Integrative Analysis of ATAC-Seq and RNA-Seq through Machine Learning Identifies 10 Signature Genes for Breast Cancer Intrinsic Subtypes

초록

Simple Summary Breast cancer is categorized into four main intrinsic subtypes, and distinguishing between these subtypes is crucial for providing personalized treatment to patients. However, systematic analyses exploring the connections between gene expression and chromatin accessibility using bulk RNA-seq and ATAC-seq data, coupled with machine learning algorithms, are lacking. In this study, we develop a classification model based on the integrative analysis of RNA-seq transcriptome and ATAC-seq epigenetic information. We identify 10 signature genes associated with these intrinsic subtypes, which are predominantly linked to immune responses, hormone signaling, cancer progression, and cellular proliferation.Abstract Breast cancer is a heterogeneous disease composed of various biologically distinct subtypes, each characterized by unique molecular features. Its formation and progression involve a complex, multistep process that includes the accumulation of numerous genetic and epigenetic alterations. Although integrating RNA-seq transcriptome data with ATAC-seq epigenetic information provides a more comprehensive understanding of gene regulation and its impact across different conditions, no classification model has yet been developed for breast cancer intrinsic subtypes based on such integrative analyses. In this study, we employed machine learning algorithms to predict intrinsic subtypes through the integrative analysis of ATAC-seq and RNA-seq data. We identified 10 signature genes (CDH3, ERBB2, TYMS, GREB1, OSR1, MYBL2, FAM83D, ESR1, FOXC1, and NAT1) using recursive feature elimination with cross-validation (RFECV) and a support vector machine (SVM) based on SHAP (SHapley Additive exPlanations) feature importance. Furthermore, we found that these genes were primarily associated with immune responses, hormone signaling, cancer progression, and cellular proliferation.

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