Abstract
Identifying specific molecular markers and developing sensitive detection methods are two of the fundamental requirements for detection and differential diagnosis of cancer. Toward this goal, we first performed cDNA array analysis using 65 non-small cell lung cancer and non-involved normal lung tissues. We then used several complementary statistical and analytical methods to examine gene expression profiles generated by us and others from four independent sets of normal and neoplastic lung tissues. We report here that several sets of roughly 20 genes were sufficient to provide a robust distinction between normal and neoplastic tissues of the lung. Next we assessed the predictive ability of these gene sets by using Flow-Thru Chips (FTC) (MetriGenix, Baltimore, MD) containing 20 genes to screen 48 primary lung tumours and normal lung tissues. Gene expression changes detected by FTC distinguished lung cancers from the normal lung tissues using an RNA amount equivalent to that present in as few as 300 cells. We also used an independent set of 24 genes and showed that their expression profile was equally effective when measured by quantitative polymerase chain reaction (Q-PCR). Our results demonstrate that lung cancers can be identified based on the expression patterns of just 20 genes and that this approach is applicable for cancer diagnosis, prognosis, and monitoring using small amount of tumour or biopsy samples.