Epigenetically reprogrammed methylation landscape drives the DNA self-assembly and serves as a universal cancer biomarker

Applications of Machine Learning (ML) and Mathematical Modeling (MM) in Healthcare with Special Focus on Cancer Prognosis and Anticancer Therapy: Current Status and Challenges

The use of data-driven high-throughput analytical techniques, which has given rise to computational oncology, is undisputed. The widespread use of machine learning (ML) and mathematical modeling (MM)-based techniques is widely acknowledged. These two approaches have fueled the advancement in cancer research and eventually led to the uptake of telemedicine in cancer care. For diagnostic, prognostic, and treatment purposes concerning different types of cancer research, vast databases of varied information with manifold dimensions are required, and indeed, all this information can only be managed by an automated system developed utilizing ML and MM. In addition, MM is being used to probe the relationship between the pharmacokinetics and pharmacodynamics (PK/PD interactions) of anti-cancer substances to improve cancer treatment, and also to refine the quality of existing treatment models by being incorporated at all steps of research and development related to cancer and in routine patient care. This review will serve as a consolidation of the advancement and benefits of ML and MM techniques with a special focus on the area of cancer prognosis and anticancer therapy, leading to the identification of challenges (data quantity, ethical consideration, and data privacy) which are yet to be fully addressed in current studies.

Functionalized nanomaterials-based electrochemiluminescent biosensors and their application in cancer biomarkers detection

To detect a range of trace biomarkers associated with human diseases, researchers have been focusing on developing biosensors that possess high sensitivity and specificity. Electrochemiluminescence (ECL) biosensors have emerged as a prominent research tool in recent years, owing to their potential superiority in low background signal, high sensitivity, straightforward instrumentation, and ease of operation. Functional nanomaterials (FNMs) exhibit distinct advantages in optimizing electrical conductivity, increasing reaction rate, and expanding specific surface area due to their small size effect, quantum size effect, and surface and interface effects, which can significantly improve the stability, reproducibility, and sensitivity of the biosensors. Thereby, various nanomaterials (NMs) with excellent properties have been developed to construct efficient ECL biosensors. This review provides a detailed summary and discussion of FNMs-based ECL biosensors and their applications in cancer biomarkers detection.

Cas12a-based direct visualization of nanoparticle-stabilized fluorescence signal for multiplex detection of DNA methylation biomarkers

The CRISPR-Cas12a RNA-guided complexes hold immense promise for nucleic acid detection. However, limitations arise from their specificity in detecting off-targets and the stability of the signal molecules. Here, we have developed a platform that integrates multiplex amplification and nanomolecular-reporting signals, allowing us to detect various clinically relevant nucleic acid targets with enhanced stability, sensitivity, and visual interpretation. Through the electrostatic co-assembly of the Oligo reporter with oppositely charged nanoparticles, we observed a significant enhancement in its stability in low-pollution environments, reaching up to a threefold increase compared to the original version. Additionally, the fluorescence efficiency was expanded by three orders of magnitude, broadening the detection range considerably. Utilizing a multiplex strategy, this assay can accomplish simultaneous detection of multiple targets and single-point indication detection of nine specific targets. This significant advancement heightened the sensitivity of disease screening and improved the accuracy of diagnosing disease-related changes. We tested this assay in a colorectal cancer model, demonstrating that it can identify DNA methylation features at the aM-level within 40-60 min. Validation using clinical samples yielded consistent results with qPCR and bisulfite sequencing, affirming the assay's reliability and potential for clinical applications.

Cancer cells possess different isotopic enrichment: Isotopic induced functionalizations of normal DNA mutations leading to cancer

Although the dynamics of telomeres during the life expectancy of normal cells has been extensively studied, there are still some unresolved issues regarding this research field. For example, the conditions required for telomere shortening leading to malignant transformations are not fully understood. In this work, we mass analyzed DNA of normal and cancer cells for comparing telomere isotopic compositions of white blood cells and cancer cells. We have found that the 1327 Da and 1672 Da characteristic telomere mass to charges cause differential mass distributions of about 1 Da among normal cells relative to cancer cells. These isotopic differences are consistent with a prior theory according to which replacing primordial, common isotopes of 1H, 12C, 14N, 16O, 24Mg, 31P and/or 32S by nonprimordial, uncommon isotopes of 2D, 13C, 15N, 17O, 25Mg and/or 33S leads to altered enzymatic dynamics. This replacement may subsequently modulate DNA and telomere codons resulting in transformation of normal cells to cancer cells (in 15 N depletion in telomeres dependent manner). The prior theory and the current data are consistent also with a recently observed non-uniform methylation pattern of the DNA of cancer cells relative to a more uniform methylation in the DNA of normal cells. We observe further evidence of nonprimordial isotopic accelerations of acetylations, methylations, hydroxylations and aminations of nucleosides with alterations of phosphorylations of nucleotides; which may explain the induction of mutations at the DNA, RNA and proteins leading to cancer and more general alterations of DNA, which are associated with aging. This difference in mass spectra between normal and cancer DNA may stem from different functionalizations and isotopic enrichments affecting the motion derived from nuclear magnetic moments (NMMs). We suggest that this phenomenon may lead to malignant transformation.

A pocket companion to cell-free DNA (cfDNA) preanalytics

The cumulative pool of cell-free DNA (cfDNA) molecules within bodily fluids represents a highly dense and multidimensional information repository. This "biological mirror" provides real-time insights into the composition, function, and dynamics of the diverse genomes within the body, enabling significant advancements in personalized molecular medicine. However, effective use of this information necessitates meticulous classification of distinct cfDNA subtypes with exceptional precision. While cfDNA molecules originating from different sources exhibit numerous genetic, epigenetic, and physico-chemical variations, they also share common features that complicate analyses. Considerable progress has been achieved in mapping the landscape of cfDNA features, their clinical correlations, and optimizing extraction procedures, analytical approaches, bioinformatics pipelines, and machine learning algorithms. Nevertheless, preanalytical workflows, despite their profound impact on cfDNA measurements, have not progressed at a corresponding pace. In this perspective article, we emphasize the pivotal role of robust preanalytical procedures in the development and clinical integration of cfDNA assays, highlighting persistent obstacles and emerging challenges.

The changing face of circulating tumor DNA (ctDNA) profiling: Factors that shape the landscape of methodologies, technologies, and commercialization

Liquid biopsies, in particular the profiling of circulating tumor DNA (ctDNA), have long held promise as transformative tools in cancer precision medicine. Despite a prolonged incubation phase, ctDNA profiling has recently experienced a strong wave of development and innovation, indicating its imminent integration into the cancer management toolbox. Various advancements in mutation-based ctDNA analysis methodologies and technologies have greatly improved sensitivity and specificity of ctDNA assays, such as optimized preanalytics, size-based pre-enrichment strategies, targeted sequencing, enhanced library preparation methods, sequencing error suppression, integrated bioinformatics and machine learning. Moreover, research breakthroughs have expanded the scope of ctDNA analysis beyond hotspot mutational profiling of plasma-derived apoptotic, mono-nucleosomal ctDNA fragments. This broader perspective considers alternative genetic features of cancer, genome-wide characterization, classical and newly discovered epigenetic modifications, structural variations, diverse cellular and mechanistic ctDNA origins, and alternative biospecimen types. These developments have maximized the utility of ctDNA, facilitating landmark research, clinical trials, and the commercialization of ctDNA assays, technologies, and products. Consequently, ctDNA tests are increasingly recognized as an important part of patient guidance and are being implemented in clinical practice. Although reimbursement for ctDNA tests by healthcare providers still lags behind, it is gaining greater acceptance. In this work, we provide a comprehensive exploration of the extensive landscape of ctDNA profiling methodologies, considering the multitude of factors that influence its development and evolution. By illuminating the broader aspects of ctDNA profiling, the aim is to provide multiple entry points for understanding and navigating the vast and rapidly evolving landscape of ctDNA methodologies, applications, and technologies.

