Subtype-WGME Enables Whole-genome-wide Multi-omics Cancer Subtyping

Overview

Journal Cell Rep Methods

Specialty Cell Biology

Date 2024 May 18

PMID 38761803

Authors

Hai Yang

Liang Zhao

Dongdong Li

Congcong An

Xiaoyang Fang

Yiwen Chen

Jingping Liu

Ting Xiao

Zhe Wang

Affiliations

Soon will be listed here.

Abstract

We present an innovative strategy for integrating whole-genome-wide multi-omics data, which facilitates adaptive amalgamation by leveraging hidden layer features derived from high-dimensional omics data through a multi-task encoder. Empirical evaluations on eight benchmark cancer datasets substantiated that our proposed framework outstripped the comparative algorithms in cancer subtyping, delivering superior subtyping outcomes. Building upon these subtyping results, we establish a robust pipeline for identifying whole-genome-wide biomarkers, unearthing 195 significant biomarkers. Furthermore, we conduct an exhaustive analysis to assess the importance of each omic and non-coding region features at the whole-genome-wide level during cancer subtyping. Our investigation shows that both omics and non-coding region features substantially impact cancer development and survival prognosis. This study emphasizes the potential and practical implications of integrating genome-wide data in cancer research, demonstrating the potency of comprehensive genomic characterization. Additionally, our findings offer insightful perspectives for multi-omics analysis employing deep learning methodologies.

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