{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,22]],"date-time":"2026-03-22T04:10:14Z","timestamp":1774152614347,"version":"3.50.1"},"reference-count":64,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,1,7]],"date-time":"2021-01-07T00:00:00Z","timestamp":1609977600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,1,7]],"date-time":"2021-01-07T00:00:00Z","timestamp":1609977600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>One of the current directions of precision medicine is the use of computational methods to aid in the diagnosis, prognosis, and treatment of disease based on data driven approaches. For instance, in oncology, there has been a particular focus on development of algorithms and biomarkers that can be used for pre-clinical and clinical applications. In particular large-scale omics-based models to predict drug sensitivity in in vitro cancer cell line panels have been used to explore the utility and aid in the development of these models as clinical tools. Additionally, a number of web-based interfaces have been constructed for researchers to explore the potential of drug perturbed gene expression as biomarkers including the NCI Transcriptional Pharmacodynamic Workbench. In this paper we explore the influence of drug perturbed gene dynamics of the NCI Transcriptional Pharmacodynamics Workbench in computational models to predict in vitro drug sensitivity for 15 drugs on the NCI60 cell line panel.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>This work presents three main findings. First, our models show that gene expression profiles that capture changes in gene expression after 24\u00a0h of exposure to a high concentration of drug generates the most accurate predictive models compared to the expression profiles under different dosing conditions. Second, signatures of 100 genes are developed for different gene expression profiles; furthermore, when the gene signatures are applied across gene expression profiles model performance is substantially decreased when gene signatures developed using changes in gene expression are applied to non-drugged gene expression. Lastly, we show that the gene interaction networks developed on these signatures show different network topologies and can be used to inform selection of cancer relevant genes.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>Our models suggest that perturbed gene signatures are predictive of drug response, but cannot be applied to predict drug response using unperturbed gene expression. Furthermore, additional drug perturbed gene expression measurements in in vitro cell lines could generate more predictive models; but, more importantly be used in conjunction with computational methods to discover important drug disease relationships.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-020-03947-y","type":"journal-article","created":{"date-parts":[[2021,1,7]],"date-time":"2021-01-07T08:05:03Z","timestamp":1610006703000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Predicting chemosensitivity using drug perturbed gene dynamics"],"prefix":"10.1186","volume":"22","author":[{"given":"Joshua D.","family":"Mannheimer","sequence":"first","affiliation":[]},{"given":"Ashok","family":"Prasad","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3063-1669","authenticated-orcid":false,"given":"Daniel L.","family":"Gustafson","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,1,7]]},"reference":[{"issue":"9","key":"3947_CR1","doi-asserted-by":"publisher","first-page":"793","DOI":"10.1056\/NEJMp1500523","volume":"372","author":"FS Collins","year":"2015","unstructured":"Collins FS, Varmus H. A new initiative on precision medicine. N Engl J Med. 2015;372(9):793\u20135.","journal-title":"N Engl J Med"},{"issue":"4","key":"3947_CR2","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1212\/CPJ.0b013e318278c328","volume":"2","author":"GR Cutter","year":"2012","unstructured":"Cutter GR, Liu Y. Personalized medicine: the return of the house call? Neurol Clin Pract. 