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

Jie      Li; Siyi      Xu; Feng      Zhu; Fei      Shen; Tianyi      Zhang; Xin      Wan; Saisai      Gong; Geyu      Liang; Yonglin      Zhou
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, multi-omics, exposure, machine learning, gastroscopy.
[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.
 [http://dx.doi.org/10.3322/caac.21660] [PMID: 33538338]

[2]
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.
 [http://dx.doi.org/10.1016/j.eclinm.2022.101404] [PMID: 35497064]

[3]
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.
 [http://dx.doi.org/10.1007/s10120-017-0716-7] [PMID: 28417260]

[4]
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.
 [http://dx.doi.org/10.1007/s10120-016-0659-4] [PMID: 27734273]

[5]
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.
 [http://dx.doi.org/10.3748/wjg.v19.i13.2092] [PMID: 23599630]

[6]
Herrera-Pariente, C.; Montori, S.; Llach, J.; Bofill, A.; Albeniz, E.; Moreira, L. Biomarkers for gastric cancer screening and early diagnosis. Biomedicines,  2021, 9(10), 1448.
 [http://dx.doi.org/10.3390/biomedicines9101448] [PMID: 34680565]

[7]
Shi, X.J.; Wei, Y.; Ji, B. Systems biology of gastric cancer: Perspectives on the omics-based diagnosis and treatment. Front. Mol. Biosci.,  2020, 7, 203.
 [http://dx.doi.org/10.3389/fmolb.2020.00203] [PMID: 33005629]

[8]
Sironi, L.; Tremoli, E.; Miller, I.; Guerrini, U.; Calvio, A.M.; Eberini, I.; Gemeiner, M.; Asdente, M.; Paoletti, R.; Gianazza, E. Acute-phase proteins before cerebral ischemia in stroke-prone rats: Identification by proteomics. Stroke,  2001, 32(3), 753-760.
 [http://dx.doi.org/10.1161/01.STR.32.3.753] [PMID: 11239198]

[9]
Gonzalez-Covarrubias, V.; Martínez-Martínez, E.; del Bosque-Plata, L. The potential of metabolomics in biomedical applications. Metabolites,  2022, 12(2), 194.
 [http://dx.doi.org/10.3390/metabo12020194] [PMID: 35208267]

[10]
Fiehn, O. Metabolomics-the link between genotypes and phenotypes. Plant Mol. Biol.,  2002, 48(1/2), 155-171.
 [http://dx.doi.org/10.1023/A:1013713905833] [PMID: 11860207]

[11]
Burton, C.; Ma, Y. Current trends in cancer biomarker discovery using urinary metabolomics: Achievements and new challenges. Curr. Med. Chem.,  2019, 26(1), 5-28.
 [http://dx.doi.org/10.2174/0929867324666170914102236] [PMID: 28914192]

[12]
Amantonico, A.; Urban, P.L.; Zenobi, R. Analytical techniques for single-cell metabolomics: State of the art and trends. Anal. Bioanal. Chem.,  2010, 398(6), 2493-2504.
 [http://dx.doi.org/10.1007/s00216-010-3850-1] [PMID: 20544183]

[13]
Wu, X.; Jian, A.; Tang, H.; Liu, W.; Liu, F.; Liu, S.; Wu, H. A multi-omics study on the effect of helicobacter pylori-related genes in the tumor immunity on stomach adenocarcinoma. Front. Cell. Infect. Microbiol.,  2022, 12, 880636.
 [http://dx.doi.org/10.3389/fcimb.2022.880636] [PMID: 35619651]

[14]
Jung, Y.S.; Xuan Tran, M.T.; Park, B.; Moon, C.M. Association between family history of gastric cancer and the risk of gastric cancer and adenoma: A nationwide population-based study. Am. J. Gastroenterol.,  2022, 117(8), 1255-1263.
 [http://dx.doi.org/10.14309/ajg.0000000000001837] [PMID: 35613561]

[15]
Herrera-Pariente, C.; Capó-García, R.; Díaz-Gay, M.; Carballal, S.; Muñoz, J.; Llach, J.; Sánchez, A.; Bonjoch, L.; Arnau-Collell, C.; Soares de Lima, Y.; Golubicki, M.; Jung, G.; Lozano, J.J.; Castells, A.; Balaguer, F.; Bujanda, L.; Castellví-Bel, S.; Moreira, L. Identification of new genes involved in germline predisposition to early-onset gastric cancer. Int. J. Mol. Sci.,  2021, 22(3), 1310.
 [http://dx.doi.org/10.3390/ijms22031310] [PMID: 33525650]

[16]
Reel, P.S.; Reel, S.; Pearson, E.; Trucco, E.; Jefferson, E. Using machine learning approaches for multi-omics data analysis: A review. Biotechnol. Adv.,  2021, 49, 107739.
 [http://dx.doi.org/10.1016/j.biotechadv.2021.107739] [PMID: 33794304]

[17]
Poirion, O.B.; Jing, Z.; Chaudhary, K.; Huang, S.; Garmire, L.X. DeepProg: An ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data. Genome Med.,  2021, 13(1), 112.
 [http://dx.doi.org/10.1186/s13073-021-00930-x] [PMID: 34261540]

[18]
Fan, P.; Zhang, Z.; Lu, L.; Guo, X.; Hao, Z.; Wang, X.; Ye, Y. Association of single nucleotide polymorphisms (SNPs) with gastric cancer susceptibility and prognosis in population in Wuwei, Gansu, China. World J. Surg. Oncol.,  2022, 20(1), 194.
 [http://dx.doi.org/10.1186/s12957-022-02663-6] [PMID: 35689286]

[19]
Zang, Z.J.; Ong, C.K.; Cutcutache, I.; Yu, W.; Zhang, S.L.; Huang, D.; Ler, L.D.; Dykema, K.; Gan, A.; Tao, J.; Lim, S.; Liu, Y.; Futreal, P.A.; Grabsch, H.; Furge, K.A.; Goh, L.K.; Rozen, S.; Teh, B.T.; Tan, P. Genetic and structural variation in the gastric cancer kinome revealed through targeted deep sequencing. Cancer Res.,  2011, 71(1), 29-39.
 [http://dx.doi.org/10.1158/0008-5472.CAN-10-1749] [PMID: 21097718]

[20]
Zhang, J.; Liu, F.; Yang, Y.; Yu, N.; Weng, X.; Yang, Y.; Gong, Z.; Huang, S.; Gan, L.; Sun, S.; Zhang, X.; Gong, Y.; Liu, Y.; Guo, W. Integrated DNA and RNA sequencing reveals early drivers involved in metastasis of gastric cancer. Cell Death Dis.,  2022, 13(4), 392.
 [http://dx.doi.org/10.1038/s41419-022-04838-1] [PMID: 35449126]

[21]
Xiao, Y.; Bi, M.; Guo, H.; Li, M. Multi-omics approaches for biomarker discovery in early ovarian cancer diagnosis. E. Bio. Medicine.,  2022, 79, 104001.
 [http://dx.doi.org/10.1016/j.ebiom.2022.104001] [PMID: 35439677]

