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Cancer staging diagnosis based on transcriptomics and variational autoencoder
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Jiarui LI, Li QIAN, Junjie SHEN, Honglin GUO, Maoyang QIN, Yazhou WU
Journal of Army Medical University | 2025, 47(6) : 613 - 622
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Journal of Army Medical University | 2025, 47(6): 613-622
• Biomedical Engineering •
Cancer staging diagnosis based on transcriptomics and variational autoencoder
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Jiarui LI, Li QIAN, Junjie SHEN, Honglin GUO, Maoyang QIN, Yazhou WU
Affiliations
  • 1Department of Military Health Statistics,Faculty of Military Preventive Medicine,Army Medical University (Third Military Medical University),Chongqing,China
Published: 2025-03-30 doi: 10.16016/j.2097-0927.202412079
Outline
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Objective‍

To conduct an in-depth analysis and feature extraction of the transcriptomics data of 10 types of cancers in order to realize the staging diagnosis of cancer samples.

Methods‍

The transcriptomics data of the top 10 cancers having the highest incidence were amassed from the UCSC Xena website, which comprised 4 938 samples and 59 428 genes. With the aid of variational autoencoder, we developed an incremental feature ranking and selection variational autoencoder (IFRSVAE) based on feature importance ranking and incorporating the masking algorithm and the Incremental Feature Selection (IFS). Subsequently, the performance efficiency of our IFRSVAE model was evaluated in conjunction with Random Forest (RF), Support Vector Machine(SVM), and eXtreme Gradient Boosting (XGboost), and it was also compared with other methods.

Results‍

Our research extracted 21 features for the ensuing classification. In comparison to the conventional variational autoencoder, recursive feature elimination, and Lasso regression models, the IFRSVAE model attained more favorable performance across all 3 classifiers (highest AUC value, and well performed other indicators). Notably, the IFRSVAE-RF exhibited the most outstanding performance, with an AUC value reaching 85.49% (95%CI: 83.24%~87.74%). Moreover, Shapley additive explanations(SHAP) interpretable model illustrated well contributions of the features in our model.

Conclusion‍

Our developed IFRSVAE shows certain effectiveness in feature extraction. The constructed IFRSVAE-RF model demonstrates relatively good performance in the task of cancer staging diagnosis, which providing a new and referable idea for research orientation of deep-learning-based diagnostic methods for cancer staging.

cancer staging  /  transcriptomics  /  variational autoencoder  /  machine learning
李佳芮, 钱力, 沈俊杰, 郭泓麟, 秦茂洋, 伍亚舟. 基于转录组学和变分自编码器的癌症分期诊断研究. 陆军军医大学学报, 2025 , 47 (6) : 613 -622 . DOI: 10.16016/j.2097-0927.202412079
Jiarui LI, Li QIAN, Junjie SHEN, Honglin GUO, Maoyang QIN, Yazhou WU. Cancer staging diagnosis based on transcriptomics and variational autoencoder[J]. Journal of Army Medical University, 2025 , 47 (6) : 613 -622 . DOI: 10.16016/j.2097-0927.202412079
Year 2025 volume 47 Issue 6
10
3
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Article Info
doi: 10.16016/j.2097-0927.202412079
  • Receive Date:2024-12-16
  • Online Date:2026-10-30
  • Published:2025-03-30
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History
  • Received:2024-12-16
  • Revised:2025-01-29
Affiliations
    1Department of Military Health Statistics,Faculty of Military Preventive Medicine,Army Medical University (Third Military Medical University),Chongqing,China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
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Percentage of
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Genus
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Number of
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Percentage of total
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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