An Unsupervised Deep Learning-Based Model Using Multiomics Data to Predict Prognosis of Patients with Stomach Adenocarcinoma.

Journal: Computational and mathematical methods in medicine
Published Date: Oct 27, 2022

Abstract

METHODS: Patients (363 in total) with stomach adenocarcinoma from The Cancer Genome Atlas (TCGA) cohort were included. An autoencoder was constructed to integrate the RNA sequencing, miRNA sequencing, and methylation data. The features of the bottleneck layer were used to perform the -means clustering algorithm to obtain different subgroups for evaluating the prognosis-related risk of stomach adenocarcinoma. The model's robustness was verified using a 10-fold cross-validation (CV). Survival was analyzed by the Kaplan-Meier method. Univariate and multivariate Cox regression was used to estimate hazard risk. The model was validated in three independent cohorts with different endpoints.

Authors

  • Sizhen Chen
    Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing 210009, China.
  • Yiteng Zang
    Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing 210009, China.
  • Biyun Xu
    Department of Biostatistics, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China.
  • Beier Lu
    Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing 210009, China.
  • Rongji Ma
    Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing 210009, China.
  • Pengcheng Miao
    Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing 210009, China.
  • Bingwei Chen
    Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing 210009, China.

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