Skip to main content
SpringerLink
Log in

Integrating Multiple Datasets to Discover Stage-Specific Cancer Related Genes and Stage-Specific Pathways

  • Conference paper
  • First Online:
Bioinformatics and Biomedical Engineering (IWBBIO 2019)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 11465))

  • 1106 Accesses

Abstract

Investigating the evolution of complex diseases through different disease stages is critical for understanding the root cause of these diseases, which is fundamental for their accurate prognosis and effective treatment. There have been numerous studies that have identified many single genes, static modules and individual pathways related cancer progression, but few attempt has been developed to identify specific genes and pathways interactions related individual disease stages via data integration. To address these issues, we have proposed a general working flow, to reveal disease stages dynamics by joint analysis of multi-level datasets. Our contribution is two-fold. Firstly, we present a classical regression method to identify stage-specific cancer genes, where the gene expression and DNA methylation datasets are integrated. Secondly, we construct a pathway evolution network, which considered interactions among specific mapped pathways and their overlapped genes. Interestingly, the potential discovered biological functions from this network together with the common bridges and genes, not only help us to understand the functional evolution and dynamics of complex diseases in a more deep fashion, but also useful for clinical management to design customized drugs with more effective therapy.

B. Chen and C. Aouiche—Equal contributors.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Horne, S., Chowdhury, S., Heng, H.: Stress, genomic adaptation, and the evolutionary trade-off. Front. Genet. 5, 92 (2014)

    Article  Google Scholar 

  2. Horne, S., Pollick, S., Heng, H.: Evolutionary mechanism unifies the hallmarks of cancer. Int. J. Cancer 136, 2012–21 (2015)

    Article  Google Scholar 

  3. Spiller, D.G., Wood, C.D., Rand, D.A., White, M.R.H.: Measurement of single-cell dynamics. Nature 465(7299), 736–748 (2010)

    Article  Google Scholar 

  4. Chen, L., Wang, R.S., Zhang, X.S.: Biomolecular Networks: Methods and Applications in Systems Biology, vol. 10. Wiley, Hoboken (2009)

    Book  Google Scholar 

  5. Chen, L., Wang, R., Li, C., Aihara, K.: Modeling Biomolecular Networks in Cells: Structures and Dynamics. Springer, London (2010). https://doi.org/10.1007/978-1-84996-214-8

    Book  MATH  Google Scholar 

  6. Lee, J., Zhao, X., Yoon, I., Lee, J., Kwon, N., Wang, Y., et al.: Integrative analysis of mutational and transcriptional profiles reveals driver mutations of metastatic breast cancers. Cell Discov. 2, 16025 (2016)

    Article  Google Scholar 

  7. Guanghui, Z., Hui, Y., Xiao, C., Jun, W., Yong, Z., Xing-Ming, Z.: CSTEA: a webserver for the cell state transition expression atlas. Nucleic Acids Res. 45, 103–108 (2017)

    Google Scholar 

  8. Bosinger, S.E., Jacquelin, B., Benecke, A., Silvestri, G., Muller-Trutwin, M.: Systems biology of natural Simian immunodeficiency virus infections. Curr. Opin. HIV AIDS 7(1), 71–78 (2012)

    Article  Google Scholar 

  9. Jordan, N.V., et al.: HER2 expression identifies dynamic functional states within circulating breast cancer cells. Nature 537(7618), 102–106 (2016)

    Article  Google Scholar 

  10. Nakamura, A., Osonoi, T., Terauchi, Y.: relationship between urinary sodium excretion and pioglitazone-induced edema. J. Diab. Invest. 1(5), 208–211 (2010)

