An early biomarker and potential therapeutic target of RUNX 3 hypermethylation in breast cancer, a system review and meta-analysis

DNA Methyltransferase 3A: A Significant Target for the Discovery of Inhibitors as Potent Anticancer Drugs

DNA methyltransferase (DNMT) is a conserved family of Cytosine methylases, which plays a crucial role in the regulation of Epigenetics. They have been considered promising therapeutic targets for cancer. Among the DNMT family, mutations in the DNMT3A subtype are particularly important in hematologic malignancies. The development of specific DNMT3A subtype inhibitors to validate the therapeutic potential of DNMT3A in certain diseases is a significant task. In this review, we summarized the small molecule inhibitors of DNMT3A discovered in recent years and their inhibitory activities, and classified them based on their inhibitory mechanisms.

RUNX3 Meets the Ubiquitin-Proteasome System in Cancer

RUNX3 is a transcription factor with regulatory roles in cell proliferation and development. While largely characterized as a tumor suppressor, RUNX3 can also be oncogenic in certain cancers. Many factors account for the tumor suppressor function of RUNX3, which is reflected by its ability to suppress cancer cell proliferation after expression-restoration, and its inactivation in cancer cells. Ubiquitination and proteasomal degradation represent a major mechanism for the inactivation of RUNX3 and the suppression of cancer cell proliferation. On the one hand, RUNX3 has been shown to facilitate the ubiquitination and proteasomal degradation of oncogenic proteins. On the other hand, RUNX3 can be inactivated through the ubiquitin-proteasome system. This review encapsulates two facets of RUNX3 in cancer: how RUNX3 suppresses cell proliferation by facilitating the ubiquitination and proteasomal degradation of oncogenic proteins, and how RUNX3 is degraded itself through interacting RNA-, protein-, and pathogen-mediated ubiquitination and proteasomal degradation.

Analysis of Intrinsic Breast Cancer Subtypes: The Clinical Utility of Epigenetic Biomarkers and TP53 Mutation Status in Triple-Negative Cases

This study aimed at analyzing the DNA methylation pattern and TP53 mutation status of intrinsic breast cancer (BC) subtypes for improved characterization and survival prediction. DNA methylation of 17 genes was tested by methylation-specific PCR in 116 non-familial BRCA mutation-negative BC and 29 control noncancerous cases. At least one gene methylation was detected in all BC specimens and a 10-gene panel statistically significantly separated tumors from noncancerous breast tissues. Methylation of FILIP1L and MT1E was predominant in triple-negative (TN) BC, while other BC subtypes were characterized by RASSF1, PRKCB, MT1G, APC, and RUNX3 hypermethylation. TP53 mutation (TP53-mut) was found in 38% of sequenced samples and mainly affected TN BC cases (87%). Cox analysis revealed that TN status, age at diagnosis, and RUNX3 methylation are independent prognostic factors for overall survival (OS) in BC. The combinations of methylated biomarkers, RUNX3 with MT1E or FILIP1L, were also predictive for shorter OS, whereas methylated FILIP1L was predictive of a poor outcome in the TP53-mut subgroup. Therefore, DNA methylation patterns of specific genes significantly separate BC from noncancerous breast tissues and distinguishes TN cases from non-TN BC, whereas the combination of two-to-three epigenetic biomarkers can be an informative tool for BC outcome predictions.

Screening of Breast Cancer Methylation Biomarkers Based on the TCGA Database

Objective

Breast cancer has become a fatal disease for women world-wide. Its incidence in China has been increasing yearly, and the identification of early-stage biomarkers is urgently required.

Methods

ANOVA was carried out in the case of a primary tumor, adjacent normal tissue, and tumor metastasis of breast cancer, and on pan-cancer samples using the genome-wide methylation data of 31 solid tumor Illumina Methylation 450K chips downloaded from The Cancer Genome Atlas (TCGA) website in September 2018. Methylation sites showing a significant difference (P ≤ 0.05) were screened and compared with the whole-genome methylation data of 31 other solid tumor species in the TCGA database using t-tests in order to screen the methylation sites of breast cancer-specific expression. The expression of the screened methylation sites was confirmed through pyrosequencing in 45 cases of breast cancer, lung cancer, gastric cancer, and colorectal cancer.

