Genome-wide DNA methylome analysis reveals methylation subtypes with different clinical outcomes for acute myeloid leukemia patients

Identification of Novel DNA Methylation Prognostic Biomarkers for AML With Normal Cytogenetics

PURPOSE

AML is a hematologic cancer that is clinically heterogeneous, with a wide range of clinical outcomes. DNA methylation changes are a hallmark of AML but are not routinely used as a criterion for risk stratification. The aim of this study was to explore DNA methylation markers that could risk stratify patients with cytogenetically normal AML (CN-AML), currently classified as intermediate-risk.

MATERIALS AND METHODS

DNA methylation profiles in whole blood samples from 77 patients with CN-AML in The Cancer Genome Atlas (LAML cohort) were analyzed. Individual 5'-cytosine-phosphate-guanine-3' (CpG) sites were assessed for their ability to predict overall survival. The output was validated using DNA methylation profiles from bone marrow samples of 79 patients with CN-AML in a separate data set from the Gene Expression Omnibus.

RESULTS

In the training set, using DNA methylation data derived from the 450K array, we identified 2,549 CpG sites that could potentially distinguish patients with CN-AML with an adverse prognosis (intermediate-poor) from those with a more favorable prognosis (intermediate-favorable) independent of age. Of these, 25 CpGs showed consistent prognostic potential across both the 450K and 27K array platforms. In a separate validation data set, nine of these 25 CpGs exhibited statistically significant differences in 2-year survival. These nine validated CpGs formed the basis for a combined prognostic biomarker panel, which includes an 8-CpG Somatic Panel and the methylation status of cg23947872. This panel displayed strong predictive ability for 2-year survival, 2-year progression-free survival, and complete remission in the validation cohort.

CONCLUSION

This study highlights DNA methylation profiling as a promising approach to enhance risk stratification in patients with CN-AML, potentially offering a pathway to more personalized treatment strategies.

Development of a prognostic model for personalized prediction of colon adenocarcinoma (COAD) patient outcomes using methylation-driven genes

The objective of this study was to identify methylation-driven genes and explore their prognostic value in colon adenocarcinoma (COAD). The Cancer Genome Atlas (TCGA) database was used to acquire collated COAD transcriptome gene expression matrix (containing 59,427 transcripts), transcriptome gene methylation level matrix (containing 29,602 methylated modified genes), which included 517 samples containing 41 samples of normal tissue (NT) & 476 samples of COAD, and patient clinical information files (including patient survival time, survival status, age, gender and tumor stage, etc.), for all COAD samples. A total of 9807 differentially expressed genes (DEGs) were obtained by DEG analysis of the COAD transcriptional expression matrix, of which 5874 were up-regulated and 3933 were down-regulated. And 46 methylation-driven DEGs (MD-DEGs) in COAD were obtained by DEG analysis, differential analysis of gene methylation levels, and correlation analysis between them. Next, three prognostic associated MD-DEGs (PMD-DEGs) (IDUA, ZBTB18 and C5orf38) were identified by Cox regression analysis, and a prognostic model composed of the three PMD-DEGs was constructed by least absolute shrinkage and selection operator (LASSO) regression analysis and cross-validation analysis. In addition, survival analysis, the receiver operating characteristics (ROC) curve analysis and independent prognostic analysis were used to evaluate and verify that the prognostic model we constructed could accurately and independently predict the prognosis of COAD patients. Finally, we constructed a nomogram based on the prognosis model to accurately and personalized predict the survival prognosis of COAD patients. In conclusion, we identified the methylation driver gene of COAD and constructed a prognostic model and nomogram to personalized predict the prognosis of patients, which opened a new prospect for accurate diagnosis and treatment in clinical practice.

Targeting DNA Methylation in Leukemia, Myelodysplastic Syndrome, and Lymphoma: A Potential Diagnostic, Prognostic, and Therapeutic Tool

DNA methylation represents a crucial mechanism of epigenetic regulation in hematologic malignancies. The methylation process is controlled by specific DNA methyl transferases and other regulators, which are often affected by genetic alterations. Global hypomethylation and hypermethylation of tumor suppressor genes are associated with hematologic cancer development and progression. Several epi-drugs have been successfully implicated in the treatment of hematologic malignancies, including the hypomethylating agents (HMAs) decitabine and azacytidine. However, combinations with other treatment modalities and the discovery of new molecules are still the subject of research to increase sensitivity to anti-cancer therapies and improve patient outcomes. In this review, we summarized the main functions of DNA methylation regulators and genetic events leading to changes in methylation landscapes. We provide current knowledge about target genes with aberrant methylation levels in leukemias, myelodysplastic syndromes, and malignant lymphomas. Moreover, we provide an overview of the clinical trials, focused mainly on the combined therapy of HMAs with other treatments and its impact on adverse events, treatment efficacy, and survival rates among hematologic cancer patients. In the era of precision medicine, a transition from genes to their regulation opens up the possibility of an epigenetic-based approach as a diagnostic, prognostic, and therapeutic tool.