[22]
Han, X.; Liu, T.; Zhai, J.; Liu, C.; Wang, W.; Nie, C.; Wang, Q.; Zhu, X.; Zhou, H.; Tian, W. Association between EPHA5 methylation status in peripheral blood leukocytes and the risk and prognosis of gastric cancer. PeerJ,  2022, 10, e13774.
 [http://dx.doi.org/10.7717/peerj.13774] [PMID: 36164608]

[23]
Zhang, Y.; Hu, S.; Li, J.; Shi, D.; Luo, B. The promoter aberrant methylation status of TMEM130 is associated with gastric cancer. Dig. Liver Dis.,  2022, 54(6), 819-825.
 [http://dx.doi.org/10.1016/j.dld.2021.05.035] [PMID: 34162508]

[24]
Guo, X.Y.; Dong, L.; Qin, B.; Jiang, J.; Shi, A.M. Decreased expression of gastrokine 1 in gastric mucosa of gastric cancer patients. World J. Gastroenterol.,  2014, 20(44), 16702-16706.
 [http://dx.doi.org/10.3748/wjg.v20.i44.16702] [PMID: 25469040]

[25]
Yamada, S.; Kato, S.; Matsuhisa, T.; Makonkawkeyoon, L.; Yoshida, M.; Chakrabandhu, T.; Lertprasertsuk, N.; Suttharat, P.; Chakrabandhu, B.; Nishiumi, S.; Chongraksut, W.; Azuma, T. Predominant mucosal IL-8 mRNA expression in non-cagA Thais is risk for gastric cancer. World J. Gastroenterol.,  2013, 19(19), 2941-2949.
 [http://dx.doi.org/10.3748/wjg.v19.i19.2941] [PMID: 23704827]

[26]
Pereira, B.S.; Wisnieski, F.; Calcagno, D.Q.; Santos, L.C.; Gigek, C.O.; Chen, E.S.; Rasmussen, L.T.; Payão, S.L.M.; Almeida, R.S.; Pinto, C.A.; Karia, B.T.R.; Artigiani, R.; Demachki, S.; Assumpção, P.P.; Lourenço, L.G.; Arasaki, C.H.; Burbano, R.R.; Leal, M.F.; Smith, M.A.C. Genetic and transcriptional analysis of 8q24.21 cluster in gastric cancer. Anticancer Res.,  2022, 42(9), 4381-4394.
 [http://dx.doi.org/10.21873/anticanres.15938] [PMID: 36039443]

[27]
Qian, H.; Appiah-Kubi, K.; Wang, Y.; Wu, M.; Tao, Y.; Wu, Y.; Chen, Y. The clinical significance of platelet-derived growth factors (PDGFs) and their receptors (PDGFRs) in gastric cancer: A systematic review and meta-analysis. Crit. Rev. Oncol. Hematol.,  2018, 127, 15-28.
 [http://dx.doi.org/10.1016/j.critrevonc.2018.05.004] [PMID: 29891108]

[28]
Zhang, C.; Liang, Y.; Ma, M.H.; Wu, K.Z.; Dai, D.Q. KRT15, INHBA, MATN3, and AGT are aberrantly methylated and differentially expressed in gastric cancer and associated with prognosis. Pathol. Res. Pract.,  2019, 215(5), 893-899.
 [http://dx.doi.org/10.1016/j.prp.2019.01.034] [PMID: 30718100]

[29]
Verma, R.; Sharma, P.C. Identification of stage-specific differentially expressed genes and SNPs in gastric cancer employing RNA-Seq based transcriptome profiling. Genomics,  2022, 114(1), 61-71.
 [http://dx.doi.org/10.1016/j.ygeno.2021.11.032] [PMID: 34839019]

[30]
Zhang, Y.; Liu, W.; Feng, W.; Wang, X.; Lei, T.; Chen, Z.; Song, W. Identification of 14 differentially-expressed metabolism-related genes as potential targets of gastric cancer by integrated proteomics and transcriptomics. Front. Cell Dev. Biol.,  2022, 10, 816249.
 [http://dx.doi.org/10.3389/fcell.2022.816249] [PMID: 35265615]

[31]
Zhao, X.; Wu, S.; Jing, J. Identifying diagnostic and prognostic biomarkers and candidate therapeutic drugs of gastric cancer based on transcriptomics and single-cell sequencing. Pathol. Oncol. Res.,  2021, 27, 1609955.
 [http://dx.doi.org/10.3389/pore.2021.1609955] [PMID: 34899080]

[32]
Zhang, P.; Yang, M.; Zhang, Y.; Xiao, S.; Lai, X.; Tan, A.; Du, S.; Li, S. Dissecting the single-cell transcriptome network underlying gastric premalignant lesions and early gastric cancer. Cell Rep.,  2020, 30(12), 4317.
 [http://dx.doi.org/10.1016/j.celrep.2020.03.020] [PMID: 32209487]

[33]
Liu, X.Y.; Zhang, T.Q.; Zhang, Q.; Guo, J.; Zhang, P.; Mao, T.; Tian, Z.B.; Zhang, C.P.; Li, X.Y. Differential long non-coding RNA expression analysis in chronic non-atrophic gastritis, gastric mucosal intraepithelial neoplasia, and gastric cancer tissues. Front. Genet.,  2022, 13, 833857.
 [http://dx.doi.org/10.3389/fgene.2022.833857] [PMID: 35571069]

[34]
Sun, M.; Xia, R.; Jin, F.; Xu, T.; Liu, Z.; De, W.; Liu, X. Downregulated long noncoding RNA MEG3 is associated with poor prognosis and promotes cell proliferation in gastric cancer. Tumour Biol.,  2014, 35(2), 1065-1073.
 [http://dx.doi.org/10.1007/s13277-013-1142-z] [PMID: 24006224]

[35]
Lee, S.; Park, J.; Oh, S.; Kwack, K. Downregulation of LOC441461 promotes cell growth and motility in human gastric cancer. Cancers,  2022, 14(5), 1149.
 [http://dx.doi.org/10.3390/cancers14051149] [PMID: 35267457]

[36]
Deng, Y.; Huang, Z.; Xu, Y.; Jin, J.; Zhuo, W.; Zhang, C.; Zhang, X.; Shen, M.; Yan, X.; Wang, L.; Wang, X.; Kang, Y.; Si, J.; Zhou, T. MiR-215 modulates gastric cancer cell proliferation by targeting RB1. Cancer Lett.,  2014, 342(1), 27-35.
 [http://dx.doi.org/10.1016/j.canlet.2013.08.033] [PMID: 23981575]

[37]
Liu, G.; Xiang, T.; Wu, Q.F.; Wang, W.X. Long noncoding RNA H19-Derived miR-675 enhances proliferation and invasion via RUNX1 in gastric cancer cells. Oncol. Res.,  2016, 23(3), 99-107.
 [http://dx.doi.org/10.3727/096504015X14496932933575] [PMID: 26931432]

[38]
Kong, Y.; Ning, L.; Qiu, F.; Yu, Q.; Cao, B. Clinical significance of serum miR-25 as a diagnostic and prognostic biomarker in human gastric cancer. Cancer Biomark.,  2019, 24(4), 477-483.
 [http://dx.doi.org/10.3233/CBM-182213] [PMID: 30909187]

