Skip to main content

Identification of cancer-specific biomarkers by using microarray gene expression profiling


Carcinogenesis is a complex biological process that is affected by multiple genes, some of which can be used as biomarkers for specific tumor stages or types. An effective method for predicting such tumor markers, which are important for both diagnosis and prevention, is gene expression profiling. Here, we used a classification method and survival tests to predict cancer biomarker genes from individual cancer gene expression profiles. To validate the ability of classification in our samples, an area under the curve was calculated using support vector machine classification methods for selected genes. Twenty-three of the candidate biomarkers were correlated with patient survival. To confirm classification performance in other samples, we validated our results by comparison with breast and ovarian cancer samples. We conclude that these 23 genes might be used as cancer biomarkers.

This is a preview of subscription content, access via your institution.


  1. 1.

    Liu, J.J. et al. Multiclass cancer classification and biomarker discovery using GA-based algorithms. Bioinformatics 21, 2691–2697 (2005).

    Article  CAS  Google Scholar 

  2. 2.

    Tseng, G.C. et al. Investigating multi-cancer biomarkers and their cross-predictability in the expression profiles of multiple cancer types. Biomarker Insights 4, 57–79 (2009).

    Google Scholar 

  3. 3.

    Bello, M.G. Enhanced training algorithms, and integrated training/architecture selection for multilayer perceptron networks. IEEE transactions on neural networks/a publication of the IEEE Neural Networks Council 3, 864–875 (1992).

    Article  CAS  Google Scholar 

  4. 4.

    Levner, I. Feature selection and nearest centroid classification for protein mass spectrometry. BMC Bioinformatics 6, 68 (2005).

    Article  Google Scholar 

  5. 5.

    Chang, C.-C. & Lin, C.-J. LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 1–27 (2011).

    Article  Google Scholar 

  6. 6.

    Boersma, B.J. et al. A stromal gene signature associated with inflammatory breast cancer. Int. J. Cancer 122, 1324–1332 (2008).

    Article  CAS  Google Scholar 

  7. 7.

    Liu, R. et al. The prognostic role of a gene signature from tumorigenic breast-cancer cells. N. Engl. J. Med. 356, 217–226 (2007).

    Article  CAS  Google Scholar 

  8. 8.

    Turashvili, G. et al. Novel markers for differentiation of lobular and ductal invasive breast carcinomas by laser microdissection and microarray analysis. BMC Cancer 7, 55 (2007).

    Article  Google Scholar 

  9. 9.

    Bowen, N.J. et al. Gene expression profiling supports the hypothesis that human ovarian surface epithelia are multipotent and capable of serving as ovarian cancer initiating cells. BMC Med. Genomics 2, 71 (2009).

    Article  Google Scholar 

  10. 10.

    Tusher, V.G., Tibshirani, R. & Chu, G. Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl. Acad. Sci. USA 98, 5116–5121 (2001).

    Article  CAS  Google Scholar 

Download references

Author information



Corresponding authors

Correspondence to Soo Young Cho or Young Seek Lee.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Chai, J.C., Park, S., Seo, H. et al. Identification of cancer-specific biomarkers by using microarray gene expression profiling. BioChip J 7, 57–62 (2013).

Download citation


  • Cancer biomarker
  • Bioinformatics
  • Classification
  • Meta-analysis
  • Gene expression profile