides insight into methylation changes in ctDNA from patients with common malignant liver tumors and can serve as a starting point for further research.

Imaging-Based In Situ Analysis of 5-Methylcytosine at Low Repetitive Single Gene Loci with Transcription-Activator-Like Effector Probes

Transcription-activator-like effectors (TALEs) are programmable DNA binding proteins that can be used for sequence-specific, imaging-based analysis of cellular 5-methylcytosine. However, this has so far been limited to highly repetitive satellite DNA. To expand this approach to the analysis of coding single gene loci, we here explore a number of signal amplification strategies for increasing imaging sensitivity with TALEs. We develop a straightforward amplification protocol and employ it to target the MUC4 gene, which features only a small cluster of repeat sequences. This offers high sensitivity imaging of MUC4, and in costaining experiments with pairs of one TALE selective for unmethylated cytosine and one universal control TALE enables analyzing methylation changes in the target independently of changes in target accessibility. These advancements offer prospects for 5-methylcytosine analysis at coding, nonrepetitive gene loci by the use of designed TALE probe collections.

Recent advances in the molecular prognostication of meningiomas

Meningiomas are the most common primary intracranial neoplasm. While traditionally viewed as benign, meningiomas are associated with significant patient morbidity, and certain meningioma subgroups display more aggressive and malignant behavior with higher rates of recurrence. Historically, the risk stratification of meningioma recurrence has been primarily associated with the World Health Organization histopathological grade and surgical extent of resection. However, a growing body of literature has highlighted the value of utilizing molecular characteristics to assess meningioma aggressiveness and recurrence risk. In this review, we discuss preclinical and clinical evidence surrounding the use of molecular classification schemes for meningioma prognostication. We also highlight how molecular data may inform meningioma treatment strategies and future directions.

P14.5 functions to inhibit cell migration and can be used as a prognostic marker in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is the most common primary liver malignancy with a high mortality rate. P14.5 is a member of the highly conserved YER057c/YIL051c/YjgF subfamily and is highly expressed in the liver. However, its low expression is associated with carcinogenesis in HCC.

OBJECTIVE

This study aimed to investigate the role and prognostic significance of P14.5 in HCC.

METHODS

The clinical significance of P14.5 in HCC was examined using ONCOMINE, UALCAN, Human Protein Atlas, and Kaplan-Meier plotter. The DNA methylation profile of the P14.5 promoter was examined in 103 HCC and paired precancerous tissues; the HCC cell lines HepG2, MHCC-97L, SMMC-7721, SK-Hep-1, and Huh7; and the normal hepatic cell line HL-7702 via MALDI-TOF mass spectrometry. In addition, in vitro experiments were performed to examine the effects of P14.5 overexpression on the proliferation and migration of SMMC-7721 cells.

RESULTS

Low expression of P14.5 was correlated with shorter overall survival (OS) and disease-free survival (DFS) in HCC. Based on the results of MALDI-TOF mass spectrometry, no difference was observed in the methylation level between HCC cells and normal human hepatic cells and between HCC and paired precancerous tissues. Additionally, P14.5 overexpression promoted the proliferation and inhibited the migration of SMMC7721 cells in vitro.

CONCLUSIONS

P14.5 may serve as a prognostic biomarker in HCC and plays a role in the migration and proliferation of HCC cells. Low expression of P14.5 during hepatocarcinogenesis is not attributed to DNA methylation.

Genome-wide methylation profiling identifies a novel gene signature for patients with synchronous colorectal cancer

BACKGROUND

There are no robust tools for the diagnosis of synchronous colorectal cancer (SyCRC). Herein, we developed the first methylation signature to identify and characterise patients with SyCRC.

METHODS

For biomarker discovery, we analysed the genome-wide methylation profiles of 16 SyCRC and 18 solitary colorectal cancer (SoCRC) specimens. We thereafter established a methylation signature risk-scoring model to identify SyCRC in an independent cohort of 38 SyCRC and 42 SoCRC patients. In addition, we evaluated the prognostic value of the identified methylation profile.

