Abstract
Genes are closely related to the occurrence of cancer. Many complex diseases and cancers have been discovered. Liver cancer is a malignant tumor of the liver, which can be divided into primary and secondary categories. Primary malignant tumors of the liver originate from the epithelial or mesenchymal tissues of the liver. Many methods have been proposed to predict genes related to liver cancer. In this article, in order to predict genes related to the occurrence and development of liver cancer, we use multi-omics data combined with a variety of analysis methods to predict genes that are differentially expressed in liver cancer. Firstly, we used different data sources to build a data set. Then, we performed GO (Gene Ontology) analysis, KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis, PPI (Protein Protein Interaction) analysis and DO (Disease Ontology) analysis methods on the potential liver cancer-related genes contained in the data. GO analysis is used to describe the role of genes and proteins in cells, so as to fully describe the properties of genes and gene products in organisms. KEGG is a database that systematically analyzes the metabolic pathways of gene products in cells, and is the most commonly used metabolic pathway analysis. PPI network analysis is helpful for studying molecular mechanisms of diseases and discovering new drug targets from a systematic perspective. DO analysis helps to analyze the relationship with the occurrence and development of the disease.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Sia, D., Villanueva, A., Friedman, S.L., Llovet, J.M.: Liver cancer cell of origin, molecular class, and effects on patient prognosis. Gastroenterology 152(4), 745–761 (2017)
Zheng, C.-H., Yuan, L., Sha, W., Sun, Z.-L.: Gene differential coexpression analysis based on biweight correlation and maximum clique. BMC Bioinformatics 15(15), 1–7 (2014)
Yuan, L., Zheng, C.-H., Xia, J.-F., Huang, D.-S.: Module based differential coexpression analysis method for type 2 diabetes. BioMed Res. Int. 2015, 836929 (2015)
Wesselhoeft, R.A., Kowalski, P.S., Anderson, D.G.: Engineering circular RNA for potent and stable translation in eukaryotic cells. Nat. Commun. 9(1), 1–10 (2018)
Ge, S.-G., Xia, J., Sha, W., Zheng, C.-H.: Cancer subtype discovery based on integrative model of multigenomic data. IEEE/ACM Trans. Comput. Biol. Bioinf. 14(5), 1115–1121 (2016)
Wei, P.-J., Zhang, D., Xia, J., Zheng, C.-H.: LNDriver: identifying driver genes by integrating mutation and expression data based on gene-gene interaction network. BMC Bioinformatics 17(17), 467 (2016)
Stagsted, L.V., Nielsen, K.M., Daugaard, I., Hansen, T.B.: Noncoding AUG circRNAs constitute an abundant and conserved subclass of circles. Life Sci. Alliance 2(3), e201900398 (2019)
Yuan, L., et al.: Nonconvex penalty based low-rank representation and sparse regression for eQTL mapping. IEEE/ACM Trans. Comput. Biol. Bioinf. 14(5), 1154–1164 (2016)
Velasco, M.X., Kosti, A., Penalva, L.O.F., Hernández, G.: The diverse roles of RNA-binding proteins in glioma development. In: Romão, L. (ed.) The mRNA Metabolism in Human Disease. AEMB, vol. 1157, pp. 29–39. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-19966-1_2
Yuan, L., Yuan, C.-A., Huang, D.-S.: FAACOSE: a fast adaptive ant colony optimization algorithm for detecting SNP epistasis. Complexity 2017, 1–10 (2017)
Freeman, J.L., et al.: Copy number variation: new insights in genome diversity. Genome Res. 16(8), 949–961 (2006)
Yuan, L., et al.: Integration of multi-omics data for gene regulatory network inference and application to breast cancer. IEEE/ACM Trans. Comput. Biol. Bioinf. 16(3), 782–791 (2018)
Lauer, S., Gresham, D.: An evolving view of copy number variants. Curr. Genet. 65(6), 1287–1295 (2019). https://doi.org/10.1007/s00294-019-00980-0
Gentile, G., La Cognata, V., Cavallaro, S.: The contribution of CNVs to the most common aging-related neurodegenerative diseases. Aging Clin. Exp. Res. 33(5), 1187–1195 (2020). https://doi.org/10.1007/s40520-020-01485-4
Xing, Z., Chu, C., Chen, L., Kong, X.: The use of gene ontology terms and KEGG pathways for analysis and prediction of oncogenes. Biochimica et Biophysica Acta (BBA)-General Subjects 1860(11), 2725–2734 (2016)
Mo, S., et al.: KEGG-expressed genes and pathways in intervertebral disc degeneration: Protocol for a systematic review and data mining. Medicine 98(21), e15796 (2019)
Zhang, T., Jiang, M., Chen, L., Niu, B., Cai, Y.: Prediction of gene phenotypes based on GO and KEGG pathway enrichment scores. BioMed Res. Int. 2013, 1–7 (2013)
Acknowledgement
This work was supported by the National Key R&D Program of China (Grant nos. 2019YFB1404700, 2018AAA0100100), supported by the grant of National Natural Science Foundation of China (No. 62002189), supported by the grant of Natural Science Foundation of Shandong Province, China (No. ZR2020QF038), and partly supported by National Natural Science Foundation of China (Grant nos. 61861146002, 61732012, 61932008).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Yuan, L., Shen, Z. (2021). Joint Association Analysis Method to Predict Genes Related to Liver Cancer. In: Huang, DS., Jo, KH., Li, J., Gribova, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science(), vol 12838. Springer, Cham. https://doi.org/10.1007/978-3-030-84532-2_33
Download citation
DOI: https://doi.org/10.1007/978-3-030-84532-2_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-84531-5
Online ISBN: 978-3-030-84532-2
eBook Packages: Computer ScienceComputer Science (R0)