Integrative analysis identifies potential DNA methylation biomarkers for pan-cancer diagnosis and prognosis

Ding, Wubin; Chen, Geng; Shi, Tieliu

doi:10.1080/15592294.2019.1568178

Cited by 105 publications

(75 citation statements)

References 49 publications

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“…Several studies had presented comprehensive characterization of DNA-methylation across multiple cancer types while focusing on the analysis of single CpG sites [38,[59][60][61][62][63]. Here, we drafted the first pan-cancer catalog of DMRs based on the characterization of more than 6,000 human samples DNA methylation data of 14 tumor types from TCGA and other recent studies.…”

Section: Discussionmentioning

confidence: 99%

Cancer methylomes characterization enabled by Rocker-meth

Benelli

Franceschini

Magi

et al. 2020

Preprint

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Differentially DNA methylated regions (DMRs) inform on the role of epigenetic changes in cancer. We present Rocker-meth, a computational method exploiting a heterogeneous hidden Markov model to detect DMRs across multiple experimental platforms. Its application to more than 6,000 methylation profiles across 14 tumor types provides a comprehensive catalog of tumor type-specific and shared DMRs, also amenable to single-cell DNA-methylation data. In depth integrative analysis including orthogonal omics shows the enhanced ability of Rocker-meth in recapitulating known associations, further uncovering the pan-cancer relationship between DNA hypermethylation and transcription factor deregulation depending on the baseline chromatin state.

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Section: Discussionmentioning

confidence: 99%

Cancer methylomes characterization enabled by Rocker-meth

Benelli

Franceschini

Magi

et al. 2020

Preprint

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“…When adding new models, a gradient descent algorithm was used to minimize the loss. The XGBoost model was widely used for diagnosis classi cation [34,35], treatment effect [36,37], and prognosis evaluation [38,39] in different diseases.…”

Section: Discussionmentioning

confidence: 99%

Importance of GWAS Risk Loci and Clinical Data in Predicting Asthma Using Machine-Learning Approaches

et al. 2020

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Asthma is a serious immune-mediated respiratory airway disease. Its pathological processes involve genetics and the environment, but it remains unclear. To understand the risk factors of asthma, we combined genome-wide association study (GWAS) risk loci and clinical data in predicting asthma using machine-learning approaches. A case-control study with 123 asthma patients and 100 healthy controls was conducted in Zhuang population in Guangxi. GWAS risk loci were detected using polymerase chain reaction, and clinical data were collected. Machine-learning approaches (e.g., extreme gradient boosting [XGBoost], decision tree, support vector machine, and random forest algorithms) were used to identify the major factors that contributed to asthma. A total of 14 GWAS risk loci with clinical data were analyzed on the basis of 10 times of 10-fold cross-validation for all machine-learning models. Using GWAS risk loci or clinical data, the best performances were area under the curve (AUC) values of 64.3% and 71.4%, respectively. Combining GWAS risk loci and clinical data, the XGBoost established the best model with an AUC of 79.7%, indicating that the combination of genetics and clinical data can enable improved performance. We then sorted the importance of features and found that the top six risk factors for predicting asthma were rs3117098, rs7775228, family history, rs2305480, rs4833095, and body mass index. Asthma-prediction models based on GWAS risk loci and clinical data can accurately predict asthma and thus provide insights into the disease pathogenesis of asthma. Further research is required to evaluate more genetic markers and clinical data and predict asthma risk.

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“…We used 192 cases for training and 98 cases for validation. We applied a sequential model-based variable selection to screen markers for predicting survival outcome ( Ding, Chen & Shi, 2019 ; Xu et al, 2017 ). Based on the candidate biomarkers, we first fitted a univariate Cox proportional hazards model by using each marker as the covariate.…”

Section: Methodsmentioning

confidence: 99%

Identification of DNA methylation patterns and biomarkers for clear-cell renal cell carcinoma by multi-omics data analysis

Liu

Tian

2020

PeerJ

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Background Tumorigenesis is highly heterogeneous, and using clinicopathological signatures only is not enough to effectively distinguish clear cell renal cell carcinoma (ccRCC) and improve risk stratification of patients. DNA methylation (DNAm) with the stability and reversibility often occurs in the early stage of tumorigenesis. Disorders of transcription and metabolism are also an important molecular mechanisms of tumorigenesis. Therefore, it is necessary to identify effective biomarkers involved in tumorigenesis through multi-omics analysis, and these biomarkers also provide new potential therapeutic targets. Method The discovery stage involved 160 pairs of ccRCC and matched normal tissues for investigation of DNAm and biomarkers as well as 318 cases of ccRCC including clinical signatures. Correlation analysis of epigenetic, transcriptomic and metabolomic data revealed the connection and discordance among multi-omics and the deregulated functional modules. Diagnostic or prognostic biomarkers were obtained by the correlation analysis, the Least Absolute Shrinkage and Selection Operator (LASSO) and the LASSO-Cox methods. Two classifiers were established based on random forest (RF) and LASSO-Cox algorithms in training datasets. Seven independent datasets were used to evaluate robustness and universality. The molecular biological function of biomarkers were investigated using DAVID and GeneMANIA. Results Based on multi-omics analysis, the epigenetic measurements uniquely identified DNAm dysregulation of cellular mechanisms resulting in transcriptomic alterations, including cell proliferation, immune response and inflammation. Combination of the gene co-expression network and metabolic network identified 134 CpG sites (CpGs) as potential biomarkers. Based on the LASSO and RF algorithms, five CpGs were obtained to build a diagnostic classifierwith better classification performance (AUC > 99%). A eight-CpG-based prognostic classifier was obtained to improve risk stratification (hazard ratio (HR) > 4; log-rank test, p-value < 0.01). Based on independent datasets and seven additional cancers, the diagnostic and prognostic classifiers also had better robustness and stability. The molecular biological function of genes with abnormal methylation were significantly associated with glycolysis/gluconeogenesis and signal transduction. Conclusion The present study provides a comprehensive analysis of ccRCC using multi-omics data. These findings indicated that multi-omics analysis could identify some novel epigenetic factors, which were the most important causes of advanced cancer and poor clinical prognosis. Diagnostic and prognostic biomarkers were identified, which provided a promising avenue to develop effective therapies for ccRCC.

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Integrative analysis identifies potential DNA methylation biomarkers for pan-cancer diagnosis and prognosis

Cited by 105 publications

References 49 publications

Cancer methylomes characterization enabled by Rocker-meth

Cancer methylomes characterization enabled by Rocker-meth

Importance of GWAS Risk Loci and Clinical Data in Predicting Asthma Using Machine-Learning Approaches

Identification of DNA methylation patterns and biomarkers for clear-cell renal cell carcinoma by multi-omics data analysis

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