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Current Bioinformatics

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

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Research Article

TMMGdb - Tumor Metastasis Mechanism-associated Gene Database

Author(s): Hsueh-Chuan Liu, Ka-Lok Ng*, Venugopala Reddy Mekala and Chien-Hung Huang

Volume 18, Issue 1, 2023

Published on: 13 December, 2022

Page: [63 - 75] Pages: 13

DOI: 10.2174/1574893618666221025105927

Price: $65

Abstract

Background: At present, all or the majority of published databases report metastasis genes based on the concept of using cancer types or hallmarks of cancer/metastasis. Since tumor metastasis is a dynamic process involving many cellular and molecular processes, those databases cannot provide information on the sequential relations and cellular and molecular mechanisms among different metastasis stages.

Objective: We incorporate the concept of tumor metastasis mechanism to construct a tumor metastasis mechanism-associated gene (TMMG) database based on using the metastasis mechanism concept.

Methods: We utilized the text mining tool, BioBERT to mine the titles and abstracts of the papers and identify TMMGs.

Results: This tumor metastasis mechanism-associated gene database (TMMGdb) contains a wealth of annotations. To check the reliability of TMMGdb, we compared the proportions of housekeeping genes (HKGs) in TMMGdb, HCMDB, and CMgene, the results showed that around 20% of the TMMGs are HKGs, and the proportions are highly consistent among the three databases. Compared with the HCMDB and CMgene databases, TMMGdb is able to find a more recent (on or after 2017) collection of publications and TMMGs. We provided six case studies to illustrate the uniqueness of the TMMGdb database.

Conclusion: TMMGdb is a comprehensive resource for the biomedical community to understand the dynamic process, molecular features, and cellular processes involved in tumor metastasis. TMMGdb provides four interfaces; ‘Browse’, ‘Search’, ‘DEG Search’ and ‘Download’, for users to investigate the causal effects among different metastasis stages; the database is freely accessible at http://hmg.asia.edu.tw/ TMMGdb.

Keywords: Tumor metastasis mechanism, cancer types, text mining, genetic mutations, cancer driver genes, protein-protein interaction networks.

