Login Register Cart 0
Generic placeholder image

Current Bioinformatics

Editor-in-Chief

ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Identification of Potential Biomarkers in Stomach Adenocarcinoma using Machine Learning Approaches

Author(s): Elham Nazari, Ghazaleh Pourali, Majid Khazaei, Alireza Asadnia, Mohammad Dashtiahangar, Reza Mohit, Mina Maftooh, Mohammadreza Nassiri, Seyed Mahdi Hassanian, Majid Ghayour-Mobarhan, Gordon A. Ferns, Soodabeh Shahidsales and Amir Avan*

Volume 18, Issue 4, 2023

Published on: 05 April, 2023

Page: [320 - 333] Pages: 14

DOI: 10.2174/1574893618666230227103427

Price: $65

conference banner
Abstract

Background: Stomach adenocarcinoma (STAD) is a common cancer with poor clinical outcomes globally. Due to a lack of early diagnostic markers of disease, the majority of patients are diagnosed at an advanced stage.

Objective: The aim of the present study is to provide some new insights into the available biomarkers for patients with STAD using bioinformatics.

Methods: RNA-Sequencing and other relevant data of patients with STAD from The Cancer Genome Atlas (TCGA) database were evaluated to identify differentially expressed genes (DEGs). Then, Machine Learning algorithms were undertaken to predict biomarkers. Additionally, Kaplan–Meier analysis was used to detect prognostic biomarkers. Furthermore, the Gene Ontology and Reactome pathways, protein-protein interactions (PPI), multiple sequence alignment, phylogenetic mapping, and correlation between clinical parameters were evaluated.

Results: The results showed 61 DEGs, and the key dysregulated genes associated with STAD are MTHFD1L (Methylenetetrahydrofolate dehydrogenase 1-like), ZWILCH (Zwilch Kinetochore Protein), RCC2 (Regulator of chromosome condensation 2), DPT (Dermatopontin), GCOM1 (GRINL1A complex locus 1), and CLEC3B (C-Type Lectin Domain Family 3 Member B). Moreover, the survival analysis reported ASPA (Aspartoacylase) as a prognostic marker.

Conclusion: Our study provides a proof of concept of the potential value of ASPA as a prognostic factor in STAD, requiring further functional investigations to explore the value of emerging markers.

