Multi-omics Combined with Machine Learning Facilitating the Diagnosis of Gastric Cancer

Title:Multi-omics Combined with Machine Learning Facilitating the Diagnosis of Gastric Cancer

Volume: 31

Author(s): Jie Li, Siyi Xu, Feng Zhu, Fei Shen, Tianyi Zhang, Xin Wan, Saisai Gong, Geyu Liang*Yonglin Zhou

Affiliation:

• Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, 210009, Jiangsu, China
• Jiangsu Provincial Key Laboratory of Critical Care Medicine, School of Public Health, Southeast University, Nanjing, 210009, China

Abstract: Gastric cancer (GC) is a highly intricate gastrointestinal malignancy. Early detection of gastric cancer forms the cornerstone of precision medicine. Several studies have been conducted to investigate early biomarkers of gastric cancer using genomics, transcriptomics, proteomics, and metabolomics, respectively. However, endogenous substances associated with various omics are concurrently altered during gastric cancer development. Furthermore, environmental exposures and family history can also induce modifications in endogenous substances. Therefore, in this study, we primarily investigated alterations in DNA mutation, DNA methylation, mRNA, lncRNA, miRNA, circRNA, and protein, as well as glucose, amino acid, nucleotide, and lipid metabolism levels in the context of GC development, employing genomics, transcriptomics, proteomics, and metabolomics. Additionally, we elucidate the impact of exposure factors, including HP, EBV, nitrosamines, smoking, alcohol consumption, and family history, on diagnostic biomarkers of gastric cancer. Lastly, we provide a summary of the application of machine learning in integrating multi-omics data. Thus, this review aims to elucidate: i) the biomarkers of gastric cancer related to genomics, transcriptomics, proteomics, and metabolomics; ii) the influence of environmental exposure and family history on multi- -omics data; iii) the integrated analysis of multi-omics data using machine learning techniques.

Cite this article as:

Li Jie, Xu Siyi, Zhu Feng, Shen Fei, Zhang Tianyi, Wan Xin, Gong Saisai, Liang Geyu*, Zhou Yonglin, Multi-omics Combined with Machine Learning Facilitating the Diagnosis of Gastric Cancer, Current Medicinal Chemistry 2024; 31 () . https://dx.doi.org/10.2174/0109298673284520240112055108