    Article  Google Scholar 

  11. Michor, F., Iwasa, Y., Nowak, M.A.: Dynamics of cancer progression. Nat. Rev. Cancer 4(3), 197 (2004)

    Article  Google Scholar 

  12. Karczewski, K.J., Snyder, M.P.: Integrative omics for health and disease. Nat. Rev. Genet. 19(5), 299 (2018)

    Article  Google Scholar 

  13. Ma, X., Sun, P.G., Zhang, Z.Y.: An integrative framework for protein interaction network and methylation data to discover epigenetic modules. IEEE/ACM Trans. Comput. Biol. Bioinform. (Early Access), 1 (2018). https://doi.org/10.1109/TCBB.2018.2831666

  14. Hsu, F., Serpedin, E., Hsiao, T., Bishop, A., Dougherty, E., Chen, Y.: Reducing confounding and suppression effects in TCGA data: an integrated analysis of chemotherapy response in ovarian cancer. BMC Genomics 13, 13 (2012)

    Article  Google Scholar 

  15. Parker, J., Mullins, M., Cheang, M., Leung, S., Voduc, D., Vickery, T., et al.: Supervised risk predictor of breast cancer based on intrinsic subtypes. J. Clin. Oncol. 27, 1160–1167 (2009)

    Article  Google Scholar 

  16. Curtis, C., Shah, S., Chin, S., Turashvili, G., Rueda, O., Dunning, M., et al.: The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 486, 346–352 (2012)

    Article  Google Scholar 

  17. Kittaneh, M., Montero, A., Gluck, S.: Molecular profiling for breast cancer: a comprehensive review. Biomark. Cancer 5, 61–70 (2013)

    Article  Google Scholar 

  18. Li, A., Walling, J., Ahn, S., Kotliarov, Y., Su, Q., Quezado, M., et al.: Unsupervised analysis of transcriptomic profiles reveals six glioma subtypes. Cancer Res. 69, 2091–2099 (2009)

    Article  Google Scholar 

  19. Shen, L., Toyota, M., Kondo, Y., Lin, E., Zhang, L., Guo, Y., et al.: Integrated genetic and epigenetic analysis identifies three different subclasses of colon cancer. Proc. Nat. Acad. Sci. U.S.A. 104, 18654–18659 (2007)

    Article  Google Scholar 

  20. van’t Veer, L., Dai, H., van de Vijver, M., He, Y., Hart, A., Mao, M., et al.: Gene expression profiling predicts clinical outcome of breast cancer. Nature 415, 530–536 (2002)

    Article  Google Scholar 

  21. Vaquerizas, J.M., Kummerfeld, S.K., Teichmann, S.A., Luscombe, N.M.: A census of human transcription factors: function, expression and evolution. Nat. Biotechnol. 10, 252–263 (2009)

    Google Scholar 

  22. Schwikowski, B., Uetz, P., Fields, S.: A network of protein-protein interactions in yeast. Nat. Biotechnol. 18, 1257–1261 (2010)

    Article  Google Scholar 

  23. Menche, J., et al.: Uncovering disease-disease relationships through the incomplete interactome. Science 347, 1257601 (2015)

    Article  Google Scholar 

  24. Tong, A.H., Lesage, G., Bader, G.D., et al.: Global mapping of the yeast genetic interaction network: discovering gene and drug function. Science 303(5659), 808–813 (2004)

    Article  Google Scholar 

  25. Glazko, G.V., Emmert-Streib, F.: Unite and conquer: univariate and multivariate approaches for finding differentially expressed gene sets. Bioinformatics 25(18), 2348–2354 (2009)

    Article  Google Scholar 

  26. Xia, Y., Yu, H., Jansen, R., Seringhaus, M., Baxter, S., Greenbaum, D., et al.: Analyzing cellular biochemistry in terms of molecular networks. Ann. Rev. Biochem. 73, 1051–1087 (2004)

    Article  Google Scholar 

  27. Zou, H., Hastie, T.: Regularization and variable selection via the elastic net. J. Royal Stat. Soc. Ser. B 67, 301–320 (2005)