[39]
Shin, V.Y.; Ng, E.K.O.; Chan, V.W.; Kwong, A.; Chu, K.M. A three-miRNA signature as promising non-invasive diagnostic marker for gastric cancer. Mol. Cancer,  2015, 14(1), 202.
 [http://dx.doi.org/10.1186/s12943-015-0473-3] [PMID: 26607322]

[40]
Huang, Z.; Zhu, D.; Wu, L.; He, M.; Zhou, X.; Zhang, L.; Zhang, H.; Wang, W.; Zhu, J.; Cheng, W.; Chen, Y.; Fan, Y.; Qi, L.; Yin, Y.; Zhu, W.; Shu, Y.; Liu, P. Six serum-based miRNAs as potential diagnostic biomarkers for gastric cancer. Cancer Epidemiol. Biomarkers Prev.,  2017, 26(2), 188-196.
 [http://dx.doi.org/10.1158/1055-9965.EPI-16-0607] [PMID: 27756776]

[41]
So, J.B.Y.; Kapoor, R.; Zhu, F.; Koh, C.; Zhou, L.; Zou, R.; Tang, Y.C.; Goo, P.C.K.; Rha, S.Y.; Chung, H.C.; Yoong, J.; Yap, C.T.; Rao, J.; Chia, C.K.; Tsao, S.; Shabbir, A.; Lee, J.; Lam, K.P.; Hartman, M.; Yong, W.P.; Too, H.P.; Yeoh, K.G. Development and validation of a serum microRNA biomarker panel for detecting gastric cancer in a high-risk population. Gut,  2021, 70(5), 829-837.
 [http://dx.doi.org/10.1136/gutjnl-2020-322065] [PMID: 33028667]

[42]
Zhao, Q.; Chen, S.; Li, T.; Xiao, B.; Zhang, X. Clinical values of circular RNA 0000181 in the screening of gastric cancer. J. Clin. Lab. Anal.,  2018, 32(4), e22333.
 [http://dx.doi.org/10.1002/jcla.22333] [PMID: 28940688]

[43]
Xie, Y.; Shao, Y.; Sun, W.; Ye, G.; Zhang, X.; Xiao, B.; Guo, J. Downregulated expression of hsa_circ_0074362 in gastric cancer and its potential diagnostic values. Biomarkers Med.,  2018, 12(1), 11-20.
 [http://dx.doi.org/10.2217/bmm-2017-0114] [PMID: 29240459]

[44]
Chen, S.; Li, T.; Zhao, Q.; Xiao, B.; Guo, J. Using circular RNA hsa_circ_0000190 as a new biomarker in the diagnosis of gastric cancer. Clin. Chim. Acta,  2017, 466, 167-171.
 [http://dx.doi.org/10.1016/j.cca.2017.01.025] [PMID: 28130019]

[45]
Li, T.; Shao, Y.; Fu, L.; Xie, Y.; Zhu, L.; Sun, W.; Yu, R.; Xiao, B.; Guo, J. Plasma circular RNA profiling of patients with gastric cancer and their droplet digital RT-PCR detection. J. Mol. Med.,  2018, 96(1), 85-96.
 [http://dx.doi.org/10.1007/s00109-017-1600-y] [PMID: 29098316]

[46]
Sun, H.; Tang, W.; Rong, D.; Jin, H.; Fu, K.; Zhang, W.; Liu, Z.; Cao, H.; Cao, X. Hsa_circ_0000520, a potential new circular RNA biomarker, is involved in gastric carcinoma. Cancer Biomark.,  2018, 21(2), 299-306.
 [http://dx.doi.org/10.3233/CBM-170379] [PMID: 29103021]

[47]
Bure, I.V.; Nemtsova, M.V. Methylation and noncoding RNAs in gastric cancer: Everything is connected. Int. J. Mol. Sci.,  2021, 22(11), 5683.
 [http://dx.doi.org/10.3390/ijms22115683] [PMID: 34073603]

[48]
Zhang, P.; Wu, W.; Chen, Q.; Chen, M. Non-coding RNAs and their integrated networks. J. Integr. Bioinform.,  2019, 16(3), 20190027.
 [http://dx.doi.org/10.1515/jib-2019-0027] [PMID: 31301674]

[49]
Goodall, G.J.; Wickramasinghe, V.O. RNA in cancer. Nat. Rev. Cancer,  2021, 21(1), 22-36.
 [http://dx.doi.org/10.1038/s41568-020-00306-0] [PMID: 33082563]

[50]
Chun-zhi, Z.; Lei, H.; An-ling, Z.; Yan-chao, F.; Xiao, Y.; Guang-xiu, W.; Zhi-fan, J.; Pei-yu, P.; Qing-yu, Z.; Chun-sheng, K. MicroRNA-221 and microRNA-222 regulate gastric carcinoma cell proliferation and radioresistance by targeting PTEN. BMC Cancer,  2010, 10(1), 367.
 [http://dx.doi.org/10.1186/1471-2407-10-367] [PMID: 20618998]

[51]
Liu, Y.; Zhang, L.; Du, W. Circular RNA circ-PVT1 contributes to paclitaxel resistance of gastric cancer cells through the regulation of ZEB1 expression by sponging miR-124-3p. Biosci. Rep.,  2019, 39(12), BSR20193045.
 [http://dx.doi.org/10.1042/BSR20193045] [PMID: 31793989]

[52]
Zhang, F.; Li, Y.; Xu, W.; He, L.; Tan, Y.; Xu, H. Long non-coding RNA ZFAS1 regulates the malignant progression of gastric cancer via the microRNA-200b-3p/Wnt1 axis. Biosci. Biotechnol. Biochem.,  2019, 83(7), 1289-1299.
 [http://dx.doi.org/10.1080/09168451.2019.1606697] [PMID: 30999814]

[53]
Liu, X.; Ma, R.; Yi, B.; Riker, A.I.; Xi, Y. MicroRNAs are involved in the development and progression of gastric cancer. Acta Pharmacol. Sin.,  2021, 42(7), 1018-1026.
 [http://dx.doi.org/10.1038/s41401-020-00540-0] [PMID: 33037405]

[54]
Wu, S.R.; Wu, Q.; Shi, Y.Q. Recent advances of miRNAs in the development and clinical application of gastric cancer. Chin. Med. J.,  2020, 133(15), 1856-1867.
 [http://dx.doi.org/10.1097/CM9.0000000000000921] [PMID: 32649523]

[55]
Zhu, C.; Ren, C.; Han, J.; Ding, Y.; Du, J.; Dai, N.; Dai, J.; Ma, H.; Hu, Z.; Shen, H.; Xu, Y.; Jin, G. A five-microRNA panel in plasma was identified as potential biomarker for early detection of gastric cancer. Br. J. Cancer,  2014, 110(9), 2291-2299.
 [http://dx.doi.org/10.1038/bjc.2014.119] [PMID: 24595006]

