Identification of DNA methylation-regulated genes as potential biomarkers for coronary heart disease via machine learning in the Framingham Heart Study

From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non-Invasive Precision Medicine in Cancer Patients

With the increasing demand for precision medicine in cancer patients, radiogenomics emerges as a promising frontier. Radiogenomics is originally defined as a methodology for associating gene expression information from high-throughput technologies with imaging phenotypes. However, with advancements in medical imaging, high-throughput omics technologies, and artificial intelligence, both the concept and application of radiogenomics have significantly broadened. In this review, the history of radiogenomics is enumerated, related omics technologies, the five basic workflows and their applications across tumors, the role of AI in radiogenomics, the opportunities and challenges from tumor heterogeneity, and the applications of radiogenomics in tumor immune microenvironment. The application of radiogenomics in positron emission tomography and the role of radiogenomics in multi-omics studies is also discussed. Finally, the challenges faced by clinical transformation, along with future trends in this field is discussed.

Multi-omics association study of DNA methylation and gene expression levels and diagnoses of cardiovascular diseases in Danish Twins

BACKGROUND

Cardiovascular diseases (CVDs) are major causes of mortality and morbidity worldwide; yet the understanding of their molecular basis is incomplete. Multi-omics studies have significant potential to uncover these mechanisms, but such studies are challenged by genetic and environmental confounding-a problem that can be effectively reduced by investigating intrapair differences in twins. Here, we linked data on all diagnoses of the circulatory system from the nationwide Danish Patient Registry (spanning 1977-2022) to a study population of 835 twins holding genome-wide DNA methylation and gene expression data. CVD diagnoses were divided into prevalent or incident cases (i.e., occurring before or after blood sample collection (2007-2011)). The diagnoses were classified into four groups: cerebrovascular diseases, coronary artery disease (CAD), arterial and other cardiovascular diseases (AOCDs), and diseases of the veins and lymphatic system. Statistical analyses were performed by linear (prevalent cases) or cox (incident cases) regression analyses at both the individual-level and twin pair-level. Significant genes (p < 0.05) in both types of biological data and at both levels were inspected by bioinformatic analyses, including gene set enrichment analysis and interaction network analysis.

RESULTS

In general, more genes were found for prevalent than for incident cases, and bioinformatic analyses primarily found pathways of the immune system, signal transduction and diseases for prevalent cases, and pathways of cell-cell communication, metabolisms of proteins and RNA, gene expression, and chromatin organization groups for incident cases. This potentially reflects biology related to response to CVD (prevalent cases) and mechanisms related to regulation and development of disease (incident cases). Of specific genes, Myosin 1E was found to be central for CAD, and DEAD-Box Helicase 5 for AOCD. These genes were observed in both the prevalent and the incident analyses, potentially reflecting that their DNA methylation and gene transcription levels change both because of disease (prevalent cases) and prior disease (incident cases).

CONCLUSION

We present novel biomarkers for CVD by performing multi-omics analysis in twins, hereby lowering the confounding due to shared genetics and early life environment-a study design that is surprisingly rare in the field of CVD, and where additional studies are highly needed.

The Use of Precision Epigenetic Methods for the Diagnosis and Care of Stable Coronary Heart Disease Reduces Healthcare Costs

INTRODUCTION

The cost of secondary prevention of coronary heart disease (CHD) is continuing to increase, with a substantial portion of this acceleration being driven by the expense of confirmatory diagnostic testing. Conceivably, newly developed precision epigenetic technologies could drive down these costs. However, at the current time, their impact on overall expense for CHD care is poorly understood. We hypothesized that the use of a newly developed, highly sensitive, and specific epigenetic test, PrecisionCHD, could decrease the costs of secondary prevention.

METHODS

To test this hypothesis, we constructed a budget impact analysis using a cost calculation model that examined the effects of substituting PrecisionCHD for conventional CHD diagnostic tests on the expenses of the initial evaluation and first year of care of stable CHD using a 1-year time horizon with no discounting.

RESULTS

The model projected that for a commercial insurer with one million members, full adoption of PrecisionCHD as the primary method of initial CHD assessment would save approximately $113.6 million dollars in the initial year.

CONCLUSION

These analyses support the use of precision epigenetic methods as part of the initial diagnosis and care of stable CHD and can meaningfully reduce cost. Real-world pilots to test the reliability of these analyses are indicated.

Epigenomic insights into common human disease pathology

The epigenome-the chemical modifications and chromatin-related packaging of the genome-enables the same genetic template to be activated or repressed in different cellular settings. This multi-layered mechanism facilitates cell-type specific function by setting the local sequence and 3D interactive activity level. Gene transcription is further modulated through the interplay with transcription factors and co-regulators. The human body requires this epigenomic apparatus to be precisely installed throughout development and then adequately maintained during the lifespan. The causal role of the epigenome in human pathology, beyond imprinting disorders and specific tumour suppressor genes, was further brought into the spotlight by large-scale sequencing projects identifying that mutations in epigenomic machinery genes could be critical drivers in both cancer and developmental disorders. Abrogation of this cellular mechanism is providing new molecular insights into pathogenesis. However, deciphering the full breadth and implications of these epigenomic changes remains challenging. Knowledge is accruing regarding disease mechanisms and clinical biomarkers, through pathogenically relevant and surrogate tissue analyses, respectively. Advances include consortia generated cell-type specific reference epigenomes, high-throughput DNA methylome association studies, as well as insights into ageing-related diseases from biological 'clocks' constructed by machine learning algorithms. Also, 3rd-generation sequencing is beginning to disentangle the complexity of genetic and DNA modification haplotypes. Cell-free DNA methylation as a cancer biomarker has clear clinical utility and further potential to assess organ damage across many disorders. Finally, molecular understanding of disease aetiology brings with it the opportunity for exact therapeutic alteration of the epigenome through CRISPR-activation or inhibition.

