Artificial Intelligence in Epigenetic Studies: Shedding Light on Rare Diseases

Nanotherapeutic approaches for delivery of long non-coding RNAs: an updated review with emphasis on cancer

The long noncoding RNAs (lncRNAs) comprise a wide range of RNA species whose length exceeds 200 nucleotides, which regulate the expression of genes and cellular functions in a wide range of organisms. Several diseases, including malignancy, have been associated with lncRNA dysregulation. Due to their functions in cancer development and progression, lncRNAs have emerged as promising biomarkers and therapeutic targets in cancer diagnosis and treatment. Several studies have investigated the anti-cancer properties of lncRNAs; however, only a few lncRNAs have been found to exhibit tumor suppressor properties. Furthermore, their length and poor stability make them difficult to synthesize. Thus, to overcome the instability of lncRNAs, poor specificity, and their off-target effects, researchers have constructed nanocarriers that encapsulate lncRNAs. Recently, translational medicine research has focused on delivering lncRNAs into tumor cells, including cancer cells, through nano-drug delivery systems in vivo. The developed nanocarriers can protect, target, and release lncRNAs under controlled conditions without appreciable adverse effects. To deliver lncRNAs to cancer cells, various nanocarriers, such as exosomes, microbubbles, polymer nanoparticles, 1,2-dioleyl-3-trimethylammoniumpropane chloride nanocarriers, and virus-like particles, have been successfully developed. Despite this, every nanocarrier has its own advantages and disadvantages when it comes to delivering nucleic acids effectively and safely. This article examines the current status of nanocarriers for lncRNA delivery in cancer therapy, focusing on their potential to enhance cancer treatment.

Artificial intelligence in rare disease diagnosis and treatment

Artificial intelligence (AI) utilization in health care has grown over the past few years. It also has demonstrated potential in improving the efficiency of diagnosis and treatment. Some types of AI, such as machine learning, allow for the efficient analysis of vast datasets, identifying patterns, and generating key insights. Predictions can then be made for medical diagnosis and personalized treatment recommendations. The use of AI can bypass some conventional limitations associated with rare diseases. Namely, it can optimize traditional randomized control trials, and may eventually reduce costs for drug research and development. Recent advancements have enabled researchers to train models based on large datasets and then fine-tune these models on smaller datasets typically associated with rare diseases. In this mini-review, we discuss recent advancements in AI and how AI can be applied to streamline rare disease diagnosis and optimize treatment.

Predicting Duodenal Cancer Risk in Patients with Familial Adenomatous Polyposis Using Machine Learning Model

BACKGROUND/AIMS

The aim of this study was to both classify data of familial adenomatous polyposis patients with and without duode- nal cancer and to identify important genes that may be related to duodenal cancer by XGboost model.

MATERIALS AND METHODS

The current study was performed using expression profile data from a series of duodenal samples from familial adenomatous polyposis patients to explore variations in the familial adenomatous polyposis duodenal adenoma-carcinoma sequence. The expression profiles obtained from cancerous, adenomatous, and normal tissues of 12 familial adenomatous polyposis patients with duodenal cancer and the tissues of 12 familial adenomatous polyposis patients without duodenal cancer were compared. The ElasticNet approach was utilized for the feature selection. Using 5-fold cross-validation, one of the machine learning approaches, XGboost, was utilized to classify duodenal cancer. Accuracy, balanced accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score performance metrics were assessed for model performance.

RESULTS

According to the variable importance obtained from the modeling, ADH1C, DEFA5, CPS1, SPP1, DMBT1, VCAN-AS1, APOB genes (cancer vs. adenoma); LOC399753, APOA4, MIR548X, and ADH1C genes (adenoma vs. adenoma); SNORD123, CEACAM6, SNORD78, ANXA10, SPINK1, and CPS1 (normal vs. adenoma) genes can be used as predictive biomarkers.