[56]
Li, J.; Sun, D.; Pu, W.; Wang, J.; Peng, Y. Circular RNAs in cancer: Biogenesis, function, and clinical significance. Trends Cancer,  2020, 6(4), 319-336.
 [http://dx.doi.org/10.1016/j.trecan.2020.01.012] [PMID: 32209446]

[57]
Ding, L.; Zhao, Y.; Dang, S.; Wang, Y.; Li, X.; Yu, X.; Li, Z.; Wei, J.; Liu, M.; Li, G. Circular RNA circ-DONSON facilitates gastric cancer growth and invasion via NURF complex dependent activation of transcription factor SOX4. Mol. Cancer,  2019, 18(1), 45.
 [http://dx.doi.org/10.1186/s12943-019-1006-2] [PMID: 30922402]

[58]
Dhondrup, R.; Zhang, X.; Feng, X.; Lobsang, D.; Hua, Q.; Liu, J.; Cuo, Y.; Zhuoma, S.; Duojie, G.; Duojie Caidan, S.; Gyal, S. Proteomic analysis reveals molecular differences in the development of gastric cancer. Evid. Based Complement. Alternat. Med.,  2022, 2022, 1-18.
 [http://dx.doi.org/10.1155/2022/8266544] [PMID: 35958927]

[59]
Uehara, T.; Kikuchi, H.; Miyazaki, S.; Iino, I.; Setoguchi, T.; Hiramatsu, Y.; Ohta, M.; Kamiya, K.; Morita, Y.; Tanaka, H.; Baba, S.; Hayasaka, T.; Setou, M.; Konno, H. Overexpression of lysophosphatidylcholine acyltransferase 1 and concomitant lipid alterations in gastric cancer. Ann. Surg. Oncol.,  2016, 23(S2), 206-213.
 [http://dx.doi.org/10.1245/s10434-015-4459-6] [PMID: 25752890]

[60]
Wang, X.; Zhi, Q.; Liu, S.; Xue, S.L.; Shen, C.; Li, Y.; Wu, C.; Tang, Z.; Chen, W.; Song, J.L.; Bao, M.; Song, Y.H.; Zhou, J. Identification of specific biomarkers for gastric adenocarcinoma by ITRAQ proteomic approach. Sci. Rep.,  2016, 6(1), 38871.
 [http://dx.doi.org/10.1038/srep38871] [PMID: 27941907]

[61]
Jiang, Z.; Sun, X.; Zhang, Q.; Ji, X.; Yu, Q.; Huang, T.; Chen, D.; Chen, H.; Mei, X.; Wang, L.; He, L.; Fang, J.; Hou, L.; Wang, L. Identification of candidate biomarkers that involved in the epigenetic transcriptional regulation for detection gastric cancer by iTRAQ based quantitative proteomic analysis. Clin. Chim. Acta,  2017, 471, 29-37.
 [http://dx.doi.org/10.1016/j.cca.2017.05.015] [PMID: 28502558]

[62]
Jiang, Z.; Zhang, C.; Gan, L.; Jia, Y.; Xiong, Y.; Chen, Y.; Wang, Z.; Wang, L.; Luo, H.; Li, J.; Zhu, R.; Ji, X.; Yu, Q.; Wang, L. iTRAQ-Based quantitative proteomics approach identifies novel diagnostic biomarkers that were essential for glutamine metabolism and redox homeostasis for gastric cancer. Proteomics Clin. Appl.,  2019, 13(4), 1800038.
 [http://dx.doi.org/10.1002/prca.201800038] [PMID: 30485682]

[63]
Yoo, M.W.; Park, J.; Han, H.S.; Yun, Y.M.; Kang, J.W.; Choi, D.Y.; Lee, J.; Jung, J.H.; Lee, K.Y.; Kim, K.P. Discovery of gastric cancer specific biomarkers by the application of serum proteomics. Proteomics,  2017, 17(6), 1600332.
 [http://dx.doi.org/10.1002/pmic.201600332] [PMID: 28133907]

[64]
Zhou, B.; Zhou, Z.; Chen, Y.; Deng, H.; Cai, Y.; Rao, X.; Yin, Y.; Rong, L. Plasma proteomics-based identification of novel biomarkers in early gastric cancer. Clin. Biochem.,  2020, 76, 5-10.
 [http://dx.doi.org/10.1016/j.clinbiochem.2019.11.001] [PMID: 31765635]

[65]
Aa, J.; Yu, L.; Sun, M.; Liu, L.; Li, M.; Cao, B.; Shi, J.; Xu, J.; Cheng, L.; Zhou, J.; Zheng, T.; Wang, X.; Zhao, C.; Gu, R.; Zhang, F.; Shi, R.; Wang, G. Metabolic features of the tumor microenvironment of gastric cancer and the link to the systemic macroenvironment. Metabolomics,  2012, 8(1), 164-173.
 [http://dx.doi.org/10.1007/s11306-011-0297-0]

[66]
Kaji, S.; Irino, T.; Kusuhara, M.; Makuuchi, R.; Yamakawa, Y.; Tokunaga, M.; Tanizawa, Y.; Bando, E.; Kawamura, T.; Kami, K.; Ohashi, Y.; Zhang, S.; Orita, H.; Lee-Okada, H.C.; Fukunaga, T.; Terashima, M. Metabolomic profiling of gastric cancer tissues identified potential biomarkers for predicting peritoneal recurrence. Gastric Cancer,  2020, 23(5), 874-883.
 [http://dx.doi.org/10.1007/s10120-020-01065-5] [PMID: 32219586]

[67]
Zhu, X.; Wang, K.; Liu, G.; Wang, Y.; Xu, J.; Liu, L.; Li, M.; Shi, J.; Aa, J.; Yu, L. Metabolic perturbation and potential markers in patients with esophageal cancer. Gastroenterol. Res. Pract.,  2017, 2017, 1-9.
 [http://dx.doi.org/10.1155/2017/5469597] [PMID: 28512469]

[68]
Ikeda, A.; Nishiumi, S.; Shinohara, M.; Yoshie, T.; Hatano, N.; Okuno, T.; Bamba, T.; Fukusaki, E.; Takenawa, T.; Azuma, T.; Yoshida, M. Serum metabolomics as a novel diagnostic approach for gastrointestinal cancer. Biomed. Chromatogr.,  2012, 26(5), 548-558.
 [http://dx.doi.org/10.1002/bmc.1671] [PMID: 21773981]

[69]
Han, Y.; Yoo, H.J.; Jee, S.H.; Lee, J.H. High serum levels of l-carnitine and citric acid negatively correlated with alkaline phosphatase are detectable in Koreans before gastric cancer onset. Metabolomics,  2022, 18(8), 62.
 [http://dx.doi.org/10.1007/s11306-022-01922-7] [PMID: 35900644]

[70]
Song, H.; Peng, J.S.; Dong-Sheng, Y.; Yang, Z.L.; Liu, H.L.; Zeng, Y.K.; Shi, X.P.; Lu, B.Y. Serum metabolic profiling of human gastric cancer based on gas chromatography/mass spectrometry. Braz. J. Med. Biol. Res.,  2012, 45(1), 78-85.
 [http://dx.doi.org/10.1590/S0100-879X2011007500158] [PMID: 22124703]

