Cancer type and survival prediction based on transcriptomic feature map.

Journal: Computers in biology and medicine
Published Date: Apr 30, 2025

Abstract

This study achieved cancer type and survival time prediction by transforming transcriptomic features into feature maps and employing deep learning models. Using transcriptomic data from 27 cancer types and survival data from 10 types in the TCGA database, a pan-cancer transcriptomic feature map was constructed through data cleaning, feature extraction, and visualization. Using Inception network and gated convolutional modules yielded a pan-cancer classification accuracy of 91.8 %. Additionally, by extracting 31 differential genes from different cancer feature maps, an interaction network diagram was drawn, identifying two key genes, ANXA5 and ACTB. These genes are potential biomarkers related to cancer progression, angiogenesis, metastasis, and treatment resistance. Survival prediction analysis on 10 cancer types, combined with feature maps and data amplification, cancer survival prediction accuracy reached from 0.75 to 0.91. This transcriptomic feature map provides a novel approach for cancer omics analysis, to facilitate personalized treatments and reflecting individual differences.

Authors

  • Ming Yan
  • Zirou Dong
    Inner Mongolia Key Laboratory of Life Health and Bioinformatics, College of Life Science and Technology, Inner Mongolia University of Science and Technology, Baotou, 014010, China.
  • Zhaopo Zhu
    Center for Medical Genetics & Hunan Key Laboratory, School of Life Sciences, Central South University, Changsha, Huna, 410008, China.
  • Chengliang Qiao
    Inner Mongolia Key Laboratory of Life Health and Bioinformatics, College of Life Science and Technology, Inner Mongolia University of Science and Technology, Baotou, 014010, China.
  • Meizhi Wang
    Inner Mongolia Key Laboratory of Life Health and Bioinformatics, College of Life Science and Technology, Inner Mongolia University of Science and Technology, Baotou, 014010, China.
  • Zhixia Teng
    School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China.
  • Yongqiang Xing
    Inner Mongolia Key Laboratory of Life Health and Bioinformatics, College of Life Science and Technology, Inner Mongolia University of Science and Technology, Baotou, 014010, China.
  • Guojun Liu
  • Guoqing Liu
    Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, Guangxi Cardiovascular Institute, Nanning, Guangxi, China.
  • Lu Cai
    School of Mechanical Engineering, Wuhan Polytechnic University, Wuhan, China.
  • Hu Meng
    Inner Mongolia Key Laboratory of Life Health and Bioinformatics, College of Life Science and Technology, Inner Mongolia University of Science and Technology, Baotou, 014010, China. Electronic address: mh_imust@foxmail.com.

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