CONCLUSIONS

The proposed model used in this study shows that the aforementioned genes can forecast the risk of duodenal cancer in patients with familial adenomatous polyposis. More comprehensive analyses should be performed in the future to assess the reliability of the genes determined.

Epigenomic signatures reveal mechanistic clues and predictive markers for autism spectrum disorder

Autism spectrum disorder (ASD) comprises a heterogeneous group of neurodevelopmental outcomes in children with a commonality in deficits in social communication and language combined with repetitive behaviors and interests. The etiology of ASD is heterogeneous, as several hundred genes have been implicated as well as multiple in utero environmental exposures. Over the past two decades, epigenetic investigations, including DNA methylation, have emerged as a novel way to capture the complex interface of multivariate ASD etiologies. More recently, epigenome-wide association studies using human brain and surrogate accessible tissues have revealed some convergent genes that are epigenetically altered in ASD, many of which overlap with known genetic risk factors. Unlike transcriptomes, epigenomic signatures defined by DNA methylation from surrogate tissues such as placenta and cord blood can reflect past differences in fetal brain gene transcription, transcription factor binding, and chromatin. For example, the discovery of NHIP (neuronal hypoxia inducible, placenta associated) through an epigenome-wide association in placenta, identified a common genetic risk for ASD that was modified by prenatal vitamin use. While epigenomic signatures are distinct between different genetic syndromic causes of ASD, bivalent chromatin and some convergent gene pathways are consistently epigenetically altered in both syndromic and idiopathic ASD, as well as some environmental exposures. Together, these epigenomic signatures hold promising clues towards improved early prediction and prevention of ASD as well genes and gene pathways to target for pharmacological interventions. Future advancements in single cell and multi-omic technologies, machine learning, as well as non-invasive screening of epigenomic signatures during pregnancy or newborn periods are expected to continue to impact the translatability of the recent discoveries in epigenomics to precision public health.

A Systematic Review of Progress toward Unlocking the Power of Epigenetics in NSCLC: Latest Updates and Perspectives

Epigenetic research has the potential to improve our understanding of the pathogenesis of cancer, specifically non-small-cell lung cancer, and support our efforts to personalize the management of the disease. Epigenetic alterations are expected to have relevance for early detection, diagnosis, outcome prediction, and tumor response to therapy. Additionally, epi-drugs as therapeutic modalities may lead to the recovery of genes delaying tumor growth, thus increasing survival rates, and may be effective against tumors without druggable mutations. Epigenetic changes involve DNA methylation, histone modifications, and the activity of non-coding RNAs, causing gene expression changes and their mutual interactions. This systematic review, based on 110 studies, gives a comprehensive overview of new perspectives on diagnostic (28 studies) and prognostic (25 studies) epigenetic biomarkers, as well as epigenetic treatment options (57 studies) for non-small-cell lung cancer. This paper outlines the crosstalk between epigenetic and genetic factors as well as elucidates clinical contexts including epigenetic treatments, such as dietary supplements and food additives, which serve as anti-carcinogenic compounds and regulators of cellular epigenetics and which are used to reduce toxicity. Furthermore, a future-oriented exploration of epigenetic studies in NSCLC is presented. The findings suggest that additional studies are necessary to comprehend the mechanisms of epigenetic changes and investigate biomarkers, response rates, and tailored combinations of treatments. In the future, epigenetics could have the potential to become an integral part of diagnostics, prognostics, and personalized treatment in NSCLC.

Scoping review and classification of deep learning in medical genetics

Deep learning (DL) is applied in many biomedical areas. We performed a scoping review on DL in medical genetics. We first assessed 14,002 articles, of which 133 involved DL in medical genetics. DL in medical genetics increased rapidly during the studied period. In medical genetics, DL has largely been applied to small data sets of affected individuals (mean = 95, median = 29) with genetic conditions (71 different genetic conditions were studied; 24 articles studied multiple conditions). A variety of data types have been used in medical genetics, including radiologic (20%), ophthalmologic (14%), microscopy (8%), and text-based data (4%); the most common data type was patient facial photographs (46%). DL authors and research subjects overrepresent certain geographic areas (United States, Asia, and Europe). Convolutional neural networks (89%) were the most common method. Results were compared with human performance in 31% of studies. In total, 51% of articles provided data access; 16% released source code. To further explore DL in genomics, we conducted an additional analysis, the results of which highlight future opportunities for DL in medical genetics. Finally, we expect DL applications to increase in the future. To aid data curation, we evaluated a DL, random forest, and rule-based classifier at categorizing article abstracts.