RESULTS

We identified six differentially methylated CpG probes/sites that distinguished SyCRC from SoCRC. In the validation cohort, we developed a methylation panel that identified patients with SyCRC from not only larger tumour (AUC = 0.91) but also the paired remaining tumour (AUC = 0.93). Moreover, high risk scores of our panel were associated with the development of metachronous CRC among patients with SyCRC (AUC = 0.87) and emerged as an independent predictor for relapse-free survival (hazard ratio = 2.72; 95% CI = 1.12-6.61). Furthermore, the risk stratification model which combined with clinical risk factors was a diagnostic predictor of recurrence (AUC = 0.90).

CONCLUSIONS

Our novel six-gene methylation panel robustly identifies patients with SyCRC, which has the clinical potential to improve the diagnosis and management of patients with CRC.

A Bayesian model for identifying cancer subtypes from paired methylation profiles

Aberrant DNA methylation is the most common molecular lesion that is crucial for the occurrence and development of cancer, but has thus far been underappreciated as a clinical tool for cancer classification, diagnosis or as a guide for therapeutic decisions. Partly, this has been due to a lack of proven algorithms that can use methylation data to stratify patients into clinically relevant risk groups and subtypes that are of prognostic importance. Here, we proposed a novel Bayesian model to capture the methylation signatures of different subtypes from paired normal and tumor methylation array data. Application of our model to synthetic and empirical data showed high clustering accuracy, and was able to identify the possible epigenetic cause of a cancer subtype.

The regulation role and diagnostic value of fibrinogen-like protein 1 revealed by pan-cancer analysis

Although the role of fibrinogen-like protein 1 (FGL1) in tumorigenesis is well known, a pan-cancer analysis of FGL1 lacks. We used bioinformatics techniques to analyze cancer data from publicly available datasets from The Cancer Genome Atlas, UALCAN, TIMER, Gene Expression Profiling Interactive Analysis, cBioPortal, Search Tool for the Retrieval of Interacting Genes, and DAVID. FGL1 expression was significantly regulated in various common tumors than in normal tissues; it was increased in lung adenocarcinoma and decreased in colon adenocarcinoma. Cox regression analysis demonstrated that the upregulation of FGL1 expression was correlated with poor overall survival (OS) and disease-free survival (DFS) in stomach adenocarcinoma, brain low-grade glioma, cervical squamous cell carcinoma, and endocervical adenocarcinoma. Decreased FGL1 methylation levels were observed in majority of tumor types. FGL1 expression was significantly associated with the levels of immune cell subtypes and immune checkpoint genes. Deep deletion was the most common genetic mutation in FGL1 that led to frame-shift mutations, which was closely associated with poor progression-free interval, disease-specific survival, and OS in patients with FGL1 mutations. Kyoto Encyclopedia of Genes and Genomes enrichment analysis showed that FGL1-related genes participate in diverse pathways. Ubiquitin-mediated proteolysis is significantly correlated to the function of FGL1, which was identified for the first time in the present study. This pan-cancer study provides a deep understanding of the functions of FGL1 in progression of many tumors and demonstrates that FGL1 may be a potential biomarker for the diagnosis, prognosis, and immune infiltration in cancer.

Excavation of Molecular Subtypes of Endometrial Cancer Based on DNA Methylation

Tumor heterogeneity makes the diagnosis and treatment of endometrial cancer difficult. As an important modulator of gene expression, DNA methylation can affect tumor heterogeneity and, therefore, provide effective information for clinical treatment. In this study, we explored specific prognostic clusters based on 482 examples of endometrial cancer methylation data in the TCGA database. By analyzing 4870 CpG clusters, we distinguished three clusters with different prognostics. Differences in DNA methylation levels are associated with differences in age, grade, clinical pathological staging, and prognosis. Subsequently, we screened out 264 specific hypermethylation and hypomethylation sites and constructed a prognostic model for Bayesian network classification, which corresponded to the classification of the test set to the classification results of the train set. Since the tumor microenvironment plays a key role in determining immunotherapy responses, we conducted relevant analyses based on clusters separated from DNA methylation data to determine the immune function of each cluster. We also predicted their sensitivity to chemotherapy drugs. Specific classifications of DNA methylation may help to address the heterogeneity of previously existing molecular clusters of endometrial cancer, as well as to develop more effective, individualized treatments.