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[1]
Welch JN, Chrysogelos SA. Positive mediators of cell proliferation in neoplastic transformation. In: Coleman WB, Tsongalis GJ, Eds. The Molecular Basis of Human Cancer. Totowa, NJ: Humana Press 2002.
[http://dx.doi.org/10.1007/978-1-59259-125-1_4]
[2]
Nowell PC. Mechanisms of tumor progression. Cancer Res 1986; 46(5): 2203-7.
[PMID: 3516380]
[3]
Seyfried TN, Huysentruyt LC. On the origin of cancer metastasis. Crit Rev Oncog 2013; 18(1 - 2): 43-73.
[http://dx.doi.org/10.1615/CritRevOncog.v18.i1-2.40] [PMID: 23237552]
[4]
Fares J, Fares MY, Khachfe HH, Salhab HA, Fares Y. Molecular principles of metastasis: A hallmark of cancer revisited. Signal Transduct Target Ther 2020; 5(1): 28.
[http://dx.doi.org/10.1038/s41392-020-0134-x] [PMID: 32296047]
[5]
Liberzon A, Birger C, Thorvaldsdóttir H, Ghandi M, Mesirov JP, Tamayo P. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst 2015; 1(6): 417-25.
[http://dx.doi.org/10.1016/j.cels.2015.12.004] [PMID: 26771021]
[6]
Liotta LA, Saidel GM, Kleinerman J. Stochastic model of metastases formation. Biometrics 1976; 32(3): 535-50.
[http://dx.doi.org/10.2307/2529743] [PMID: 963169]
[7]
Tan WY. A stochastic model for the formation of metastatic foci at distant sites. Math Comput Model 1989; 12(9): 1093-102.
[http://dx.doi.org/10.1016/0895-7177(89)90230-6]
[8]
Sherratt JA. Predictive mathematical modeling in metastasis. Methods Mol Med 2001; 57: 309-15.
[PMID: 21340907]
[9]
Anderson ARA, Chaplain MAJ, Newman EL, Steele RJC, Thompson AM. Mathematical modelling of tumour invasion and metastasis. J Theor Med 2000; 2(2): 129-54.
[http://dx.doi.org/10.1080/10273660008833042]
[10]
Haustein V, Schumacher U. A dynamic model for tumour growth and metastasis formation. J Clin Bioinforma 2012; 2(1): 11-.
[http://dx.doi.org/10.1186/2043-9113-2-11] [PMID: 22548735]
[11]
Franssen LC, Lorenzi T, Burgess AEF, Chaplain MAJ. A Mathematical framework for modelling the metastatic spread of cancer. Bull Math Biol 2019; 81(6): 1965-2010.
[http://dx.doi.org/10.1007/s11538-019-00597-x] [PMID: 30903592]
[12]
Divoli A, Mendonça EA, Evans JA, Rzhetsky A. Conflicting biomedical assumptions for mathematical modeling: The case of cancer metastasis. PLOS Comput Biol 2011; 7(10)e1002132
[http://dx.doi.org/10.1371/journal.pcbi.1002132] [PMID: 21998558]
[13]
Lambert AW, Pattabiraman DR, Weinberg RA. Emerging biological principles of metastasis. Cell 2017; 168(4): 670-91.
[http://dx.doi.org/10.1016/j.cell.2016.11.037] [PMID: 28187288]
[14]
Laranga R, Duchi S, Ibrahim T, Guerrieri AN, Donati DM, Lucarelli E. Trends in bone metastasis modeling. Cancers (Basel) 2020; 12(8): 2315.
[http://dx.doi.org/10.3390/cancers12082315] [PMID: 32824479]
[15]
Richard V, Kumar TRS, Pillai RM. Transitional dynamics of cancer stem cells in invasion and metastasis. Transl Oncol 2021; 14(1): 100909-9.
[http://dx.doi.org/10.1016/j.tranon.2020.100909] [PMID: 33049522]
[16]
Lee J, Yoon W, Kim S, et al. BioBERT: A pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 2019.btz682
[http://dx.doi.org/10.1093/bioinformatics/btz682] [PMID: 31501885]
[17]
Yu J, Vodyanik MA, Smuga-Otto K, et al. Induced pluripotent stem cell lines derived from human somatic cells. Science 2007; 318(5858): 1917-20.
[http://dx.doi.org/10.1126/science.1151526] [PMID: 18029452]
[18]
Kanehisa M, et al. KEGG: Integrating viruses and cellular organisms. Nucleic Acids Res 2020.
[PMID: 33125081]
[19]
Jassal B, Matthews L, Viteri G, et al. The reactome pathway knowledgebase. Nucleic Acids Res 2020; 48(D1): D498-503.
[PMID: 31691815]
[20]
Karagkouni D, Paraskevopoulou MD, Chatzopoulos S, et al. DIANA-TarBase v8: A decade-long collection of experimentally supported miRNA–gene interactions. Nucleic Acids Res 2018; 46(D1): D239-45.
[http://dx.doi.org/10.1093/nar/gkx1141] [PMID: 29156006]
[21]
Forbes SA, Beare D, Boutselakis H, et al. COSMIC: Somatic cancer genetics at high-resolution. Nucleic Acids Res 2017; 45(D1): D777-83.
[http://dx.doi.org/10.1093/nar/gkw1121] [PMID: 27899578]
[22]
Oughtred R, Stark C, Breitkreutz BJ, et al. The BioGRID interaction database: 2019 update. Nucleic Acids Res 2019; 47(D1): D529-41.
[http://dx.doi.org/10.1093/nar/gky1079] [PMID: 30476227]
[23]
