Developing DNA methylation-based prognostic biomarkers of acute myeloid leukemia

A DNA Damage Response Related Signature to Predict Prognosis in Patients with Acute Myeloid Leukemia

The prognosis of acute myeloid leukemia (AML) is disappointing in most subtypes and varies widely. DNA damage response (DDR) is associated with prognosis and immunotherapy in multiple cancers. Here, we identify a signature of eight DDR-related genes associated with overall survival, which stratifies AML patients into high- and low-risk groups. Patients in low-risk group were more likely to respond to sorafenib. The signature could be an independent prognostic predictor for patients treated with ADE and ADE plus gemtuzumab ozogamicin. Therefore, this DDR prognostic signature might be applied to prognostic stratification and treatment selection in AML patients, which warrants further studies.

Identification of High-Frequency Methylation Sites in RNF180 Promoter Region Affecting Expression and Their Relationship with Prognosis of Gastric Cancer

Background

Ring finger protein 180 (RNF180) is a tumor suppressor gene regulated by promoter methylation. We previously demonstrated that the RNF180 promoter methylation could be a risk factor for gastric cancer (GC); and eight high-frequency hypermethylated CpG sites were associated with GC. However, it is not clear whether these key sites can affect gene expression and involve in prognosis. The aim of this study was to investigate the effects of above CpG sites on the gene expression and prognosis of GC.

Patients and Methods

A total of 164 GC tissues were enrolled and followed up. Tissue samples were used for DNA and RNA isolation. Methylation status of RNF180 was detected using bisulfite sequencing PCR (BSP). Expression levels of RNF180 were detected using quantitative real-time reverse transcription-polymerase chain reaction (qRT-PCR). JASPAR and PROMO databases were used to predict the transcription factors (TFs) binding to the CpG site.

Results

The methylation in RNF180 promoter region increased and mRNA expression decreased in GC tissue. Correlation analysis revealed that the average methylation rate (AMR) and four CpG sites methylation rate were negatively related to RNF180 expression, including M3(−165)(Chr5:64165942), M5(−148)(Chr5:64,165,959), M7(−133)(Chr5:64,165,974) and M8(−130)(Chr5:64,165,977). Furthermore, the methylation rate of M5(−148)(Chr5:64,165,959) and M27(−26)(Chr5:64,166,081) above 0.3 indicated poor prognosis (P_M5 = 0.008, P_M27 = 0.003, HR_M5(−148) = 2.000 (1.201,3.332), HR_M27(−26)=2.389 (1.336,4.271)), which could be independent factors of prognosis.

Conclusion

By focusing on the methylation sites in the RNF180 promoter region, we identified two high-frequency methylation sites, M5(−148)(Chr5:64,165,959) and M27(−26)(Chr5:64,166,081), which could affect gene expression and predict the prognosis of GC. In the future, the possible molecular mechanism involved needs to be further studied.

Impact of mutations in DNA methylation modification genes on genome-wide methylation landscapes and downstream gene activations in pan-cancer

Background

In cancer, mutations of DNA methylation modification genes have crucial roles for epigenetic modifications genome-wide, which lead to the activation or suppression of important genes including tumor suppressor genes. Mutations on the epigenetic modifiers could affect the enzyme activity, which would result in the difference in genome-wide methylation profiles and, activation of downstream genes. Therefore, we investigated the effect of mutations on DNA methylation modification genes such as DNMT1, DNMT3A, MBD1, MBD4, TET1, TET2 and TET3 through a pan-cancer analysis.

Methods

First, we investigated the effect of mutations in DNA methylation modification genes on genome-wide methylation profiles. We collected 3,644 samples that have both of mRNA and methylation data from 12 major cancer types in The Cancer Genome Atlas (TCGA). The samples were divided into two groups according to the mutational signature. Differentially methylated regions (DMR) that overlapped with the promoter region were selected using minfi and differentially expressed genes (DEG) were identified using EBSeq. By integrating the DMR and DEG results, we constructed a comprehensive DNA methylome profiles on a pan-cancer scale. Second, we investigated the effect of DNA methylations in the promoter regions on downstream genes by comparing the two groups of samples in 11 cancer types. To investigate the effects of promoter methylation on downstream gene activations, we performed clustering analysis of DEGs. Among the DEGs, we selected highly correlated gene set that had differentially methylated promoter regions using graph based sub-network clustering methods.

Results

We chose an up-regulated DEGs cluster where had hypomethylated promoter in acute myeloid leukemia (LAML) and another down-regulated DEGs cluster where had hypermethylated promoter in colon adenocarcinoma (COAD). To rule out effects of gene regulation by transcription factor (TF), if differentially expressed TFs bound to the promoter of DEGs, that DEGs did not included to the gene set that effected by DNA methylation modifiers. Consequently, we identified 54 hypomethylated promoter DMR up-regulated DEGs in LAML and 45 hypermethylated promoter DMR down-regulated DEGs in COAD.

Conclusions

Our study on DNA methylation modification genes in mutated vs. non-mutated groups could provide useful insight into the epigenetic regulation of DEGs in cancer.