Dual-Responsive Carbon Quantum Dots for the Simultaneous Detection of Cytosine and 5-Methylcytosine Interpreted by a Machine Learning-Assisted Smartphone

DNA methylation is an epigenetic alteration that results in 5-methylcytosine (5-mC) through the addition of a methyl group to the fifth carbon of a cytosine (C) residue. The methylation level, the ratio of 5-mC to C, in urine might be related to the whole-body epigenetic status and the occurrence of common cancers. To date, never before have any nanomaterials been developed to simultaneously determine C and 5-mC in urine samples. Herein, a dual-responsive fluorescent sensor for the urinary detection of C and 5-mC has been developed. This assay relied on changes in the optical properties of nitrogen-doped carbon quantum dots (CQDs) prepared by microwave-assisted pyrolysis. In the presence of C, the blue-shifted fluorescence intensity of the CQDs increased. However, fluorescence quenching was observed upon the addition of 5-mC. This was primarily due to photoinduced electron transfer as confirmed by the density functional theory calculation. In urine samples, our sensitive fluorescent sensor had detection limits for C and 5-mC of 43.4 and 74.4 μM, respectively, and achieved satisfactory recoveries ranging from 103.5 to 115.8%. The simultaneous detection of C and 5-mC leads to effective methylation level detection, achieving recoveries in the range of 104.6-109.5%. Besides, a machine learning-enabled smartphone was also developed, which can be effectively applied to the determination of methylation levels (0-100%). These results demonstrate a simple but very effective approach for detecting the methylation level in urine, which could have significant implications for predicting the clinical prognosis.

DoSurvive: A webtool for investigating the prognostic power of a single or combined cancer biomarker

We present DoSurvive, a user-friendly survival analysis web tool and a cancer prognostic biomarker centered database. DoSurvive is the first database that allows users to perform multivariant survival analysis for cancers with customized gene/patient list. DoSurvive offers three survival analysis methods, Log rank test, Cox regression and accelerated failure time model (AFT), for users to analyze five types of quantitative features (mRNA, miRNA, lncRNA, protein and methylation of CpG islands) with four survival types, i.e. overall survival, disease-specific survival, disease-free interval, and progression-free interval, in 33 cancer types. Notably, the implemented AFT model provides an alternative method for genes/features which failed the proportional hazard assumption in Cox regression. With the unprecedented number of survival models implemented and high flexibility in analysis, DoSurvive is a unique platform for the identification of clinically relevant targets for cancer researcher and practitioners. DoSurvive is freely available at http://dosurvive.lab.nycu.edu.tw/.

Targeting altered glycosylation in secreted tumor glycoproteins for broad cancer detection

There is an urgent need to develop new tumor biomarkers for early cancer detection, but the variability of tumor-derived antigens has been a limitation. Here we demonstrate a novel anti-Tn antibody microarray platform to detect Tn+ glycoproteins, a near universal antigen in carcinoma-derived glycoproteins, for broad detection of cancer. The platform uses a specific recombinant IgG1 to the Tn antigen (CD175) as a capture reagent and a recombinant IgM to the Tn antigen as a detecting reagent. These reagents were validated by immunohistochemistry in recognizing the Tn antigen using hundreds of human tumor specimens. Using this approach, we could detect Tn+ glycoproteins at subnanogram levels using cell lines and culture media, serum, and stool samples from mice engineered to express the Tn antigen in intestinal epithelial cells. The development of a general cancer detection platform using recombinant antibodies for detection of altered tumor glycoproteins expressing a unique antigen could have a significant impact on cancer detection and monitoring.

Nonlinear optical phase shift in blood plasmas for neoplasia diagnosis

Detecting cancer at an early stage is crucial for timely treatment and better chances of survival. This research focuses on a scanning method for detecting cancer by examining the nonlinear optical characteristics of blood plasma samples. The study used both cancerous and noncancerous plasma samples and presented the results statistically by utilizing an incident laser power-dependent nonlinear optical phase shift variable called ζ in the Z-scan technique. The results showed a clear difference between the cancerous and non-cancerous samples with an accuracy of 92%. Furthermore, the study suggests the potential for measuring the cancer staging from the cancerous plasma. The study also confirmed a significant difference in ζ for plasma samples undergoing chemotherapy. A red laser with high power (above 18mW) was used to avoid the involvement of fluorophores or other chemical reagents in the plasma samples during the measurement.

Impact of Circulating Cell-Free DNA (cfDNA) as a Biomarker of the Development and Evolution of Periodontitis

In the last few decades, circulating cell-free DNA (cfDNA) has been shown to have an important role in cell apoptosis or necrosis, including in the development and evolution of several tumors and inflammatory diseases in humans. In this regard, periodontitis, a chronic inflammatory disease that can induce the destruction of supporting components of the teeth, could represent a chronic inflammatory stimulus linked to a various range of systemic inflammatory diseases. Recently, a possible correlation between periodontal disease and cfDNA has been shown, representing new important diagnostic-therapeutic perspectives. During the development of periodontitis, cfDNA is released in biological fluids such as blood, saliva, urine and other body fluids and represents an important index of inflammation. Due to the possibility of withdrawing some of these liquids in a non-invasive way, cfDNA could be used as a possible biomarker for periodontal disease. In addition, discovering a proportional relationship between cfDNA levels and the severity of periodontitis, expressed through the disease extent, could open the prospect of using cfDNA as a possible therapeutic target. The aim of this article is to report what researchers have discovered in recent years about circulating cfDNA in the development, evolution and therapy of periodontitis. The analyzed literature review shows that cfDNA has considerable potential as a diagnostic, therapeutic biomarker and therapeutic target in periodontal disease; however, further studies are needed for cfDNA to be used in clinical practice.

A high-throughput test enables specific detection of hepatocellular carcinoma

High-throughput tests for early cancer detection can revolutionize public health and reduce cancer morbidity and mortality. Here we show a DNA methylation signature for hepatocellular carcinoma (HCC) detection in liquid biopsies, distinct from normal tissues and blood profiles. We developed a classifier using four CpG sites, validated in TCGA HCC data. A single F12 gene CpG site effectively differentiates HCC samples from other blood samples, normal tissues, and non-HCC tumors in TCGA and GEO data repositories. The markers were validated in a separate plasma sample dataset from HCC patients and controls. We designed a high-throughput assay using next-generation sequencing and multiplexing techniques, analyzing plasma samples from 554 clinical study participants, including HCC patients, non-HCC cancers, chronic hepatitis B, and healthy controls. HCC detection sensitivity was 84.5% at 95% specificity and 0.94 AUC. Implementing this assay for high-risk individuals could significantly decrease HCC morbidity and mortality.

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.

Development of a Point-of-Care Cervico-Vaginal Sampling/Testing Device for the Colorimetric Detection of Cervical Cancer

This paper reports the colorimetric analysis of cervical-cancer-affected clinical samples by the in situ formation of gold nanoparticles (AuNPs) formed with cervico-vaginal fluids collected from healthy and cancer-affected patients in a clinical setup, termed "C-ColAur". We evaluated the efficacy of the colorimetric technique against the clinical analysis (biopsy/Pap smear) and reported the sensitivity and specificity. We investigated if the aggregation coefficient and size of the nanoparticles responsible for the change in color of the AuNPs (formed with clinical samples) could also be used as a measure of detecting malignancy. We estimated the protein and lipid concentrations in the clinical samples and attempted to investigate if either of these components was solely responsible for the color change, enabling their colorimetric detection. We also propose a self-sampling device, CerviSelf, that could enable the rapid frequency of screening. We discuss two of the designs in detail and demonstrate the 3D-printed prototypes. These devices, in conjugation with the colorimetric technique C-ColAur, have the potential to be self-screening techniques, enabling women to undergo rapid and frequent screening in the comfort and privacy of their homes, allowing a chance at an early diagnosis and improved survival rates.