[71]
Miyagi, Y.; Higashiyama, M.; Gochi, A.; Akaike, M.; Ishikawa, T.; Miura, T.; Saruki, N.; Bando, E.; Kimura, H.; Imamura, F.; Moriyama, M.; Ikeda, I.; Chiba, A.; Oshita, F.; Imaizumi, A.; Yamamoto, H.; Miyano, H.; Horimoto, K.; Tochikubo, O.; Mitsushima, T.; Yamakado, M.; Okamoto, N. Plasma free amino acid profiling of five types of cancer patients and its application for early detection. PLoS One,  2011, 6(9), e24143.
 [http://dx.doi.org/10.1371/journal.pone.0024143] [PMID: 21915291]

[72]
Wang, H.; Zhang, H.; Deng, P.; Liu, C.; Li, D.; Jie, H.; Zhang, H.; Zhou, Z.; Zhao, Y.L. Tissue metabolic profiling of human gastric cancer assessed by 1H NMR. BMC Cancer,  2016, 16(1), 371.
 [http://dx.doi.org/10.1186/s12885-016-2356-4] [PMID: 27356757]

[73]
Lario, S.; Ramírez-Lázaro, M.J.; Sanjuan-Herráez, D.; Brunet-Vega, A.; Pericay, C.; Gombau, L.; Junquera, F.; Quintás, G.; Calvet, X. Plasma sample based analysis of gastric cancer progression using targeted metabolomics. Sci. Rep.,  2017, 7(1), 17774.
 [http://dx.doi.org/10.1038/s41598-017-17921-x] [PMID: 29259332]

[74]
Jung, J.; Jung, Y.; Bang, E.J.; Cho, S.; Jang, Y.J.; Kwak, J.M.; Ryu, D.H.; Park, S.; Hwang, G.S. Noninvasive diagnosis and evaluation of curative surgery for gastric cancer by using NMR-based metabolomic profiling. Ann. Surg. Oncol.,  2014, 21(S4), 736-742.
 [http://dx.doi.org/10.1245/s10434-014-3886-0] [PMID: 25092158]

[75]
Dai, D.; Yang, Y.; Yu, J.; Dang, T.; Qin, W.; Teng, L.; Ye, J.; Jiang, H. Interactions between gastric microbiota and metabolites in gastric cancer. Cell Death Dis.,  2021, 12(12), 1104.
 [http://dx.doi.org/10.1038/s41419-021-04396-y] [PMID: 34819503]

[76]
Wu, H.; Xue, R.; Tang, Z.; Deng, C.; Liu, T.; Zeng, H.; Sun, Y.; Shen, X. Metabolomic investigation of gastric cancer tissue using gas chromatography/mass spectrometry. Anal. Bioanal. Chem.,  2010, 396(4), 1385-1395.
 [http://dx.doi.org/10.1007/s00216-009-3317-4] [PMID: 20012946]

[77]
Huang, S.; Guo, Y.; Li, Z.W.; Shui, G.; Tian, H.; Li, B.W.; Kadeerhan, G.; Li, Z.X.; Li, X.; Zhang, Y.; Zhou, T.; You, W.C.; Pan, K.F.; Li, W.Q. Identification and validation of plasma metabolomic signatures in precancerous gastric lesions that progress to cancer. JAMA Netw. Open,  2021, 4(6), e2114186.
 [http://dx.doi.org/10.1001/jamanetworkopen.2021.14186] [PMID: 34156450]

[78]
Yu, L.; Lai, Q.; Feng, Q.; Li, Y.; Feng, J.; Xu, B. Serum metabolic profiling analysis of chronic gastritis and gastric cancer by untargeted metabolomics. Front. Oncol.,  2021, 11, 636917.
 [http://dx.doi.org/10.3389/fonc.2021.636917] [PMID: 33777793]

[79]
Zou, L.; Guo, L.; Zhu, C.; Lai, Z.; Li, Z.; Yang, A. Serum phospholipids are potential biomarkers for the early diagnosis of gastric cancer. Clin. Chim. Acta,  2021, 519, 276-284.
 [http://dx.doi.org/10.1016/j.cca.2021.05.002] [PMID: 33989614]

[80]
Hung, C.Y.; Yeh, T.S.; Tsai, C.K.; Wu, R.C.; Lai, Y.C.; Chiang, M.H.; Lu, K.Y.; Lin, C.N.; Cheng, M.L.; Lin, G. Glycerophospholipids pathways and chromosomal instability in gastric cancer: Global lipidomics analysis. World J. Gastrointest. Oncol.,  2019, 11(3), 181-194.
 [http://dx.doi.org/10.4251/wjgo.v11.i3.181] [PMID: 30918592]

[81]
Zhang, H.; Cui, L.; Liu, W.; Wang, Z.; Ye, Y.; Li, X.; Wang, H. 1H NMR metabolic profiling of gastric cancer patients with lymph node metastasis. Metabolomics,  2018, 14(4), 47.
 [http://dx.doi.org/10.1007/s11306-018-1344-x] [PMID: 29541009]

[82]
Jin, H.; Qiao, F.; Chen, L.; Lu, C.; Xu, L.; Gao, X. Serum metabolomic signatures of lymph node metastasis of esophageal squamous cell carcinoma. J. Proteome Res.,  2014, 13(9), 4091-4103.
 [http://dx.doi.org/10.1021/pr500483z] [PMID: 25162382]

[83]
Gu, J.; Hu, X.; Shao, W.; Ji, T.; Yang, W.; Zhuo, H.; Jin, Z.; Huang, H.; Chen, J.; Huang, C.; Lin, D. Metabolomic analysis reveals altered metabolic pathways in a rat model of gastric carcinogenesis. Oncotarget,  2016, 7(37), 60053-60073.
 [http://dx.doi.org/10.18632/oncotarget.11049] [PMID: 27527852]

[84]
Xu, J.; Chen, Y.; Zhang, R.; Song, Y.; Cao, J.; Bi, N.; Wang, J.; He, J.; Bai, J.; Dong, L.; Wang, L.; Zhan, Q.; Abliz, Z. Global and targeted metabolomics of esophageal squamous cell carcinoma discovers potential diagnostic and therapeutic biomarkers. Mol. Cell. Proteomics,  2013, 12(5), 1306-1318.
 [http://dx.doi.org/10.1074/mcp.M112.022830] [PMID: 23397110]

[85]
Hirayama, A.; Kami, K.; Sugimoto, M.; Sugawara, M.; Toki, N.; Onozuka, H.; Kinoshita, T.; Saito, N.; Ochiai, A.; Tomita, M.; Esumi, H.; Soga, T. Quantitative metabolome profiling of colon and stomach cancer microenvironment by capillary electrophoresis time-of-flight mass spectrometry. Cancer Res.,  2009, 69(11), 4918-4925.
 [http://dx.doi.org/10.1158/0008-5472.CAN-08-4806] [PMID: 19458066]

