Gene Sets of Gene Ontology are More Stable Diagnostic Biomarkers than Genes in Oral Squamous Cell Carcinoma

Tao      Huang; Wei      Wu; Honglai      Jin; Yu-Dong      Cai

doi:10.2174/1574893611308050009

Abstract

Many people suffer from oral squamous cell carcinoma (OSCC), a kind of cancer with high severity and prevalence. The usual diagnostic method of oral squamous cell carcinoma is still very primitive by visually examining the mouth. When patients have a definitive diagnosis, they usually have missed the optimal therapeutic period. Therefore, it is essential to diagnose OSCC as early as possible. Microarray technology has been widely used for biomarker identification, but individual gene biomarkers excavated from microarray studies are often limited by poor reproducibility and robustness, since there was little or no overlap between different studies in term of their results. Here, we used both gene based approach and gene set based approach to identify biomarkers of oral squamous cell carcinoma using five independent data sets. Then, we evaluated the reproducibility of differentially expressed genes in five data sets quantified by t-test p values, and the reproducibility of Gene Ontology (GO) gene sets in five data sets, quantified by Matthews’s correlation coefficient (MCC) using leave-one-out cross validation (LOOCV). Very weak correlation was found between the differentially expressed genes in most data set pairs - the average Pearson correlation coefficient of ten data set pairs was merely 0.048. However, the GO gene sets among data set pairs are significantly correlated – Pearson correlation test p value is 0 for all data set pairs and the average Pearson correlation coefficient is 0.510. Our study shows that it is feasible to identify stable and reproducible gene set biomarkers and pave a way for discovering diagnostic biomarkers of oral squamous cell carcinoma using GO gene sets.

Keywords: Biomarker, gene set, oral squamous cell carcinoma, reproducibility, robust.

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