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Stratified epithelium consists of several stacked layers of cells. They provide the durability to withstand constant physical and chemical attacks. Stratified epithelium is named after the shape of the most apical layer of cells. Stratified squamous epithelium is the most common type found in the human body. In this tissue, the apical cells are squamous, whereas the basal layer contains either columnar or cuboidal cells. The basal cells divide to form new daughter cells, which gradually become...
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Updated: Sep 14, 2025

Cancer-Associated Fibroblasts from Mouse Mammary Tumors as Tools for Molecular and Computational Studies
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A densely connected framework for cancer subtype classification.

Yu Li1, Denggao Zheng1, Kaijie Sun1

  • 1School of Medical Information Engineering, Anhui University of Chinese Medicine, Hefei, China.

BMC Bioinformatics
|July 18, 2025
PubMed
Summary
This summary is machine-generated.

We developed DEGCN, a deep learning model integrating multi-omics data for precise cancer subtype identification. This approach achieved high accuracy in classifying renal, breast, and gastric cancers, aiding personalized treatment strategies.

Keywords:
Cancer subtypesDenseNetKidney cancerMulti-omicsVariational autoencoder

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Accurate cancer subtype identification is essential for personalized medicine.
  • Multi-omics data integration offers a comprehensive approach to understanding cancer biology.
  • Combining diverse molecular data enhances insights into disease mechanisms.

Purpose of the Study:

  • To introduce DEGCN, a novel deep learning model for cancer subtype classification.
  • To leverage multi-omics data for improved diagnostic accuracy.
  • To demonstrate the model's effectiveness across different cancer types.

Main Methods:

  • Developed DEGCN, integrating a three-channel Variational Autoencoder (VAE) for dimensionality reduction.
  • Employed a densely connected Graph Convolutional Network (GCN) for classification.
  • Utilized multi-omics datasets from renal, breast, and gastric cancers (TCGA).

Main Results:

  • DEGCN achieved 97.06% ± 2.04% cross-validated accuracy for renal cancer subtypes.
  • Demonstrated strong generalization with 89.82% ± 2.29% (breast) and 88.64% ± 5.24% (gastric) accuracy.
  • Outperformed conventional machine learning and existing deep learning models.

Conclusions:

  • DEGCN shows exceptional performance in integrating heterogeneous data and accurate classification.
  • The model holds significant potential for advancing cancer subtype prediction.
  • DEGCN can aid in guiding clinical treatment decisions for cancer patients.