Filtering High-Dimensional Methylation Marks With Extremely Small Sample Size: An Application to Gastric Cancer Data.

Xin Chen,Qingrun Zhang,Thierry Chekouo

doi:10.3389/fgene.2021.705708

Xin Chen, Qingrun Zhang + Show 1 more

Open Access

https://doi.org/10.3389/fgene.2021.705708

Copy DOI

Abstract

DNA methylations in critical regions are highly involved in cancer pathogenesis and drug response. However, to identify causal methylations out of a large number of potential polymorphic DNA methylation sites is challenging. This high-dimensional data brings two obstacles: first, many established statistical models are not scalable to so many features; second, multiple-test and overfitting become serious. To this end, a method to quickly filter candidate sites to narrow down targets for downstream analyses is urgently needed. BACkPAy is a pre-screening Bayesian approach to detect biological meaningful patterns of potential differential methylation levels with small sample size. BACkPAy prioritizes potentially important biomarkers by the Bayesian false discovery rate (FDR) approach. It filters non-informative sites (i.e., non-differential) with flat methylation pattern levels across experimental conditions. In this work, we applied BACkPAy to a genome-wide methylation dataset with three tissue types and each type contains three gastric cancer samples. We also applied LIMMA (Linear Models for Microarray and RNA-Seq Data) to compare its results with what we achieved by BACkPAy. Then, Cox proportional hazards regression models were utilized to visualize prognostics significant markers with The Cancer Genome Atlas (TCGA) data for survival analysis. Using BACkPAy, we identified eight biological meaningful patterns/groups of differential probes from the DNA methylation dataset. Using TCGA data, we also identified five prognostic genes (i.e., predictive to the progression of gastric cancer) that contain some differential methylation probes, whereas no significant results was identified using the Benjamin-Hochberg FDR in LIMMA. We showed the importance of using BACkPAy for the analysis of DNA methylation data with extremely small sample size in gastric cancer. We revealed that RDH13, CLDN11, TMTC1, UCHL1, and FOXP2 can serve as predictive biomarkers for gastric cancer treatment and the promoter methylation level of these five genes in serum could have prognostic and diagnostic functions in gastric cancer patients.

Highlights

DNA methylation is a biochemical process of adding a methyl group at the 5’ carbon of the cytosine ring in a nucleotide (Du et al, 2010; Li et al, 2015)
We used a dataset with very small sample size and a large number of features, which is available from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) public functional genomics data repository with GEO number GSE97686 and GSE107161 (Najgebauer et al, 2019)
In BACkPAy, non-differentially probes belong to group FlatFlatFlatFlat i.e., probes that do not have a significant change between tissue types for male and female samples

Summary

Introduction

DNA methylation is a biochemical process of adding a methyl group at the 5’ carbon of the cytosine ring in a nucleotide (Du et al, 2010; Li et al, 2015). The number of features (probes) in methylation dataset is typically at least on the order of several thousand, whereas the number of samples may be few, presenting challenges in multiple hypothesis testing as well as overfitting. In this manuscript, we are interested in identifying or filtering groups of potential probes that show significant methylation level differences (and similar patterns) among experimental conditions while accounting for another demographic factor (e.g., sex). Using a DNA methylation dataset in gastric cancer with extremely small sample size (e.g., in cell line experiments), we would like to analyse differential methylation probes among experimental groups for both male and female (see Figure 1 for instance)

Objectives

Methods

Results