« Previous Next »
[1]
Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2021; 71(3): 209-49.
[http://dx.doi.org/10.3322/caac.21660] [PMID: 33538338]
[2]
Machlowska J, Baj J, Sitarz M, Maciejewski R, Sitarz R. Gastric cancer: Epidemiology, risk factors, classification, genomic characteristics and treatment strategies. Int J Mol Sci 2020; 21(11): 4012.
[http://dx.doi.org/10.3390/ijms21114012] [PMID: 32512697]
[3]
Yusefi AR, Bagheri Lankarani K, Bastani P, Radinmanesh M, Kavosi Z. Risk factors for gastric cancer: A systematic review. APJCP 2018; 19(3): 591-603.
[PMID: 29579788]
[4]
Thrift AP, El-Serag HB. Burden of gastric cancer. Clin Gastroenterol Hepatol 2020; 18(3): 534-42.
[http://dx.doi.org/10.1016/j.cgh.2019.07.045] [PMID: 31362118]
[5]
Rawla P, Barsouk A. Epidemiology of gastric cancer: Global trends, risk factors and prevention. Prz Gastroenterol 2019; 14(1): 26-38.
[http://dx.doi.org/10.5114/pg.2018.80001]
[6]
Matsuoka T, Yashiro M. Biomarkers of gastric cancer: Current topics and future perspective. World J Gastroenterol 2018; 24(26): 2818-32.
[http://dx.doi.org/10.3748/wjg.v24.i26.2818] [PMID: 30018477]
[7]
Sun C, Yuan Q, Wu D, Meng X, Wang B. Identification of core genes and outcome in gastric cancer using bioinformatics analysis. Oncotarget 2017; 8(41): 70271-80.
[http://dx.doi.org/10.18632/oncotarget.20082] [PMID: 29050278]
[8]
Liu X, Wu J, Zhang D, et al. Identification of potential key genes associated with the pathogenesis and prognosis of gastric cancer based on integrated bioinformatics analysis. Front Genet 2018; 9: 265.
[http://dx.doi.org/10.3389/fgene.2018.00265] [PMID: 30065754]
[9]
Wang X, Zhi J. A machine learning-based analytical framework for employee turnover prediction. J Manag Anal 2021; 8(3): 351-70.
[http://dx.doi.org/10.1080/23270012.2021.1961318]
[10]
Maksum Y, Amirli A, Amangeldi A, et al. Computational acceleration of topology optimization using parallel computing and machine learning methods – analysis of research trends. J Ind Inf Integr 2022; 28: 100352.
[http://dx.doi.org/10.1016/j.jii.2022.100352]
[11]
Uysal MP. Machine learning-enabled healthcare information systems in view of industrial information integration engineering. J Ind Inf Integr 2022; 30: 100382.
[http://dx.doi.org/10.1016/j.jii.2022.100382]
[12]
Azeem M, Haleem A, Javaid M. Symbiotic relationship between machine learning and Industry 4.0: A review. J Ind Inf Integr 2021; 2021: 2130002.
[13]
Pradhan K, Chawla P. Medical Internet of things using machine learning algorithms for lung cancer detection. J Manag Anal 2020; 7(4): 591-623.
[http://dx.doi.org/10.1080/23270012.2020.1811789]
[14]
Nazari E, Aghemiri M, Avan A, Mehrabian A, Tabesh H. Machine learning approaches for classification of colorectal cancer with and without feature selection method on microarray data. Gene Rep 2021; 25: 101419.
[http://dx.doi.org/10.1016/j.genrep.2021.101419]
[15]
Nazari E, Farzin AH, Aghemiri M, Avan A, Tara M, Tabesh H. Deep learning for acute myeloid leukemia diagnosis. J Med Life 2020; 13(3): 382-7.
[http://dx.doi.org/10.25122/jml-2019-0090] [PMID: 33072212]
[16]
Kushwaha S, Bahl S, Bagha AK, et al. Significant applications of machine learning for COVID-19 pandemic. J Ind Inf Integr 2020; 5(4): 453-79.
[http://dx.doi.org/10.1142/S2424862220500268]
[17]
Li T, Gao X, Han L, Yu J, Li H. Identification of hub genes with prognostic values in gastric cancer by bioinformatics analysis. World J Surg Oncol 2018; 16(1): 114.