Prognostic and therapeutic prediction by screening signature combinations from transcriptome-methylome interactions in oral squamous cell carcinoma

DNA methylation pattern in oral squamous cell carcinoma (OSCC) remains poorly described. This study aimed to perform a genome-wide integrated analysis of the transcriptome and methylome and assess the efficacy of their prognostic signature model in patients with OSCC. We analyzed transcriptome and methylome data from 391 OSCC samples and 41 adjacent normal samples. A total of 8074 differentially expressed genes (DEGs) and 10,084 differentially expressed CpGs (DMCpGs) were identified. Then 241 DEGs with DMCpGs were identified. According to the prognostic analysis, the prognostic signature of methylation-related differentially expressed genes (mrDEGPS) was established. mrDEGPS consisted of seven prognostic methylation-related genes, including ESRRG, CCNA1, SLC20A1, COL6A6, FCGBP, CDKN2A, and ZNF43. mrDEGPS was a significant stratification factor of survival (P < 0.00001) irrespective of the clinical stage. The immune effector components, including B cells, CD4⁺ T cells, and CD8⁺ T cells, were decreased in the tumor environment of patients with high mrDEGPS. Immune checkpoint expressions, including CTLA-4, PD-1, LAG3, LGALS9, HAVCR2, and TIGHT, were comprehensively elevated (P < 0.001). The estimated half-maximal inhibitory concentration difference between low- and high-risk patients was inconsistent among chemotherapeutic drugs. In conclusion, the transcriptome–methylome interaction pattern in OSCC is complex. mrDEGPS can predict patient survival and responses to immunotherapy and chemotherapy and facilitate clinical decision-making in patients with OSCC.

Integrative Analysis of Multi-Omics Identified the Prognostic Biomarkers in Acute Myelogenous Leukemia

Background

Acute myelogenous leukemia (AML) is a common pediatric malignancy in children younger than 15 years old. Although the overall survival (OS) has been improved in recent years, the mechanisms of AML remain largely unknown. Hence, the purpose of this study is to explore the differentially methylated genes and to investigate the underlying mechanism in AML initiation and progression based on the bioinformatic analysis.

Methods

Methylation array data and gene expression data were obtained from TARGET Data Matrix. The consensus clustering analysis was performed using ConsensusClusterPlus R package. The global DNA methylation was analyzed using methylationArrayAnalysis R package and differentially methylated genes (DMGs), and differentially expressed genes (DEGs) were identified using Limma R package. Besides, the biological function was analyzed using clusterProfiler R package. The correlation between DMGs and DEGs was determined using psych R package. Moreover, the correlation between DMGs and AML was assessed using varElect online tool. And the overall survival and progression-free survival were analyzed using survival R package.

Results

All AML samples in this study were divided into three clusters at k = 3. Based on consensus clustering, we identified 1,146 CpGs, including 40 hypermethylated and 1,106 hypomethylated CpGs in AML. Besides, a total 529 DEGs were identified, including 270 upregulated and 259 downregulated DEGs in AML. The function analysis showed that DEGs significantly enriched in AML related biological process. Moreover, the correlation between DMGs and DEGs indicated that seven DMGs directly interacted with AML. CD34, HOXA7, and CD96 showed the strongest correlation with AML. Further, we explored three CpG sites cg03583857, cg26511321, cg04039397 of CD34, HOXA7, and CD96 which acted as the clinical prognostic biomarkers.

Conclusion

Our study identified three novel methylated genes in AML and also explored the mechanism of methylated genes in AML. Our finding may provide novel potential prognostic markers for AML.

Genome-wide DNA methylome analysis reveals methylation subtypes with different clinical outcomes for acute myeloid leukemia patients

Leukemia is the second common blood cancer after lymphoma, and its incidence rate has an increasing trend in recent years. Acute myeloid leukemia (AML) is one of the prevalent forms of leukemia. Although previous studies have investigated the methylation profile for AML patients, the AML methylation subtypes based on the genome‐wide methylome are still unclear. In the present study, we identified three methylation subtypes for AML samples based on the methylation profiles at CGI, CGI shore, CGI shelf, and opensea genomic contexts. Analyzing the molecular characteristics and clinical factors of the three subtypes revealed different methylation patterns and clinical outcomes between them. Further analysis revealed subtype dependent marker genes and their promoter CpG sites with regulatory function. Finally, we found that combining the AML patient age and methylation pattern brought better clinical outcome classification. In conclusion, we identified AML methylation subtypes and their marker genes, these results may help to excavate potential targets for clinical therapy and the development of precision medicine for AML patients.