4.5 Article

Global expression-based classification of lymph node metastasis and extracapsular spread of oral tongue squamous cell carcinoma

Journal

NEOPLASIA
Volume 8, Issue 11, Pages 925-932

Publisher

ELSEVIER SCIENCE INC
DOI: 10.1593/neo.06430

Keywords

squamous cell carcinoma; lymph node metastasis; extracapsular spread; microarray; leave-one-out cross-validation

Categories

Funding

  1. NCI NIH HHS [R03 CA114688, P01 CA106451] Funding Source: Medline
  2. NIDCR NIH HHS [K22 DE014847, R01 DE015970, R03 DE016569] Funding Source: Medline

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Regional lymph nodemetastasis is a critical event in oral tongue squamous cell carcinoma (OTSCC) progression. The identification of biomarkers associated with the metastatic process would provide critical prognostic information to facilitate clinical decision making for improved management of OTSCC patients. Global expressional profiles were obtained for 25 primary OTSCCs, where 11 cases showed lymph node metastasis (pN(+)) histologically and 14 cases were nonmetastatic (pN(-)). Seven of pN(+) cases also exhibited extracapsular spread (ECS) of metastatic nodes. Multiple expression indices were used to generate signature gene sets for pN(+/-) and ECS+/- cases. Selected genes from signature gene sets were validated using quantitative reverse transcription - polymerase chain reaction (qRT-PCR). The classification powers of these genes were then evaluated using a logistic model, receiver operating characteristic curve analysis, and leave-oneout cross-validation. qRT-PCR validation data showed that differences at RNA levels are either statistically significant (P <.05) or suggestive (P <.1) for six of eight genes tested (BMP2, CTTN, EEF1A1, GTSE1, MMP9, and EGFR) for pN(+/-) cases, and for five of eight genes tested ( BMP2, CTTN, EEF1A1, MMP9, and EGFR) for ECS+/- cases. Logistic models with specific combinations of genes (CTTN+MMP9+EGFR for pN and CTTN+EEF1A1+ MMP9 for ECS) achieved perfect specificity and sensitivity. Leave-one-out cross-validation showed overall accuracy rates of 85% for both pN and ECS prediction models. Our results demonstrated that the pN and the ECS of OTSCCs can be predicted by gene expression analyses of primary tumors.

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