Shifting the Cancer Screening Paradigm: The Rising Potential of Blood-Based Multi-Cancer Early Detection Tests

Cancer remains a leading cause of death worldwide, partly owing to late detection which entails limited and often ineffective therapeutic options. Most cancers lack validated screening procedures, and the ones available disclose several drawbacks, leading to low patient compliance and unnecessary workups, adding up the costs to healthcare systems. Hence, there is a great need for innovative, accurate, and minimally invasive tools for early cancer detection. In recent years, multi-cancer early detection (MCED) tests emerged as a promising screening tool, combining molecular analysis of tumor-related markers present in body fluids with artificial intelligence to simultaneously detect a variety of cancers and further discriminate the underlying cancer type. Herein, we aim to provide a highlight of the variety of strategies currently under development concerning MCED, as well as the major factors which are preventing clinical implementation. Although MCED tests depict great potential for clinical application, large-scale clinical validation studies are still lacking.

Multiomics characteristics and immunotherapeutic potential of EZH2 in pan-cancer

Enhancer of zeste homolog 2 (EZH2) is a significant epigenetic regulator that plays a critical role in the development and progression of cancer. However, the multiomics features and immunological effects of EZH2 in pan-cancer remain unclear. Transcriptome and clinical raw data of pan-cancer samples were acquired from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and subsequent data analyses were conducted by using R software (version 4.1.0). Furthermore, numerous bioinformatics analysis databases also reapplied to comprehensively explore and elucidate the oncogenic mechanism and therapeutic potential of EZH2 from pan-cancer insight. Finally, quantitative reverse transcription polymerase chain reaction and immunohistochemical assays were performed to verify the differential expression of EZH2 gene in various cancers at the mRNA and protein levels. EZH2 was widely expressed in multiple normal and tumor tissues, predominantly located in the nucleoplasm. Compared with matched normal tissues, EZH2 was aberrantly expressed in most cancers either at the mRNA or protein level, which might be caused by genetic mutations, DNA methylation, and protein phosphorylation. Additionally, EZH2 expression was correlated with clinical prognosis, and its up-regulation usually indicated poor survival outcomes in cancer patients. Subsequent analysis revealed that EZH2 could promote tumor immune evasion through T-cell dysfunction and T-cell exclusion. Furthermore, expression of EZH2 exhibited a strong correlation with several immunotherapy-associated responses (i.e., immune checkpoint molecules, tumor mutation burden (TMB), microsatellite instability (MSI), mismatch repair (MMR) status, and neoantigens), suggesting that EZH2 appeared to be a novel target for evaluating the therapeutic efficacy of immunotherapy.

Rapid detection of cancer DNA in human blood using cysteamine-capped AuNPs and a machine learning-enabled smartphone

DNA methylation occurs when a methyl group is added to a cytosine (C) residue's fifth carbon atom, forming 5-methylcytosine (5-mC). Cancer genomes have a distinct methylation landscape (Methylscape), which could be used as a universal cancer biomarker. This study developed a simple, low-cost, and straightforward Methylscape sensing platform using cysteamine-decorated gold nanoparticles (Cyst/AuNPs), in which the sensing principle is based on methylation-dependent DNA solvation. Normal and cancer DNAs have distinct methylation profiles; thus, they can be distinguished by observing the dispersion of Cyst/AuNPs adsorbed on these DNA aggregates in MgCl2 solution. After optimising the MgCl2, Cyst/AuNPs, DNA concentration, and incubation time, the optimised conditions were used for leukemia screening, by comparing the relative absorbance (ΔA 650/525). Following the DNA extraction from actual blood samples, this sensor demonstrated effective leukemia screening in 15 minutes with high sensitivity, achieving 95.3% accuracy based on the measurement by an optical spectrophotometer. To further develop for practical realisation, a smartphone assisted by machine learning was used to screen cancer patients, achieving 90.0% accuracy in leukemia screening. This sensing platform can be applied not only for leukemia screening but also for other cancers associated with epigenetic modification.

Detection of Circulating Tumor DNA in Plasma Using Targeted Sequencing

Cell-free DNA (cfDNA) is the degradation product of extracellular DNA. Circulating tumor DNA (ctDNA), as a fraction of cfDNA, comes from tumor cells and contains variations, including mutation, deletion, insertion, rearrangement, copy number variation, and methylation. Therefore, biomarkers identified in ctDNA show promising clinical applications in early diagnosis, recurrence monitoring, and conducting individualized treatment. In this chapter, we introduce experimental workflow and bioinformatic pipeline of targeted sequencing of cfDNA.

Methylation biomarkers for early cancer detection and diagnosis: Current and future perspectives

The increase in recent scientific studies on cancer biomarkers has brought great new insights into the field. Moreover, novel technological breakthroughs such as long read sequencing and microarrays have enabled high throughput profiling of many biomarkers, while advances in bioinformatic tools have made the possibility of developing highly reliable and accurate biomarkers a reality. These changes triggered renewed interest in biomarker research and provided tremendous opportunities for enhancing cancer management and improving early disease detection. DNA methylation alterations are known to accompany and contribute to carcinogenesis, making them promising biomarkers for cancer, namely due to their stability, frequency and accessibility in bodily fluids. The advent of newer minimally invasive experimental methods such as liquid biopsies provide the perfect setting for methylation-based biomarker development and application. Despite their huge potential, accurate and robust biomarkers for the conclusive diagnosis of most cancer types are still not routinely used, hence a strong need for sustained research in this field is still needed. This review provides a brief exposition of current methylation biomarkers for cancer diagnosis and early detection, including markers already in clinical use as well as various upcoming ones. It also outlines how recent big data and novel technologies will revolutionise the next generation of cancer tests in supplementing or replacing currently existing invasive techniques.

Nanoscale Investigation of DNA Demethylation in Leukemia Cells by Means of Ultrasensitive Vibrational Spectroscopy

DNA methylation is a crucial epigenetic hallmark of cancer development but the experimental methods able to prove nanoscale modifications are very scarce. Over time, Raman and its counterpart, surface-enhanced Raman scattering (SERS), became one of the most promising techniques capable to investigate nanoscale modifications of DNA bases. In our study, we employed Raman/SERS to highlight the differences between normal and leukemia DNA samples and to evaluate the effects of a 5-azacytidine treatment on leukemia cells. To obtain spectral information related to DNA base modifications, a DNA incubation step of 4 min at 94 °C, similar to the one performed in the case of RT-PCR experiments, was conducted prior to any measurements. In this way, reproducible Raman/SERS spectra were collected for all genomic DNA samples. Our Raman results allowed discrimination between normal and cancer DNAs based on their different aggregation behavior induced by the distinct methylation landscape present in the DNA samples. On the other hand, the SERS spectra collected on the same DNA samples show a very intense vibrational band located at 1008 cm^-1 assigned to a rocking vibration of 5-methyl-cytosine. The intensity of this band strongly decreases in cancer DNA due to the modification of the methylation landscape occurring in cancers. We believe that under controlled experimental conditions, this vibrational band could be used as a powerful marker for demonstrating epigenetic reprogramming in cancer by means of SERS.