Insights on E1-like enzyme ATG7: functional regulation and relationships with aging-related diseases

Autophagy is a dynamic self-renovation biological process that maintains cell homeostasis and is responsible for the quality control of proteins, organelles, and energy metabolism. The E1-like ubiquitin-activating enzyme autophagy-related gene 7 (ATG7) is a critical factor that initiates classic autophagy reactions by promoting the formation and extension of autophagosome membranes. Recent studies have identified the key functions of ATG7 in regulating the cell cycle, apoptosis, and metabolism associated with the occurrence and development of multiple diseases. This review summarizes how ATG7 is precisely programmed by genetic, transcriptional, and epigenetic modifications in cells and the relationship between ATG7 and aging-related diseases.

Data science using the human epigenome for predicting multifactorial diseases and symptoms

Tweetable abstract This article reviews machine learning models that leverages epigenomic data for predicting multifactorial diseases and symptoms as well as how such models can be utilized to explore new research questions.

DHCR24 in Tumor Diagnosis and Treatment: A Comprehensive Review

As an important nutrient in the human body, cholesterol can not only provide structural components for the body's cells, but also can be transformed into a variety of active substances to regulate cell signaling pathways. As an important cholesterol synthase, DHCR24 participates in important regulatory processes in the body. The application of DHCR24 in tumor clinical diagnosis and treatment also attracts much attention. This article reviews the structure and regulatory characteristics of DHCR24, and the research of DHCR24 on tumor progression. We summarize the possible mechanisms of DHCR24 promoting tumor progression through reactive oxygen species (ROS), p53, Ras and PI3K-AKT pathways. Through our review, we hope to provide more research ideas and reference value for the application of DHCR24 in tumor prevention and treatment.

Validation of an Integrated Genetic-Epigenetic Test for the Assessment of Coronary Heart Disease

BACKGROUND

Coronary heart disease (CHD) is the leading cause of death in the world. Unfortunately, many of the key diagnostic tools for CHD are insensitive, invasive, and costly; require significant specialized infrastructure investments; and do not provide information to guide postdiagnosis therapy. In prior work using data from the Framingham Heart Study, we provided in silico evidence that integrated genetic-epigenetic tools may provide a new avenue for assessing CHD.

METHODS AND RESULTS

In this communication, we use an improved machine learning approach and data from 2 additional cohorts, totaling 449 cases and 2067 controls, to develop a better model for ascertaining symptomatic CHD. Using the DNA from the 2 new cohorts, we translate and validate the in silico findings into an artificial intelligence-guided, clinically implementable method that uses input from 6 methylation-sensitive digital polymerase chain reaction and 10 genotyping assays. Using this method, the overall average area under the curve, sensitivity, and specificity in the 3 test cohorts is 82%, 79%, and 76%, respectively. Analysis of targeted cytosine-phospho-guanine loci shows that they map to key risk pathways involved in atherosclerosis that suggest specific therapeutic approaches.

CONCLUSIONS

We conclude that this scalable integrated genetic-epigenetic approach is useful for the diagnosis of symptomatic CHD, performs favorably as compared with many existing methods, and may provide personalized insight to CHD therapy. Furthermore, given the dynamic nature of DNA methylation and the ease of methylation-sensitive digital polymerase chain reaction methodologies, these findings may pave a pathway for precision epigenetic approaches for monitoring CHD treatment response.

Precision epigenetics provides a scalable pathway for improving coronary heart disease care globally

Coronary heart disease (CHD) is the world's leading cause of death. Up to 90% of all CHD deaths are preventable, but effective prevention of this mortality requires more scalable, precise methods for assessing CHD status and monitoring treatment response. Unfortunately, current diagnostic methods have barriers to implementation, particularly in rural areas and lower-income countries. This gap may be bridged by highly scalable advances in DNA methylation testing methods and artificial intelligence. Herein, we review prior studies of CHD related to methylation alone and in combination with other biovariables. We compare these new methods with established methods for diagnosing CHD. Finally, we outline pathways through which methylation-based testing methods may allow the democratization of improved methods for assessing CHD globally.

The Reversion of the Epigenetic Signature of Coronary Heart Disease in Response to Smoking Cessation

Coronary heart disease (CHD) is the leading cause of death worldwide. However, current diagnostic tools for CHD, such as coronary computed tomography angiography (CCTA), are poorly suited for monitoring treatment response. Recently, we have introduced an artificial-intelligence-guided integrated genetic-epigenetic test for CHD whose core consists of six assays that determine methylation in pathways known to moderate the pathogenesis of CHD. However, whether methylation at these six loci is sufficiently dynamic to guide CHD treatment response is unknown. To test that hypothesis, we examined the relationship of changes in these six loci to changes in cg05575921, a generally accepted marker of smoking intensity, using DNA from a cohort of 39 subjects undergoing a 90-day smoking cessation intervention and methylation-sensitive digital PCR (MSdPCR). We found that changes in epigenetic smoking intensity were significantly associated with reversion of the CHD-associated methylation signature at five of the six MSdPCR predictor sites: cg03725309, cg12586707, cg04988978, cg17901584, and cg21161138. We conclude that methylation-based approaches could be a scalable method for assessing the clinical effectiveness of CHD interventions, and that further studies to understand the responsiveness of these epigenetic measures to other forms of CHD treatment are in order.