Blood-based DNA methylation profiling for the detection of ovarian cancer

OBJECTIVES

Ovarian cancer is a fatal gynecological cancer due to the lack of effective screening strategies at early stage. This study explored the utility of DNA methylation profiling of blood samples for the detection of ovarian cancer.

METHODS

Targeted bisulfite sequencing was performed on tissue (n = 152) and blood samples (n = 373) obtained from healthy women, women with benign ovarian tumors, or malignant epithelial ovarian tumors. Based on the tissue-derived differentially-methylated regions, a supervised machine learning algorithm was implemented and cross-validated using the blood-derived DNA methylation profiles of the training cohort (n = 178) to predict and classify each blood sample as malignant or non-malignant. The model was further evaluated using an independent test cohort (n = 184).

RESULTS

Comparison of the DNA methylation profiles of normal/benign and malignant tumor samples identified 1272 differentially-methylated regions, with 49.4% hypermethylated regions and 50.6% hypomethylated regions. Five-fold cross-validation of the model using the training dataset yielded an area under the curve of 0.94. Using the test dataset, the model accurately predicted non-malignancy in 96.2% of healthy women (n = 53) and 93.5% of women with benign tumors (n = 46). For patients with malignant tumors, the model accurately predicted malignancy in 44.4% of stage I-II (n = 9), 86.4% of stage III (n = 59), 100.0% of stage IV tumors (n = 6), and 81.8% of tumors with unknown stage (n = 11). Overall, the model yielded a predictive accuracy of 89.5%.

CONCLUSIONS

Our study demonstrates the potential clinical application of blood-based DNA methylation profiling for the detection of ovarian cancer.

Comprehensive Analysis of GDF10 Methylation Site-Associated Genes as Prognostic Markers for Endometrial Cancer

Growth differentiation factor-10 (GDF10) with its methylation trait has recently been found to play a crucial regulatory and communication role in cancers. This investigation aims to identify GDF10 methylation site-associated genes that are closely associated with endometrial cancer (EC) patients' survival based on normal and UCEC samples from the UCSC Xena database. Our study revealed for the first time that EC exhibited significantly higher levels of GDF10 promoter methylation in comparison with normal tissues. Multiple differentiated methylation sites, which have prognostic value due to their apparent survival differences, were found in the GDF10 promoter region. We performed weighted gene coexpression network analysis (WGCNA) on EC tissues and paraneoplastic tissues while using these differentially methylated sites as phenotypes for selecting the most correlated key modules and their internal genes. To obtain a gene set, the key module genes and differentially expressed genes (DEGs) of EC were intersected. The least absolute shrinkage and selection operator (LASSO) regression along with multivariate Cox regression were performed from the gene set and we screened out the key genes B4GALNT3, DNAJC22, and GREB1. Finally, a prognostic model was validated for effectiveness based on these genes. Additionally, Kaplan-Meier analysis and time-dependent receiver operating characteristics (ROC) were applied to assess and verify the model, and they showed good prognosis prediction. Moreover, the differences in risk scores were statistically significant with age, tumor stage, and grade. They may be related to the immune infiltration of tumors as well. In conclusion, based on the methylation-related genes associated with GDF10, we developed a prognosis model for EC patients. It might provide a fresh view for further research and treatment of EC.

Comprehensive landscape of the functions and prognostic value of RNA binding proteins in uterine corpus endometrial carcinoma

Background: The dysregulation of RNA binding proteins (RBPs) is involved in tumorigenesis and progression. However, information on the overall function of RNA binding proteins in Uterine Corpus Endometrial Carcinoma (UCEC) remains to be studied. This study aimed to explore Uterine Corpus Endometrial Carcinoma-associated molecular mechanisms and develop an RNA-binding protein-associated prognostic model.

