Exploring Gene Signatures in Different Molecular Subtypes of Gastric Cancer (MSS/ TP53+, MSS/TP53-): A Network-based and Machine Learning Approach

Document Type : Research Article

Authors

1 Department of Cell and Molecular Biology, Faculty of Science, Semnan University, Semnan, Iran

2 Research Institute for Fundamental Sciences (RIFS), University of Tabriz, Tabriz, Iran

3 Department of Medical Biotechnology, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran

Abstract

Gastric cancer (GC) is one of the leading causes of cancer mortality, worldwide. Molecular understanding of GC’s different subtypes is still dismal and it is necessary to develop new subtype-specific diagnostic and therapeutic approaches. Therefore developing comprehensive research in this area is demanding to have a deeper insight into molecular processes, underlying these subtypes. In this study, a three-step methodology was developed to identify important genes and subnetworks in two subtypes of GC (TP53+ and TP53-). First, weighted gene co-expression network analysis was performed to explore co-expressed gene modules in both subtypes. Afterward, the relationship of each module with the tumor pathological stage (as a clinical trait indicating tumor progression) was studied by decision tree machine learning algorithm and the best predicting module was selected for further analysis (modules with 241 genes for TP53+ and  1441 genes for TP53- were identified). Subsequently, a motif exploring and motif ranking analysis was implemented to explore three-member signature gene motifs in the selected modules' biological network. These motifs may have key regulatory roles in the studied GC subtypes. Motif members of TP53- mostly contain MAPK signaling pathway genes which show their key role in this subtype of GC. In the case of the TP53+ subtype, our findings demonstrated that alternative splicing and SNARE proteins could prompt the initiation and advancement of the disease. These findings can be used to develop new diagnostic and therapeutic approaches based on the personalized medicine concept. This methodology could be implemented to unravel underlying mechanisms and pathways in other complex phenotypes and diseases.

