Abstract
Many studies assumed gene expression to be normally distributed. However, some were found to have left-skewed distribution, while others have right-skewed distribution. Here, we investigated the gene expression distribution of five lung adenocarcinoma data sets. We assumed that samples in the tail and non-tail of a skewed distribution were drawn from different populations with different survival outcomes. To investigate this hypothesis, skewed genes were detected to build a tail indicator matrix comprising of binary values. Survival analysis revealed that patients with more skewed genes in their tails had worse survival. Hierarchical clustering of the tail indicator matrices discovered a gene set with similar tail configurations for either left or right skewed genes. The two gene sets divided patients into three groups with different survivals. In conclusion, there is a direct association between genes with skewed distribution and the prognosis of lung adenocarcinoma patients.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Der, S.D., Sykes, J., Pintilie, M., Zhu, C.Q., Strumpf, D., Liu, N., Jurisica, I., Shepherd, F.A., Tsao, M.S.: Validation of a histology-independent prognostic gene signature for early-stage, non-small-cell lung cancer including stage IA patients. J. Thorac. Oncol. 9(1), 59–64 (2014)
Gjerstorff, M.F., Pøhl, M., Olsen, K.E., Ditzel, H.J.: Analysis of GAGE, NY-ESO-1 and SP17 cancer/testis antigen expression in early stage non-small cell lung carcinoma. BMC Cancer 13(1), 466 (2013)
Goldman, M., Craft, B., Swatloski, T., Cline, M., Morozova, O., Diekhans, M., Haussler, D., Zhu, J.: The UCSC cancer genomics browser: update 2015. Nucleic Acids Res. 43, D812–D817 (2014)
Guo, Y., Sheng, Q., Li, J., Ye, F., Samuels, D.C., Shyr, Y.: Large scale comparison of gene expression levels by microarrays and RNAseq using TCGA data. PLoS one 8(8), e71462 (2013)
Li, C.M.C., Gocheva, V., Oudin, M.J., Bhutkar, A., Wang, S.Y., Date, S.R., Ng, S.R., Whittaker, C.A., Bronson, R.T., Snyder, E.L., et al.: Foxa2 and Cdx2 cooperate with NKX2-1 to inhibit lung adenocarcinoma metastasis. Genes devel. 29(17), 1850–1862 (2015)
Marko, N.F., Weil, R.J.: Non-gaussian distributions affect identification of expression patterns, functional annotation, and prospective classification in human cancer genomes. PLoS one 7(10), e46935 (2012)
Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A., Leisch, F., Chang, C.C., Lin, C.C., Meyer, M.D.: Package e1071 (2017)
Network, C.G.A.R., et al.: Comprehensive molecular profiling of lung adenocarcinoma. Nature 511(7511), 543–550 (2014)
Okayama, H., Kohno, T., Ishii, Y., Shimada, Y., Shiraishi, K., Iwakawa, R., Furuta, K., Tsuta, K., Shibata, T., Yamamoto, S., et al.: Identification of genes upregulated in ALK-positive and EGFR/KRAS/ALK-negative lung adenocarcinomas. Cancer Res. 72(1), 100–111 (2012)
Sayers, E.W., Barrett, T., Benson, D.A., Bolton, E., Bryant, S.H., Canese, K., Chetvernin, V., Church, D.M., DiCuccio, M., Federhen, S., et al.: Database resources of the national center for biotechnology information. Nucleic Acids Res. 39(suppl 1), D38–D51 (2011)
Schabath, M.B., Welsh, E.A., Fulp, W.J., Chen, L., Teer, J.K., Thompson, Z.J., Engel, B.E., Xie, M., Berglund, A.E., Creelan, B.C., et al.: Differential association of STK11 and TP53 with KRAS mutation-associated gene expression, proliferation and immune surveillance in lung adenocarcinoma. Oncogene 35, 3209 (2015)
Shedden, K., Taylor, J.M., Enkemann, S.A., Tsao, M.S., Yeatman, T.J., Gerald, W.L., Eschrich, S., Jurisica, I., Giordano, T.J., Misek, D.E., et al.: Gene expression-based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study. Nat. Med. 14(8), 822–827 (2008)
Stewart, B., Wild, C.P., et al.: World cancer report 2014 (2014)
Taguchi, A., Hanash, S., Rundle, A., McKeague, I.W., Tang, D., Darakjy, S., Gaziano, J.M., Sesso, H.D., Perera, F.: Circulating pro-surfactant protein B as a risk biomarker for lung cancer. Cancer Epidemiol. Prev. Biomark. 22(10), 1756–1761 (2013)
Thomas, R., de la Torre, L., Chang, X., Mehrotra, S.: Validation and characterization of DNA microarray gene expression data distribution and associated moments. BMC Bioinform. 11(1), 576 (2010)
Trost, B., Moir, C.A., Gillespie, Z.E., Kusalik, A., Mitchell, J.A., Eskiw, C.H.: Concordance between RNA-sequencing data and DNA microarray data in transcriptome analysis of proliferative and quiescent fibroblasts. Roy. Soc. Open Sci. 2(9), 150402 (2015)
Wang, Y., Yang, W., Pu, Q., Yang, Y., Ye, S., Ma, Q., Ren, J., Cao, Z., Zhong, G., Zhang, X., et al.: The effects and mechanisms of SLC34A2 in tumorigenesis and progression of human non-small cell lung cancer. J. Biomed. Sci. 22(1), 52 (2015)
Watanabe, H., Francis, J.M., Woo, M.S., Etemad, B., Lin, W., Fries, D.F., Peng, S., Snyder, E.L., Tata, P.R., Izzo, F., et al.: Integrated cistromic and expression analysis of amplified NKX2-1 in lung adenocarcinoma identifies LMO3 as a functional transcriptional target. Genes Dev. 27(2), 197–210 (2013)
Winslow, M.M., Dayton, T.L., Verhaak, R.G., Kim-Kiselak, C., Snyder, E.L., Feldser, D.M., Hubbard, D.D., DuPage, M.J., Whittaker, C.A., Hoersch, S., et al.: Suppression of lung adenocarcinoma progression by NKX2-1. Nature 473(7345), 101–104 (2011)
Zhao, S., Fung-Leung, W.P., Bittner, A., Ngo, K., Liu, X.: Comparison of RNA-Seq and microarray in transcriptome profiling of activated T cells. PloS one 9(1), e78644 (2014)
Acknowledgments
This work was supported by the Zhi-Yuan chair professorship start-up grant (WF220103010) from Shanghai Jiao Tong University.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Chen, Y., Tu, S., Xu, L. (2017). The Prognostic Role of Genes with Skewed Expression Distribution in Lung Adenocarcinoma. In: Sun, Y., Lu, H., Zhang, L., Yang, J., Huang, H. (eds) Intelligence Science and Big Data Engineering. IScIDE 2017. Lecture Notes in Computer Science(), vol 10559. Springer, Cham. https://doi.org/10.1007/978-3-319-67777-4_57
Download citation
DOI: https://doi.org/10.1007/978-3-319-67777-4_57
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67776-7
Online ISBN: 978-3-319-67777-4
eBook Packages: Computer ScienceComputer Science (R0)