Prognostic value of TMEM59L and its genomic and immunological characteristics in cancer

Background

TMEM59L is a newly discovered transmembrane protein; its functions in cancer remain unknown. This study was designed to reveal the prognostic value and the functional role of TMEM59L in cancer.

Methods

The gene expression profiles, methylation data, and corresponding clinical data of TMEM59L were retrieved from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression database. Survival analysis was employed to calculate the pan-cancer prognostic value of TMEM59L. The correlation between TMEM59L expression and tumor immune microenvironment, as well as DNA methylation dynamics and genomic heterogeneity across cancers were assessed based on data from TCGA.

Results

Our findings revealed that distinct differences of TMEM59L mRNA expression were observed in different cancer types and that higher TMEM59L expression was observed in the advanced pathological stage and associated with worse prognosis in kidney renal papillary cell carcinoma, bladder urothelial carcinoma, colon adenocarcinoma, and kidney renal clear cell carcinoma. Pathway analysis indicated that TMEM59L exerted a key influence in cancer development and in immune- and cancer-associated pathways such as epithelial-mesenchymal transition and TGF-β signaling. Moreover, correlation analysis hinted at a negative correlation of TMEM59L expression with CD8 T cells, activated CD4 T cells, and several immunomodulators, including IDO1, TIGIT, PD-L1, CTLA-4, and BTLA in various cancers. Survival analysis indicated that the hypermethylation of TMEM59L gene was associated with longer survival times. A significant correlation was also observed between TMEM59L expression and immunophenoscore, homologous recombination deficiency, loss of heterozygosity, tumor stemness score, and neoantigens in various cancers. Importantly, we also identified numerous potential agents that may target TMEM59L.

Conclusion

Our study revealed the prognostic value as well as the genomic and immunological characteristics of TMEM59L in cancers, highlighting the promising potential for TMEM59L as a prognostic cancer biomarker and a therapeutic target.

Nanotechnology: A Promising Approach for Cancer Diagnosis, Therapeutics and Theragnosis

Cancer remains the most devastating disease and the major cause of mortality worldwide. Although early diagnosis and treatment are the key approach in fighting against cancer, the available conventional diagnostic and therapeutic methods are not efficient. Besides, ineffective cancer cell selectivity and toxicity of traditional chemotherapy remain the most significant challenge. These limitations entail the need for the development of both safe and effective cancer diagnosis and treatment options. Due to its robust application, nanotechnology could be a promising method for in-vivo imaging and detection of cancer cells and cancer biomarkers. Nanotechnology could provide a quick, safe, cost-effective, and efficient method for cancer management. It also provides simultaneous diagnosis and treatment of cancer using nano-theragnostic particles that facilitate early detection and selective destruction of cancer cells. Updated and recent discussions are important for selecting the best cancer diagnosis, treatment, and management options, and new insights on designing effective protocols are utmost important. This review discusses the application of nanotechnology in cancer diagnosis, therapeutics, and theragnosis and provides future perspectives in the field.

New Perspectives on the Importance of Cell-Free DNA Biology

Body fluids are constantly replenished with a population of genetically diverse cell-free DNA (cfDNA) fragments, representing a vast reservoir of information reflecting real-time changes in the host and metagenome. As many body fluids can be collected non-invasively in a one-off and serial fashion, this reservoir can be tapped to develop assays for the diagnosis, prognosis, and monitoring of wide-ranging pathologies, such as solid tumors, fetal genetic abnormalities, rejected organ transplants, infections, and potentially many others. The translation of cfDNA research into useful clinical tests is gaining momentum, with recent progress being driven by rapidly evolving preanalytical and analytical procedures, integrated bioinformatics, and machine learning algorithms. Yet, despite these spectacular advances, cfDNA remains a very challenging analyte due to its immense heterogeneity and fluctuation in vivo. It is increasingly recognized that high-fidelity reconstruction of the information stored in cfDNA, and in turn the development of tests that are fit for clinical roll-out, requires a much deeper understanding of both the physico-chemical features of cfDNA and the biological, physiological, lifestyle, and environmental factors that modulate it. This is a daunting task, but with significant upsides. In this review we showed how expanded knowledge on cfDNA biology and faithful reverse-engineering of cfDNA samples promises to (i) augment the sensitivity and specificity of existing cfDNA assays; (ii) expand the repertoire of disease-specific cfDNA markers, thereby leading to the development of increasingly powerful assays; (iii) reshape personal molecular medicine; and (iv) have an unprecedented impact on genetics research.

Whole-genome circulating tumor DNA methylation landscape reveals sensitive biomarkers of breast cancer

The changes in circulating tumor DNA (ctDNA) methylation are believed to be early events in breast cancer initiation, which makes them suitable as promising biomarkers for early diagnosis. However, applying ctDNA in breast cancer early diagnosis remains highly challenging due to the contamination of background DNA from blood and low DNA methylation signals. Here, we report an improved way to extract ctDNA, reduce background contamination, and build a whole-genome bisulfite sequencing (WGBS) library from different stages of breast cancer. We first compared the DNA methylation data of 74 breast cancer patients with those of seven normal controls to screen candidate methylation CpG site biomarkers for breast cancer diagnosis. The obtained 26 candidate ctDNA methylation biomarkers produced high accuracy in breast cancer patients (area under the curve [AUC] = 0.889; sensitivity: 100%; specificity: 75%). Furthermore, we revealed potential ctDNA methylated CpG sites for detecting early-stage breast cancer (AUC = 0.783; sensitivity: 93.44%; specificity: 50%). In addition, different subtypes of breast cancer could be well distinguished by the ctDNA methylome, which was obtained through our improved ctDNA-WGBS method. Overall, we identified high specificity and sensitivity breast cancer-specific methylation CpG site biomarkers, and they will be expected to have the potential to be translated to clinical practice.

Differentiating between liver diseases by applying multiclass machine learning approaches to transcriptomics of liver tissue or blood-based samples

Background & Aims

Liver disease carries significant healthcare burden and frequently requires a combination of blood tests, imaging, and invasive liver biopsy to diagnose. Distinguishing between inflammatory liver diseases, which may have similar clinical presentations, is particularly challenging. In this study, we implemented a machine learning pipeline for the identification of diagnostic gene expression biomarkers across several alcohol-associated and non-alcohol-associated liver diseases, using either liver tissue or blood-based samples.

Methods

We collected peripheral blood mononuclear cells (PBMCs) and liver tissue samples from participants with alcohol-associated hepatitis (AH), alcohol-associated cirrhosis (AC), non-alcohol-associated fatty liver disease, chronic HCV infection, and healthy controls. We performed RNA sequencing (RNA-seq) on 137 PBMC samples and 67 liver tissue samples. Using gene expression data, we implemented a machine learning feature selection and classification pipeline to identify diagnostic biomarkers which distinguish between the liver disease groups. The liver tissue results were validated using a public independent RNA-seq dataset. The biomarkers were computationally validated for biological relevance using pathway analysis tools.

Results

Utilizing liver tissue RNA-seq data, we distinguished between AH, AC, and healthy conditions with overall accuracies of 90% in our dataset, and 82% in the independent dataset, with 33 genes. Distinguishing 4 liver conditions and healthy controls yielded 91% overall accuracy in our liver tissue dataset with 39 genes, and 75% overall accuracy in our PBMC dataset with 75 genes.

Conclusions

Our machine learning pipeline was effective at identifying a small set of diagnostic gene biomarkers and classifying several liver diseases using RNA-seq data from liver tissue and PBMCs. The methodologies implemented and genes identified in this study may facilitate future efforts toward a liquid biopsy diagnostic for liver diseases.

