Epigenetically Silenced Candidate Tumor Suppressor Genes in Prostate Cancer: Identified by Modeling Methylation Stratification and Applied to Progression Prediction

Aberrations of DNA methylation in cancer

DNA methylation is a chromatin modification that plays an important role in the epigenetic regulation of gene expression. Changes in DNA methylation patterns are characteristic of many malignant neoplasms. DNA methylation is occurred by DNA methyltransferases (DNMTs), while demethylation is mediated by TET family proteins. Mutations and changes in the expression profile of these enzymes lead to DNA hypo- and hypermethylation and have a strong impact on carcinogenesis. In this review, we considered the key aspects of the mechanisms of regulation of DNA methylation and demethylation, and also analyzed the role of DNA methyltransferases and TET family proteins in the pathogenesis of various malignant neoplasms.During the preparation of the review, we used the following biomedical literature information bases: Scopus (504), PubMed (553), Web of Science (1568), eLibrary (190). To obtain full-text documents, the electronic resources of PubMed Central (PMC), Science Direct, Research Gate, CyberLeninka were used. To analyze the mutational profile of epigenetic regulatory enzymes, we used the cBioportal portal (https://www.cbioportal.org / ), data from The AACR Project GENIE Consortium (https://www.mycancergenome.org / ), COSMIC, Clinvar, and The Cancer Genome Atlas (TCGA).

Deciphering the Increased Prevalence of TP53 Mutations in Metastatic Prostate Cancer

The prevalence of TP53 mutations in advanced prostate cancers (PCa) is 3 to 5 times of the quantity in primary PCa. By an integrative analysis of the Cancer Genome Atlas and Catalogue of Somatic Mutations in Cancer data, we revealed the supporting evidence for 2 complementary hypotheses: H₁ - TP53 abnormalities promote metastasis or therapy-resistance of PCa cells, and H₂—part of TP53 mutations in PCa metastases occur after the diagnosis of original cancers. The plausibility of these hypotheses can explain the increased prevalence of TP53 mutations in PCa metastases. With H₁ and H₂ as the general assumptions, we developed mathematical models to decipher the change of the percentage frequency (prevalence) of TP53 mutations from primary tumors to metastases. The following results were obtained. Compared to TP53-normal patients, TP53-mutated patients had poorer biochemical relapse-free survival, higher Gleason scores, and more advanced t-stages (P < .01). Single-nucleotide variants in metastases more frequently occurred on G bases of the coding sequence than those in primary cancers (P = .03). The profile of TP53 hotspot mutations was significantly different between primary and metastatic PCa as demonstrated in a set of statistical tests (P < .05). By the derived formulae, we estimated that about 40% TP53 mutation records collected from metastases occurred after the diagnosis of the original cancers. Our study provided significant insight into PCa progression. The proposed models can also be applied to decipher the prevalence of mutations on TP53 (or other driver genes) in other cancer types.

Epigenetic Drugs and Their Immune Modulating Potential in Cancers

Epigenetic drugs are used for the clinical treatment of hematologic malignancies; however, their therapeutic potential in solid tumors is still under investigation. Current evidence suggests that epigenetic drugs may lead to antitumor immunity by increasing antigen presentation and may enhance the therapeutic effect of immune checkpoint inhibitors. Here, we highlight their impact on the tumor epigenome and discuss the recent evidence that epigenetic agents may optimize the immune microenvironment and promote antiviral response.

An overview of advances in multi-omics analysis in prostate cancer

Prostate cancer (PCa) is a deadly disease for men, and studies of all types of omics data are necessary to promote precision medicine. The maturity of sequencing technology, the improvements of computer processing power, and the progress achieved in omics analysis methods have improved research efficiency and saved research costs. The occurrence and development of PCa is due to multisystem and multilevel pathological changes. Although omics research at a single level is important, this approach often has limitations. In contrast, the combined analysis of multiple types of omics data can better analyze PCa changes as a whole, thus ensuring the validity of research results to the greatest extent. This paper introduces the applications of single omics in PCa and then summarizes research progress in the combined analysis of two or more types of omics data, so as to systematically and comprehensively analyze the necessity of combined analysis of multiple omics data in PCa.