2012;2(4):343\u201351.","journal-title":"Neurol Clin Pract"},{"issue":"13","key":"3947_CR3","doi-asserted-by":"publisher","first-page":"3112","DOI":"10.1002\/cncr.25206","volume":"116","author":"M Toi","year":"2010","unstructured":"Toi M, Iwata H, Yamanaka T, Masuda N, Ohno S, Nakamura S, Nakayama T, Kashiwaba M, Kamigaki S, Kuroi K. Clinical significance of the 21-gene signature (Oncotype DX) in hormone receptor-positive early stage primary breast cancer in the Japanese population. Cancer. 2010;116(13):3112\u20138.","journal-title":"Cancer"},{"issue":"16","key":"3947_CR4","doi-asserted-by":"publisher","first-page":"1673","DOI":"10.1056\/NEJMoa052122","volume":"353","author":"EH Romond","year":"2005","unstructured":"Romond EH, Perez EA, Bryant J, Suman VJ, Geyer CE, Davidson NE, Tan-Chiu E, Martino S, Paik S, Kaufman PA, et al. Trastuzumab plus adjuvant chemotherapy for operable HER2-positive breast cancer. N Engl J Med. 2005;353(16):1673\u201384.","journal-title":"N Engl J Med"},{"issue":"8","key":"3947_CR5","doi-asserted-by":"publisher","first-page":"1093","DOI":"10.1001\/jamaoncol.2018.1660","volume":"4","author":"J Marquart","year":"2018","unstructured":"Marquart J, Chen EY, Prasad V. Estimation of the percentage of US patients with cancer who benefit from genome-driven oncology. JAMA Oncol. 2018;4(8):1093\u20138.","journal-title":"JAMA Oncol"},{"key":"3947_CR6","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1038\/73439","volume":"24","author":"U Scherf","year":"2000","unstructured":"Scherf U, Ross DT, Waltham M, Smith LH, Lee JK, Tanabe L, Kohn KW, Reinhold WC, Myers TG, Andrews DT, et al. A gene expression database for the molecular pharmacology of cancer. Nat Genet. 2000;24:236.","journal-title":"Nat Genet"},{"issue":"5","key":"3947_CR7","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1002\/bies.950180513","volume":"18","author":"M Schena","year":"1996","unstructured":"Schena M. Genome analysis with gene expression microarrays. BioEssays. 1996;18(5):427\u201331.","journal-title":"BioEssays"},{"key":"3947_CR8","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1038\/nrclinonc.2014.6","volume":"11","author":"HMJ Werner","year":"2014","unstructured":"Werner HMJ, Mills GB, Ram PT. Cancer systems biology: a peek into the future of patient care? Nat Rev Clin Oncol. 2014;11:167.","journal-title":"Nat Rev Clin Oncol"},{"issue":"2","key":"3947_CR9","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1016\/j.soncn.2018.03.008","volume":"34","author":"KL Fessele","year":"2018","unstructured":"Fessele KL. The rise of big data in oncology. Semin Oncol Nurs. 2018;34(2):168\u201376.","journal-title":"Semin Oncol Nurs"},{"issue":"12","key":"3947_CR10","doi-asserted-by":"publisher","first-page":"730","DOI":"10.1038\/nrc4029","volume":"15","author":"PM Altrock","year":"2015","unstructured":"Altrock PM, Liu LL, Michor F. The mathematics of cancer: integrating quantitative models. Nat Rev Cancer. 2015;15(12):730\u201345.","journal-title":"Nat Rev Cancer"},{"key":"3947_CR11","doi-asserted-by":"crossref","unstructured":"Flaig TW, Tangen CM, Daneshmand S, Alva AS, Lerner SP, Lucia MS, McConkey DJ, Theodorescu D, Goldkorn A, Milowsky MI et al. SWOG S1314: A randomized phase II study of co-expression extrapolation (COXEN) with neoadjuvant chemotherapy for localized, muscle-invasive bladder cancer. J Clin Oncol. 