Join transcriptomic analysis of tumoral progression in Lung cancer

Objectives: The analysis of genetic expression data sets that compare healthy tissue with tumoral or pathologic tissue available in specialized repositories allows the identification of an important deregulated genes only in lung cancer and no in other lung diseases. Methods: 10 Microarray datasets (6 lung cancer, 4 lung diseases) comparing normal and diseases lung tissue were carefully chosen to identify differentiated expressed genes (DEGs) only in lung cancer, through the utilization of R language, specialized libraries, and DAVID bioinformatics tool for gene enrichment analyses. The functional annotation analysis allows to identify genes with experimental evidence associated with tumoral processes. The PPIs networks shows the connectivity patterns of deregulated genes in lung cancer and signaling pathways related to tumoral processes. And the survival analysis shows the association between the expression of the main biomarkers identified with the survival of lung cancer patients. Results: Functional annotation analysis shows that these unique DEGs of lung cancer are related to tumoral acquisition characteristics and pathological processes. And among them, there are some genes that codify for transcription factors (AP1, STAT5A, MEIS1, ELK1 and TFDP2) that have relevant theoretical and experimental evidence about their ability to bind and regulate an important number of the winner DEGs found uniquely deregulated in lung cancer. Conclusion: Our results indicate that lung cancer have exclusive deregulated genes and specifically transcription factors that are not deregulated with other lung diseases, related to main characteristic tumoral processes, suggesting that could become important biomarkers of tumoral progression and targets for lung cancer treatment.