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作者:

Zheng, Weisheng (Zheng, Weisheng.) | Pu, Mengchen (Pu, Mengchen.) | Li, Xiaorong (Li, Xiaorong.) | Du, Zhaolan (Du, Zhaolan.) | Jin, Sutong (Jin, Sutong.) | Li, Xingshuai (Li, Xingshuai.) | Zhou, Jielong (Zhou, Jielong.) | Zhang, Yingsheng (Zhang, Yingsheng.)

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Scopus SCIE

摘要:

Metastatic propagation is the leading cause of death for most cancers. Prediction and elucidation of metastatic process is crucial for the treatment of cancer. Even though somatic mutations have been linked to tumorigenesis and metastasis, it is less explored whether metastatic events can be identified through genomic mutational signatures, which are concise descriptions of the mutational processes. Here, we developed MetaWise, a Deep Neural Network (DNN) model, by applying mutational signatures as input features calculated from Whole-Exome Sequencing (WES) data of TCGA and other metastatic cohorts. This model can accurately classify metastatic tumors from primary tumors and outperform traditional machine learning (ML) models and a deep learning (DL) model, DiaDeL. Signatures of non-coding mutations also have a major impact on the model's performance. SHapley Additive exPlanations (SHAP) and Local Surrogate (LIME) analyses identify several mutational signatures which are directly correlated to metastatic spread in cancers, including APOBEC-mutagenesis, UV-induced signatures, and DNA damage response deficiency signatures.

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作者机构:

  • [ 1 ] [Zheng, Weisheng]Beijing StoneWise Technol Co Ltd, Beijing, Peoples R China
  • [ 2 ] [Pu, Mengchen]Beijing StoneWise Technol Co Ltd, Beijing, Peoples R China
  • [ 3 ] [Li, Xiaorong]Beijing StoneWise Technol Co Ltd, Beijing, Peoples R China
  • [ 4 ] [Du, Zhaolan]Beijing StoneWise Technol Co Ltd, Beijing, Peoples R China
  • [ 5 ] [Jin, Sutong]Beijing StoneWise Technol Co Ltd, Beijing, Peoples R China
  • [ 6 ] [Li, Xingshuai]Beijing StoneWise Technol Co Ltd, Beijing, Peoples R China
  • [ 7 ] [Zhou, Jielong]Beijing StoneWise Technol Co Ltd, Beijing, Peoples R China
  • [ 8 ] [Zhang, Yingsheng]Beijing StoneWise Technol Co Ltd, Beijing, Peoples R China
  • [ 9 ] [Li, Xiaorong]Minzu Univ China, Beijing, Peoples R China
  • [ 10 ] [Du, Zhaolan]Beijing Univ Technol, Beijing, Peoples R China
  • [ 11 ] [Jin, Sutong]Harbin Inst Technol, Weihai, Shandong, Peoples R China

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来源 :

SCIENTIFIC REPORTS

ISSN: 2045-2322

年份: 2023

期: 1

卷: 13

4 . 6 0 0

JCR@2022

ESI学科: Multidisciplinary;

ESI高被引阀值:20

被引次数:

WoS核心集被引频次:

SCOPUS被引频次: 3

ESI高被引论文在榜: 0 展开所有

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