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Predicting the clinical behavior of ovarian cancer from gene expression profiles
  1. F. De Smet*,
  2. N. L.M.M. Pochet*,
  3. K. Engelen*,
  4. T. Van Gorp,
  5. P. Van Hummelen,
  6. K. Marchal*,
  7. F. Amant,
  8. D. Timmerman,
  9. B. L.R. De Moor* and
  10. I. B. Vergote
  1. * Department of Electrical Engineering ESAT-SCD, K.U.Leuven, Leuven-Heverlee, Belgium
  2. Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, University Hospitals, K.U.Leuven, Leuven, Belgium
  3. MicroArray Facility, Flanders Interuniversity Institute of Biotechnology (V.I.B.), Leuven, Belgium
  1. Address correspondence and reprint requests to: Ignace Vergote, MD, PhD, Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, University Hospitals, K.U. Leuven, Herestraat 49, 3000 Leuven, Belgium. Email: ignace.vergote{at}


We investigated whether prognostic information is reflected in the expression patterns of ovarian carcinoma samples. RNA obtained from seven FIGO stage I without recurrence, seven platin-sensitive advanced-stage (III or IV), and six platin-resistant advanced-stage ovarian tumors was hybridized on a complementary DNA microarray with 21,372 spotted clones. The results revealed that a considerable number of genes exhibit nonaccidental differential expression between the different tumor classes. Principal component analysis reflected the differences between the three tumor classes and their order of transition. Using a leave-one-out approach together with least squares support vector machines, we obtained an estimated classification test accuracy of 100% for the distinction between stage I and advanced-stage disease and 76.92% for the distinction between platin-resistant versus platin-sensitive disease in FIGO stage III/IV. These results indicate that gene expression patterns could be useful in clinical management of ovarian cancer.

  • clinical
  • FIGO stage
  • microarrays
  • ovarian cancer
  • platin resistance

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