2019, 37(15_suppl):4506\u20134506.","DOI":"10.1200\/JCO.2019.37.15_suppl.4506"},{"issue":"7391","key":"3947_CR12","doi-asserted-by":"publisher","first-page":"570","DOI":"10.1038\/nature11005","volume":"483","author":"MJ Garnett","year":"2012","unstructured":"Garnett MJ, Edelman EJ, Heidorn SJ, Greenman CD, Dastur A, Lau KW, Greninger P, Thompson IR, Luo X, Soares J, et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature. 2012;483(7391):570\u20135.","journal-title":"Nature"},{"key":"3947_CR13","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1038\/nature11003","volume":"483","author":"J Barretina","year":"2012","unstructured":"Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S, Wilson CJ, Leh\u00e1r J, Kryukov GV, Sonkin D, et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature. 2012;483:603.","journal-title":"Nature"},{"issue":"12","key":"3947_CR14","doi-asserted-by":"publisher","first-page":"1202","DOI":"10.1038\/nbt.2877","volume":"32","author":"JC Costello","year":"2014","unstructured":"Costello JC, Heiser LM, Georgii E, G\u00f6nen M, Menden MP, Wang NJ, Bansal M, Ammad-ud-din M, Hintsanen P, Khan SA, et al. A community effort to assess and improve drug sensitivity prediction algorithms. Nat Biotechnol. 2014;32(12):1202\u201312.","journal-title":"Nat Biotechnol"},{"issue":"D1","key":"3947_CR15","doi-asserted-by":"publisher","first-page":"D955","DOI":"10.1093\/nar\/gks1111","volume":"41","author":"W Yang","year":"2013","unstructured":"Yang W, Soares J, Greninger P, Edelman EJ, Lightfoot H, Forbes S, Bindal N, Beare D, Smith JA, Thompson IR, et al. Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res. 2013;41(D1):D955\u201361.","journal-title":"Nucleic Acids Res"},{"issue":"1","key":"3947_CR16","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1186\/s12920-019-0519-2","volume":"12","author":"JD Mannheimer","year":"2019","unstructured":"Mannheimer JD, Duval DL, Prasad A, Gustafson DL. A systematic analysis of genomics-based modeling approaches for prediction of drug response to cytotoxic chemotherapies. BMC Med Genomics. 2019;12(1):87.","journal-title":"BMC Med Genomics"},{"issue":"5795","key":"3947_CR17","doi-asserted-by":"publisher","first-page":"1929","DOI":"10.1126\/science.1132939","volume":"313","author":"J Lamb","year":"2006","unstructured":"Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel MJ, Lerner J, Brunet J-P, Subramanian A, Ross KN, et al. The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease. Science. 2006;313(5795):1929.","journal-title":"Science"},{"issue":"6","key":"3947_CR18","doi-asserted-by":"publisher","first-page":"1437","DOI":"10.1016\/j.cell.2017.10.049","volume":"171","author":"A Subramanian","year":"2017","unstructured":"Subramanian A, Narayan R, Corsello SM, Peck DD, Natoli TE, Lu X, Gould J, Davis JF, Tubelli AA, Asiedu JK, et al. A next generation connectivity map: L1000 platform and the first 1,000,000 profiles. Cell. 2017;171(6):1437-1452.e1417.","journal-title":"Cell"},{"issue":"1","key":"3947_CR19","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1016\/j.ygyno.2010.10.003","volume":"120","author":"SB Rho","year":"2011","unstructured":"Rho SB, Kim B-R, Kang S. A gene signature-based approach identifies thioridazine as an inhibitor of phosphatidylinositol-3\u2032-kinase (PI3K)\/AKT pathway in ovarian cancer cells. Gynecol Oncol. 