Abstract
Breast cancer is a malignant disease that is caused by multiple factors, and the prognosis of breast cancer patients is the focus of medical research. In the present study, we have proposed a novel computational framework which identifies the prognostic biomarkers of breast cancer based on multiple network fusion and multiple scoring strategies. In order to eliminate the heterogeneity of samples, we first clustered the patient samples according to the principle components of gene expression. For each cluster, we used the fusion network to reduce the incompleteness of interactome and to take into account more disease-related information. Genes were weighted from the perspectives of biological functions, prognostic ability and correlation with known disease genes, and a network propagation model was applied to the fusion network so as to comprehensively evaluate the influence of genes on breast cancer patients. To evaluate the performance of our method, we have compared our approach with three state-of-the-art approaches. The results demonstrated that biomarkers captured by our method have both strong discriminative power in differentiating patients with different prognostic outcomes and biological significance.
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
Weigelt, B., Peterse, J.L., Van’t Veer, L.J.: Breast cancer metastasis: markers and models. Nat. Rev. Cancer 5, 591 (2005)
Slamon, D.J., Clark, G.M., Wong, S.G., Levin, W.J., Ullrich, A., McGuire, W.L.: Human breast cancer: correlation of relapse and survival with amplification of the HER-2/neu oncogene. Science 235, 177–182 (1987)
Key, T.J., Verkasalo, P.K., Banks, E.: Epidemiology of breast cancer. Lancet Oncol. 2, 133–140 (2001)
Sawyers, C.L.: The cancer biomarker problem. Nature 452, 548 (2008)
Winter, C., et al.: Google goes cancer: improving outcome prediction for cancer patients by network-based ranking of marker genes. PLoS Comput. Biol. 8, e1002511 (2012)
Cun, Y., Fröhlich, H.: Network and data integration for biomarker signature discovery via network smoothed t-statistics. PLoS One 8, e73074 (2013)
Wang, X., Wang, S.-S., Zhou, L., Yu, L., Zhang, L.-M.: A network-pathway based module identification for predicting the prognosis of ovarian cancer patients. J. Ovarian Res. 9, 73 (2016)
Choi, J., Park, S., Yoon, Y., Ahn, J.: Improved prediction of breast cancer outcome by identifying heterogeneous biomarkers. Bioinformatics 33, 3619–3626 (2017)
Liu, W., et al.: Topologically inferring risk-active pathways toward precise cancer classification by directed random walk. Bioinformatics 29, 2169–2177 (2013)
Edgar, R., Domrachev, M., Lash, A.E.: Gene expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 30, 207–210 (2002)
Pawitan, Y., et al.: Gene expression profiling spares early breast cancer patients from adjuvant therapy: derived and validated in two population-based cohorts. Breast Cancer Res. 7, R953 (2005)
Wang, Y., et al.: Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet 365, 671–679 (2005)
Menche, J., et al.: Uncovering disease-disease relationships through the incomplete interactome. Science 347, 1257601 (2015)
Ashburner, M., et al.: Gene Ontology: tool for the unification of biology. Nat. Genet. 25, 25 (2000)
Consortium, G.O.: Expansion of the Gene Ontology knowledgebase and resources. Nucleic Acids Res. 45, D331–D338 (2016)
Piñero, J., et al.: DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants. Nucleic Acids Res. 45, D833–D839 (2016)
Vanunu, O., Magger, O., Ruppin, E., Shlomi, T., Sharan, R.: Associating genes and protein complexes with disease via network propagation. PLoS Comput. Biol. 6, e1000641 (2010)
Acknowledgements
This work was supported in part by the National Natural Science Foundation of China (61622213, 61732009, 61420106009, 61702054), the 111 Project (No. B18059), the Hunan Provincial Science and Technology Program (2018WK4001), the Training Program for Excellent Young Innovators of Changsha (Grant No. kq1802024), and the Hunan Provincial Natural Science Foundation of China (Grant No. 2018JJ3568).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Li, X., Xiang, J., Wang, J., Wu, FX., Li, M. (2019). Identification of Prognostic and Heterogeneous Breast Cancer Biomarkers Based on Fusion Network and Multiple Scoring Strategies. In: Huang, DS., Jo, KH., Huang, ZK. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11644. Springer, Cham. https://doi.org/10.1007/978-3-030-26969-2_50
Download citation
DOI: https://doi.org/10.1007/978-3-030-26969-2_50
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-26968-5
Online ISBN: 978-3-030-26969-2
eBook Packages: Computer ScienceComputer Science (R0)