Lay summary

Distinguishing between inflammatory liver diseases without multiple tests can be challenging due to their clinically similar characteristics. To lay the groundwork for the development of a non-invasive blood-based diagnostic across a range of liver diseases, we compared samples from participants with alcohol-associated hepatitis, alcohol-associated cirrhosis, chronic hepatitis C infection, and non-alcohol-associated fatty liver disease. We used a machine learning computational approach to demonstrate that gene expression data generated from either liver tissue or blood samples can be used to discover a small set of gene biomarkers for effective diagnosis of these liver diseases.

Nano-omics: nanotechnology-based multidimensional harvesting of the blood-circulating cancerome

Over the past decade, the development of 'simple' blood tests that enable cancer screening, diagnosis or monitoring and facilitate the design of personalized therapies without the need for invasive tumour biopsy sampling has been a core ambition in cancer research. Data emerging from ongoing biomarker development efforts indicate that multiple markers, used individually or as part of a multimodal panel, are required to enhance the sensitivity and specificity of assays for early stage cancer detection. The discovery of cancer-associated molecular alterations that are reflected in blood at multiple dimensions (genome, epigenome, transcriptome, proteome and metabolome) and integration of the resultant multi-omics data have the potential to uncover novel biomarkers as well as to further elucidate the underlying molecular pathways. Herein, we review key advances in multi-omics liquid biopsy approaches and introduce the 'nano-omics' paradigm: the development and utilization of nanotechnology tools for the enrichment and subsequent omics analysis of the blood-circulating cancerome.

Molecular Biomarkers in Cancer

Molecular cancer biomarkers are any measurable molecular indicator of risk of cancer, occurrence of cancer, or patient outcome. They may include germline or somatic genetic variants, epigenetic signatures, transcriptional changes, and proteomic signatures. These indicators are based on biomolecules, such as nucleic acids and proteins, that can be detected in samples obtained from tissues through tumor biopsy or, more easily and non-invasively, from blood (or serum or plasma), saliva, buccal swabs, stool, urine, etc. Detection technologies have advanced tremendously over the last decades, including techniques such as next-generation sequencing, nanotechnology, or methods to study circulating tumor DNA/RNA or exosomes. Clinical applications of biomarkers are extensive. They can be used as tools for cancer risk assessment, screening and early detection of cancer, accurate diagnosis, patient prognosis, prediction of response to therapy, and cancer surveillance and monitoring response. Therefore, they can help to optimize making decisions in clinical practice. Moreover, precision oncology is needed for newly developed targeted therapies, as they are functional only in patients with specific cancer genetic mutations, and biomarkers are the tools used for the identification of these subsets of patients. Improvement in the field of cancer biomarkers is, however, needed to overcome the scientific challenge of developing new biomarkers with greater sensitivity, specificity, and positive predictive value.

Current and Future Perspectives of Cell-Free DNA in Liquid Biopsy

A liquid biopsy is a minimally invasive or non-invasive method to analyze a range of tumor material in blood or other body fluids, including circulating tumor cells (CTCs), cell-free DNA (cfDNA), messenger RNA (mRNA), microRNA (miRNA), and exosomes, which is a very promising technology. Among these cancer biomarkers, plasma cfDNA is the most widely used in clinical practice. Compared with a tissue biopsy of traditional cancer diagnosis, in assessing tumor heterogeneity, a liquid biopsy is more reliable because all tumor sites release cfDNA into the blood. Therefore, a cfDNA liquid biopsy is less invasive and comprehensive. Moreover, the development of next-generation sequencing technology makes cfDNA sequencing more sensitive than a tissue biopsy, with higher clinical applicability and wider application. In this publication, we aim to review the latest perspectives of cfDNA liquid biopsy clinical significance and application in cancer diagnosis, treatment, and prognosis. We introduce the sequencing techniques and challenges of cfDNA detection, analysis, and clinical applications, and discuss future research directions.

Promoter hypermethylation of GALR1 acts as an early epigenetic susceptibility event in colorectal carcinogenesis

Epigenetics play an essential role in colorectal neoplasia process. There is a need to determine the appropriateness of epigenetic biomarkers for early detection as well as expand our understanding of the carcinogenic process. Therefore, the aim of the study was to assess how DNA methylation pattern of GALR1 gene evolves in a sample set representing colorectal neoplastic progression. The study was designed into three phases. Firstly, Methylation status of GALR1 was assessed with genome-wide DNA methylation beadchip and pyrosequencing assays in colorectal lesions and paired normal tissues. Then, linear mixed-effects modeling analyses were applied to describe the trend of DNA methylation during the progression of colorectal neoplasia. In the third phase, quantitative RT-PCR was used to examine GALR1 expression in patients with precursor lesion and colorectal cancer. We found that significant hypermethylation of GALR1 promoter was a widely existent modification in CRCs (P < 0.001). When further examined methylation pattern of GALR1 during neoplastic progression of CRC, we found that DNA methylation level of GALR1 showed a significant stepwise increase from normal to hyperplastic polyps, to adenomas and to carcinoma samples (P < 0.001). Besides, loss of mRNA expression is a common accompaniment to adenomas and carcinomas. Public omics data analyses showed an inverse correlation between gene expression and DNA methylation (P < 0.001). Our findings indicate that epigenetic alteration of GALR1 promoter is gradually accumulated during the colorectal neoplastic progression. It can potentially be a promising biomarker used for screening and surveillance of colorectal cancer.

LncRNA-mediated DNA methylation: an emerging mechanism in cancer and beyond

DNA methylation is one of the most important epigenetic mechanisms to regulate gene expression, which is highly dynamic during development and specifically maintained in somatic cells. Aberrant DNA methylation patterns are strongly associated with human diseases including cancer. How are the cell-specific DNA methylation patterns established or disturbed is a pivotal question in developmental biology and cancer epigenetics. Currently, compelling evidence has emerged that long non-coding RNA (lncRNA) mediates DNA methylation in both physiological and pathological conditions. In this review, we provide an overview of the current understanding of lncRNA-mediated DNA methylation, with emphasis on the roles of this mechanism in cancer, which to the best of our knowledge, has not been systematically summarized. In addition, we also discuss the potential clinical applications of this mechanism in RNA-targeting drug development.

Magnetic Immunosensor Coupled to Enzymatic Signal for Determination of Genomic DNA Methylation

Aberrations of genomic DNA methylation have been confirmed to be involved in the evolution of human cancer and have thus gained the potential to be depicted as biomarkers for cancer diagnostics and prognostic predictions, which implicates an urgent need for detection of total genomic DNA methylation. In this work, we suggested an assay for the quantification of global DNA methylation, utilizing methylation specific antibody (5mC) modified magnetic beads (MBs) for immunorecognition and affinity enrichment. Subsequently, the captured DNA on the surface of MBs interacted with the glucose oxidase-conjugated DNA antibody whose catalytic reaction product was engaged in electrochemical detection of the overall level of DNA methylation on a PB-doped screen-printed electrode. With 15 pg of input DNA, which, to our best knowledge, is the lowest required amount of DNA without sodium bisulfite treatment or amplification, this test strategy was able to perceive as low as 5% methylation level within 70 min including the preparation of anti-5mC-MBs. We believe this detection technique offers a promising option to detect global DNA methylation in both academic and clinical scenarios.

