Dataset Information

DNA methylation data-based molecular subtype classification and prediction in patients with gastric cancer.

ABSTRACT: Background:Genetic and epigenetic alterations have been indicated to be closely correlated with the carcinogenesis, DNA methylation is one of most frequently occurring molecular behavior that take place early during this complicated process in gastric cancer (GC). Methods:In this study, 398 samples were collected from the cancer genome atlas (TCGA) database and were analyzed, so as to mine the specific DNA methylation sites that affected the prognosis for GC patients. Moreover, the 23,588 selected CpGs that were markedly correlated with patient prognosis were used for consistent clustering of the samples into 6 subgroups, and samples in each subgroup varied in terms of M, Stage, Grade, and Age. In addition, the levels of methylation sites in each subgroup were calculated, and 347 methylation sites (corresponding to 271 genes) were screened as the intrasubgroup specific methylation sites. Meanwhile, genes in the corresponding promoter regions that the above specific methylation sites were located were performed signaling pathway enrichment analysis. Results:The specific genes were enriched to the biological pathways that were reported to be closely correlated with GC; moreover, the subsequent transcription factor enrichment analysis discovered that, these genes were mainly enriched into the cell response to transcription factor B, regulation of MAPK signaling pathways, and regulation of cell proliferation and metastasis. Eventually, the prognosis prediction model for GC patients was constructed using the Random Forest Classifier model, and the training set and test set data were carried out independent verification and test. Conclusions:Such specific classification based on specific DNA methylation sites can well reflect the heterogeneity of GC tissues, which contributes to developing the individualized treatment and accurately predicting patient prognosis.

SUBMITTER: Lian Q

PROVIDER: S-EPMC7388223 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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DNA methylation data-based molecular subtype classification and prediction in patients with gastric cancer.

Lian Qixin Q Wang Bo B Fan Lijun L Sun Junqiang J Wang Guilai G Zhang Jidong J

Cancer cell international 20200729

<h4>Background</h4>Genetic and epigenetic alterations have been indicated to be closely correlated with the carcinogenesis, DNA methylation is one of most frequently occurring molecular behavior that take place early during this complicated process in gastric cancer (GC).<h4>Methods</h4>In this study, 398 samples were collected from the cancer genome atlas (TCGA) database and were analyzed, so as to mine the specific DNA methylation sites that affected the prognosis for GC patients. Moreover, th ...[more]

PMID: 32742196

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Project description:BackgroundDNA methylation (DNAm) has important regulatory roles in many biological processes and diseases. It is the only epigenetic mark with a clear mechanism of mitotic inheritance and the only one easily available on a genome scale. Aberrant cytosine-phosphate-guanine (CpG) methylation has been discussed in the context of disease aetiology, especially cancer. CpG hypermethylation of promoter regions is often associated with silencing of tumour suppressor genes and hypomethylation with activation of oncogenes.Supervised principal component analysis (SPCA) is a popular machine learning method. However, in a recent application to phenotype prediction from DNAm data SPCA was inferior to the specific method EVORA.ResultsWe present Model-Selection-SPCA (MS-SPCA), an enhanced version of SPCA. MS-SPCA applies several models that perform well in the training data to the test data and selects the very best models for final prediction based on parameters of the test data.We have applied MS-SPCA for phenotype prediction from genome-wide DNAm data. CpGs used for prediction are selected based on the quantification of three features of their methylation (average methylation difference, methylation variation difference and methylation-age-correlation). We analysed four independent case-control datasets that correspond to different stages of cervical cancer: (i) cases currently cytologically normal, but will later develop neoplastic transformations, (ii, iii) cases showing neoplastic transformations and (iv) cases with confirmed cancer. The first dataset was split into several smaller case-control datasets (samples either Human Papilloma Virus (HPV) positive or negative). We demonstrate that cytology normal HPV+ and HPV- samples contain DNAm patterns which are associated with later neoplastic transformations. We present evidence that DNAm patterns exist in cytology normal HPV- samples that (i) predispose to neoplastic transformations after HPV infection and (ii) predispose to HPV infection itself. MS-SPCA performs significantly better than EVORA.ConclusionsMS-SPCA can be applied to many classification problems. Additional improvements could include usage of more than one principal component (PC), with automatic selection of the optimal number of PCs. We expect that MS-SPCA will be useful for analysing recent larger DNAm data to predict future neoplastic transformations.

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Dataset Information

DNA methylation data-based molecular subtype classification and prediction in patients with gastric cancer.

Publications

DNA methylation data-based molecular subtype classification and prediction in patients with gastric cancer.

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