DNA methylation patterns-based subtype distinction and identification of soft tissue sarcoma prognosis

The role of dietary polyphenols in alternating DNA methylation in cancer

Natural products such as curcumin, quercetin, and resveratrol have been shown to have antitumor effectsand several studies have examined their role in treating cancer, either alone or in combination with other chemotherapeutic drugs. These compounds are capable of affecting different cancer-related mechanisms, such as proliferation, inflammation, invasion, and metastasis. Along with all of the benefits of these agents, affecting epigenetic processes is one of the most important aspects of their impact. Epigenetic modifications can be categorized into three main processes that include DNA methylation, histone modification, and regulation of small non-coding RNAs. Therefore, targeting DNA methylation can be used as a cancer treatment strategy by identifying or developing methylation modulators. Herein, we take a look into the studies investigating the role of natural products (e.g. curcumin, resveratrol, epigallocatechin gallate (EGCG), and quercetin) in alternating the DNA methylation status of various cancer cells. We discuss how these compounds reduce the expression of enzymes mediating the methylation of tumor suppressor genes and thereby, increasing the expression of tumor suppressors while reactivating antitumor signaling pathways.

Machine learning in the identification of prognostic DNA methylation biomarkers among patients with cancer: A systematic review of epigenome-wide studies

BACKGROUND

DNA methylation biomarkers have great potential in improving prognostic classification systems for patients with cancer. Machine learning (ML)-based analytic techniques might help overcome the challenges of analyzing high-dimensional data in relatively small sample sizes. This systematic review summarizes the current use of ML-based methods in epigenome-wide studies for the identification of DNA methylation signatures associated with cancer prognosis.

METHODS

We searched three electronic databases including PubMed, EMBASE, and Web of Science for articles published until 2 January 2023. ML-based methods and workflows used to identify DNA methylation signatures associated with cancer prognosis were extracted and summarized. Two authors independently assessed the methodological quality of included studies by a seven-item checklist adapted from 'A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies (PROBAST)' and from the 'Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK). Different ML methods and workflows used in included studies were summarized and visualized by a sunburst chart, a bubble chart, and Sankey diagrams, respectively.

RESULTS

Eighty-three studies were included in this review. Three major types of ML-based workflows were identified. 1) unsupervised clustering, 2) supervised feature selection, and 3) deep learning-based feature transformation. For the three workflows, the most frequently used ML techniques were consensus clustering, least absolute shrinkage and selection operator (LASSO), and autoencoder, respectively. The systematic review revealed that the performance of these approaches has not been adequately evaluated yet and that methodological and reporting flaws were common in the identified studies using ML techniques.

CONCLUSIONS

There is great heterogeneity in ML-based methodological strategies used by epigenome-wide studies to identify DNA methylation markers associated with cancer prognosis. In theory, most existing workflows could not handle the high multi-collinearity and potentially non-linearity interactions in epigenome-wide DNA methylation data. Benchmarking studies are needed to compare the relative performance of various approaches for specific cancer types. Adherence to relevant methodological and reporting guidelines are urgently needed.