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Review | Open Access

Deciphering gastric inflammation-induced tumorigenesis through multi-omics data and AI methods

Qian Zhang,*, Mingran Yang,*, Peng Zhang, Bowen Wu, Xiaosen Wei, Shao Li ( )
Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China

*These authors contributed equally to this work.

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Abstract

Gastric cancer (GC), the fifth most common cancer globally, remains the leading cause of cancer deaths worldwide. Inflammation-induced tumorigenesis is the predominant process in GC development; therefore, systematic research in this area should improve understanding of the biological mechanisms that initiate GC development and promote cancer hallmarks. Here, we summarize biological knowledge regarding gastric inflammation-induced tumorigenesis, and characterize the multi-omics data and systems biology methods for investigating GC development. Of note, we highlight pioneering studies in multi-omics data and state-of-the-art network-based algorithms used for dissecting the features of gastric inflammation-induced tumorigenesis, and we propose translational applications in early GC warning biomarkers and precise treatment strategies. This review offers integrative insights for GC research, with the goal of paving the way to novel paradigms for GC precision oncology and prevention.

Keywords

artificial intelligencemulti-omicsGastric cancerinflammation-induced tumorigenesisnetwork-based methods

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Cancer Biology & Medicine
Volume 21 Issue 4,
April 2024
Pages 312-330
DOI: 10.20892/j.issn.2095-3941.2023.0129
Cite this article:
Zhang Q, Yang M, Zhang P, et al. Deciphering gastric inflammation-induced tumorigenesis through multi-omics data and AI methods. Cancer Biology & Medicine, 2024, 21(4): 312-330. https://doi.org/10.20892/j.issn.2095-3941.2023.0129

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Received: 17 April 2023
Accepted: 26 June 2023
Published: 17 August 2023
©2024 The Authors.

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