[http://dx.doi.org/10.1186/s12957-018-1409-3] [PMID: 29921304]
[18]
Nie K, Shi L, Wen Y, et al. Identification of hub genes correlated with the pathogenesis and prognosis of gastric cancer via bioinformatics methods. Minerva Med 2020; 111(3): 213-25.
[http://dx.doi.org/10.23736/S0026-4806.19.06166-4] [PMID: 31638362]
[19]
Liu D, Sun C, Kim N, et al. Comprehensive analysis of SFRP family members prognostic value and immune infiltration in gastric cancer. Life 2021; 11(6): 522.
[http://dx.doi.org/10.3390/life11060522] [PMID: 34205081]
[20]
Deng LY, Zeng XF, Tang D, Deng W, Liu HF, Xie YK. Expression and prognostic significance of thrombospondin gene family in gastric cancer. J Gastrointest Oncol 2021; 12(2): 355-64.
[http://dx.doi.org/10.21037/jgo-21-54] [PMID: 34012631]
[21]
Yan X, Fu X, Guo ZX, Liu XP, Liu TZ, Li S. Construction and validation of an eight-gene signature with great prognostic value in bladder cancer. J Cancer 2020; 11(7): 1768-79.
[http://dx.doi.org/10.7150/jca.38741] [PMID: 32194788]
[22]
Lv X, Zhao Y, Zhang L, et al. Development of a novel gene signature in patients without Helicobacter pylori infection gastric cancer. J Cell Biochem 2020; 121(2): 1842-54.
[http://dx.doi.org/10.1002/jcb.29419] [PMID: 31633246]
[23]
Robinson MD, McCarthy DJ, Smyth GK. edgeR: A Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 2010; 26(1): 139-40.
[24]
Sundar R, Barr Kumarakulasinghe N, Huak Chan Y, et al. Machine-learning model derived gene signature predictive of paclitaxel survival benefit in gastric cancer: Results from the randomised phase III SAMIT trial. Gut 2022; 71(4): 676-85.
[http://dx.doi.org/10.1136/gutjnl-2021-324060] [PMID: 33980610]
[25]
Szász AM, Lánczky A, Nagy Á, et al. Cross-validation of survival associated biomarkers in gastric cancer using transcriptomic data of 1,065 patients. Oncotarget 2016; 7(31): 49322-33.
[http://dx.doi.org/10.18632/oncotarget.10337] [PMID: 27384994]
[26]
Franceschini A, Szklarczyk D, Frankild S, et al. STRING v9.1: Protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res 2013; 41: D808-15.
[PMID: 23203871]
[27]
Pruitt KD, Tatusova T, Maglott DR. NCBI reference sequences (RefSeq): A curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res 2007; 35 (Suppl. 1): D61-5.
[http://dx.doi.org/10.1093/nar/gkl842] [PMID: 17130148]
[28]
Sievers F, Wilm A, Dineen D, et al. Fast, scalable generation of high‐quality protein multiple sequence alignments using Clustal Omega. Mol Syst Biol 2011; 7(1): 539.
[http://dx.doi.org/10.1038/msb.2011.75] [PMID: 21988835]
[29]
Yan Y, Tao H, He J, Huang SY. The HDOCK server for integrated protein–protein docking. Nat Protoc 2020; 15(5): 1829-52.
[http://dx.doi.org/10.1038/s41596-020-0312-x] [PMID: 32269383]
[30]
Dhankhar P, Dalal V, Singh V, Tomar S, Kumar P. Computational guided identification of novel potent inhibitors of N-terminal domain of nucleocapsid protein of severe acute respiratory syndrome coronavirus 2. J Biomol Struct Dyn 2022; 40(9): 4084-99.
[http://dx.doi.org/10.1080/07391102.2020.1852968] [PMID: 33251943]
[31]
Kumari R, Dalal V. Identification of potential inhibitors for LLM of Staphylococcus aureus: structure-based pharmacophore modeling, molecular dynamics, and binding free energy studies. J Biomol Struct Dyn 2022; 40(20): 9833-47.
[PMID: 34096457]
[32]
Tanzadehpanah H, Asoodeh A, Saberi MR, Chamani J. Identification of a novel angiotensin-I converting enzyme inhibitory peptide from ostrich egg white and studying its interactions with the enzyme. Innov Food Sci Emerg Technol 2013; 18: 212-9.