2011;120(1):121\u20137.","journal-title":"Gynecol Oncol"},{"issue":"9","key":"3947_CR20","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1182\/blood-2009-07-235143","volume":"115","author":"T Sanda","year":"2010","unstructured":"Sanda T, Li X, Gutierrez A, Ahn Y, Neuberg DS, O\u2019Neil J, Strack PR, Winter CG, Winter SS, Larson RS, et al. Interconnecting molecular pathways in the pathogenesis and drug sensitivity of T-cell acute lymphoblastic leukemia. Blood. 2010;115(9):1735\u201345.","journal-title":"Blood"},{"issue":"1","key":"3947_CR21","doi-asserted-by":"publisher","first-page":"1940","DOI":"10.1038\/s41467-017-02160-5","volume":"8","author":"M Choi","year":"2017","unstructured":"Choi M, Shi J, Zhu Y, Yang R, Cho K-H. Network dynamics-based cancer panel stratification for systemic prediction of anticancer drug response. Nat Commun. 2017;8(1):1940.","journal-title":"Nat Commun"},{"issue":"24","key":"3947_CR22","doi-asserted-by":"publisher","first-page":"6807","DOI":"10.1158\/0008-5472.CAN-18-0989","volume":"78","author":"A Monks","year":"2018","unstructured":"Monks A, Zhao Y, Hose C, Hamed H, Krushkal J, Fang J, Sonkin D, Palmisano A, Polley EC, Fogli LK, et al. The NCI transcriptional pharmacodynamics workbench: a tool to examine dynamic expression profiling of therapeutic response in the NCI-60 cell line panel. Can Res. 2018;78(24):6807.","journal-title":"Can Res"},{"key":"3947_CR23","unstructured":"Hall M. Correlation-based feature selection for machine learning. New Zealand Waikato University; 1999."},{"key":"3947_CR24","doi-asserted-by":"publisher","first-page":"682","DOI":"10.1038\/nchembio.118","volume":"4","author":"AL Hopkins","year":"2008","unstructured":"Hopkins AL. Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol. 2008;4:682.","journal-title":"Nat Chem Biol"},{"issue":"11","key":"3947_CR25","first-page":"2764","volume":"60","author":"MJ Seraj","year":"2000","unstructured":"Seraj MJ, Samant RS, Verderame MF, Welch DR. Functional evidence for a novel human breast carcinoma metastasis suppressor, BRMS1, encoded at chromosome 11q13. Cancer Res. 2000;60(11):2764\u20139.","journal-title":"Cancer Res"},{"issue":"1","key":"3947_CR26","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1186\/1741-7015-10-19","volume":"10","author":"AI Riker","year":"2012","unstructured":"Riker AI, Samant RS. Location, location, location: the BRMS1 protein and melanoma progression. BMC Med. 2012;10(1):19.","journal-title":"BMC Med"},{"issue":"2","key":"3947_CR27","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1016\/j.canlet.2008.11.024","volume":"276","author":"PW Smith","year":"2009","unstructured":"Smith PW, Liu Y, Siefert SA, Moskaluk CA, Petroni GR, Jones DR. Breast cancer metastasis suppressor 1 (BRMS1) suppresses metastasis and correlates with improved patient survival in non-small cell lung cancer. Cancer Lett. 2009;276(2):196\u2013203.","journal-title":"Cancer Lett"},{"issue":"4","key":"3947_CR28","doi-asserted-by":"publisher","first-page":"720","DOI":"10.1016\/j.bbrc.2011.10.154","volume":"415","author":"B Kim","year":"2011","unstructured":"Kim B, Nam HJ, Pyo KE, Jang MJ, Kim IS, Kim D, Boo K, Lee SH, Yoon JB, Baek SH, et al. Breast cancer metastasis suppressor 1 (BRMS1) is destabilized by the Cul3-SPOP E3 ubiquitin ligase complex. Biochem Biophys Res Commun. 2011;415(4):720\u20136.","journal-title":"Biochem Biophys Res Commun"},{"key":"3947_CR29","unstructured":"Zhao XL, Wang P: [Expression of SATB1 and BRMS1 in ovarian serous adenocarcinoma and its relationship with clinieopathological features]. Sichuan Da Xue Xue Bao Yi Xue Ban 2011, 42(1):82\u201385."