Results

A total of 10 specific breast cancer methylation sites (cg13683194, cg07996594, cg21646032, cg07671949, cg21185686, cg03625109, cg16429070, cg23601468, cg24818566, and cg01240931) were analyzed; nine genes (C9orf125, RARB, ESR1, RUNX3, PCDHGB7, DBC1, PDGFRB, TIMP3, and APC) were involved. The overall effect was excellent; a total of 4 methylation sites (2 in the DBC1 gene [cg03625109 and cg24818566], 1 in the C9orf125 gene [cg13683194], and 1 in the PDGFRB gene [cg16429070]) could effectively distinguish breast cancer from 31 other cancer species. The pyrosequencing results revealed that 7 screened methylation sites could significantly distinguish between breast cancer, lung cancer, gastric cancer, and colorectal cancer samples; these sites could also specifically distinguish between luminal A, luminal B, HER2, and Basal-like types of breast cancer.

Conclusion

The 10 breast cancer methylation sites screened in the present study can effectively distinguish breast cancer from 31 other solid tumors, and they are expected to be used as biomarkers for early screening of breast cancer.

Expression, clinical significance and correlation of RUNX3 and HER2 in colorectal cancer

Background

The incidence of colorectal cancer is high and on the rise. The genetic and protein expressions of RUNT-associated transcription factor 3 (RUNX3) and human epidermal growth factor receptor 2 (HER2) in colorectal cancer (CRC) and adjacent normal tissues were detected to preliminarily explore their correlation and clinical significance.

Methods

CRC specimens excised during general surgery were selected for localization and quantitative analysis of protein and gene expression by SP (Streptavidin-peroxidase conjugated method) immunohistochemical staining, reverse transcription-polymerase chain reaction (RT-qPCR) and western blot. Combined with the patients' data, the relationship between the expression of the two genes and tumor characteristics was analyzed. Log-rank test was used to analyze the correlation between the two proteins and survival prognosis of CRC patients. The expression of RUNX3 in RKO and HCT-116 was knocked down, and the relative expression of HER2 in the two cell lines was detected.

Results

Immunohistochemical, RT-qPCR and Western blot results showed that the positive expression rate of RUNX3 in CRC was lower than in the normal group, and HER2 in CRC was higher than in the normal group. The positive expression of the two proteins correlated with the pT and pN stages of CRC. A significant negative correlation between the expression of the two genes in CRC. Follow-up results showed that the number of Runx3-positive patients was higher than negative ones, while HER2 positive were fewer than negative ones. In vitro experiments showed that RUNX3 protein and gene expression decreased, HER2 protein and gene expression increased in RUNX3 knockdown RKO and HCT-116 cells, respectively (P<0.05).

Conclusions

The expression of RUNX3 and HER2 in CRC is related to the occurrence and development of CRC, and the two genes have a negative regulating effect.

Prediction of Disease Genes Based on Stage-Specific Gene Regulatory Networks in Breast Cancer

Breast cancer is one of the most common malignant tumors in women, which seriously endangers women’s health. Great advances have been made over the last decades, however, most studies predict driver genes of breast cancer using biological experiments and/or computational methods, regardless of stage information. In this study, we propose a computational framework to predict the disease genes of breast cancer based on stage-specific gene regulatory networks. Firstly, we screen out differentially expressed genes and hypomethylated/hypermethylated genes by comparing tumor samples with corresponding normal samples. Secondly, we construct three stage-specific gene regulatory networks by integrating RNA-seq profiles and TF-target pairs, and apply WGCNA to detect modules from these networks. Subsequently, we perform network topological analysis and gene set enrichment analysis. Finally, the key genes of specific modules for each stage are screened as candidate disease genes. We obtain seven stage-specific modules, and identify 20, 12, and 22 key genes for three stages, respectively. Furthermore, 55%, 83%, and 64% of the genes are associated with breast cancer, for example E2F2, E2F8, TPX2, BUB1, and CKAP2L. So it may be of great importance for further verification by cancer experts.

DNA methylation analysis with methylation-sensitive high-resolution melting (MS-HRM) reveals gene panel for glioma characteristics

INTRODUCTION

Local DNA hypermethylation is a potential source of cancer biomarkers. While the evaluation of single gene methylation has limited value, their selected panel may provide better information.