Combined inhibition of G9a and EZH2 suppresses tumor growth via synergistic induction of IL24-mediated apoptosis

G9a and EZH2 are two histone methyltransferases commonly upregulated in several cancer types, yet the precise roles that these enzymes play cooperatively in cancer is unclear. We demonstrate here that frequent concurrent upregulation of both G9a and EZH2 occurs in several human tumors. These methyltransferases cooperatively repressed molecular pathways responsible for tumor cell death. In genetically distinct tumor subtypes, concomitant inhibition of G9a and EZH2 potently induced tumor cell death, highlighting the existence of tumor cell survival dependency at the epigenetic level. G9a and EZH2 synergistically repressed expression of genes involved in the induction of endoplasmic reticulum (ER) stress and the production of reactive oxygen species. IL24 was essential for the induction of tumor cell death and was identified as a common target of G9a and EZH2. Loss-of-function of G9a and EZH2 activated the IL24-ER stress axis and increased apoptosis in cancer cells while not affecting normal cells. These results indicate that G9a and EZH2 promotes the evasion of ER stress-mediated apoptosis by repressing IL24 transcription, therefore suggesting that their inhibition may represent a potential therapeutic strategy for solid cancers.

Opportunities for Early Cancer Detection: The Rise of ctDNA Methylation-Based Pan-Cancer Screening Technologies

The efficiency of conventional screening programs to identify early-stage malignancies can be limited by the low number of cancers recommended for screening as well as the high cumulative false-positive rate, and associated iatrogenic burden, resulting from repeated multimodal testing. The opportunity to use minimally invasive liquid biopsy testing to screen asymptomatic individuals at-risk for multiple cancers simultaneously could benefit from the aggregated diseases prevalence and a fixed specificity. Increasing both latter parameters is paramount to mediate high positive predictive value—a useful metric to evaluate a screening test accuracy and its potential harm-benefit. Thus, the use of a single test for multi-cancer early detection (stMCED) has emerged as an appealing strategy for increasing early cancer detection rate efficiency and benefit population health. A recent flurry of these stMCED technologies have been reported for clinical potential; however, their development is facing unique challenges to effectively improve clinical cost–benefit. One promising avenue is the analysis of circulating tumour DNA (ctDNA) for detecting DNA methylation biomarker fingerprints of malignancies—a hallmark of disease aetiology and progression holding the potential to be tissue- and cancer-type specific. Utilizing panels of epigenetic biomarkers could potentially help to detect earlier stages of malignancies as well as identify a tumour of origin from blood testing, useful information for follow-up clinical decision making and subsequent patient care improvement. Overall, this review collates the latest and most promising stMCED methodologies, summarizes their clinical performances, and discusses the specific requirements multi-cancer tests should meet to be successfully implemented into screening guidelines.

Recent Advances in Device Engineering and Computational Analysis for Characterization of Cell-Released Cancer Biomarkers

During cancer progression, tumors shed different biomarkers into the bloodstream, including circulating tumor cells (CTCs), extracellular vesicles (EVs), circulating cell-free DNA (cfDNA), and circulating tumor DNA (ctDNA). The analysis of these biomarkers in the blood, known as 'liquid biopsy' (LB), is a promising approach for early cancer detection and treatment monitoring, and more recently, as a means for cancer therapy. Previous reviews have discussed the role of CTCs and ctDNA in cancer progression; however, ctDNA and EVs are rapidly evolving with technological advancements and computational analysis and are the subject of enormous recent studies in cancer biomarkers. In this review, first, we introduce these cell-released cancer biomarkers and briefly discuss their clinical significance in cancer diagnosis and treatment monitoring. Second, we present conventional and novel approaches for the isolation, profiling, and characterization of these markers. We then investigate the mathematical and in silico models that are developed to investigate the function of ctDNA and EVs in cancer progression. We convey our views on what is needed to pave the way to translate the emerging technologies and models into the clinic and make the case that optimized next-generation techniques and models are needed to precisely evaluate the clinical relevance of these LB markers.

SERS-based DNA methylation profiling allows the differential diagnosis of malignant lymphadenopathy

This study highlights the potential of surface-enhanced Raman scattering (SERS) to differentiate between B-cell lymphoma (BCL), T-cell lymphoma (TCL), lymph node metastasis of melanoma (Met) and control (Ctr) samples based on the specific SERS signal of DNA extracted from lymph node tissue biopsy. Differences in the methylation profiles as well as the specific interaction of malignant and non-malignant DNA with the metal nanostructure are captured in specific variations of the band at 1005 cm^-1, attributed to 5-methylcytosine and the band at 730 cm^-1, attributed to adenine. Thus, using the area ratio of these two SERS marker bands as input for univariate classification, an area under the curve (AUC) of 0.70 was achieved in differentiating between malignant and non-malignant DNA. In addition, DNA from the BCL and TCL groups exhibited differences in the area of the SERS band at 730 cm^-1, yielding an AUC of 0.84 in differentiating between these two lymphadenopathies. Lastly, using multivariate data analysis techniques, an overall accuracy of 94.7% was achieved in the differential diagnosis between the BCL, TCL, Met and Ctr groups. These results pave the way towards the implementation of SERS as a novel tool in the clinical setting for improving the diagnosis of malignant lymphadenopathy.

Single-molecule analysis of genome-wide DNA methylation by fiber FISH coupled with atomic force microscopy

A straightforward single-molecule approach was developed for identifying whole-genome DNA methylation through fiber-FISH coupled with AFM. This method has advantages of low DNA input, reproduction, long reads and low cost.

Epigenetic approaches for cervical neoplasia screening (Review)

Human papillomavirus (HPV) infection is the leading cause of cervical cancer. The Papanicolaou cytology test is the usually employed type of screening for this infection; however, its sensibility is limited. Only a small percentage of women infected with high-risk HPV develop cervical cancer with an array of genetic and epigenetic modifications. Thus, it is necessary to develop rapid, reproducible and minimally invasive technologies for screening. DNA methylation has gained attention as an alternative method for molecular diagnosis and prognosis in HPV infection. The aim of the present review was to highlight the potential of DNA methylation in cervical neoplasia screening for clinical applications. It was observed that the methylation human and viral genes was correlated with high-grade lesions and cancer. Methylation biomarkers have shown a good capacity to discriminate between high-grade lesions with a transformative potential and cervical cancer, being able to detect these modifications at an early stage. With further research, the epigenetic profiles and subtypes of the tumors could be elaborated, which would aid in therapy selection by opening avenues in personalized precision medicine. Response to therapy could also be evaluated through such methods and the accessibility of liquid biopsies would allow a constant monitoring of the patient's status without invasive sampling techniques.

Detection of average methylation level of specific genes by binary-probe hybridization

We developed a simple and highly-selective method for 5-methylcytosine detection of specific gene sequence based on binary-probe DNA hybridization. The sequence complementary to the target was designed into two probes, and each fragment of binary probes bound to a relatively short sequence of the target, which made it sensitive to the base mismatches introduced by bisulfite treatment. The advantages of a low detection limit of methylation abundance of 0.1% for the fully methylated target and high sensitivity of 10 pM have been proved by the successful design of binary-probe hybridization. The successful design of the binary probes makes it possible to quantify the average methylation levels of five CpG sites. Thirty-two DNA strands containing 5, 4, 3, 2, 1 and 0 CpG sites were successfully analyzed with the same pair of binary probes. The higher the average methylation level of the target was, the higher the degree of the hybridization reaction. Based on the simple construction of the binary-probe hybridization, the developed biosensor exhibited signals proportional to the average methylation level of the vimentin gene and could evaluate the average methylation level of artificial mixtures. Furthermore, the method has been used to detect vimentin methylation in a genomic context with good specificity, which indicated its potential in the pre-diagnosis of methylation related disease.