},{"issue":"10","key":"3947_CR30","doi-asserted-by":"publisher","first-page":"4006","DOI":"10.1021\/pr0703167","volume":"6","author":"J Rivera","year":"2007","unstructured":"Rivera J, Megias D, Bravo J. Proteomics-based strategy to delineate the molecular mechanisms of the metastasis suppressor gene BRMS1. J Proteome Res. 2007;6(10):4006\u201318.","journal-title":"J Proteome Res"},{"issue":"14","key":"3947_CR31","doi-asserted-by":"publisher","first-page":"3779","DOI":"10.1158\/0008-5472.CAN-13-3430","volume":"74","author":"F Tang","year":"2014","unstructured":"Tang F, Zhang L, Xue G, Hynx D, Wang Y, Cron PD, Hundsrucker C, Hergovich A, Frank S, Hemmings BA, et al. hMOB3 modulates MST1 apoptotic signaling and supports tumor growth in glioblastoma multiforme. Cancer Res. 2014;74(14):3779\u201389.","journal-title":"Cancer Res"},{"issue":"35","key":"3947_CR32","doi-asserted-by":"publisher","first-page":"38016","DOI":"10.18632\/oncotarget.5697","volume":"6","author":"L Liu","year":"2015","unstructured":"Liu L, Huang J, Wang K, Li L, Li Y, Yuan J, Wei S. Identification of hallmarks of lung adenocarcinoma prognosis using whole genome sequencing. Oncotarget. 2015;6(35):38016\u201328.","journal-title":"Oncotarget"},{"issue":"7","key":"3947_CR33","doi-asserted-by":"publisher","first-page":"946","DOI":"10.1093\/neuonc\/not308","volume":"16","author":"DG van Vuurden","year":"2014","unstructured":"van Vuurden DG, Aronica E, Hulleman E, Wedekind LE, Biesmans D, Malekzadeh A, Bugiani M, Geerts D, Noske DP, Vandertop WP, et al. Pre-B-cell leukemia homeobox interacting protein 1 is overexpressed in astrocytoma and promotes tumor cell growth and migration. Neuro Oncol. 2014;16(7):946\u201359.","journal-title":"Neuro Oncol"},{"issue":"7","key":"3947_CR34","doi-asserted-by":"publisher","first-page":"728","DOI":"10.4161\/cbt.10.7.12965","volume":"10","author":"MJ Wheater","year":"2010","unstructured":"Wheater MJ, Johnson PW, Blaydes JP. The role of MNK proteins and eIF4E phosphorylation in breast cancer cell proliferation and survival. Cancer Biol Ther. 2010;10(7):728\u201335.","journal-title":"Cancer Biol Ther"},{"issue":"1","key":"3947_CR35","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1007\/s11060-010-0233-6","volume":"101","author":"D Muta","year":"2011","unstructured":"Muta D, Makino K, Nakamura H, Yano S, Kudo M, Kuratsu J. Inhibition of eIF4E phosphorylation reduces cell growth and proliferation in primary central nervous system lymphoma cells. J Neurooncol. 2011;101(1):33\u20139.","journal-title":"J Neurooncol"},{"issue":"10","key":"3947_CR36","doi-asserted-by":"publisher","first-page":"2750","DOI":"10.1158\/0008-5472.CAN-13-2509","volume":"74","author":"ZJ Zhou","year":"2014","unstructured":"Zhou ZJ, Dai Z, Zhou SL, Hu ZQ, Chen Q, Zhao YM, Shi YH, Gao Q, Wu WZ, Qiu SJ, et al. HNRNPAB induces epithelial-mesenchymal transition and promotes metastasis of hepatocellular carcinoma by transcriptionally activating SNAIL. Cancer Res. 2014;74(10):2750\u201362.","journal-title":"Cancer Res"},{"issue":"2","key":"3947_CR37","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1016\/j.ccell.2015.07.005","volume":"28","author":"L Wang","year":"2015","unstructured":"Wang L, Yu Y, Chow DC, Yan F, Hsu CC, Stossi F, Mancini MA, Palzkill T, Liao L, Zhou S, et al. Characterization of a steroid receptor coactivator small molecule stimulator that overstimulates cancer cells and leads to cell stress and death. Cancer Cell. 2015;28(2):240\u201352.","journal-title":"Cancer Cell"},{"issue":"8","key":"3947_CR38","doi-asserted-by":"publisher","first-page":"1580","DOI":"10.1016\/j.molonc.2015.04.010","volume":"9","author":"C Baldeyron","year":"2015","unstructured":"Baldeyron C, Brisson A, Tesson B, N\u00e9mati F, Koundrioukoff S, Saliba E, De Koning L, Martel E, Ye M, Rigaill G, et al. TIPIN depletion leads to apoptosis in breast cancer cells. Mol Oncol. 