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Biocybernetics and Biomedical Engineering

Tytuł artykułu

A multi-layered incremental feature selection algorithm for adjuvant chemotherapy effectiveness/futileness assessment in non-small cell lung cancer

Autorzy Naftchali, R. E.  Abadeh, M. S. 
Treść / Zawartość
Warianty tytułu
Języki publikacji EN
EN Non-small cell lung cancer (NSCLC) is the most common type of lung cancer; and is one of the leading causes of death in the world. Surgery combined with chemotherapy is the recommended treatment for NSCLC. Since chemotherapy is an expensive treatment for either medical staff or patients suffering from pain, this study attempts to construct an intelligent predictive model to predict the adjuvant chemotherapy (ACT) effectiveness/ futileness in the patients, in order to help futile cases for unnecessary applications. There is a 2-step method: preprocessing and predicting. First a purposefully preprocessing tech-nique: chi-square test, SVM-RFE and correlation matrix, were employed in NSCLC gene expression dataset as a novel multi-layered feature selection method to defeat the curse of dimension and detect the chemotherapy target genes from tens of thousands features, based on which the patients can be classified into two groups, with NB classifier at second step. 10-Fold cross-validation was found with accuracy of 68.93% for 2 genes, TGFA (205015_s_at) and SEMA6C (208100_x_at), which is preferable compared to earlier studies, even though more than 2 input features are employed for the prediction. According to the results found in this study, one can concludes that the multi-layered feature selection approach has increased the classification accuracy in terms of finding the fitted patient for receiving ACT by reducing the number of features and has significant power to be used in medical datasets with small train samples and large number of features.
Słowa kluczowe
PL ekspresja genu   klasyfikator naiwny Bayesa   macierz korelacji   selekcja cech   chemioterapia  
EN gene expression   naïve Bayes   correlation matrix   feature selection   chemotherapy  
Wydawca Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences
Czasopismo Biocybernetics and Biomedical Engineering
Rocznik 2017
Tom Vol. 37, no. 3
Strony 477--488
Opis fizyczny Bibliogr. 52 poz., rys., tab., wykr.
autor Naftchali, R. E.
autor Abadeh, M. S.
  • Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran,
[1] Herbst RS, Heymach JV, Lippman SM. Lung cancer. N Engl J Med 2008;359:1367–80.
[2] Thunnissen E, van der Oord K, den Bakker M. Prognostic and predictive biomarkers in lung cancer. A review. Virchows Arch 2014;464:347–58.
[3] Scagliotti G. The Italian/European experience with adjuvant chemotherapy in resectable non-small lung cancer. Clin Cancer Res 2005;11:5011–6.
[4] Waller D, Fairlamb D, Gower N. Determining the value of cisplatin-based chemotherapy for all patients with non-small cell lung cancer (NSCLC). Preliminary results in the surgical setting. Lung Cancer 2003;41:s54.
[5] Arriagada R, Dunant A, Pignon J. Long-term results of the international adjuvant lung cancer trial evaluating adjuvant Cisplatin-based chemotherapy in resected lung cancer. J Clin Oncol 2010;35–42.
[6] Strauss G, Herndon J, Maddaus M. Adjuvant paclitaxel plus carboplatin compared with observation in stage IB non-small-cell lung cancer: CALGB 9633 with the Cancer and Leukemia Group B, Radiation Therapy Oncology Group, and North Central Cancer Treatment Group Study Groups. J Clin Oncol 2008;26:5043–51.
[7] Butts C, Ding K, Seymour L. Randomized phase III trial of vinorelbine plus cisplatin compared with observation in completely resected stage IB and II non-small-cell lung cancer: updated survival analysis of JBR-10. J Clin Oncol 2010;28:29–34.
[8] Douillard J. Adjuvant chemotherapy for non-small-cell lung cancer: it does not always fade with time. J Clin Oncol 2010;28:3–5.
[9] Tang H, Xiao G, Behrens C. A 12-gene set predicts survival benefits from adjuvant chemotherapy in non-small cell lung cancer patients. Clin Cancer Res 2013;19:1577–86.
