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A multi-gene expression profile panel for predicting liver metastasis: An algorithmic approach

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  • Kanisha Shah
  • Shanaya Patel
  • Sheefa Mirza
  • Rakesh M Rawal

Abstract

Background & aim: Liver metastasis has been found to affect outcome in prostate, pancreatic and colorectal cancers, but its role in lung cancer is unclear. The 5 year survival rate remains extensively low owing to intrinsic resistance to conventional therapy which can be attributed to the genetic modulators involved in the pathogenesis of the disease. Thus, this study aims to generate a model for early diagnosis and timely treatment of liver metastasis in lung cancer patients. Methods: mRNA expression of 15 genes was quantified by real time PCR on lung cancer specimens with (n = 32) and without (n = 30) liver metastasis and their normal counterparts. Principal Component analysis, linear discriminant analysis and hierarchical clustering were conducted to obtain a predictive model. The accuracy of the models was tested by performing Receiver Operating Curve analysis. Results: The expression profile of all the 15 genes were subjected to PCA and LDA analysis and 5 models were generated. ROC curve analysis was performed for all the models and the individual genes. It was observed that out of the 15 genes only 8 genes showed significant sensitivity and specificity. Another model consisting of the selected eight genes was generated showing a specificity and sensitivity of 90.0 and 96.87 respectively (p

Suggested Citation

  • Kanisha Shah & Shanaya Patel & Sheefa Mirza & Rakesh M Rawal, 2018. "A multi-gene expression profile panel for predicting liver metastasis: An algorithmic approach," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-14, November.
    • Handle: RePEc:plo:pone00:0206400
    DOI: 10.1371/journal.pone.0206400
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    References listed on IDEAS

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    1. Dudoit S. & Fridlyand J. & Speed T. P, 2002. "Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 77-87, March.
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