Genomic dissection for characterization of cancerous oral epithelium tissues using transcription profiling

Daehee Hwang, Ilias Alevizos, William A. Schmitt, Jatin Misra, Hiroe Ohyama, Randy Todd, Mamatha Mahadevappa, Janet A. Warrington, George Stephanopoulos, David T. Wong, Gregory Stephanopoulos

Research output: Contribution to journalArticlepeer-review

30 Scopus citations

Abstract

Genome-wide and high-throughput functional genomic tools offer the potential of identifying disease-associated genes and dissecting disease regulatory patterns. There is a need for a set of systematic bioinformatic tools that handles efficiently a large number of variables for extracting biological meaning from experimental outputs. We present well-characterized statistical tools to discover genes that are differentially expressed between malignant oral epithelial and normal tissues in microarray experiments and to construct a robust classifier using the identified discriminatory genes. Those tools include Wilks' lambda score, error rate estimated from leave-one out cross-validation (LOOCV) and Fisher Discriminant Analysis (FDA). High Density DNA microarrays and Real Time Quantitative PCR were employed for the generation and validation of the transcription profile of the oral cancer and normal samples. We identified 45 genes that are strongly correlated with malignancy. Of the 45 genes identified, six have been previously implicated in the disease, and two are uncharacterized clones.

Original languageEnglish (US)
Pages (from-to)259-268
Number of pages10
JournalOral Oncology
Volume39
Issue number3
DOIs
StatePublished - Apr 1 2003
Externally publishedYes

Keywords

  • DNA microarray
  • Discriminatory genes
  • Oral epithelial cancer
  • Pattern recognition
  • Statistical analysis

ASJC Scopus subject areas

  • Oral Surgery
  • Oncology
  • Cancer Research

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