Keywords

References

Adler AS, McCleland M L, Yee S, Yaylaoglu M, Hussain S, Cosino E, Chopra VS. 2014. An integrative analysis of colon cancer identifies an essential function for PRPF6 in tumor growth. Genes Dev 28:1068-1084.
Antonacopoulou AG, Grivas PD, Skarlas L, Kalofonos M, Scopa CD, Kalofonos HP. 2008. POLR2F, ATP6V0A1 and PRNP expression in colorectal cancer: new molecules with prognostic significance? Anticancer Res 28:1221-1227.
Arif S, Qudsia S, Urooj S, Chaudry N, Arshad A, Andleeb S. 2015. Blueprint of quartz crystal microbalance biosensor for early detection of breast cancer through salivary autoantibodies against ATP6AP1. Biosens Bioelectron 65:62-70.
Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. 2018. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 68:394-424.
Carvalho BS, Irizarry RA. 2010. A framework for oligonucleotide microarray preprocessing. Bioinformatics 26:2363-2367.
Chang W, Ma L, Lin L, Gu L, Liu X, Cai H, Zhang M. 2009. Identification of novel hub genes associated with liver metastasis of gastric cancer. Int J Cancer 125:2844-53.
Cristescu R, Lee J, Nebozhyn M, Kim KM, Ting JC, Wong SS, Xiang SY. 2015. Molecular analysis of gastric cancer identifies subtypes associated with distinct clinical outcomes. Nat Med 21:449-56.
David CJ, Manley JL. 2010. Alternative pre-mRNA splicing regulation in cancer: pathways and programs unhinged. Genes Dev 24:2343-64.
Dettling M, Bühlmann P. 2003. Boosting for tumor classification with gene expression data. Bioinformatics 19:1061-1069.
Edge SB, Compton CC. 2010. The American joint committee on cancer: the 7th edition of the AJCC cancer staging manual and the future of TNM. Ann Surg Oncol (6):1471-1474.
Ferro A, Peleteiro B, Malvezzi M, Bosetti C, Bertuccio P, Levi F, Lunet N. 2014. Worldwide trends in stomach cancer mortality and incidence (1980-2011) and predictions to 2015. Eur J Cancer 50: 1330-1344.
Gullo I, Carneiro F, Oliveira C, Almeida GM. 2018. Heterogeneity in gastric cancer: from pure morphology to molecular classifications. Pathobiology 85:50-63.
Guo X, Shi Y, Gou Y, Li J, Han S, Zhang Y, Huo J, Sun S. 2011.  Human ribosomal protein S13 promotes gastric cancer growth through down-regulating p27Kip1. J Cell Mol Med 15:296-306.
Hira ZM, Gillies DF. 2015. A review of feature selection and feature extraction methods applied on microarray data. Adv Bioinform 2015.
Hsin IL, Sheu GT, Jan MS, Sun HL, Wu TC, Chiu LY, Ko JL. 2012. Inhibition of lysosome degradation on autophagosome formation and responses to GMI, an immunomodulatory protein from Ganoderma microsporum. Br J Pharmacol  167:1287-300.
Huang H, Han Y, Zhang C, Wu J, Feng J, Qu L, Shou C. 2016. HNRNPC as a candidate biomarker for chemoresistance in gastric cancer. Tumor Biol 37:3527-34.
Jiang B, Li S, Jiang Z, Shao P. 2017. Gastric cancer associated genes identified by an integrative analysis of gene expression data. Biomed Res Int 2017 :7259097.
Katoh M, Katoh M. 2009. Transcriptional mechanisms of WNT5A based on NF-κB, Hedgehog, TGFβ, and Notch signaling cascades. Int J Mol Med 23:763-769.
Khan FM, Marquardt S, Gupta SK, Knoll S, Schmitz U, Spitschak A, Engelmann D. 2017. Unraveling a tumor type-specific regulatory core underlying E2F1-mediated epithelial-mesenchymal transition to predict receptor protein signatures. Nat Commun 8:1-5.
Kim IJ, Kang HC, Park JG. 2004. Microarray applications in cancer research. Cancer research and treatment: J Korean Cancer Assoc 36:207-213.
Kim JS, Shin OR, Kim HK, Cho YS, An CH, Lim KW, Kim SS. 2010. Overexpression of protein phosphatase non-receptor type 11 (PTPN11) in gastric carcinomas. Dig Dis Sci 55:1565-1569.
Kim KH, Yeo SG, Yoo BC, Myung JK. 2017. Identification of calgranulin B interacting proteins and network analysis in gastrointestinal cancer cells. PLoS One 12:e0171232.
Kuhn M, Johnson K. 2013. A Short Tour of the Predictive Modeling Process. InApplied predictive modeling  (pp. 19-26). Springer, New York, NY.
Langfelder P, Horvath S. 2008. WGCNA: an R package for weighted correlation network analysis. BMC Bioinform 9:559. doi:10.1186/1471-2105-9-559.
Le DH, Pham VH. 2017. HGPEC: a Cytoscape app for prediction of novel disease-gene and disease-disease associations and evidence collection based on a random walk on heterogeneous network. BMC Syst Biol 11:61. doi:10.1186/s12918-017-0437-x.