AIMS

This study aimed to analyze the promoter methylation level in a 7-gene panel in brain tumors and verifies the usefulness of methylation-sensitive high-resolution melting (MS-HRM) for this purpose.

METHODS

Forty-six glioma samples and one non-neoplastic brain sample were analyzed by MS-HRM in terms of SFRP1, SFRP2, RUNX3, CBLN4, INA, MGMT, and RASSF1A promoter methylation. The results were correlated with patients' clinicopathological features.

RESULTS

DNA methylation level of all analyzed genes was significantly higher in brain tumor samples as compared to non-neoplastic brain and commercial, unmethylated DNA control. RASSF1A was the most frequently methylated gene, with statistically significant differences depending on the tumor WHO grade. Higher MGMT methylation levels were observed in females, whereas the levels of SFRP1 and INA promoter methylation significantly increased with patients' age. A positive correlation of promoter methylation levels was observed between pairs of genes, for example, CBLN4 and INA or MGMT and RASSF1A.

CONCLUSIONS

Our 7-gene panel of promoter methylation can be helpful in brain tumor diagnosis or characterization, and MS-HRM is a suitable method for its analysis.

Gene promoter methylation and cancer: An umbrella review

Gene promoter methylation is a common epigenetic event, taking place in the early phase of tumorigenesis, which has a great potential as a diagnostic and prognostic cancer biomarker. In this umbrella review, we provide an overview on the association between gene-promoter methylation of protein-coding genes and cancer risk based on currently available meta-analyses data on gene promoter methylation. We searched MEDLINE via PubMed and the Cochrane Database of Systematic Reviews for meta-analyses that examine the association between gene-promoter methylation and cancer, published until January 2019 in English. We used AMSTAR to assess the quality of the included studies and applied a set of pre-specified criteria to evaluate the magnitude of each association. We provide a comprehensive overview of 80 unique combinations between 22 different genes and 18 cancer outcomes, all of which indicated a positive association between promoter hypermethylation and cancer. In total, the 70 meta-analyses produced significant results under a random-effects model with odds ratios that ranged from 1.94 to 26.60, with the summary effect being in favor of the unmethylated group in all cases. Three of the strong evidence associations involve RASSF1 methylation on bladder cancer risk (OR = 18.46; 95% CI: 12.69-26.85; I² = 0%), MGMT methylation on NSCLC (OR = 4.25; 95% CI: 2.83-6.38; I² = 22.4%) and RARB methylation on prostate cancer (OR = 6.87; 95% CI: 4.68-10.08; I² = 0%). Meta-analyses showed a moderate quality, AMSTAR score ranging from 4 to 9 (Mdn = 8; IQR: 7.0 to 8.0). As primary studies and meta-analyses on the subject accumulate, more genetic loci may be found to be highly associated with specific cancer types and hence the biomarker sets will become wider.

Deconvolution of DNA methylation identifies differentially methylated gene regions on 1p36 across breast cancer subtypes

Breast cancer is a complex disease consisting of four distinct molecular subtypes. DNA methylation-based (DNAm) studies in tumors are complicated further by disease heterogeneity. In the present study, we compared DNAm in breast tumors with normal-adjacent breast samples from The Cancer Genome Atlas (TCGA). We constructed models stratified by tumor stage and PAM50 molecular subtype and performed cell-type reference-free deconvolution to control for cellular heterogeneity. We identified nineteen differentially methylated gene regions (DMGRs) in early stage tumors across eleven genes (AGRN, C1orf170, FAM41C, FLJ39609, HES4, ISG15, KLHL17, NOC2L, PLEKHN1, SAMD11, WASH5P). These regions were consistently differentially methylated in every subtype and all implicated genes are localized to the chromosomal cytoband 1p36.3. Seventeen of these DMGRs were independently validated in a similar analysis of an external data set. The identification and validation of shared DNAm alterations across tumor subtypes in early stage tumors advances our understanding of common biology underlying breast carcinogenesis and may contribute to biomarker development. We also discuss evidence of the specific importance and potential function of 1p36 in cancer.