[86]
Chen, J.L.; Tang, H.Q.; Hu, J.D.; Fan, J.; Hong, J.; Gu, J.Z. Metabolomics of gastric cancer metastasis detected by gas chromatography and mass spectrometry. World J. Gastroenterol.,  2010, 16(46), 5874-5880.
 [http://dx.doi.org/10.3748/wjg.v16.i46.5874] [PMID: 21155010]

[87]
Zhang, H.; Wang, L.; Hou, Z.; Ma, H.; Mamtimin, B.; Hasim, A.; Sheyhidin, I. Metabolomic profiling reveals potential biomarkers in esophageal cancer progression using liquid chromatography-mass spectrometry platform. Biochem. Biophys. Res. Commun.,  2017, 491(1), 119-125.
 [http://dx.doi.org/10.1016/j.bbrc.2017.07.060] [PMID: 28711496]

[88]
Wang, L.; Chen, J.; Chen, L.; Deng, P.; bu, Q.; Xiang, P.; Li, M.; Lu, W.; Xu, Y.; Lin, H.; Wu, T.; Wang, H.; Hu, J.; Shao, X.; Cen, X.; Zhao, Y.L. 1H-NMR based metabonomic profiling of human esophageal cancer tissue. Mol. Cancer,  2013, 12(1), 25.
 [http://dx.doi.org/10.1186/1476-4598-12-25] [PMID: 23556477]

[89]
Chen, J.L.; Fan, J.; Lu, X. CE-MS based on moving reaction boundary method for urinary metabolomic analysis of gastric cancer patients. Electrophoresis,  2014, 35(7), 1032-1039.
 [http://dx.doi.org/10.1002/elps.201300243] [PMID: 23900894]

[90]
Han, J.; Meng, Q.; Shen, L.; Wu, G. Interleukin-6 induces fat loss in cancer cachexia by promoting white adipose tissue lipolysis and browning. Lipids Health Dis.,  2018, 17(1), 14.
 [http://dx.doi.org/10.1186/s12944-018-0657-0] [PMID: 29338749]

[91]
Boison, D.; Yegutkin, G.G. Adenosine metabolism: Emerging concepts for cancer therapy. Cancer Cell,  2019, 36(6), 582-596.
 [http://dx.doi.org/10.1016/j.ccell.2019.10.007] [PMID: 31821783]

[92]
Nie, S.; Zhao, Y.; Qiu, X.; Wang, W.; Yao, Y.; Yi, M.; Wang, D. Metabolomic study on nude mice models of gastric cancer treated with modified Si Jun Zi Tang via HILIC UHPLC-Q-TOF/MS analysis. Evid. Based Complement. Alternat. Med.,  2019, 2019, 1-18.
 [http://dx.doi.org/10.1155/2019/3817879] [PMID: 31341492]

[93]
Liu, Z.C.; Wu, W.H.; Huang, S.; Li, Z.W.; Li, X.; Shui, G.H.; Lam, S.M.; Li, B.W.; Li, Z.X.; Zhang, Y.; Zhou, T.; You, W.C.; Pan, K.F.; Li, W.Q. Plasma lipids signify the progression of precancerous gastric lesions to gastric cancer: A prospective targeted lipidomics study. Theranostics,  2022, 12(10), 4671-4683.
 [http://dx.doi.org/10.7150/thno.74770] [PMID: 35832080]

[94]
Jin, G.; Lv, J.; Yang, M.; Wang, M.; Zhu, M.; Wang, T.; Yan, C.; Yu, C.; Ding, Y.; Li, G.; Ren, C.; Ni, J.; Zhang, R.; Guo, Y.; Bian, Z.; Zheng, Y.; Zhang, N.; Jiang, Y.; Chen, J.; Wang, Y.; Xu, D.; Zheng, H.; Yang, L.; Chen, Y.; Walters, R.; Millwood, I.Y.; Dai, J.; Ma, H.; Chen, K.; Chen, Z.; Hu, Z.; Wei, Q.; Shen, H.; Li, L. Genetic risk, incident gastric cancer, and healthy lifestyle: A meta-analysis of genome-wide association studies and prospective cohort study. Lancet Oncol.,  2020, 21(10), 1378-1386.
 [http://dx.doi.org/10.1016/S1470-2045(20)30460-5] [PMID: 33002439]

[95]
Matsuoka, K.; Nishiumi, S.; Yoshida, M.; Kodama, Y. Effects of Helicobacter pylori on the glutathione-related pathway in gastric epithelial cells. Biochem. Biophys. Res. Commun.,  2020, 526(4), 1118-1124.
 [http://dx.doi.org/10.1016/j.bbrc.2020.04.019] [PMID: 32312521]

[96]
Liu, D.; Zhu, J.; Ma, X.; Zhang, L.; Wu, Y.; Zhu, W.; Xing, Y.; Jia, Y.; Wang, Y. Transcriptomic and metabolomic profiling in Helicobacter pylori–induced gastric cancer identified prognosis- and immunotherapy-relevant gene signatures. Front. Cell Dev. Biol.,  2021, 9, 769409.
 [http://dx.doi.org/10.3389/fcell.2021.769409] [PMID: 35004676]

[97]
Jabini, R.; Eghbali, S.A.; Ayatollahi, H.; Sheikhi, M.; Farzanehfar, M. Analysis of KRAS gene mutation associated with Helicobacter pylori infection in patients with gastric cancer. Iran. J. Basic Med. Sci.,  2019, 22(5), 529-533.
 [PMID: 31217933]

[98]
Nakajima, T.; Yamashita, S.; Maekita, T.; Niwa, T.; Nakazawa, K.; Ushijima, T. The presence of a methylation fingerprint of Helicobacter pylori infection in human gastric mucosae. Int. J. Cancer,  2009, 124(4), 905-910.
 [http://dx.doi.org/10.1002/ijc.24018] [PMID: 19035455]

[99]
Niwa, T.; Tsukamoto, T.; Toyoda, T.; Mori, A.; Tanaka, H.; Maekita, T.; Ichinose, M.; Tatematsu, M.; Ushijima, T. Inflammatory processes triggered by Helicobacter pylori infection cause aberrant DNA methylation in gastric epithelial cells. Cancer Res.,  2010, 70(4), 1430-1440.
 [http://dx.doi.org/10.1158/0008-5472.CAN-09-2755] [PMID: 20124475]

[100]
Shin, C.M.; Kim, N.; Jung, Y.; Park, J.H.; Kang, G.H.; Kim, J.S.; Jung, H.C.; Song, I.S. Role of Helicobacter pylori infection in aberrant DNA methylation along multistep gastric carcinogenesis. Cancer Sci.,  2010, 101(6), 1337-1346.
 [http://dx.doi.org/10.1111/j.1349-7006.2010.01535.x] [PMID: 20345486]

[101]
Zhou, H.; Sun, H.; Liu, X.; Chen, J.; Zhang, L.; Lin, S.; Han, X.; Nie, C.; Liu, Y.; Tian, W.; Zhao, Y. Combined effect between WT1 methylation and Helicobacter pylori infection, smoking, and alcohol consumption on the risk of gastric cancer. Helicobacter,  2019, 24(5), e12650.
 [http://dx.doi.org/10.1111/hel.12650] [PMID: 31361067]