Lin X, Zhao Y, Song WM, Zhang B. 2015. Molecular classification and prediction in gastric cancer. Comput Struct Biotechnol J 13:448-458.
Liu G, Li DZ, Jiang CS, Wang W. 2014. Transduction motif analysis of gastric cancer based on a human signaling network. Braz J Med Biol Res 47:369-375.
Liu X, Ye L, Wang J, Fan D. 1999. Expression of heat shock protein 90 beta in human gastric cancer tissue and SGC7901/VCR of MDR-type gastric cancer cell line. Chin Med J 112:1133-1137.
Maghvan PV, Rezaei-Tavirani M, Zali H, Nikzamir A, Abdi S, Khodadoostan M, Asadzadeh-Aghdaei H. 2017. Network analysis of common genes related to esophageal, gastric, and colon cancers. Gastroenterol Hepatol Bed Bench 10:295-302.
Meng J, Wang J. 2015. Role of SNARE proteins in tumourigenesis and their potential as targets for novel anti-cancer therapeutics. Biochim Biophys Acta Rev Cancer 1856:1-2.
Mering CV, Huynen M, Jaeggi D, Schmidt S, Bork P, Snel B. 2003. STRING: a database of predicted functional associations between proteins. Nucleic Acids Res 31:258-261.
Muller P, Ruckova E, Halada P, Coates PJ, Hrstka R, Lane DP, Vojtesek B. 2013. C-terminal phosphorylation of Hsp70 and Hsp90 regulates alternate binding to co-chaperones CHIP and HOP to determine cellular protein folding/degradation balances. Oncogene  32:3101-3110.
Parray AA, Baba RA, Bhat HF, Wani L, Mokhdomi TA, Mushtaq U, Khanday FA. 2014. MKK6 is upregulated in human esophageal, stomach, and colon cancers. Cancer Invest 32:416-422.
Pritchard AL, Hayward NK. 2013. Molecular pathways: mitogen-activated protein kinase pathway mutations and drug resistance. Clin Cancer Res 19:2301-2309.
Quesada V, Conde L, Villamor N, Ordóñez GR, Jares P, Bassaganyas L, López-Guerra M. 2012. Exome sequencing identifies recurrent mutations of the splicing factor SF3B1 gene in chronic lymphocytic leukemia. Nat Genet 44:47-52.
Rinnone F, Micale G, Bonnici V, Bader GD, Shasha D, Ferro A, Giugno R. 2015. NetMatchStar: an enhanced Cytoscape network querying app. F1000Research. 4: 479. doi: 10.12688/f1000research.6656.2.
Shimizu D, Kanda M, Kodera Y. 2017. Review of recent molecular landscape knowledge of gastric cancer. Histol Histopathol 33:11-26.
Siegel RL, Miller KD, Fedewa SA, Ahnen DJ, Meester RG, Barzi A, Jemal A. 2017. Colorectal cancer statistics, 2017. CA Cancer J Clin 67:177-193.
Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, Kuhn M. 2015. STRING v10: protein–protein interaction networks, integrated over the tree of life.Nucleic Acids Res 43: 447-452.
Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, Jensen LJ. 2016. The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible. Nucleic Acids Res 45: 362–368.
Tomsic J, He H, Akagi K, Liyanarachchi S, Pan Q, Bertani B, De La Chapelle A. 2015. A germline mutation in SRRM2, a splicing factor gene, is implicated in papillary thyroid carcinoma predisposition. Sci Rep 5:1-3.
Wang J, Cui S, Zhang X, Wu Y, Tang H. 2013. High expression of heat shock protein 90 is associated with tumor aggressiveness and poor prognosis in patients with advanced gastric cancer. PLoS One 8:e62876.
Wong SH, Zhang T, Xu Y, Subramaniam VN, Griffiths G, Hong W. 1998. Endobrevin, a novel synaptobrevin/VAMP-like protein preferentially associated with the early endosome. Mol Biol Cell 9:1549-1563.
Wu Q, Gou Y, Wang Q, Jin H, Cui L, Zhang Y, Fan D. 2011. Downregulation of RPL6 by siRNA inhibits proliferation and cell cycle progression of human gastric cancer cell lines. PLoS One 6:e26401.
Xu K, Mao X, Mehta M, Cui J, Zhang C, Mao F, Xu Y. 2013. Elucidation of how cancer cells avoid acidosis through comparative transcriptomic data analysis. PLoS One  8:e71177.
Xu ZY, Chen JS, Shu YQ. 2010. Gene expression profile towards the prediction of patient survival of gastric cancer. Biomed Pharmacother 64:133-139.
Yeger-Lotem E, Sattath S, Kashtan N, Itzkovitz S, Milo R, Pinter RY, Margalit H. 2004. Network motifs in integrated cellular networks of transcription-regulation and protein–protein interaction. Proc Natl Acad Sci 101:5934-5939.
Zhang B, Horvath S. 2005. A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol 4 (1). doi: 10.2202/1544-6115.1128.
Zuehlke AD, Beebe K, Neckers L, Prince T. 2015. Regulation and function of the human HSP90AA1 gene. Gene 570:8-16.
Journal of Genetic Resources
Volume 6, Issue 2
2020
Pages 195-208

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