Methods: Differently expressed RNA binding proteins were identified between Uterine Corpus Endometrial Carcinoma tumor tissues and normal tissues by R packages (DESeq2, edgeR) from The Cancer Genome Atlas (TCGA) database. Hub RBPs were subsequently identified by univariate and multivariate Cox regression analyses. The cBioPortal platform, R packages (ggplot2), Human Protein Atlas (HPA), and TIMER online database were used to explore the molecular mechanisms of Uterine Corpus Endometrial Carcinoma. Kaplan-Meier (K-M), Area Under Curve (AUC), and the consistency index (c-index) were used to test the performance of our model.

Results: We identified 128 differently expressed RNA binding proteins between Uterine Corpus Endometrial Carcinoma tumor tissues and normal tissues. Seven RNA binding proteins genes (NOP10, RBPMS, ATXN1, SBDS, POP5, CD3EAP, ZC3H12C) were screened as prognostic hub genes and used to construct a prognostic model. Such a model may be able to predict patient prognosis and acquire the best possible treatment. Further analysis indicated that, based on our model, the patients in the high-risk subgroup had poor overall survival (OS) compared to those in the low-risk subgroup. We also established a nomogram based on seven RNA binding proteins. This nomogram could inform individualized diagnostic and therapeutic strategies for Uterine Corpus Endometrial Carcinoma.

Conclusion: Our work focused on systematically analyzing a large cohort of Uterine Corpus Endometrial Carcinoma patients in the The Cancer Genome Atlas database. We subsequently constructed a robust prognostic model based on seven RNA binding proteins that may soon inform individualized diagnosis and treatment.

DNA Methylation in Gestational Diabetes and its Predictive Value for Postpartum Glucose Disturbances

CONTEXT

DNA methylation in the diagnosis of gestational diabetes.

OBJECTIVE

To assess the value of DNA methylation in the diagnosis of gestational diabetes (GDM) and in the prediction of maternal postpartum glucose disturbances.

METHODS

Two-stage observational study performed between July 2006 and December 2010, at University Hospital. Forty-eight randomly selected pregnant women formed the discovery cohort (24 with GDM and 24 controls) and 252 pregnant women (94 with GDM and 158 controls) formed the replication cohort. GDM women were re-evaluated 4 years postpartum. The main outcome measures were GDM, type 2 diabetes or prediabetes at 4 years postpartum.

RESULTS

We identified 3 CpG sites related to LINC00917, TRAPPC9, and LEF1 that were differentially methylated in women with GDM and abnormal glucose tolerance; and sites associated with LINC00917 and TRAPPC9 were independently associated with an abnormal glucose tolerance status 4 years postpartum after controlling for clinical variables. Moreover, the site associated with LINC00917 and the combination of the 3 sites had the highest predictive values.

CONCLUSION

Our results suggest that some of these sites may be implicated in the development of GDM and postpartum abnormal glucose tolerance.

Computational Analysis of High-Dimensional DNA Methylation Data for Cancer Prognosis

Developing cancer prognostic models using multiomics data is a major goal of precision oncology. DNA methylation provides promising prognostic biomarkers, which have been used to predict survival and treatment response in solid tumor or plasma samples. This review article presents an overview of recently published computational analyses on DNA methylation for cancer prognosis. To address the challenges of survival analysis with high-dimensional methylation data, various feature selection methods have been applied to screen a subset of informative markers. Using candidate markers associated with survival, prognostic models either predict risk scores or stratify patients into subtypes. The model's discriminatory power can be assessed by multiple evaluation metrics. Finally, we discuss the limitations of existing studies and present the prospects of applying machine learning algorithms to fully exploit the prognostic value of DNA methylation.

