Abstract
Gene Sub-Network-based Feature Selection (GSNFS) is an efficient method for handling case-control and multiclass studies for gene sub-network biomarker identification by an integrated analysis of gene expression, gene-set and network data. However, GSNFS has produce considerably high number of sub-network and has not assessed the importance of each sub-network. Recently, we have incorporated 2 feature selection techniques; correlation-based and information gain into the GSNFS workflow to help reduce the number and assess the importance of each individual sub-network. The extended GSNFS method was clearly shown to identify good candidate gene subnetwork markers in lung cancer. In this work, we applied a similar work flow to colorectal cancer. First, the top- and bottom- 5 ranked gene-sets were selected and investigated the classification performance. Similarly, the top-ranked gene-sets showed a better performance than the bottom-ranked gene-sets. The identified top-ranked gene-sets such as TNF-beta and MAPK signaling pathway were known to relate to cancer. In addition, the characteristic of top identified pathway network was further analyzed and visualized. SMAD3, a gene that was reported to be related to cancer by many studies, was mostly found to have the highest neighbor in 4 datasets. The results in this study has confirmed that GSNFS combined with feature selection is very promising as significantly fewer subnetworks were needed to build a classifier and gave a comparable performance to a full dataset classifier.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chen, L., Xuan, J., Riggins, R.B., Clarke, R., Wang, Y.: Identifying cancer biomarkers by network-constrained support vector machines. BMC Syst. Biol. 5(1), 161 (2011). https://doi.org/10.1186/1752-0509-5-161
Tyson, J.J., Baumann, W.T., Chen, C., Verdugo, A., Tavassoly, I., Wang, Y., Clarke, R.: Dynamic modelling of estrogen signaling and cell fate in breast cancer cells. Nat. Rev. Cancer 11(7), 523–532 (2011). https://doi.org/10.1038/nrc3081
Curtis, R.K., Orešič, M., Vidal-Puig, A.: Pathways to the analysis of microarray data. Trends Biotechnol. 23(8), 429–435 (2005). https://doi.org/10.1016/j.tibtech.2005.05.011
Chuang, H.Y., Lee, E., Liu, Y.T., Lee, D., Ideker, T.: Network-based classification of breast cancer metastasis. Mol. Syst. Biol. 3, 140 (2007). https://doi.org/10.1038/msb4100180
Doungpan, N., Engchuan, W., Chan, J.H., Meechai, A.: GSNFS: gene subnetwork biomarker identification of lung cancer expression data. BMC Med. Genomics 9(S3) (2016). https://doi.org/10.1186/s12920-016-0231-4
Chan, J.H., Sootanan, P., Larpeampaisarl, P.: Feature selection of pathway markers for microarray-based disease classification using negatively correlated feature sets. In: The 2011 International Joint Conference on Neural Networks, pp. 3293–3299 (2011). https://doi.org/10.1109/ijcnn.2011.6033658
Kozuevanich S., Meechai A., Chan J.H.: Feature selection in GSNFS-based marker identification. In: The 10th International Conference on Computational Systems-Biology and Bioinformatics (CSBio 2019). (2019). https://doi.org/10.1145/3365953.3365964
Barrett, T.: NCBI GEO: mining millions of expression profiles–database and tools. Nucleic Acids Res. 33(Database issue), D562–D566 (2004). https://doi.org/10.1093/nar/gki022
Soh, D., Dong, D., Guo, Y., Wong, L.: Consistency, comprehensiveness, and compatibility of pathway databases. BMC Bioinform. 11(1), 449 (2010). https://doi.org/10.1186/1471-2105-11-449
Hong, Y., Ho, K.S., Eu, K.W., Cheah, P.Y.: A susceptibility gene set for early onset colorectal cancer that integrates diverse signaling pathways: implication for tumorigenesis. Clin. Cancer Res. 13(4), 1107–1114 (2007). https://doi.org/10.1158/1078-0432.ccr-06-1633
Sabates-Bellver, J., Van der Flier, L.G., de Palo, M., Cattaneo, E., Maake, C., Rehrauer, H., et al.: transcriptome profile of human colorectal adenomas. Mol. Cancer Res. 5(12), 1263–1275 (2007). https://doi.org/10.1158/1541-7786.mcr-07-0267
Hong, Y., Downey, T., Eu, K.W., Koh, P.K., Cheah, P.Y.: A “metastasis-prone” signature for early-stage mismatch-repair proficient sporadic colorectal cancer patients and its implications for possible therapeutics. Clin. Exp. Metas. 27(2), 83–90 (2010). https://doi.org/10.1007/s10585-010-9305-4
Khamas, A., Ishikawa, T., Shimokawa, K., Mogushi, K., et al.: Screening for epigenetically masked genes in colorectal cancer using 5-Aza-2’-deoxycytidine, microarray and gene expression profile. Cancer Genomics Proteomics 9(2), 67–75 (2012). PMID: 22399497
Stark, C.: BioGRID: a general repository for interaction datasets. Nucleic Acids Res. 34(90001), D535–D539 (2006). https://doi.org/10.1093/nar/gkj109
Hall, M.A., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. 11, 10–18 (2009)
Shannon, P.: Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13(11), 2498–2504 (2003). https://doi.org/10.1101/gr.1239303
Syed, V.: TGF-β signaling in cancer. J. Cell. Biochem. 117(6), 1279–1287 (2016). https://doi.org/10.1002/jcb.25496
Millet, C., Zhang, Y.E.: Roles of Smad3 in TGF- β signaling during carcinogenesis. Crit. Rev. Eukaryot. Gene Expr. 17(4), 281–293 (2009). PMID: 17725494
Acknowledgements
The first author would like to acknowledge the graduate scholarship from the Department of Chemical Engineering, KMUTT for funding of his Master study.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Kozuevanich, S., Meechai, A., Chan, J.H. (2020). Biomarker Identification in Colorectal Cancer Using Subnetwork Analysis with Feature Selection. In: Meesad, P., Sodsee, S. (eds) Recent Advances in Information and Communication Technology 2020. IC2IT 2020. Advances in Intelligent Systems and Computing, vol 1149. Springer, Cham. https://doi.org/10.1007/978-3-030-44044-2_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-44044-2_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-44043-5
Online ISBN: 978-3-030-44044-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)