2015;9(8):1580\u201398.","journal-title":"Mol Oncol"},{"issue":"9","key":"3947_CR39","doi-asserted-by":"publisher","first-page":"1297","DOI":"10.1038\/sj.onc.1210740","volume":"27","author":"JA Lee","year":"2008","unstructured":"Lee JA, Park JE, Lee DH, Park SG, Myung PK, Park BC, Cho S. G1 to S phase transition protein 1 induces apoptosis signal-regulating kinase 1 activation by dissociating 14-3-3 from ASK1. Oncogene. 2008;27(9):1297\u2013305.","journal-title":"Oncogene"},{"issue":"21","key":"3947_CR40","doi-asserted-by":"publisher","first-page":"17386","DOI":"10.1074\/jbc.M111.321158","volume":"287","author":"N Li","year":"2012","unstructured":"Li N, Zhong X, Lin X, Guo J, Zou L, Tanyi JL, Shao Z, Liang S, Wang L-P, Hwang W-T, et al. Lin-28 homologue A (LIN28A) promotes cell cycle progression via regulation of cyclin-dependent kinase 2 (CDK2), cyclin D1 (CCND1), and cell division cycle 25 homolog A (CDC25A) expression in cancer. J Biol Chem. 2012;287(21):17386\u201397.","journal-title":"J Biol Chem"},{"issue":"12","key":"3947_CR41","first-page":"3049","volume":"63","author":"K Yamane","year":"2003","unstructured":"Yamane K, Chen J, Kinsella TJ. Both DNA topoisomerase II-binding protein 1 and BRCA1 regulate the G2-M cell cycle checkpoint. Cancer Res. 2003;63(12):3049\u201353.","journal-title":"Cancer Res"},{"issue":"38","key":"3947_CR42","doi-asserted-by":"publisher","first-page":"4572","DOI":"10.1038\/onc.2012.470","volume":"32","author":"R Amato","year":"2013","unstructured":"Amato R, Scumaci D, D\u2019Antona L, Iuliano R, Menniti M, Di Sanzo M, Faniello MC, Colao E, Malatesta P, Zingone A, et al. Sgk1 enhances RANBP1 transcript levels and decreases taxol sensitivity in RKO colon carcinoma cells. Oncogene. 2013;32(38):4572\u20138.","journal-title":"Oncogene"},{"issue":"8","key":"3947_CR43","doi-asserted-by":"publisher","first-page":"1406","DOI":"10.1007\/s00018-002-8518-3","volume":"59","author":"N Zaffaroni","year":"2002","unstructured":"Zaffaroni N, Pennati M, Colella G, Perego P, Supino R, Gatti L, Pilotti S, Zunino F, Daidone MG. Expression of the anti-apoptotic gene survivin correlates with taxol resistance in human ovarian cancer. Cell Mol Life Sci. 2002;59(8):1406\u201312.","journal-title":"Cell Mol Life Sci"},{"issue":"4","key":"3947_CR44","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1007\/s12032-015-0557-3","volume":"32","author":"G Kara","year":"2015","unstructured":"Kara G, Tuncer S, T\u00fcrk M, Denkba\u015f EB. Downregulation of ABCE1 via siRNA affects the sensitivity of A549 cells against chemotherapeutic agents. Med Oncol. 2015;32(4):103.","journal-title":"Med Oncol"},{"issue":"9","key":"3947_CR45","first-page":"5495","volume":"7","author":"L Wang","year":"2014","unstructured":"Wang L, Zhang M, Liu D-X. Knock-down of ABCE1 gene induces G1\/S arrest in human oral cancer cells. Int J Clin Exp Pathol. 2014;7(9):5495\u2013504.","journal-title":"Int J Clin Exp Pathol"},{"issue":"9","key":"3947_CR46","first-page":"10072","volume":"8","author":"D Zheng","year":"2015","unstructured":"Zheng D, Dai Y, Wang S, Xing X. MicroRNA-299-3p promotes the sensibility of lung cancer to doxorubicin through directly targeting ABCE1. Int J Clin Exp Pathol. 