    Article  MathSciNet  Google Scholar 

  28. Pineda, S., Milne, R.L., Calle, M.L., Rothman, N., De Maturana, E., et al.: Genetic variation in the TP53 pathway and bladder cancer risk. A comprehensive analysis. PLoS One 9(5), e89952 (2014)

    Article  Google Scholar 

  29. Cho, S., Kim, K., Kim, Y.J., Lee, J.K., Cho, Y.S., et al.: Joint identification of multiple genetic variants via elastic-net variable selection in a genome-wide association analysis. Ann. Hum. Genet. 74, 416–428 (2010)

    Article  Google Scholar 

  30. Zhou, H., Sehl, M.E., Sinsheimer, J.S., Lange, K.: Association screening of common and rare genetic variants by penalized regression. Bioinformatics 26, 2375–2382 (2010)

    Article  Google Scholar 

  31. Mankoo, P.K., Shen, R., Schultz, N., Levine, D.A., Sander, C.: Time to recurrence and survival in serous ovarian tumors predicted from integrated genomic profiles. PLoS One 6, e24709 (2011)

    Article  Google Scholar 

  32. Lee, H., Flaherty, P., Ji, H.: Systematic genomic identification of colorectal cancer genes delineating advanced from early clinical stage and metastasis. BMC Med. Genomics 6, 54 (2013)

    Article  Google Scholar 

  33. Lee, H., Palm, J., Grimes, S., Ji, H.: The cancer genome atlas clinical explorer: a web and mobile interface for identifying clinical-genomic driver associations. Genome Med. 7, 112 (2015)

    Article  Google Scholar 

  34. Ahn, T., Lee, E., Huh, N., Park, T.: Personalized identification of altered pathways in cancer using accumulated normal tissue data. Bioinformatics 30, i422–i429 (2014)

    Article  Google Scholar 

  35. Perez, R., Wu, N., Klipfel, A.A., Beart Jr., R.W.: A better cell cycle target for gene therapy of colorectal cancer: cyclin G. J. Gastrointest. Surg. 7, 884–889 (2003)

    Article  Google Scholar 

  36. Maurer, G., Tarkowski, B., Baccarini, M.: Raf kinases in cancer-roles and therapeutic opportunities. Oncogene 30(32), 3477–3488 (2011)

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the National Natural Science Foundation of China under [Grant No. 61602386, 61772426 and 61332014]; the Natural Science Foundation of Shaanxi Province under [Grant No. 2017JQ6008]; and the Top International University Visiting Program for Outstanding Young scholars of Northwestern Polytechnical University.

Author information

Authors and Affiliations

  1. School of Computer Science, Northwestern Polytechnical University, Xi’an, 710072, China

    Bolin Chen, Chaima Aouiche & Xuequn Shang

  2. Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi’an, China

    Bolin Chen & Xuequn Shang

Authors
  1. Bolin Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chaima Aouiche
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xuequn Shang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bolin Chen .

Editor information

Editors and Affiliations

  1. Department of Computer Architecture and Computer Technology Higher Technical School of Information Technology and Telecommunications Engineering, CITIC-UGR, Granada, Spain

    Ignacio Rojas

  2. ETSIIT, University of Granada, Granada, Spain

    Olga Valenzuela

  3. CITIC-UGR, University of Granada, Granada, Spain

    Fernando Rojas

  4. Fundacion Progreso y Salud, Granada, Spain

    Francisco Ortuño

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (xlsx 47 KB)

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chen, B., Aouiche, C., Shang, X. (2019). Integrating Multiple Datasets to Discover Stage-Specific Cancer Related Genes and Stage-Specific Pathways. In: Rojas, I., Valenzuela, O., Rojas, F., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2019. Lecture Notes in Computer Science(), vol 11465. Springer, Cham. https://doi.org/10.1007/978-3-030-17938-0_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-17938-0_22

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-17937-3

  • Online ISBN: 978-3-030-17938-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation