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Review
. 2024;31(40):6692-6712.
doi: 10.2174/0109298673284520240112055108.

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

Jie Li  1   2 , Siyi Xu  2 , Feng Zhu  3 , Fei Shen  3 , Tianyi Zhang  1 , Xin Wan  1 , Saisai Gong  1 , Geyu Liang  1   2 , Yonglin Zhou  3
Affiliations
Review

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

Jie Li et al. Curr Med Chem. 2024.

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 multiomics data; iii) the integrated analysis of multi-omics data using machine learning techniques.

Keywords: Gastric cancer; biomarkers; exposure; gastroscopy.; machine learning; multi-omics.

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References

    1. Sung H.; Ferlay J.; Siegel R.L.; Laversanne M.; Soerjomataram I.; Jemal A.; Bray F.; Global cancer statistics 2020: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2021,71(3),209-249 - DOI - PubMed
    1. Morgan E.; Arnold M.; Camargo M.C.; Gini A.; Kunzmann A.T.; Matsuda T.; Meheus F.; Verhoeven R.H.A.; Vignat J.; Laversanne M.; Ferlay J.; Soerjomataram I.; The current and future incidence and mortality of gastric cancer in 185 countries, 2020–40: A population-based modelling study. E Clinical Medicine 2022,47,101404 - DOI - PubMed
    1. Katai H.; Ishikawa T.; Akazawa K.; Isobe Y.; Miyashiro I.; Oda I.; Tsujitani S.; Ono H.; Tanabe S.; Fukagawa T.; Nunobe S.; Kakeji Y.; Nashimoto A.; Five-year survival analysis of surgically resected gastric cancer cases in Japan: A retrospective analysis of more than 100,000 patients from the nationwide registry of the Japanese Gastric Cancer Association (2001–2007). Gastric Cancer 2018,21(1),144-154 - DOI - PubMed
    1. Sumiyama K.; Past and current trends in endoscopic diagnosis for early stage gastric cancer in Japan. Gastric Cancer 2017,20(S1)(Suppl. 1),20-27 - DOI - PubMed
    1. Ren W.; Yu J.; Zhang Z.M.; Song Y.K.; Li Y.H.; Wang L.; Missed diagnosis of early gastric cancer or high-grade intraepithelial neoplasia. World J Gastroenterol 2013,19(13),2092-2096 - DOI - PubMed

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