[10] Van laar R. Genomic signatures for predicting survival and adjuvant chemotherapy benefit in patients with non-small- cell lung cancer. BMC Med Genom 2012;5:30–41.
[11] Zhu CQ, Ding K, Strumpf D. Prognostic and predictive gene signature for adjuvant chemotherapy in resected non-small-cell lung cancer. J Clin Oncol 2010;28:4417–24.
[12] Rosell R, Taron M, Massuti B. Predicting response to chemotherapy with early-stage lung cancer. Cancer J 2011;14:49–56.
[13] Chen D, Hsu Y, Fulp W. Prognostic and predictive value of a malignancy-risk gene signature in early-stage non-small cell lung cancer. J Natl Cancer Inst 2011;103:1859–70.
[14] Chen YC, Ke WC, Chiu HW. Risk classification of cancer survival using ANN with gene expression data from multiple laboratories. Comput Biol Med 2014;48:1–7.
[15] Xie Y, Minna JD. Non-small-cell lung cancer mRNA expression signature predicting response to adjuvant chemotherapy. J Clin Oncol 2010;28:4404–7.
[16] Berns A. Cancer: gene expression in diagnosis. Nature 2000;403:491–8.
[17] Lakhani S, Ashworth A. Microarray and histopathological analysis of tumours: the future and the past? Nat Rev Cancer 2001;1:151–7.
[18] Lu Y, Han J. Cancer classification using gene expression data. Inf Syst 2003;28:243–68.
[19] Schena M, Shalon D, Davis RW, Brown PO. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 1995;270:467–70.
[20] Sharma A, Paliwal KK. Cancer classification by gradient LDA technique using microarray gene expression data. Data Knowl Eng 2008;66:338–47.
[21] Karimi S, Farrokhnia M. Leukemia and small round blue-cell tumor cancer detection using microarray gene expression data set: combining data dimension reduction and variable selection technique. Chemom Intell Lab Syst 2014;139:6–14.
[22] Somorjai RL, Dolenko B, Baumgartner R. Class prediction and discovery using gene microarray and proteomics mass spectroscopy data: curses, caveats, cautions. Bioinformatics 2003;19:1484–91.
[23] Saeys Y, Inza I, Naga P. A review of feature selection techniques in bioinformatics. Bioinformatics 2007;23: 2507–17.
[24] Bolón-Canedo V, Sánchez-Maroño N, Alonso-Betanzos A. Distributed feature selection: an application to microarray dataclassification. Appl Soft Comput 2015;30:136–50.
[25] Fabregue M, Bringay S, Poncelet P, Teisseire T, Orsetti B. Mining microarray data to predict the histological grade of a breast cancer. J Biomed Inform 2011;44:512–6.
[26] Mao Z. Selecting significant genes by randomization test for cancer classification using gene expression data. J Biomed Inform 2013;46:594–601.
[27] Chen KH, Wang KJ, Wang KM, Angelia MA. Applying particle swarm optimization-based decision tree classifier. Appl Soft Comput 2014;24:773–80.
[28] Bermejo P, de la Ossa L, Gámez JA, Puerta JM. Fast wrapper feature subset selection in high-dimensional datasets by means of filter re-ranking. Knowl Based Syst 2012;25: 35–44.
[29] Latkowski T, Osowski S. Data mining for feature selection in gene expression autism data. Expert Syst Appl 2015;42:864–72.
[30] Guyon I, Weston J, Barnhill S, Vapnik V. Gene selection for cancer classification using support vector machines. Mach Learn 2002;46:389–422.
[31] Tang Y, Zhang Y, Huang Z. FCM-SVM-RFE gene feature selection algorithm for leukemia classification from microarray gene expression data. FUZZ'05; 2005.
[32] Elyasigomari V. Cancer classification using a novel gene selection approach by means of shuffling based on data clustering with optimization. Appl Soft Comput 2015;35:43–51.
[33] Novakovic J, Strbac P, Bulatovic D. Toward optimal feature selection. Yugosl J Oper Res 2011;21:119–35.
