The Prognostic Role of Genes with Skewed Expression Distribution in Lung Adenocarcinoma

  • Yajing Chen
  • Shikui Tu
  • Lei XuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10559)


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.


Skewed distribution Gene expression RNA-sequencing Microarray Survival Lung adenocarcinoma 



This work was supported by the Zhi-Yuan chair professorship start-up grant (WF220103010) from Shanghai Jiao Tong University.


  1. 1.
    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)CrossRefGoogle Scholar
  2. 2.
    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)CrossRefGoogle Scholar
  3. 3.
    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)CrossRefGoogle Scholar
  4. 4.
    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)CrossRefGoogle Scholar
  5. 5.
    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)CrossRefGoogle Scholar
  6. 6.
    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)CrossRefGoogle Scholar
  7. 7.
    Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A., Leisch, F., Chang, C.C., Lin, C.C., Meyer, M.D.: Package e1071 (2017)Google Scholar
  8. 8.
    Network, C.G.A.R., et al.: Comprehensive molecular profiling of lung adenocarcinoma. Nature 511(7511), 543–550 (2014)CrossRefGoogle Scholar
  9. 9.
    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)CrossRefGoogle Scholar
  10. 10.
    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)CrossRefGoogle Scholar
  11. 11.
    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)CrossRefGoogle Scholar
  12. 12.
    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)CrossRefGoogle Scholar
  13. 13.
    Stewart, B., Wild, C.P., et al.: World cancer report 2014 (2014)Google Scholar
  14. 14.
    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)CrossRefGoogle Scholar
  15. 15.
    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)CrossRefGoogle Scholar
  16. 16.
    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)CrossRefGoogle Scholar
  17. 17.
    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)CrossRefGoogle Scholar
  18. 18.
    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)CrossRefGoogle Scholar
  19. 19.
    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)CrossRefGoogle Scholar
  20. 20.
    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)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Center for Cognitive Machines and Computational Health, and Department of Computer Science and EngineeringShanghai Jiao Tong UniversityShanghaiChina
  2. 2.Department of Computer Science and EngineeringThe Chinese University of Hong KongHong Kong SARChina

Personalised recommendations