Dataset Information

MethylClass: an R package to construct DNA methylation-based classification models.

ABSTRACT: DNA methylation profiling is a useful tool to increase the accuracy of a cancer diagnosis. However, a comprehensive R package specially for it is lacking. Hence, we developed the R package methylClass for methylation-based classification. Within it, we provide the eSVM (ensemble-based support vector machine) model to achieve much higher accuracy in methylation data classification than the popular random forest model and overcome the time-consuming problem of the traditional SVM. In addition, some novel feature selection methods are included in the package to improve the classification. Furthermore, because methylation data can be converted to other omics, such as copy number variation data, we also provide functions for multi-omics studies. The testing of this package on four datasets shows the accurate performance of our package, especially eSVM, which can be used in both methylation and multi-omics models and outperforms other methods in both cases. methylClass is available at: https://github.com/yuabrahamliu/methylClass.

SUBMITTER: Liu Y

PROVIDER: S-EPMC10782803 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature

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methylClass: an R package to construct DNA methylation-based classification models.

Liu Yu Y

Briefings in bioinformatics 20231101 1

DNA methylation profiling is a useful tool to increase the accuracy of a cancer diagnosis. However, a comprehensive R package specially for it is lacking. Hence, we developed the R package methylClass for methylation-based classification. Within it, we provide the eSVM (ensemble-based support vector machine) model to achieve much higher accuracy in methylation data classification than the popular random forest model and overcome the time-consuming problem of the traditional SVM. In addition, som ...[more]

PMID: 38205965

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Project description:Sinonasal undifferentiated carcinoma (SNUC) is an aggressive malignancy harboring IDH2 R172 mutations in >80% cases. We explored the potential of genome-wide DNA methylation profiling to elucidate tumor biology and improve the diagnosis of sinonasal undifferentiated carcinoma and its histologic mimics. Forty-two cases, including sinonasal undifferentiated, large cell neuroendocrine, small cell neuroendocrine, and SMARCB1-deficient carcinomas and olfactory neuroblastoma, were profiled by Illumina Infinium Methylation EPIC array interrogating >850,000 CpG sites. The data were analyzed using a custom bioinformatics pipeline. IDH2 mutation status was determined by the targeted exome sequencing (MSK-IMPACTTM) in most cases. H3K27 methylation level was assessed by the immunohistochemistry-based H-score. DNA methylation-based semi-supervised hierarchical clustering analysis segregated IDH2 mutants, mostly sinonasal undifferentiated (n = 10) and large cell neuroendocrine carcinomas (n = 4), from other sinonasal tumors, and formed a single cluster irrespective of the histologic type. t-distributed stochastic neighbor embedding dimensionality reduction analysis showed no overlap between IDH2 mutants, SMARCB1-deficient carcinoma and olfactory neuroblastoma. IDH2 mutants demonstrated a global methylation phenotype and an increase in repressive trimethylation of H3K27 in comparison to IDH2 wild-type tumors (p < 0.001). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed no difference in pathway activation between IDH2-mutated sinonasal undifferentiated and large cell neuroendocrine carcinomas. In comparison to SMARCB1-deficient, IDH2-mutated carcinomas were associated with better disease-free survival (p = 0.034) and lower propensity for lung metastasis (p = 0.002). ARID1A mutations were common in small cell neuroendocrine carcinoma but not among IDH2 mutants (3/3 versus 0/18 and p < 0.001). IDH2 mutations in sinonasal carcinomas induce a hypermethylator phenotype and define a molecular subgroup of tumors arising in this location. IDH2-mutated sinonasal undifferentiated carcinoma and large cell neuroendocrine carcinoma likely represent a phenotypic spectrum of the same entity, which is distinct from small cell neuroendocrine and SMARCB1-deficient sinonasal carcinomas. DNA methylation-based analysis of the sinonasal tumors has potential to improve the diagnostic accuracy and classification of tumors arising in this location.

Dataset Information

MethylClass: an R package to construct DNA methylation-based classification models.

Publications

methylClass: an R package to construct DNA methylation-based classification models.

Similar Datasets

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