[34] Chen Y, Ke WC, Chiu HW, Chang YC. Cancer adjuvant chemotherapy strategic classification by artificial neural network with gene expression data: an example for non-small cell lung cancer. J Biomed Inform 2015;56:1–7.
[35] Tricoli JV, Nakai H, Byers M, Rall MG. The gene for human transforming growth factor alpha is on the short arm of chromosome 2. Cytogenet Cell Genet 1986;42:94–8.
[36] Qu X, Wei H, Zhai Y, Que H. Identification, characterization, and functional study of the two novel human members of the semaphorin gene family. J Biol Chem 2002;277:35574–85.
[37] Romanienko P, Camerini-Otero R. Cloning, characterization, and localization of mouse and human SPO11. Genomics 1999;61:156–9.
[38] Pereira-Leal J, Seabra MC. Evolution of the rab family of small GTP-binding proteins. J Mol Biol 2001;313(4):889–901.
[39] Badawy AA, El-Hindawi A, Hammam O, Moussa M, Gabal S, Said N. Impact of epidermal growth factor receptor and transforming growth factor-a on hepatitis C virus-induced hepatocarcinogenesis. APMIS 2015;123:823–31.
[40] Giricz O, Calvo V, Peterson EA. TACE-dependent TGF-alpha shedding drives triple-negative breast cancer cell invasion. Int J Cancer 2013;133:2587–95.
[41] DeHaan AM, Wolters NM, Keller ET, Ignatoski KM. EGFR ligand switch in late stage prostate cancer contributes to changes in cell signaling and bone remodeling. Prostate 2009;69:528–37.
[42] Chen S, Sun K-X, Liu BL, Zong ZH, Zhao Y. MicroRNA-505 functions as a tumor suppressor in endometrial cancer by targeting TGF-a. Mol Cancer 2016;15:224–9.
[43] Hsieh WT, Tzeng KR, Ciou JS, Tsai JJ, Kurubanjerdjit N, Huang CH, et al. Transcription factor and microRNA-regulated network motifs for cancer and signal transduction networks. BMC Syst Biol 2015;9:51–5.
[44] Mukohara T, Kudoh S, Matsuura K, Yamauchi S, Kimura T. Activated Akt expression has significant correlation with EGFR and TGF-alpha expressions in stage I NSCLC. Anticancer Res 2004;24:11–7.
[45] Prislei S, Mozzetti S, Filippetti F, De Donato M, Raspaglio G, Cicchillitti L. From plasma membrane to cytoskeleton: a novel function for semaphorin 6A. Mol Cancer Ther 2008;7:233–41.
[46] Salem H, Attiya G, El-Fishawy N. Classification of human cancer diseases by gene expression profiles. Appl Soft Comput 2017;50:124–34.
[47] Nguyen T, Khosravi A, Creighton D, Nahavandi S. Hidden Markov models for cancer classification using gene expression profiles. Inf Sci 2015;316:293–307.
[48] Hernandez JC, Duval V, Hao JK. A genetic embedded approach for gene selection and classification of microarray data. Berlin: Springer; 2007.
[49] Glaab E, Bacardit J, Garibaldi JM, Krasnogor N. Using rule- based machine learning for candidate disease gene prioritization and sample classification of cancer gene expression data. PLoS One 2012;7:1–8.
[50] Yu H, Ni J, Dan Y, Xu S. Mining and integrating reliable decision rules for imbalanced cancer gene expression datasets. Tsinghua Sci 2012;17:666–73.
[51] Hengpraprohm S. GA-based classifier with SNR weighted features for cancer microarray data classification. Int J Signal Process Syst 2013;1:29–33.
[52] Gunavathi C, Premalatha K. Performance analysis of genetic algorithm with kNN and SVM for feature selection in tumor classification. Int J Comput Electr Autom Control Inf Eng 2014;8:1390–7.
PL Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Kolekcja BazTech
Identyfikator YADDA bwmeta1.element.baztech-d317c4e6-167c-451f-ab33-8b860e1ef5e0
DOI 10.1016/j.bbe.2017.05.002