Abstract
Recent studies using high-throughput sequencing technologies have demonstrated that transposable elements (TEs) seem to be involved not only in some cancer onset but also in cancer development. New dedicated tools have been recently designed to quantify the global expression of the different families of TEs from RNA-seq data, but the identification of the particular, differentially expressed TEs would provide more profitable results. To fill the gap, here it is presented NearTrans, a bioinformatic workflow that takes advantage of gEVE (a database of endogenous viral elements) to determine differentially expressed TEs as well as the activity of genes surrounding them to study if changes in TE expression is correlated with nearby genes. An especial requirement is that input RNA-seq reads must derive from normal and cancerous tissue from the same patient. NearTrans has been tested using RNA-seq data from 14 patients with prostate cancer, where two HERVs (HERVH-int and HERV17-int) and three LINE-1 (L1PA3, L1PA4 and L1PA7) were over-expressed in separate positions of the genome. Only one of the nearby genes (ACSM1) is over-expressed in prostate cancer, in agreement with the literature. Three (PLA2G5, UBE2MP1 and MIR4675) change their expression between normal and tumor cell, although the change is not statistically significant. The fifth (LOC101928437) is highly distant to the L1PA7 and their correlation is unlikely. These results are supporting that, in some cases such as the HERVs, TE expression can be governed by the genome context related with cancer, while in others, such as the LINEs, their expression is less related with the genome context, even though they are surrounded by genes potentially involved in cancer. Therefore, NearTrans seems to be a suitable and useful workflow to discover or corroborate genes involved in cancer that might be used as specific biomarkers for the diagnosis, prognosis or treatment of cancer.
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References
Alinezhad, S., Väänänen, R.M., Mattsson, J., Li, Y., Tallgrén, T., Tong Ochoa, N., Bjartell, A., Åkerfelt, M., Taimen, P., Boström, P.J., Pettersson, K., Nees, M.: Validation of novel biomarkers for prostate cancer progression by the combination of bioinformatics, clinical and functional studies. PLoS ONE 11(5), e0155901 (2016)
Alinezhad, S., Väänänen, R.M., Ochoa, N.T., Vertosick, E.A., Bjartell, A., Boström, P.J., Taimen, P., Pettersson, K.: Global expression of AMACR transcripts predicts risk for prostate cancer - a systematic comparison of AMACR protein and MRNA expression in cancerous and noncancerous prostate. BMC Urol. 16(1), 10 (2016)
Alinezhad, S., Väänänen, R.M., Tallgrén, T., Perez, I.M., Jambor, I., Aronen, H., Kähkönen, E., Ettala, O., Syvänen, K., Nees, M., Kallajoki, M., Taimen, P., Boström, P.J., Pettersson, K.: Stratification of aggressive prostate cancer from indolent disease—prospective controlled trial utilizing expression of 11 genes in apparently benign tissue. Urol. Oncol.: Semin. Orig. Investig. 34(6), 255.e15–255.e22 (2016). Seminar on Preservation Strategies in Bladder Cancer
Babaian, A., Lever, J., Gagnier, L., Mager, D.L.: LIONS: analysis suite for detecting and quantifying transposable element initiated transcription from RNA-seq. bioRxiv (2017)
Balestrieri, B., Arm, J.P.: Group V sPLA2: classical and novel functions. Biochimica et Biophysica Acta (BBA) - Mol. Cell Biol. Lipids 1761(11), 1280–1288 (2006)
Best, M.G., Sol, N., Kooi, I., Tannous, J., Westerman, B.A., Rustenburg, F., Schellen, P., Verschueren, H., Post, E., Koster, J., Ylstra, B., Ameziane, N., Dorsman, J., Smit, E.F., Verheul, H.M., Noske, D.P., Reijneveld, J.C., Nilsson, R.J.A., Tannous, B.A., Wesseling, P., Wurdinger, T.: RNA-seq of tumor-educated platelets enables blood-based pan-cancer, multiclass, and molecular pathway cancer diagnostics. Cancer Cell 28(5), 666–676 (2015)
Bockmayr, M., Klauschen, F., Györffy, B., Denkert, C., Budczies, J.: New network topology approaches reveal differential correlation patterns in breast cancer. BMC Syst. Biol. 7, 78 (2013)
Boilard, E., Lai, Y., Larabee, K., Balestrieri, B., Ghomashchi, F., Fujioka, D., Gobezie, R., Coblyn, J.S., Weinblatt, M.E., Massarotti, E.M., Thornhill, T.S., Divangahi, M., Remold, H., Lambeau, G., Gelb, M.H., Arm, J.P., Lee, D.M.: A novel anti-inflammatory role for secretory phospholipase A2 in immune complex-mediated arthritis. EMBO Mol. Med. 2(5), 172–187 (2010)
Criscione, S.W., Zhang, Y., Thompson, W., Sedivy, J.M., Neretti, N.: Transcriptional landscape of repetitive elements in normal and cancer human cells. BMC Genom. 15(583), 1–17 (2014)
Dobin, A., Davis, C.A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., Gingeras, T.R.: STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29(1), 15–21 (2013)