2015;8(9):10072\u201381.","journal-title":"Int J Clin Exp Pathol"},{"issue":"4","key":"3947_CR47","doi-asserted-by":"publisher","first-page":"301","DOI":"10.2174\/138920371604150429153309","volume":"16","author":"X Li","year":"2015","unstructured":"Li X, Li X, Liao D, Wang X, Wu Z, Nie J, Bai M, Fu X, Mei Q, Han W. Elevated microRNA-23a expression enhances the chemoresistance of colorectal cancer cells with microsatellite instability to 5-fluorouracil by directly targeting ABCF1. Curr Protein Pept Sci. 2015;16(4):301\u20139.","journal-title":"Curr Protein Pept Sci"},{"issue":"24","key":"3947_CR48","doi-asserted-by":"publisher","first-page":"2968","DOI":"10.1007\/s00018-006-6298-x","volume":"63","author":"K Uhland","year":"2006","unstructured":"Uhland K. Matriptase and its putative role in cancer. Cell Mol Life Sci. 2006;63(24):2968\u201378.","journal-title":"Cell Mol Life Sci"},{"issue":"7","key":"3947_CR49","doi-asserted-by":"publisher","first-page":"639","DOI":"10.1023\/A:1020985632550","volume":"19","author":"CM Benaud","year":"2002","unstructured":"Benaud CM, Oberst M, Dickson RB, Lin CY. Deregulated activation of matriptase in breast cancer cells. Clin Exp Metastasis. 2002;19(7):639\u201349.","journal-title":"Clin Exp Metastasis"},{"issue":"1","key":"3947_CR50","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1097\/PAI.0b013e31817c3334","volume":"17","author":"M Warren","year":"2009","unstructured":"Warren M, Twohig M, Pier T, Eickhoff J, Lin CY, Jarrard D, Huang W. Protein expression of matriptase and its cognate inhibitor HAI-1 in human prostate cancer: a tissue microarray and automated quantitative analysis. Appl Immunohistochem Mol Morphol. 2009;17(1):23\u201330.","journal-title":"Appl Immunohistochem Mol Morphol"},{"key":"3947_CR51","doi-asserted-by":"publisher","first-page":"3881","DOI":"10.1038\/ncomms4881","volume":"5","author":"RM Larive","year":"2014","unstructured":"Larive RM, Moriggi G, Menacho-M\u00e1rquez M, Ca\u00f1amero M, de \u00c1lava E, Alarc\u00f3n B, Dosil M, Bustelo XR. Contribution of the R-Ras2 GTP-binding protein to primary breast tumorigenesis and late-stage metastatic disease. Nat Commun. 2014;5:3881.","journal-title":"Nat Commun"},{"issue":"4","key":"3947_CR52","first-page":"853","volume":"37","author":"H Luo","year":"2010","unstructured":"Luo H, Hao X, Ge C, Zhao F, Zhu M, Chen T, Yao M, He X, Li J. TC21 promotes cell motility and metastasis by regulating the expression of E-cadherin and N-cadherin in hepatocellular carcinoma. Int J Oncol. 2010;37(4):853\u20139.","journal-title":"Int J Oncol"},{"issue":"7","key":"3947_CR53","doi-asserted-by":"publisher","first-page":"e47","DOI":"10.1093\/nar\/gkv007","volume":"43","author":"ME Ritchie","year":"2015","unstructured":"Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47\u2013e47.","journal-title":"Nucleic Acids Res"},{"key":"3947_CR54","volume-title":"The biology of cancer","author":"RA Weinberg","year":"2014","unstructured":"Weinberg RA. The biology of cancer. 2nd ed. New York: Garland Science; 2014.","edition":"2"},{"issue":"4B","key":"3947_CR55","first-page":"2205","volume":"28","author":"S Ohhashi","year":"2008","unstructured":"Ohhashi S, Ohuchida K, Mizumoto K, Fujita H, Egami T, Yu J, Toma H, Sadatomi S, Nagai E. TANAKA M: down-regulation of deoxycytidine kinase enhances acquired resistance to gemcitabine in pancreatic cancer. Anticancer Res. 2008;28(4B):2205\u201312.","journal-title":"Anticancer Res"},{"key":"3947_CR56","doi-asserted-by":"crossref","unstructured":"Lin A, Giuliano CJ, Palladino A, John KM, Abramowicz C, Yuan ML, Sausville EL, Lukow DA, Liu L, Chait AR et al. Off-target toxicity is a common mechanism of action of cancer drugs undergoing clinical trials. Sci Transl