Small extracellular vesicles in cancer

Extracellular vesicles (EV) are lipid-bilayer enclosed vesicles in submicron size that are released from cells. A variety of molecules, including proteins, DNA fragments, RNAs, lipids, and metabolites can be selectively encapsulated into EVs and delivered to nearby and distant recipient cells. In tumors, through such intercellular communication, EVs can regulate initiation, growth, metastasis and invasion of tumors. Recent studies have found that EVs exhibit specific expression patterns which mimic the parental cell, providing a fingerprint for early cancer diagnosis and prognosis as well as monitoring responses to treatment. Accordingly, various EV isolation and detection technologies have been developed for research and diagnostic purposes. Moreover, natural and engineered EVs have also been used as drug delivery nanocarriers, cancer vaccines, cell surface modulators, therapeutic agents and therapeutic targets. Overall, EVs are under intense investigation as they hold promise for pathophysiological and translational discoveries. This comprehensive review examines the latest EV research trends over the last five years, encompassing their roles in cancer pathophysiology, diagnostics and therapeutics. This review aims to examine the full spectrum of tumor-EV studies and provide a comprehensive foundation to enhance the field. The topics which are discussed and scrutinized in this review encompass isolation techniques and how these issues need to be overcome for EV-based diagnostics, EVs and their roles in cancer biology, biomarkers for diagnosis and monitoring, EVs as vaccines, therapeutic targets, and EVs as drug delivery systems. We will also examine the challenges involved in EV research and promote a framework for catalyzing scientific discovery and innovation for tumor-EV-focused research.

DNA Studies: Latest Spectroscopic and Structural Approaches

This review looks at the different approaches, techniques, and materials devoted to DNA studies. In the past few decades, DNA nanotechnology, micro-fabrication, imaging, and spectroscopies have been tailored and combined for a broad range of medical-oriented applications. The continuous advancements in miniaturization of the devices, as well as the continuous need to study biological material structures and interactions, down to single molecules, have increase the interdisciplinarity of emerging technologies. In the following paragraphs, we will focus on recent sensing approaches, with a particular effort attributed to cutting-edge techniques for structural and mechanical studies of nucleic acids.

SERS-Based Evaluation of the DNA Methylation Pattern Associated With Progression in Clonal Leukemogenesis of Down Syndrome

Here we show that surface-enhanced Raman scattering (SERS) analysis captures the relative hypomethylation of DNA from patients with acute leukemia associated with Down syndrome (AL-DS) compared with patients diagnosed with transient leukemia associated with Down syndrome (TL-DS), an information inferred from the area under the SERS band at 1005 cm-1 attributed to 5-methycytosine. The receiver operating characteristic (ROC) analysis of the area under the SERS band at 1005 cm-1 yielded an area under the curve (AUC) of 0.77 in differentiating between the AL-DS and TL-DS groups. In addition, we showed that DNA from patients with non-DS myeloproliferative neoplasm (non-DS-MPN) is hypomethylated compared to non-DS-AL, the area under the SERS band at 1005 cm-1 yielding an AUC of 0.78 in separating between non-DS-MPN and non-DS-AL. Overall, in this study, the area of the 1005 cm-1 DNA SERS marker band shows a stepwise decrease in DNA global methylation as cells progress from a pre-leukemia to a full-blown acute leukemia, highlighting thus the potential of SERS as an emerging method of analyzing the methylation landscape of DNA in the context of leukemia genesis and progression.

Computational Insights into Molecular Adsorption Characteristics of Methylated DNA on Graphene Oxide for Multicancer Early Detection

DNA methylation is an epigenetic modification involving the transfer of a methyl group to cytosine residues of a DNA molecule. Altered DNA methylation of certain genes is associated with several diseases including cancer. Nanomaterials, such as graphene oxide (GO), offer great potential as sensing elements for methylated DNA (mDNA) detection due to their distinct properties. Understanding molecular interactions between mDNA and GO can make provision for developing a universal cancer screening test. Molecular dynamics (MD) simulation and density functional theory (DFT) calculation have been employed for investigating their detailed macro- and microscale interactions. Based upon the MD simulation, different adsorption levels of methylated and unmethylated DNAs on GO were represented by a contacting surface area (CSA), which depends on surrounding conditions (in water or a MgCl₂ solution). In water, the CSAs of the methylated and unmethylated single-stranded DNA (ssDNA) were ≈13 and ≈5 nm², respectively, representing more preferable adsorption on GO for the methylated ssDNA. In the presence of divalent ions (Mg²⁺), the CSAs of both methylated and unmethylated DNA molecules were ≈8 nm², suggesting that there was no significant difference in adsorption in a saline solution. To reveal the electrical property of GO covered by either methylated or unmethylated DNA, its electronic structure was investigated by the DFT calculation. The energy gaps of pristine graphene (pG) and GO adsorbed by 5-methylcytosine (5mC) were 1.6 and 12.9 meV, respectively, while cytosine adsorption resulted in lower energy gaps (1.2 meV for pG and 9.5 meV for GO). When comparing methylated DNA-covered GO with that covered with unmethylated DNA, remarkable differences in electrical conductivity, which were caused by the electronic structure of GO, were observed. These findings will provide a new route for an efficient detection method of DNA methylation, which can further be used to develop a universal cancer test.

DNA Methylation, Deamination, and Translesion Synthesis Combine to Generate Footprint Mutations in Cancer Driver Genes in B-Cell Derived Lymphomas and Other Cancers

Cancer genomes harbor numerous genomic alterations and many cancers accumulate thousands of nucleotide sequence variations. A prominent fraction of these mutations arises as a consequence of the off-target activity of DNA/RNA editing cytosine deaminases followed by the replication/repair of edited sites by DNA polymerases (pol), as deduced from the analysis of the DNA sequence context of mutations in different tumor tissues. We have used the weight matrix (sequence profile) approach to analyze mutagenesis due to Activation Induced Deaminase (AID) and two error-prone DNA polymerases. Control experiments using shuffled weight matrices and somatic mutations in immunoglobulin genes confirmed the power of this method. Analysis of somatic mutations in various cancers suggested that AID and DNA polymerases η and θ contribute to mutagenesis in contexts that almost universally correlate with the context of mutations in A:T and G:C sites during the affinity maturation of immunoglobulin genes. Previously, we demonstrated that AID contributes to mutagenesis in (de)methylated genomic DNA in various cancers. Our current analysis of methylation data from malignant lymphomas suggests that driver genes are subject to different (de)methylation processes than non-driver genes and, in addition to AID, the activity of pols η and θ contributes to the establishment of methylation-dependent mutation profiles. This may reflect the functional importance of interplay between mutagenesis in cancer and (de)methylation processes in different groups of genes. The resulting changes in CpG methylation levels and chromatin modifications are likely to cause changes in the expression levels of driver genes that may affect cancer initiation and/or progression.

e-MagnetoMethyl IP: a magnetic nanoparticle-mediated immunoprecipitation and electrochemical detection method for global DNA methylation

The quantification of global 5-methylcytosine (5mC) content has emerged as a promising approach for the diagnosis and prognosis of cancers. However, conventional methods for the global 5mC analysis require large quantities of DNA and may not be useful for liquid biopsy applications, where the amount of DNA available is limited. Herein, we report magnetic nanoparticles-assisted methylated DNA immunoprecipitation (e-MagnetoMethyl IP) coupled with electrochemical quantification of global DNA methylation. Carboxyl (-COOH) group-functionalized iron oxide nanoparticles (C-IONPs) synthesized by a novel starch-assisted gel formation method were conjugated with anti-5mC antibodies through EDC/NHS coupling (anti-5mC/C-IONPs). Anti-5mC/C-IONPs were subsequently mixed with DNA samples, in which they acted as dispersible capture agents to selectively bind 5mC residues and capture the methylated fraction of genomic DNA. The target-bound Anti-5mC/C-IONPs were magnetically separated and directly adsorbed onto the gold electrode surface using gold-DNA affinity interaction. The amount of DNA adsorbed on the electrode surface, which corresponds to the DNA methylation level in the sample, was electrochemically estimated by differential pulse voltammetric (DPV) study of an electroactive indicator [Ru(NH₃)₆]³⁺ bound to the surface-adsorbed DNA. Using a 200 ng DNA sample, the assay could successfully detect differences as low as 5% in global DNA methylation levels with high reproducibility (relative standard deviation (% RSD) = <5% for n = 3). The method could also reproducibly analyze various levels of global DNA methylation in synthetic samples as well as in cell lines. The method avoids bisulfite treatment, does not rely on enzymes for signal generation, and can detect global DNA methylation using clinically relevant quantities of sample DNA without PCR amplification. We believe that this proof-of-concept method could potentially find applications for liquid biopsy-based global DNA methylation analysis in point-of-care settings.

