Tumor type classification and candidate cancer-specific biomarkers discovery via semi-supervised learning

Peng Chen; Zhenlei Li; Zhaolin Hong; Haoran Zheng; Rong Zeng

doi:10.52601/bpr.2023.230005

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Peng Chen, Zhenlei Li, Zhaolin Hong, Haoran Zheng, Rong Zeng. Tumor type classification and candidate cancer-specific biomarkers discovery via semi-supervised learning[J]. Biophysics Reports. doi: 10.52601/bpr.2023.230005

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Tumor type classification and candidate cancer-specific biomarkers discovery via semi-supervised learning

doi: 10.52601/bpr.2023.230005

Peng Chen¹,
Zhenlei Li¹,
Zhaolin Hong¹,
Haoran Zheng^{1, 2, 3
,
,},
Rong Zeng^{4, 5
,
,}

1.
School of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, China
2.
Anhui Key Laboratory of Software Engineering in Computing and Communication, University of Science and Technology of China, Hefei 230026, China
3.
Department of Systems Biology, University of Science and Technology of China, Hefei 230026, China
4.
CAS Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
5.
School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China

More Information

Corresponding author: hrzheng@ustc.edu.cn (H. Zheng); zr@sibcb.ac.cn (R. Zeng)
Received Date: 22 March 2023
Accepted Date: 26 April 2023

Available Online: 25 May 2023

Abstract

Abstract

Identifying cancer-related differentially expressed genes provides significant information for diagnosing tumors, predicting prognoses, and effective treatments. Recently, deep learning methods have been used to perform gene differential expression analysis using microarray-based high-throughput gene profiling and have achieved good results. In this study, we proposed a new robust multiple-datasets-based semi-supervised learning model, MSSL, to perform tumor type classification and candidate cancer-specific biomarkers discovery across multiple tumor types and multiple datasets, which addressed the following long-lasting obstacles: (1) the data volume of the existing single dataset is not enough to fully exert the advantages of deep learning; (2) a large number of datasets from different research institutions cannot be effectively used due to inconsistent internal variances and low quality; (3) relatively uncommon cancers have limited effects on deep learning methods. In our article, we applied MSSL to The Cancer Genome Atlas (TCGA) and the Gene Expression Comprehensive Database (GEO) pan-cancer normalized-level3 RNA-seq data and got 97.6% final classification accuracy, which had a significant performance leap compared with previous approaches. Finally, we got the ranking of the importance of the corresponding genes for each cancer type based on classification results and validated that the top genes selected in this way were biologically meaningful for corresponding tumors and some of them had been used as biomarkers, which showed the efficacy of our method.
- Tumor type classification,
- Cancer-specific biomarkers,
- MSSL,
- Deep learning

Peng Chen, Zhenlei Li, Zhaolin Hong, Haoran Zheng and Rong Zeng declare that they have no conflict of interest.
This article does not contain any studies with human or animal subjects performed by the any of the authors.

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References(33)

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