[102]
Li, X.; Zheng, N.R.; Wang, L.H.; Li, Z.W.; Liu, Z.C.; Fan, H.; Wang, Y.; Dai, J.; Ni, X.T.; Wei, X.; Liu, M.W.; Li, K.; Li, Z.X.; Zhou, T.; Zhang, Y.; Zhang, J.Y.; Kadeerhan, G.; Huang, S.; Wu, W.H.; Liu, W.D.; Wu, X.Z.; Zhang, L.F.; Xu, J.M.; Gerhard, M.; You, W.C.; Pan, K.F.; Li, W.Q.; Qin, J. Proteomic profiling identifies signatures associated with progression of precancerous gastric lesions and risk of early gastric cancer. E. Bio. Medicine. ,  2021, 74, 103714.
 [http://dx.doi.org/10.1016/j.ebiom.2021.103714] [PMID: 34818622]

[103]
Prinz, C.; Mese, K.; Weber, D. MicroRNA changes in gastric carcinogenesis: Differential dysregulation during Helicobacter pylori and EBV infection. Genes,  2021, 12(4), 597.
 [http://dx.doi.org/10.3390/genes12040597] [PMID: 33921696]

[104]
Zhang, X. Induction of fibroblast growth factor receptor 4 by helicobacter pylori via signal transducer and activator of transcription 3 with a feedforward activation loop involving steroid receptor coactivator signaling in gastric cancer. Gastroenterology,  2022, 163(3), 620-636.e9.
 [http://dx.doi.org/10.1053/j.gastro.2022.05.016] [PMID: 35588797]

[105]
Yoon, S.J.; Kim, J.Y.; Long, N.P.; Min, J.E.; Kim, H.M.; Yoon, J.H.; Anh, N.H.; Park, M.C.; Kwon, S.W.; Lee, S.K. Comprehensive multi-omics analysis reveals aberrant metabolism of Epstein–Barr-virus-associated gastric carcinoma. Cells,  2019, 8(10), 1220.
 [http://dx.doi.org/10.3390/cells8101220] [PMID: 31597357]

[106]
Wang, Z.; Lv, Z.; Xu, Q.; Sun, L.; Yuan, Y. Identification of differential proteomics in Epstein-Barr virus-associated gastric cancer and related functional analysis. Cancer Cell Int.,  2021, 21(1), 368.
 [http://dx.doi.org/10.1186/s12935-021-02077-6] [PMID: 34247602]

[107]
Gao, Y.; Fu, Y.; Wang, J.; Zheng, X.; Zhou, J.; Ma, J. EBV as a high infection risk factor promotes RASSF10 methylation and induces cell proliferation in EBV-associated gastric cancer. Biochem. Biophys. Res. Commun.,  2021, 547, 1-8.
 [http://dx.doi.org/10.1016/j.bbrc.2021.02.014] [PMID: 33588233]

[108]
Chen, Y.; Fu, R.; Xu, M.; Huang, Y.; Sun, G.; Xu, L. N-methyl-N-nitro-N-nitrosoguanidine-mediated ING4 downregulation contributed to the angiogenesis of transformed human gastric epithelial cells. Life Sci.,  2018, 199, 179-187.
 [http://dx.doi.org/10.1016/j.lfs.2018.02.034] [PMID: 29496496]

[109]
Yang, S.; Lv, Y.; Wu, C.; Liu, B.; Shu, Z.; Lin, Y. Pickled vegetables intake impacts the metabolites for gastric cancer. Cancer Manag. Res.,  2020, 12, 8263-8273.
 [http://dx.doi.org/10.2147/CMAR.S271277] [PMID: 32982422]

[110]
Suzuki, S.; Muroishi, Y.; Nakanishi, I.; Oda, Y. Relationship between genetic polymorphisms of drug-metabolizing enzymes (CYP1A1, CYP2E1, GSTM1, and NAT2), drinking habits, histological subtypes, and p53 gene point mutations in Japanese patients with gastric cancer. J. Gastroenterol.,  2004, 39(3), 220-230.
 [http://dx.doi.org/10.1007/s00535-003-1281-x] [PMID: 15064998]

[111]
Nan, H.M.; Song, Y.J.; Yun, H.Y.; Park, J.S.; Kim, H. Effects of dietary intake and genetic factors on hypermethylation of the hMLH1 gene promoter in gastric cancer. World J. Gastroenterol.,  2005, 11(25), 3834-3841.
 [http://dx.doi.org/10.3748/wjg.v11.i25.3834] [PMID: 15991278]

[112]
Shimazu, T.; Asada, K.; Charvat, H.; Kusano, C.; Otake, Y.; Kakugawa, Y.; Watanabe, H.; Gotoda, T.; Ushijima, T.; Tsugane, S. Association of gastric cancer risk factors with DNA methylation levels in gastric mucosa of healthy Japanese: a cross-sectional study. Carcinogenesis,  2015, 36(11), 1291-1298.
 [http://dx.doi.org/10.1093/carcin/bgv125] [PMID: 26354778]

[113]
Corona, G.; Cannizzaro, R.; Miolo, G.; Caggiari, L.; De Zorzi, M.; Repetto, O.; Steffan, A.; De Re, V. Use of metabolomics as a complementary omic approach to implement risk criteria for first-degree relatives of gastric cancer patients. Int. J. Mol. Sci.,  2018, 19(3), 750.
 [http://dx.doi.org/10.3390/ijms19030750] [PMID: 29518896]

[114]
Braga-Neto, M.B.; Costa, D.V.S.; Queiroz, D.M.M.; Maciel, F.S.; de Oliveira, M.S.; Viana-Junior, A.B.; Santos, F.A.; Leitao, R.F.C.; Brito, G.A.C.; Vasconcelos, P.R.L.; Braga, L.L.B.C. Increased oxidative stress in gastric cancer patients and their first-degree relatives: A prospective study from northeastern brazil. Oxid. Med. Cell. Longev.,  2021, 2021, 1-9.
 [http://dx.doi.org/10.1155/2021/6657434] [PMID: 34873431]

[115]
Kim, H.J.; Kim, N.; Kim, H.W.; Park, J.H.; Shin, C.M.; Lee, D.H. Promising aberrant DNA methylation marker to predict gastric cancer development in individuals with family history and long-term effects of H. pylori eradication on DNA methylation. Gastric Cancer,  2021, 24(2), 302-313.
 [http://dx.doi.org/10.1007/s10120-020-01117-w] [PMID: 32915372]

[116]
Kim, J.J.; Chung, S.W.; Kim, J.H.; Kim, J.W.; Oh, J.S.; Kim, S.; Song, S.Y.; Park, J.; Kim, D.H. Promoter methylation of helicase-like transcription factor is associated with the early stages of gastric cancer with family history. Ann. Oncol.,  2006, 17(4), 657-662.
 [http://dx.doi.org/10.1093/annonc/mdl018] [PMID: 16497821]

[117]
Hai-jiang, Y. Preliminary study on EB virus and gastric cancer. Shiyong Zhongliu Zazhi, 2010.

