TY - JOUR
T1 - Genomic dissection for characterization of cancerous oral epithelium tissues using transcription profiling
AU - Hwang, Daehee
AU - Alevizos, Ilias
AU - Schmitt, William A.
AU - Misra, Jatin
AU - Ohyama, Hiroe
AU - Todd, Randy
AU - Mahadevappa, Mamatha
AU - Warrington, Janet A.
AU - Stephanopoulos, George
AU - Wong, David T.
AU - Stephanopoulos, Gregory
N1 - Funding Information:
This work was supported by the Engineering Research Program of the Office of Basic Energy Science at the Deptartment of Energy, Grant No. DE-FG02-94ER-14487 and DE-FG02-99ER-15015. Additional support was provided by NIH grant number 1-RO1-DK58533-01.
PY - 2003/4
Y1 - 2003/4
N2 - 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.
AB - 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.
KW - DNA microarray
KW - Discriminatory genes
KW - Oral epithelial cancer
KW - Pattern recognition
KW - Statistical analysis
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U2 - 10.1016/S1368-8375(02)00108-2
DO - 10.1016/S1368-8375(02)00108-2
M3 - Article
C2 - 12618198
AN - SCOPUS:0037399879
SN - 1368-8375
VL - 39
SP - 259
EP - 268
JO - Oral Oncology
JF - Oral Oncology
IS - 3
ER -