Predicting High Blood Pressure Using DNA Methylome-Based Machine Learning Models

DNA methylation modification plays a vital role in the pathophysiology of high blood pressure (BP). Herein, we applied three machine learning (ML) algorithms including deep learning (DL), support vector machine, and random forest for detecting high BP using DNA methylome data. Peripheral blood samples of 50 elderly individuals were collected three times at three visits for DNA methylome profiling. Participants who had a history of hypertension and/or current high BP measure were considered to have high BP. The whole dataset was randomly divided to conduct a nested five-group cross-validation for prediction performance. Data in each outer training set were independently normalized using a min–max scaler, reduced dimensionality using principal component analysis, then fed into three predictive algorithms. Of the three ML algorithms, DL achieved the best performance (AUPRC = 0.65, AUROC = 0.73, accuracy = 0.69, and F1-score = 0.73). To confirm the reliability of using DNA methylome as a biomarker for high BP, we constructed mixed-effects models and found that 61,694 methylation sites located in 15,523 intragenic regions and 16,754 intergenic regions were significantly associated with BP measures. Our proposed models pioneered the methodology of applying ML and DNA methylome data for early detection of high BP in clinical practices.

Deep Learning for Human Disease Detection, Subtype Classification, and Treatment Response Prediction Using Epigenomic Data

Deep learning (DL) is a distinct class of machine learning that has achieved first-class performance in many fields of study. For epigenomics, the application of DL to assist physicians and scientists in human disease-relevant prediction tasks has been relatively unexplored until very recently. In this article, we critically review published studies that employed DL models to predict disease detection, subtype classification, and treatment responses, using epigenomic data. A comprehensive search on PubMed, Scopus, Web of Science, Google Scholar, and arXiv.org was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Among 1140 initially identified publications, we included 22 articles in our review. DNA methylation and RNA-sequencing data are most frequently used to train the predictive models. The reviewed models achieved a high accuracy ranged from 88.3% to 100.0% for disease detection tasks, from 69.5% to 97.8% for subtype classification tasks, and from 80.0% to 93.0% for treatment response prediction tasks. We generated a workflow to develop a predictive model that encompasses all steps from first defining human disease-related tasks to finally evaluating model performance. DL holds promise for transforming epigenomic big data into valuable knowledge that will enhance the development of translational epigenomics.

Analyzing Modern Biomolecules: The Revolution of Nucleic-Acid Sequencing - Review

Recent developments have revolutionized the study of biomolecules. Among them are molecular markers, amplification and sequencing of nucleic acids. The latter is classified into three generations. The first allows to sequence small DNA fragments. The second one increases throughput, reducing turnaround and pricing, and is therefore more convenient to sequence full genomes and transcriptomes. The third generation is currently pushing technology to its limits, being able to sequence single molecules, without previous amplification, which was previously impossible. Besides, this represents a new revolution, allowing researchers to directly sequence RNA without previous retrotranscription. These technologies are having a significant impact on different areas, such as medicine, agronomy, ecology and biotechnology. Additionally, the study of biomolecules is revealing interesting evolutionary information. That includes deciphering what makes us human, including phenomena like non-coding RNA expansion. All this is redefining the concept of gene and transcript. Basic analyses and applications are now facilitated with new genome editing tools, such as CRISPR. All these developments, in general, and nucleic-acid sequencing, in particular, are opening a new exciting era of biomolecule analyses and applications, including personalized medicine, and diagnosis and prevention of diseases for humans and other animals.