[118]
Fujii, T.; Nishikawa, J.; Fukuda, S.; Kubota, N.; Nojima, J.; Fujisawa, K.; Ogawa, R.; Goto, A.; Hamabe, K.; Hashimoto, S.; Wai, A.P.; Iizasa, H.; Yoshiyama, H.; Sakai, K.; Suehiro, Y.; Yamasaki, T.; Takami, T. MC180295 inhibited Epstein–Barr virus-associated gastric carcinoma cell growth by suppressing DNA repair and the cell cycle. Int. J. Mol. Sci.,  2022, 23(18), 10597.
 [http://dx.doi.org/10.3390/ijms231810597] [PMID: 36142506]

[119]
Lu, L.; Mullins, C.S.; Schafmayer, C.; Zeißig, S.; Linnebacher, M. A global assessment of recent trends in gastrointestinal cancer and lifestyle-associated risk factors. Cancer Commun.,  2021, 41(11), 1137-1151.
 [http://dx.doi.org/10.1002/cac2.12220] [PMID: 34563100]

[120]
Song, P.; Wu, L.; Guan, W. Dietary nitrates, nitrites, and nitrosamines intake and the risk of gastric cancer: A meta-analysis. Nutrients,  2015, 7(12), 9872-9895.
 [http://dx.doi.org/10.3390/nu7125505] [PMID: 26633477]

[121]
Picetti, R.; Deeney, M.; Pastorino, S.; Miller, M.R.; Shah, A.; Leon, D.A.; Dangour, A.D.; Green, R. Nitrate and nitrite contamination in drinking water and cancer risk: A systematic review with meta-analysis. Environ. Res.,  2022, 210, 112988.
 [http://dx.doi.org/10.1016/j.envres.2022.112988] [PMID: 35217009]

[122]
Dong, E.Y.; Giap, A.Q.; Lustigova, E.; Wu, B.U. Gastric cancer screening in first-degree relatives: A pilot study in a diverse integrated healthcare system. Clin. Transl. Gastroenterol.,  2022, 13(11), e00531.
 [http://dx.doi.org/10.14309/ctg.0000000000000531] [PMID: 36113027]

[123]
Youn Nam, S.; Park, B.J.; Nam, J.H.; Ryu, K.H.; Kook, M.C.; Kim, J.; Lee, W.K. Association of current Helicobacter pylori infection and metabolic factors with gastric cancer in 35,519 subjects: A cross-sectional study. United European Gastroenterol. J.,  2019, 7(2), 287-296.
 [http://dx.doi.org/10.1177/2050640618819402] [PMID: 31080613]

[124]
Yaghoobi, M.; Bijarchi, R.; Narod, S.A. Family history and the risk of gastric cancer. Br. J. Cancer,  2010, 102(2), 237-242.
 [http://dx.doi.org/10.1038/sj.bjc.6605380] [PMID: 19888225]

[125]
Dhillon, A.; Singh, A.; Vohra, H.; Ellis, C.; Varghese, B.; Gill, S.S. IoTPulse: Machine learning-based enterprise health information system to predict alcohol addiction in Punjab (India) using IoT and fog computing. Enterprise Inf. Syst.,  2022, 16(7), 1820583.
 [http://dx.doi.org/10.1080/17517575.2020.1820583]

[126]
Li, X.; Ma, J.; Leng, L.; Han, M.; Li, M.; He, F.; Zhu, Y. MoGCN: A multi-omics integration method based on graph convolutional network for cancer subtype analysis. Front. Genet.,  2022, 13, 806842.
 [http://dx.doi.org/10.3389/fgene.2022.806842] [PMID: 35186034]

[127]
Dhillon, A.; Singh, A. Machine learning in healthcare data analysis: A survey. J. Biol. Todays World,  2019, 8, 1-10.

[128]
McCulloch, W.; Pitts, W. A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biol.,  1990, 52(1-2), 99-115.
 [http://dx.doi.org/10.1016/S0092-8240(05)80006-0] [PMID: 2185863]

[129]
Momeni, Z.; Hassanzadeh, E.; Saniee Abadeh, M.; Bellazzi, R. A survey on single and multi omics data mining methods in cancer data classification. J. Biomed. Inform.,  2020, 107, 103466.
 [http://dx.doi.org/10.1016/j.jbi.2020.103466] [PMID: 32525020]

[130]
Ritchie, M.D.; Holzinger, E.R.; Li, R.; Pendergrass, S.A.; Kim, D. Methods of integrating data to uncover genotype– phenotype interactions. Nat. Rev. Genet.,  2015, 16(2), 85-97.
 [http://dx.doi.org/10.1038/nrg3868] [PMID: 25582081]

[131]
EL-Manzalawy, Y.; Hsieh, T.Y.; Shivakumar, M.; Kim, D.; Honavar, V. Min-redundancy and max-relevance multi-view feature selection for predicting ovarian cancer survival using multi-omics data. BMC Med. Genomics,  2018, 11(S3)(Suppl. 3), 71.
 [http://dx.doi.org/10.1186/s12920-018-0388-0] [PMID: 30255801]

[132]
Malik, V.; Kalakoti, Y.; Sundar, D. Deep learning assisted multi-omics integration for survival and drug-response prediction in breast cancer. BMC Genomics,  2021, 22(1), 214.
 [http://dx.doi.org/10.1186/s12864-021-07524-2] [PMID: 33761889]

[133]
Zhang, X.; Wang, J.; Lu, J.; Su, L.; Wang, C.; Huang, Y.; Zhang, X.; Zhu, X. Robust prognostic subtyping of muscle-invasive bladder cancer revealed by deep learning-based multi-omics data integration. Front. Oncol.,  2021, 11, 689626.
 [http://dx.doi.org/10.3389/fonc.2021.689626] [PMID: 34422643]

[134]
Chaudhary, K.; Poirion, O.B.; Lu, L.; Garmire, L.X. Deep learning–based multi-omics integration robustly predicts survival in liver cancer. Clin. Cancer Res.,  2018, 24(6), 1248-1259.
 [http://dx.doi.org/10.1158/1078-0432.CCR-17-0853] [PMID: 28982688]

[135]
Xu, J.; Yao, Y.; Xu, B.; Li, Y.; Su, Z. Unsupervised learning of cross-modal mappings in multi-omics data for survival stratification of gastric cancer. Future Oncol.,  2022, 18(2), 215-230.
 [http://dx.doi.org/10.2217/fon-2021-1059] [PMID: 34854737]

[136]
Chen, S.; Zang, Y.; Xu, B.; Lu, B.; Ma, R.; Miao, P.; Chen, B. An unsupervised deep learning-based model using multiomics data to predict prognosis of patients with stomach adenocarcinoma. Comput. Math. Methods Med.,  2022, 2022, 1-20.
 [http://dx.doi.org/10.1155/2022/5844846] [PMID: 36339684]

[137]
Hu, C.; Jia, W. Multi-omics profiling: The way toward precision medicine in metabolic diseases. J. Mol. Cell Biol.,  2021, 13(8), mjab051.
 [http://dx.doi.org/10.1093/jmcb/mjab051] [PMID: 34406397]
Cite As
Current Medicinal Chemistry

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

Abstract