Han H, Cho JW, Lee S, et al. TRRUST v2: An expanded reference database of human and mouse transcriptional regulatory interactions. Nucleic Acids Res 2018; 46(D1): D380-6.
[http://dx.doi.org/10.1093/nar/gkx1013] [PMID: 29087512]
[24]
Khimani AH, Mhashilkar AM, Mikulskis A, et al. Housekeeping genes in cancer: Normalization of array data. Biotechniques 2005; 38(5): 739-45.
[http://dx.doi.org/10.2144/05385ST04] [PMID: 15948292]
[25]
Tilli TM, Castro CS, Tuszynski JA, Carels N. A strategy to identify housekeeping genes suitable for analysis in breast cancer diseases. BMC Genomics 2016; 17(1): 639-9.
[http://dx.doi.org/10.1186/s12864-016-2946-1] [PMID: 27526934]
[26]
Eisenberg E, Levanon EY. Human housekeeping genes, revisited. Trends Genet 2013; 29(10): 569-74.
[http://dx.doi.org/10.1016/j.tig.2013.05.010] [PMID: 23810203]
[27]
Hounkpe BW, Chenou F, de Lima F, De Paula EV. HRT Atlas v1.0 database: Redefining human and mouse housekeeping genes and candidate reference transcripts by mining massive RNA-seq datasets. Nucleic Acids Res 2021; 49(D1): D947-55.
[http://dx.doi.org/10.1093/nar/gkaa609] [PMID: 32663312]
[28]
Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 2014; 15(12): 550.
[http://dx.doi.org/10.1186/s13059-014-0550-8] [PMID: 25516281]
[29]
Therneau TM, Grambsch PM. The cox model. In: Modeling survival data: Extending the Cox model. Springer 2000; pp. 39-77.
[http://dx.doi.org/10.1007/978-1-4757-3294-8_3]
[30]
Zheng G, Ma Y, Zou Y, Yin A, Li W, Dong D. HCMDB: The human cancer metastasis database. Nucleic Acids Res 2018; 46(D1): D950-5.
[http://dx.doi.org/10.1093/nar/gkx1008] [PMID: 29088455]
[31]
Zhang H, Luo S, Zhang X, et al. SEECancer: A resource for somatic events in evolution of cancer genome. Nucleic Acids Res 2018; 46(D1): D1018-26.
[http://dx.doi.org/10.1093/nar/gkx964] [PMID: 29069402]
[32]
Yu F, Li K, Li S, et al. CFEA: A cell-free epigenome atlas in human diseases. Nucleic Acids Res 2020; 48(D1): D40-4.
[http://dx.doi.org/10.1093/nar/gkz715] [PMID: 31428785]
[33]
Gao Y, Shang S, Guo S, et al. Lnc2Cancer 3.0: An updated resource for experimentally supported lncRNA/circRNA cancer associations and web tools based on RNA-seq and scRNA-seq data. Nucleic Acids Res 2021; 49(D1): D1251-8.
[http://dx.doi.org/10.1093/nar/gkaa1006] [PMID: 33219685]
[34]
Wang P, Guo Q, Hao Y, et al. LnCeCell: A comprehensive database of predicted lncRNA-associated ceRNA networks at single-cell resolution. Nucleic Acids Res 2021; 49(D1): D125-33.
[http://dx.doi.org/10.1093/nar/gkaa1017] [PMID: 33219686]
[35]
Semenova G, Stepanova DS, Dubyk C, et al. Targeting group I p21-activated kinases to control malignant peripheral nerve sheath tumor growth and metastasis. Oncogene 2017; 36(38): 5421-31.
[http://dx.doi.org/10.1038/onc.2017.143] [PMID: 28534510]
[36]
Cai JP, Wu YJ, Li C, et al. Panax ginseng polysaccharide suppresses metastasis via modulating Twist expression in gastric cancer. Int J Biol Macromol 2013; 57: 22-5.
[http://dx.doi.org/10.1016/j.ijbiomac.2013.03.010] [PMID: 23500436]
[37]
Hanahan D, Weinberg RA. Hallmarks of cancer: The next generation. Cell 2011; 144(5): 646-74.
[http://dx.doi.org/10.1016/j.cell.2011.02.013] [PMID: 21376230]
[38]
Hanahan D. Hallmarks of Cancer: New Dimensions. Cancer Discov 2022; 12(1): 31-46.
[http://dx.doi.org/10.1158/2159-8290.CD-21-1059] [PMID: 35022204]
[39]
Kölbl AC, Hiller RA, Ilmer M, et al. Glycosyltransferases as marker genes for the quantitative polymerase chain reaction-based detection of circulating tumour cells from blood samples of patients with breast cancer undergoing adjuvant therapy. Mol Med Rep 2015; 12(2): 2933-8.
[http://dx.doi.org/10.3892/mmr.2015.3732] [PMID: 25955084]
[40]
Massagué J, Obenauf AC. Metastatic colonization by circulating tumour cells. Nature 2016; 529(7586): 298-306.
[http://dx.doi.org/10.1038/nature17038] [PMID: 26791720]
[41]
Piñero J, Saüch J, Sanz F, Furlong LI. The DisGeNET cytoscape app: Exploring and visualizing disease genomics data. Comput Struct Biotechnol J 2021; 19: 2960-7.
[http://dx.doi.org/10.1016/j.csbj.2021.05.015] [PMID: 34136095]
[42]
Ren Q, Khoo WH, Corr AP, Phan TG, Croucher PI, Stewart SA. Gene expression predicts dormant metastatic breast cancer cell phenotype. Breast Cancer Res 2022; 24(1): 10.
[http://dx.doi.org/10.1186/s13058-022-01503-5] [PMID: 35093137]

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