Metagenes Associated with Survival in Non-Small Cell Lung Cancer

• Published in: Cancer informatics 2011-01, Vol.2011 (10), p.175-183
• Main Authors: Urgard, Egon, Vooder, Tõnu, Võsa, Urmo, Välk, Kristjan, Liu, Mingming, Luo, Cheng, Hoti, Fabian, Roosipuu, Retlav, Annilo, Tarmo, Laine, Jukka, Frenz, Christopher M., Zhang, Liqing, Metspalu, Andres
• Format: Article
• Language: English
• Subjects: Analysis; Gene expression; Genetic aspects; Lung cancer; Lung cancer, Non-small cell; Original Research
• ISSN: 1176-9351; 1176-9351
• DOI: 10.4137/CIN.S7135

NSCLC (non-small cell lung cancer) comprises about 80% of all lung cancer cases worldwide. Surgery is most effective treatment for patients with early-stage disease. However, 30%–55% of these patients develop recurrence within 5 years. Therefore, markers that can be used to accurately classify early-stage NSCLC patients into different prognostic groups may be helpful in selecting patients who should receive specific therapies. A previously published dataset was used to evaluate gene expression profiles of different NSCLC subtypes. A moderated two-sample t-test was used to identify differentially expressed genes between all tumor samples and cancer-free control tissue, between SCC samples and AC/BC samples and between stage I tumor samples and all other tumor samples. Gene expression microarray measurements were validated using qRT-PCR. Bayesian regression analysis and Kaplan-Meier survival analysis were performed to determine metagenes associated with survival. We identified 599 genes which were down-regulated and 402 genes which were up-regulated in NSCLC compared to the normal lung tissue and 112 genes which were up-regulated and 101 genes which were down-regulated in AC/BC compared to the SCC. Further, for stage Ib patients the metagenes potentially associated with survival were identified. Genes that expressed differently between normal lung tissue and cancer showed enrichment in gene ontology terms which were associated with mitosis and proliferation. Bayesian regression and Kaplan-Meier analysis showed that gene-expression patterns and metagene profiles can be applied to predict the probability of different survival outcomes in NSCLC patients.