Jindong Li1, Dongfang Li1, Xudong Wei1, Yanhe Su2. 1. Department of Thoracic Surgery, the Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, P.R. China. 2. Department of Thoracic Surgery, the Second Affiliated Hospital of Zhengzhou University, Zhengzhou, P.R. China.
Abstract
BACKGROUND/AIM: Lung adenocarcinoma (AC) and squamous cell lung carcinoma (SCC) are two main subtypes of non-small cell lung cancer. In order to understand their biological differences, we conducted an in silico comparative genomic analysis of their expression profiles. MATERIALS AND METHODS: We utilized the published microarray data of 18 SCC samples and 40 AC samples to discriminate genes differentially expressed in SCC and AC. Genes were employed to construct a functional module network and build a support vector machine classifier. Another set of published non-small cell lung cancer microarray data was used to test the predictive accuracy of support vector machine classifier. RESULTS: Our analysis showed that SCC shows an elevated expression of genes related to cell division and DNA replication while AC presents an elevated expression of the genes related to protein transport and cell junction. ROC analysis demonstrates that the support vector machine classifier has a high classification accuracy for AC and SCC. CONCLUSION: AC and SCC are distinctively different in certain biological network modules. This proposes different pathological mechanisms involved in these two non-small cell lung cancer subtypes. Copyright
BACKGROUND/AIM: Lung adenocarcinoma (AC) and squamous cell lung carcinoma (SCC) are two main subtypes of non-small cell lung cancer. In order to understand their biological differences, we conducted an in silico comparative genomic analysis of their expression profiles. MATERIALS AND METHODS: We utilized the published microarray data of 18 SCC samples and 40 AC samples to discriminate genes differentially expressed in SCC and AC. Genes were employed to construct a functional module network and build a support vector machine classifier. Another set of published non-small cell lung cancer microarray data was used to test the predictive accuracy of support vector machine classifier. RESULTS: Our analysis showed that SCC shows an elevated expression of genes related to cell division and DNA replication while AC presents an elevated expression of the genes related to protein transport and cell junction. ROC analysis demonstrates that the support vector machine classifier has a high classification accuracy for AC and SCC. CONCLUSION: AC and SCC are distinctively different in certain biological network modules. This proposes different pathological mechanisms involved in these two non-small cell lung cancer subtypes. Copyright