Prognostic and therapeutic prediction by screening signature combinations from transcriptome-methylome interactions in oral squamous cell carcinoma

DNA methylation pattern in oral squamous cell carcinoma (OSCC) remains poorly described. This study aimed to perform a genome-wide integrated analysis of the transcriptome and methylome and assess the efficacy of their prognostic signature model in patients with OSCC. We analyzed transcriptome and methylome data from 391 OSCC samples and 41 adjacent normal samples. A total of 8074 differentially expressed genes (DEGs) and 10,084 differentially expressed CpGs (DMCpGs) were identified. Then 241 DEGs with DMCpGs were identified. According to the prognostic analysis, the prognostic signature of methylation-related differentially expressed genes (mrDEGPS) was established. mrDEGPS consisted of seven prognostic methylation-related genes, including ESRRG, CCNA1, SLC20A1, COL6A6, FCGBP, CDKN2A, and ZNF43. mrDEGPS was a significant stratification factor of survival (P < 0.00001) irrespective of the clinical stage. The immune effector components, including B cells, CD4⁺ T cells, and CD8⁺ T cells, were decreased in the tumor environment of patients with high mrDEGPS. Immune checkpoint expressions, including CTLA-4, PD-1, LAG3, LGALS9, HAVCR2, and TIGHT, were comprehensively elevated (P < 0.001). The estimated half-maximal inhibitory concentration difference between low- and high-risk patients was inconsistent among chemotherapeutic drugs. In conclusion, the transcriptome–methylome interaction pattern in OSCC is complex. mrDEGPS can predict patient survival and responses to immunotherapy and chemotherapy and facilitate clinical decision-making in patients with OSCC.

Epigenetic insights in the diagnosis, prognosis, and treatment selection in CRC, an updated review

BACKGROUND/AIM

The gradual accumulation of genetic and epigenetic alterations can lead to the development of colorectal cancer. In the last decade much research has been done to discover how methylation as an epigenetic alteration leads to carcinogenesis. While Methylation is a biological process, it can influence gene expression by affecting the promoter activity. This article reviews the role of methylation in critical pathways in CRC.

METHODS

In this study using appropriate keywords, all research and review articles related to the role of methylation on different cancers were collected and analyzed. Also, existing information on methylation detection methods and therapeutic sensitivity or resistance due to DNA methylation were reviewed.

RESULTS

The results of this survey revealed that while Methylation is a biological process, it can influence gene expression by affecting the promoter activity. Promoter methylation is associated with up or downregulation of genes involved in critical pathways, including cell cycle, DNA repair, and cell adherence. Hence promoter methylation can be used as a molecular tool for early diagnosis, improving treatment, and predicting treatment resistance.

CONCLUSION

Current knowledge on potential methylation biomarkers for diagnosis and prognoses of CRC has also been discussed. Our survey proposes that a multi-biomarker panel is more efficient than a single biomarker in the early diagnosis of CRC.

Methylation Heterogeneity and Gene Expression of SPG20 in Solid Tumors

Introduction. The downregulation of the Spastic Paraplegia-20 (SPG20) gene is correlated with a rare autosomal recessive disorder called Troyer Syndrome. Only in recent years has SPG20 been studied and partially characterized in cancer. SPG20 has been shown to be hypermethylated in colorectal cancer, gastric cancer, non-Hodgkin’s lymphoma and hepatocellular carcinoma. In this study, we analyze the methylation status and the gene expression of SPG20 in different tumors of various histological origins. Methods. We analyzed the data generated through Infinium Human Methylation 450 BeadChip arrays and RNA-seq approaches extrapolated from The Cancer Genome Atlas (TCGA) database. The statistics were performed with R 4.0.4. Results. We aimed to assess whether the hypermethylation of this target gene was a common characteristic among different tumors and if there was a correlation between the m-values and the gene expression in paired tumor versus solid tissue normal. Overall, our analysis highlighted that SPG20 open sea upstream the TSS is altogether hypermethylated, and the tumor tissues display a higher methylation heterogeneity compared to the solid tissue normal. The gene expression evidences a reproducible, higher gene expression in normal tissues. Conclusion. Our research, based on data mining from TCGA, evidences that colon and liver tumors display a consistent methylation heterogeneity compared to their normal counterparts. This parallels a downregulation of SPG20 gene expression in tumor samples and suggests a role for this multifunctional protein in the control of tumor progression.