[http://dx.doi.org/10.1016/j.ifset.2013.02.002]
[33]
Vakser IA. Protein-protein docking: From interaction to interactome. Biophys J 2014; 107(8): 1785-93.
[http://dx.doi.org/10.1016/j.bpj.2014.08.033] [PMID: 25418159]
[34]
Hsin KY, Ghosh S, Kitano H. Combining machine learning systems and multiple docking simulation packages to improve docking prediction reliability for network pharmacology. PLoS One 2013; 8(12): e83922.
[http://dx.doi.org/10.1371/journal.pone.0083922] [PMID: 24391846]
[35]
Van Cutsem E, Sagaert X, Topal B, Haustermans K, Prenen H. Gastric cancer. Lancet 2016; 388(10060): 2654-64.
[http://dx.doi.org/10.1016/S0140-6736(16)30354-3] [PMID: 27156933]
[36]
Zong L, Abe M, Seto Y, Ji J. The challenge of screening for early gastric cancer in China. Lancet 2016; 388(10060): 2606.
[http://dx.doi.org/10.1016/S0140-6736(16)32226-7] [PMID: 27894662]
[37]
Kulasingam V, Diamandis EP. Strategies for discovering novel cancer biomarkers through utilization of emerging technologies. Nat Clin Pract Oncol 2008; 5(10): 588-99.
[http://dx.doi.org/10.1038/ncponc1187] [PMID: 18695711]
[38]
Cheong JH, Wang SC, Park S, et al. Development and validation of a prognostic and predictive 32-gene signature for gastric cancer. Nat Commun 2022; 13(1): 774.
[http://dx.doi.org/10.1038/s41467-022-28437-y] [PMID: 35140202]
[39]
Chen J, Hao Y, Wang T, Huang D, Liu X. Discovery of Stomach Adenocarcinoma Biomarkers by Consensus Scoring of Random Sampling and Machine Learning Modeling. In2022 10th International Conference on Bioinformatics and Computational Biology (ICBCB) 2022 May 13; 112-5. IEEE
[40]
Tibbetts AS, Appling DR. Compartmentalization of Mammalian folate-mediated one-carbon metabolism. Annu Rev Nutr 2010; 30(1): 57-81.
[http://dx.doi.org/10.1146/annurev.nutr.012809.104810] [PMID: 20645850]
[41]
Lee D, Xu IMJ, Chiu DKC, et al. Folate cycle enzyme MTHFD1L confers metabolic advantages in hepatocellular carcinoma. J Clin Invest 2017; 127(5): 1856-72.
[http://dx.doi.org/10.1172/JCI90253] [PMID: 28394261]
[42]
Yang YS, Yuan Y, Hu WP, Shang QX, Chen LQ. The role of mitochondrial folate enzyme MTHFD1L in esophageal squamous cell carcinoma. Scand J Gastroenterol 2018; 53(5): 533-40.
[http://dx.doi.org/10.1080/00365521.2017.1407440] [PMID: 29171320]
[43]
Agarwal S, Behring M, Hale K, et al. MTHFD1L, a folate cycle enzyme, is involved in progression of colorectal cancer. Transl Oncol 2019; 12(11): 1461-7.
[http://dx.doi.org/10.1016/j.tranon.2019.07.011] [PMID: 31421459]
[44]
Mollinari C, Reynaud C, Martineau-Thuillier S, et al. The mammalian passenger protein TD-60 is an RCC1 family member with an essential role in prometaphase to metaphase progression. Dev Cell 2003; 5(2): 295-307.
[http://dx.doi.org/10.1016/S1534-5807(03)00205-3] [PMID: 12919680]
[45]
Papini D, Langemeyer L, Abad MA, et al. TD-60 links RalA GTPase function to the CPC in mitosis. Nat Commun 2015; 6(1): 7678.
[http://dx.doi.org/10.1038/ncomms8678] [PMID: 26158537]
[46]
Williamson RC, Cowell CAM, Hammond CL, et al. Coronin-1C and RCC2 guide mesenchymal migration by trafficking Rac1 and controlling GEF exposure. J Cell Sci 2014; 127(Pt 19): jcs.154864..
[http://dx.doi.org/10.1242/jcs.154864] [PMID: 25074804]
[47]
Wang P, Zhang W, Wang L, et al. RCC2 interacts with small GTPase RalA and regulates cell proliferation and motility in gastric cancer. OncoTargets Ther 2020; 13: 3093-103.
[http://dx.doi.org/10.2147/OTT.S228914] [PMID: 32341655]
[48]