Falgueras, J., Lara, A.J., Fernandez-Pozo, N., Canton, F.R., Perez-Trabado, G., Claros, M.G.: SeqTrim: a high-throughput pipeline for preprocessing any type of sequence reads. BMC Bioinform. 11(1), 38 (2010)
Fiancette, R., Vincent, C., Donnard, M., Bordessoule, D., Turlure, P., Trimoreau, F., Denizot, Y.: Genes encoding multiple forms of phospholipase A2 are expressed in immature forms of human leukemic blasts. Leukemia 23(6), 1196–1199 (2009)
Ghosh, S., Chan, C.K.K.: Analysis of RNA-seq data using TopHat and Cufflinks. Methods Mol. Biol. 1374, 339–361 (2016)
Jin, Y., Tam, O.H., Paniagua, E., Hammell, M.: TEtranscripts: a package for including transposable elements in differential expression analysis of RNA-seq datasets. Bioinformatics 31(22), 3593–3599 (2015)
Kassiotis, G.: Endogenous retroviruses and the development of cancer. J. Immunol. (Baltim. Md.: 1950) 192(4), 1343–1349 (2014)
Lerat, E., Fablet, M., Modolo, L., Lopez-Maestre, H., Vieira, C.: TEtools facilitates big data expression analysis of transposable elements and reveals an antagonism between their activity and that of piRNA genes. Nucleic Acids Res. 45(4), e17 (2017)
Menschikowski, M., Hagelgans, A., Kostka, H., Eisenhofer, G., Siegert, G.: Involvement of epigenetic mechanisms in the regulation of secreted phospholipase A2 expressions in Jurkat leukemia cells. Neoplasia (N.Y.) 10(11), 1195–1203 (2008)
Menschikowski, M., Hagelgans, A., Nacke, B., Jandeck, C., Mareninova, O.A., Asatryan, L., Siegert, G.: Epigenetic control of group V phospholipase A2 expression in human malignant cells. Tumor Biol. 37(6), 8097–8105 (2016)
Mounier, C.M., Wendum, D., Greenspan, E., Fléjou, J.F., Rosenberg, D.W., Lambeau, G.: Distinct expression pattern of the full set of secreted phospholipases A2 in human colorectal adenocarcinomas: sPLA2-III as a biomarker candidate. Br. J. Cancer 98(3), 587–595 (2008)
Nakagawa, S., Takahashi, M.U.: gEVE: a genome-based endogenous viral element database provides comprehensive viral protein-coding sequences in mammalian genomes. Database 2016, baw087 (2016)
Ning, Q.Y., Wu, J.Z., Zang, N., Liang, J., Hu, Y.L., Mo, Z.N.: Key pathways involved in prostate cancer based on gene set enrichment analysis and meta analysis. Genet. Mol. Res. 10(4), 3856–3887 (2011)
Quinlan, A.R., Hall, I.M.: BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26(6), 841–842 (2010)
Rajan, P., Stockley, J., Sudbery, I.M., Fleming, J.T., Hedley, A., Kalna, G., Sims, D., Ponting, C.P., Heger, A., Robson, C.N., McMenemin, R.M., Pedley, I.D., Leung, H.Y.: Identification of a candidate prognostic gene signature by transcriptome analysis of matched pre- and post-treatment prostatic biopsies from patients with advanced prostate cancer. BMC Cancer 14(1), 977 (2014)
Ren, S., Peng, Z., Mao, J.H., Yu, Y., Yin, C., Gao, X., Cui, Z., Zhang, J., Yi, K., Xu, W., Chen, C., Wang, F., Guo, X., Lu, J., Yang, J., Wei, M., Tian, Z., Guan, Y., Tang, L., Xu, C., Wang, L., Gao, X., Tian, W., Wang, J., Yang, H., Wang, J., Sun, Y.: RNA-seq analysis of prostate cancer in the Chinese population identifies recurrent gene fusions, cancer-associated long noncoding RNAs and aberrant alternative splicings. Cell Res. 22(5), 806–821 (2012)
Trapnell, C., Roberts, A., Goff, L., Pertea, G., Kim, D., Kelley, D.R., Pimentel, H., Salzberg, S.L., Rinn, J.L., Pachter, L.: Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protoc. 7(3), 562–578 (2012)
Väänänen, R.M., Lilja, H., Kauko, L., Helo, P., Kekki, H., Cronin, A.M., Vickers, A.J., Nurmi, M., Alanen, K., Bjartell, A., Pettersson, K.: Cancer-associated changes in expression of TMPRSS2-ERG, PCA3 and SPINK1 in histologically benign tissue from cancerous versus non-cancerous prostatectomy specimens. Urology 83(2), 511.e1–511.e7 (2014)
Acknowledgements
This work was funded by the Neumosur grants 12/2015 and 14/2016, and was also co-funded by the European Union through the ERDF 2014-2020 “Programa Operativo de Crecimiento Inteligente” to the RTA2013-00068-C03-02 of the Spanish INIA and MINECO. The authors also thankfully acknowledge the computer resources and the technical support provided by the Plataforma Andaluza de Bioinformática of the University of Málaga.
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Larrosa, R., Arroyo, M., Bautista, R., López-Rodríguez, C.M., Claros, M.G. (2018). NearTrans Can Identify Correlated Expression Changes Between Retrotransposons and Surrounding Genes in Human Cancer. In: Rojas, I., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2018. Lecture Notes in Computer Science(), vol 10813. Springer, Cham. https://doi.org/10.1007/978-3-319-78723-7_32
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