Med. 2019, 11(509).","DOI":"10.1126\/scitranslmed.aaw8412"},{"issue":"20","key":"3947_CR57","doi-asserted-by":"publisher","first-page":"6371","DOI":"10.1158\/1078-0432.CCR-07-5287","volume":"14","author":"LM Ellis","year":"2008","unstructured":"Ellis LM, Hicklin DJ. Pathways mediating resistance to vascular endothelial growth factor-targeted therapy. Clin Cancer Res. 2008;14(20):6371.","journal-title":"Clin Cancer Res"},{"issue":"3","key":"3947_CR58","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/j.molmed.2018.12.009","volume":"25","author":"AJ Sabnis","year":"2019","unstructured":"Sabnis AJ, Bivona TG. Principles of resistance to targeted cancer therapy: lessons from basic and translational cancer biology. Trends Mol Med. 2019;25(3):185\u201397.","journal-title":"Trends Mol Med"},{"issue":"1","key":"3947_CR59","doi-asserted-by":"publisher","first-page":"615","DOI":"10.1146\/annurev.med.53.082901.103929","volume":"53","author":"MM Gottesman","year":"2002","unstructured":"Gottesman MM. Mechanisms of cancer drug resistance. Annu Rev Med. 2002;53(1):615\u201327.","journal-title":"Annu Rev Med"},{"issue":"10","key":"3947_CR60","doi-asserted-by":"publisher","first-page":"714","DOI":"10.1038\/nrc3599","volume":"13","author":"C Holohan","year":"2013","unstructured":"Holohan C, Van Schaeybroeck S, Longley DB, Johnston PG. Cancer drug resistance: an evolving paradigm. Nat Rev Cancer. 2013;13(10):714\u201326.","journal-title":"Nat Rev Cancer"},{"issue":"2","key":"3947_CR61","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1093\/biostatistics\/kxp059","volume":"11","author":"MN McCall","year":"2010","unstructured":"McCall MN, Bolstad BM, Irizarry RA. Frozen robust multiarray analysis (fRMA). Biostatistics. 2010;11(2):242\u201353.","journal-title":"Biostatistics"},{"issue":"14 Supplement","key":"3947_CR62","doi-asserted-by":"publisher","first-page":"1522","DOI":"10.1158\/1538-7445.AM2016-1522","volume":"76","author":"J Mannheimer","year":"2016","unstructured":"Mannheimer J, Fowles JS, Shaumberg K, Duval DL, Prasad A, Gustafson DL. Abstract 1522: predicting drug sensitivity based on gene array data for cytotoxic chemotherapeutic agents. Can Res. 2016;76(14 Supplement):1522.","journal-title":"Can Res"},{"key":"3947_CR63","unstructured":"Pedregosa F, Ga, #235, Varoquaux l, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P et al. Scikit-learn: machine learning in python. J Mach Learn Res. 2011, 12:2825\u20132830."},{"key":"3947_CR64","unstructured":"Aric A. Hagber DAS, Pieter J. Swart: exploring netwrok structure, dynamics, and function using NetworkX. In: 7th Python science conference (SciPy2008): August 2008 2008; Pasadena, CA. 11\u201315."}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-020-03947-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-020-03947-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-020-03947-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,27]],"date-time":"2023-01-27T19:06:38Z","timestamp":1674846398000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-020-03947-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,7]]},"references-count":64,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["3947"],"URL":"https:\/\/doi.org\/10.1186\/s12859-020-03947-y","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,7]]},"assertion":[{"value":"14 July 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 December 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 January 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Not applicable.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"15"}}