Abstract
Lung cancer accounts for the highest fatalities amongst cancers worldwide. Within the frames of the Metaboli-Ca project, we explored the effects of non-small cell lung cancer (NSCLC) by utilizing next generation sequencing (NGS) technologies. Here, we update and expand our previous work; exploiting RNA sequencing data for the derivation of differentially expressed genes and alternatively spliced genes in cancer cells compared to the adjacent normal tissue and carry out functional analysis to discover the underlying molecular mechanisms altered in cancer cells. We used our established pipeline for quantitative analysis, which utilizes a range of state-of-the-art tools, and investigated the modifications performed in cancer cells. A significant number of 1449 genes were found as differentially expressed, while 368 genes as alternatively spliced. Focusing in alternative splicing events, a number of important molecular mechanisms emerged, such as proteasome functionality, stemness, and regulation of mitosis. Our analysis suggests several molecular players that could enhance the understanding of NSCLC pathophysiology.
Similar content being viewed by others
References
Reis-Filho JS. Next-generation sequencing. Breast Cancer Res. 2009;11(3):1.
Kontogianni, G., Papadodima, O., Mitrakas, A., Maglogiannis, I., Koukourakis, M. I., Giatromanolaki, A., & Chatziioannou, A.. Exploring the Molecular Determinants of Tumor-Stroma Interaction in Non-small Cell Lung Cancer Through the Utilization of RNA-seq Data from Lung Biopsies. In XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016 (pp. 489–493). Springer International Publishing. (2016)
American Cancer Society. Accessed at http://www.cancer.org/ on June 2016.
World Cancer Research Fund International. Accessed at http://www.wcrf.org/int/cancer-facts-figures/worldwide-data on June 2016.
Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics, btu. 2014:170.
Kim D, Pertea G, Trapnell C, Pimentel H, Kelley R, Salzberg SL. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 2013;14(4):1.
Langmead B, Salzberg SL. Fast gapped-read alignment with bowtie 2. Nat Methods. 2012;9(4):357–9.
Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley DR, et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and cufflinks. Nat Protoc. 2012;7(3):562–78.
Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: tool for the unification of biology. Nat Genet. 2000;25(1):25–9.
Milacic M, Haw R, Rothfels K, Wu G, Croft D, Hermjakob H, et al. Annotating cancer variants and anti-cancer therapeutics in reactome. Cancer. 2012;4(4):1180–211.
Croft D, Mundo AF, Haw R, Milacic M, Weiser J, Wu G, et al. The reactome pathway knowledgebase. Nucleic Acids Res. 2014;42(D1):D472–7.
Smith CL, Eppig JT. The mammalian phenotype ontology: enabling robust annotation and comparative analysis. Wiley Interdiscip Rev Syst Biol Med. 2009;1(3):390–9.
Köhler S, Doelken SC, Mungall CJ, Bauer S, Firth HV, Bailleul-Forestier I, et al. The human phenotype ontology project: linking molecular biology and disease through phenotype data. Nucleic Acids Res, gkt1026. 2013; doi:10.1093/nar/gkt1026.
Wang G, Yang ZQ, Zhang K. Endoplasmic reticulum stress response in cancer: molecular mechanism and therapeutic potential. Am J Transl Res. 2010;2(1):65–74.
Sekido Y, Fong KM, Minna JD. Molecular genetics of lung cancer. Annu Rev Med. 2003;54(1):73–87.
Brambilla E, Gazdar A. Pathogenesis of lung cancer signalling pathways: roadmap for therapies. Eur Respir J. 2009;33(6):1485–97.
Tu Y, Chen C, Pan J, Xu J, Zhou ZG, Wang CY. The ubiquitin proteasome pathway (UPP) in the regulation of cell cycle control and DNA damage repair and its implication in tumorigenesis. Int J Clin Exp Pathol. 2012;5(8):726–38.
Tang Y, Geng Y, Luo J, Shen W, Zhu W, Meng C, et al. Downregulation of ubiquitin inhibits the proliferation and radioresistance of non-small cell lung cancer cells in vitro and in vivo. Sci Report. 2015;5
Cunningham F, Amode MR, Barrell D, Beal K, Billis K, Brent S, et al. Ensembl 2015. Nucleic Acids Res. 2015;43(D1):D662–9.
Trapnell C, Hendrickson DG, Sauvageau M, Goff L, Rinn JL, Pachter L. Differential analysis of gene regulation at transcript resolution with RNA-seq. Nat Biotechnol. 2013;31(1):46–53.
Koutsandreas T, Binenbaum I, Pilalis E, Valavanis I, Papadodima O, Chatziioannou A. Analyzing and visualizing genomic complexity for the derivation of the emergent molecular networks. Intern J Monit Surveil Technol Res (IJMSTR). 2016;4(2):30–49.
Pilalis, E. D., & Chatziioannou, A. A.. Prioritized functional analysis of biological experiments using resampling and noise control methodologies. In Bioinformatics and Bioengineering (BIBE), 2013 I.E. 13th International Conference on (pp. 1–3). IEEE. (2013)
Moutselos K, Maglogiannis I, Chatziioannou A. GOrevenge: a novel generic reverse engineering method for the identification of critical molecular players, through the use of ontologies. IEEE Trans Biomed Eng. 2011;58(12):3522–7.
Acknowledgements
This study has been supported by the project entitled “Targeting tumor stroma and cancer cell metabolic co-operation for Lung Cancer Therapy (Metaboli-CA)”, in the context of the Program ΑRΙSΤΕΙΑ ΙΙ (code: 81320).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
This article is part of the Topical Collection on Systems Medicine
Rights and permissions
About this article
Cite this article
Kontogianni, G., Papadodima, O., Mitrakas, A. et al. “An RNA-seq analysis from non-small cell lung cancer biopsies suggests an important role for aberrant alternative splicing in its pathophysiology”. Health Technol. 7, 133–140 (2017). https://doi.org/10.1007/s12553-016-0158-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12553-016-0158-y