Ezh2 Is Essential for Patterning of Multiple Musculoskeletal Tissues but Dispensable for Tendon Differentiation

An efficient musculoskeletal system depends on the precise assembly and coordinated growth and function of muscles, skeleton, and tendons. However, the mechanisms that drive integrated musculoskeletal development and coordinated growth and differentiation of each of these tissues are still being uncovered. Epigenetic modifiers have emerged as critical regulators of cell fate differentiation, but so far almost nothing is known about their roles in tendon biology. Previous studies have shown that epigenetic modifications driven by Enhancer of zeste homolog 2 (EZH2), a major histone methyltransferase, have significant roles in vertebrate development including skeletal patterning and bone formation. We now find that targeting Ezh2 through the limb mesenchyme also has significant effects on tendon and muscle patterning, likely reflecting the essential roles of early mesenchymal cues mediated by Ezh2 for coordinated patterning and development of all tissues of the musculoskeletal system. Conversely, loss of Ezh2 in the tendon cells did not disrupt overall tendon structure or collagen organization suggesting that tendon differentiation and maturation are independent of Ezh2 signaling.

Perspectives on Epigenetics Alterations Associated with Smoking and Vaping

Epigenetic alterations, including DNA methylation, microRNA, and long noncoding RNA, play important roles in the pathogenesis of numerous respiratory health conditions and diseases. Exposure to tobacco smoking has been found to be associated with epigenetic changes in the respiratory tract. Marketed as a less harmful alternative to combustible cigarettes, electronic cigarette (e-cigarette) has rapidly gained popularity in recent years, especially among youth and young adults. Accumulative evidence from both animal and human studies has shown that e-cigarette use (vaping) is also linked to similar respiratory health conditions as observed with cigarette smoking, including wheezing, asthma, and COPD. This review aims to provide an overview of current studies on associations of smoking and vaping with epigenetic alterations in respiratory cells and provide future research directions in epigenetic studies related to vaping.

Electrochemically detecting DNA methylation in the EN1 gene promoter: implications for understanding ageing and disease

There is a growing need for biomarkers which predict age-onset pathology. Although this is challenging, the methylome offers significant potential. Cancer is associated with the hypermethylation of many gene promoters, among which are developmental genes. Evolutionary theory suggests developmental genes arbitrate early-late life trade-offs, causing epimutations that increase disease vulnerability. Such genes could predict age-related disease. The aim of this work was to optimise an electrochemical procedure for the future investigation of a broad range of ageing-related pathologies. An electrochemical approach, which adopted three analytical techniques, was used to investigate DNA methylation in the engrailed-1 (EN1) gene promoter. Using synthetic single-stranded DNA, one technique was able to detect DNA at concentrations as low as 10 nM, with methylation status distinguishable at concentrations >25 nM. A negative correlation could be observed between % methylation of a heterogeneous solution and the key electrochemical parameter, charge transfer resistance (Rct; r = -0.982, P<0.01). The technique was applied to the breast cancer cell line Michigan Cancer Foundation-7 (MCF-7), where a similar correlation was observed (r = -0.965, P<0.01). These results suggest electrochemistry can effectively measure DNA methylation at low concentrations of DNA. This has implications for the future detection of age-related disease.

Salivary Outer Membrane Vesicles and DNA Methylation of Small Extracellular Vesicles as Biomarkers for Periodontal Status: A Pilot Study

Periodontitis is an inflammatory disease, associated with a microbial dysbiosis. Early detection using salivary small extracellular vesicles (sEVs) biomarkers may facilitate timely prevention. sEVs derived from different species (i.e., humans, bacteria) are expected to circulate in saliva. This pilot study recruited 22 participants (seven periodontal healthy, seven gingivitis and eight periodontitis) and salivary sEVs were isolated using the size-exclusion chromatography (SEC) method. The healthy, gingivitis and periodontitis groups were compared in terms of salivary sEVs in the CD9+ sEV subpopulation, Gram-negative bacteria-enriched lipopolysaccharide (LPS+) outer membrane vesicles (OMVs) and global DNA methylation pattern of 5-methylcytosine (5mC), 5-hydroxymethylcytosine (5hmC) and N6-Methyladenosine (m6dA). It was found that LPS+ OMVs, global 5mC methylation and four periodontal pathogens (T. denticola, E. corrodens, P. gingivalis and F. nucleatum) that secreted OMVs were significantly increased in periodontitis sEVs compared to those from healthy groups. These differences were more pronounced in sEVs than the whole saliva and were more superior in distinguishing periodontitis than gingivitis, in comparison to healthy patients. Of note, global 5mC hypermethylation in salivary sEVs can distinguish periodontitis patients from both healthy controls and gingivitis patients with high sensitivity and specificity (AUC = 1). The research findings suggest that assessing global sEV methylation may be a useful biomarker for periodontitis.

Diagnostic and Prognostic Characteristics of Circulating Free DNA Methylation Detected by the Electrochemical Method in Malignant Tumors

Simple Summary

Previous studies have established an electrochemical detection method for the rapid detection of cfDNA (circulating free DNA) methylation and found that this technology might be a potential method for cancer diagnosis. However, the underlying mechanism and its role in the diagnosis and prognosis of malignant tumors are not well-characterized. In present study, we utilized the electrochemical detection method to detect the DNA methylation status by using electron microscopies and infrared spectroscopy and found that DNA with different methylated levels adsorbed to the gold surface differently, which was likely mediated by hydrophobic bonds. In addition, after detection of the cfDNA methylation status from 505 normal individuals, 725 cancer patients before treatment, and 549 patients after treatment, we found that the cfDNA adsorption rate could be used as an indicator for the diagnosis and prognosis prediction of pan-cancer. Our research provides a novel method for the liquid biopsy of cancer for diagnosis and prognosis predictions.

Abstract

Prior research has established an electrochemical method based on the differential adsorption capacity of gold surfaces with different methylated DNA degrees and found that this method might be valuable for cancer diagnosis by detecting circulating free DNA methylation. However, further investigation on the underlying mechanism and validation of its diagnostic and prognostic values in a large cohort of malignant tumors was limited. We found that DNA with different methylation levels formed particles of diverse sizes on the gold surface. Hydrophobic bonds played a significant role in the binding process of methylated DNA to the gold surface. The detection condition of an adsorption time of 10 min and temperature of 20 °C was optimal. In a large cohort of plasma samples from the patients with different malignant tumors, as well as normal individuals, we found that the electrochemical detection method based on the differential adsorption capacity of methylated DNA degree on a gold surface could be used as a noninvasive tool for malignant tumor diagnosis and prognostic evaluation. The diagnostic efficiency of this method in malignant tumors was even slightly better than that of the current tumor biomarkers widely used in routine clinical practice (circulating free DNA (cfDNA) vs. carcinoembryonic antigen (CEA), 0.8131 vs. 0.7191 and cfDNA vs. CA19-9, 0.7687 vs. 0.6693).