Pang B, Wu N, Guan R, et al. Overexpression of RCC2 enhances cell motility and promotes tumor metastasis in lung adenocarcinoma by inducing epithelial–mesenchymal transition. Clin Cancer Res 2017; 23(18): 5598-610.
[http://dx.doi.org/10.1158/1078-0432.CCR-16-2909] [PMID: 28606921]
[49]
Bruun J, Kolberg M, Ahlquist TC, et al. Regulator of chromosome condensation 2 identifies high-risk patients within both major phenotypes of colorectal cancer. Clin Cancer Res 2015; 21(16): 3759-70.
[http://dx.doi.org/10.1158/1078-0432.CCR-14-3294] [PMID: 25910952]
[50]
Kops GJPL, Weaver BAA, Cleveland DW. On the road to cancer: Aneuploidy and the mitotic checkpoint. Nat Rev Cancer 2005; 5(10): 773-85.
[http://dx.doi.org/10.1038/nrc1714] [PMID: 16195750]
[51]
Chen W, Gao C, Liu Y, Wen Y, Hong X, Huang Z. Bioinformatics analysis of prognostic miRNA signature and potential critical genes in colon cancer. Front Genet 2020; 11: 478.
[http://dx.doi.org/10.3389/fgene.2020.00478] [PMID: 32582275]
[52]
Okamoto O, Fujiwara S. Dermatopontin, a novel player in the biology of the extracellular matrix. Connect Tissue Res 2006; 47(4): 177-89.
[http://dx.doi.org/10.1080/03008200600846564] [PMID: 16987749]
[53]
Unamuno X, Gómez-Ambrosi J, Ramírez B, et al. Dermatopontin, a novel adipokine promoting adipose tissue extracellular matrix remodelling and inflammation in obesity. J Clin Med 2020; 9(4): 1069.
[http://dx.doi.org/10.3390/jcm9041069] [PMID: 32283761]
[54]
Tian P, Liang C. Transcriptome profiling of cancer tissues in Chinese patients with gastric cancer by high-throughput sequencing. Oncol Lett 2018; 15(2): 2057-64.
[PMID: 29434905]
[55]
Huang S, Ma L, Lan B, Liu N, Nong W, Huang Z. Comprehensive analysis of prognostic genes in gastric cancer. Aging 2021; 13(20): 23637-51.
[http://dx.doi.org/10.18632/aging.203638] [PMID: 34686626]
[56]
Fong MY, McDunn J, Kakar SS. Identification of metabolites in the normal ovary and their transformation in primary and metastatic ovarian cancer. PLoS One 2011; 6(5): e19963.
[http://dx.doi.org/10.1371/journal.pone.0019963] [PMID: 21625518]
[57]
Ben Sellem D, Elbayed K, Neuville A, Moussallieh FM, Lang-Averous G, Piotto M. Metabolomic characterization of ovarian epithelial carcinomas by HRMAS-NMR spectroscopy. J Oncol 2011; 2011: 174019.
[http://dx.doi.org/10.1155/2011/174019]
[58]
Schug ZT, Vande Voorde J, Gottlieb E. The metabolic fate of acetate in cancer. Nat Rev Cancer 2016; 16(11): 708-17.
[http://dx.doi.org/10.1038/nrc.2016.87] [PMID: 27562461]
[59]
Sun C, Gu Y, Chen G, Du Y. Bioinformatics analysis of stromal molecular signatures associated with breast and prostate cancer. J Comput Biol 2019; 26(10): 1130-9.
[http://dx.doi.org/10.1089/cmb.2019.0045] [PMID: 31180245]
[60]
Lou TF, Sethuraman D, Dospoy P, et al. Cancer-specific production of N-Acetylaspartate via NAT8L overexpression in non–small cell lung cancer and its potential as a circulating biomarker. Cancer Prev Res (Phila) 2016; 9(1): 43-52.
[http://dx.doi.org/10.1158/1940-6207.CAPR-14-0287] [PMID: 26511490]
[61]
Weindl D, Cordes T, Battello N, et al. Bridging the gap between non-targeted stable isotope labeling and metabolic flux analysis. Cancer Metab 2016; 4(1): 10.
[http://dx.doi.org/10.1186/s40170-016-0150-z] [PMID: 27110360]
[62]
Zand B, Previs RA, Zacharias NM, et al. Role of increased n-acetylaspartate levels in cancer. J Natl Cancer Inst 2016; 108(6): djv426.
[http://dx.doi.org/10.1093/jnci/djv426] [PMID: 26819345]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy