Deciphering and identifying pan-cancer RAS pathway activation based on graph autoencoder and ClassifierChain

Jianting Gong; Yingwei Zhao; Xiantao Heng; Yongbing Chen; Pingping Sun; Fei He; Zhiqiang Ma; Zilin Ren; Jianting Gong; Yingwei Zhao; Xiantao Heng; Yongbing Chen; Pingping Sun; Fei He; Zhiqiang Ma; Zilin Ren

doi:10.3934/era.2023253

Electronic Research Archive

2023, Volume 31, Issue 8: 4951-4967. doi: 10.3934/era.2023253

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Research article Special Issues

Deciphering and identifying pan-cancer RAS pathway activation based on graph autoencoder and ClassifierChain

1.
School of Information Science and Technology, Northeast Normal University, Changchun 130117, China
2.
Department of Computer Science, College of Humanities & Sciences of Northeast Normal University, Changchun 130119, China
3.
Changchun Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Changchun 130122, China

Received: 28 May 2023 Revised: 02 July 2023 Accepted: 05 July 2023 Published: 12 July 2023

The goal of precision oncology is to select more effective treatments or beneficial drugs for patients. The transcription of ‘‘hidden responders’’ which precision oncology often fails to identify for patients is important for revealing responsive molecular states. Recently, a RAS pathway activation detection method based on machine learning and a nature-inspired deep RAS activation pan-cancer has been proposed. However, we note that the activating gene variations found in KRAS, HRAS and NRAS vary substantially across cancers. Besides, the ability of a machine learning classifier to detect which KRAS, HRAS and NRAS gain of function mutations or copy number alterations causes the RAS pathway activation is not clear. Here, we proposed a deep neural network framework for deciphering and identifying pan-cancer RAS pathway activation (DIPRAS). DIPRAS brings a new insight into deciphering and identifying the pan-cancer RAS pathway activation from a deeper perspective. In addition, we further revealed the identification and characterization of RAS aberrant pathway activity through gene ontological enrichment and pathological analysis. The source code is available by the URL https://github.com/zhaoyw456/DIPRAS.
- pan-cancer,
- RAS pathway,
- multi-label classification,
- graph autoencoder
Citation: Jianting Gong, Yingwei Zhao, Xiantao Heng, Yongbing Chen, Pingping Sun, Fei He, Zhiqiang Ma, Zilin Ren. Deciphering and identifying pan-cancer RAS pathway activation based on graph autoencoder and ClassifierChain[J]. Electronic Research Archive, 2023, 31(8): 4951-4967. doi: 10.3934/era.2023253

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Abstract

The goal of precision oncology is to select more effective treatments or beneficial drugs for patients. The transcription of ‘‘hidden responders’’ which precision oncology often fails to identify for patients is important for revealing responsive molecular states. Recently, a RAS pathway activation detection method based on machine learning and a nature-inspired deep RAS activation pan-cancer has been proposed. However, we note that the activating gene variations found in KRAS, HRAS and NRAS vary substantially across cancers. Besides, the ability of a machine learning classifier to detect which KRAS, HRAS and NRAS gain of function mutations or copy number alterations causes the RAS pathway activation is not clear. Here, we proposed a deep neural network framework for deciphering and identifying pan-cancer RAS pathway activation (DIPRAS). DIPRAS brings a new insight into deciphering and identifying the pan-cancer RAS pathway activation from a deeper perspective. In addition, we further revealed the identification and characterization of RAS aberrant pathway activity through gene ontological enrichment and pathological analysis. The source code is available by the URL https://github.com/zhaoyw456/DIPRAS.

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