Cancer classification based on gene expression using neural networks

Transcription

1 Cancer classificaion based on gene expression using neural neworks H.P. Hu, Z.J. Niu, Y.P. Bai and X.H. Tan School of Science, Norh Universiy of China, Taiyuan, Shanxi, China Corresponding auhor: H.P. Hu Gene. Mol. Res. 14 (4): (2015) Received June 29, 2015 Acceped Sepember 25, 2015 Published December 21, 2015 DOI hp://dx.doi.org/ /2015.december ABSTRACT. Based on gene expression, we have classified 53 colon cancer paiens wih UICC II ino wo groups: relapse and no relapse. Samples were aken from each paien, and gene informaion was exraced. Of he 53 samples examined, 500 genes were considered proper hrough analyses by S-Kohonen, BP, and SVM neural neworks. Classificaion accuracy obained by S-Kohonen neural nework reaches 91%, which was more accurae han classificaion by BP and SVM neural neworks. The resuls show ha S-Kohonen neural nework is more plausible for classificaion and has a cerain feasibiliy and validiy as compared wih BP and SVM neural neworks. Key words: Colon cancer; Gene expression; S-Kohonen neural nework; BP neural nework; SVM neural nework

2 H.P. Hu e al INTRODUCTION Wih he rapid developmen of science and echnology, grea changes have aken place in he human die srucure. In addiion, various exernal facors also pose hreas o he human healh. Cancer is one of he major diseases hreaening human life and healh. The correc classificaion of umor ype is imporan for paien diagnosis and reamens. Two major challenges are encounered in cancer classificaion: class discovery and class predicion by gene expression monioring. In cases such as acue leukemia, cancer suscepibiliy is associaed wih changed in gene expression. The mehod of weighed voing genes is used o classify leukemia ino wo subypes AML and ALL (Golub e al., 1999). Arificial neural neworks are also applied for survival rae predicion. In breas cancer, area under he ROC curve (AUC) can be used as a measuremen of accuracy when evaluaing survival rae esimaes for 5, 10, and 15 years in paiens (Lundin e al., 1999). Ovarian cancer, colon cancer, and leukemia are researched by exracing feaure genes using he signal-o-noise raio, analyzing he gene expression daa wih SVM, and seing up predicing models of umors (Carsen e al., 2003). A muliple-random validaion sraegy was used for predicion of cancer oucome wih microarrays by use of saisical analysis (Michiels e al., 2005). Ineresingly, i was found ha expression of cerain genes unrelaed o cancer such as of posprandial laugher and of skin fibroblas localizaion was significanly associaed wih breas cancer oucomes (Vene e al., 2011). Neural nework and logisic regression were used o predic disan measasis (DM) of colorecal cancer (CRC) paiens (Biglarian e al., 2012). In early breas cancer predicion, digial mammography ensures paiens early deecion hrough localizaion of suspicious areas wih benign/malignan microcalcificaions using Gabor filer wih discree cosine ransform and benign/malignan classificaion using neural nework (Valarmahi e al., 2013). Gene expression is relaed o he process of glycosylaion beween breas cancer subypes, and is applied o predic he survival of breas cancer paiens (Poapenko, 2015). Screening he differenial gene expression paerns beween bladder carcinoma paiens and normal subjecs by use of empirical Bayes mehods of he linear models, co-gene-expression is obained (Bi e al., 2015). This allows discovery of complex gene neworks ha play imporan roles in bladder cancer research. Colon cancer places as he 4-6 h mos common malignan umors, and is incidence is sill rapidly rising. Aside from acue leukemia, breas cancer, and bladder cancer, colon cancer is also conneced o changes in gene expression. We found ha based on gene expression, colon cancer paiens wih UICC II who were reaed by elecive sandard oncological resecion is classified ino wo cases: relapse and no relapse afer surgery by use of S-Kohonen, BP and SVM neural neworks. By comparison, resuls show ha S-Kohonen neural nework is more effecive for cancer classificaion. MATERIAL AND METHODS Daa sources Research and analysis of gene expression is an imporan research opic in bioinformaics. Therefore, classificaion models for probabiliy of UICC II cancer recurrence and idenificaion

3 Cancer classificaion based on gene expression based on gene expression can be imporan for reamen and disease prognosis. In his paper, gene expression daa wih colon cancer came from daabase of NCBI NLM NIH. The daabase is composed of gene expressions of 53 colon cancer paiens: 13 relapse cancer paiens and 40 no relapse cancer paiens. Every colon paien has 54,675 gene feaures. By experimen, 500 gene feaures are randomly chosen. Thus, dimension samples wih color cancer daa were obained. Samples (53) were normalized and randomly disribued. A no-relapse o relapse raio of 4:1 was found, as shown in Table 1. Table 1. Disribuion of samples of colon cancer wih UICC II sage. Ses No relapse coun Relapse coun Toal Training Tes Kohonen neural nework Topological srucure of Kohonen neural nework is shown in Figure 1. Figure 1. Topological srucure of Kohonen neural nework. Kohonen neural nework is a self-organizing neural nework proposed by Dr. Teuvo Kohonen of Universiy of Helsinki Finland, whose learning process is unsupervised and whose weighs are adjused by self-organizing map o recognize he feaures in order o obain auomaic clusering. Kohonen neural nework is a wo-layer feedforward neural nework: he firs layer is he inpu layer, whose nodes are equal o he dimension of he inpu sample, and he second layer is he compeiive layer (ha is, oupu layer) whose nodes disribuion is a 2-dimension array. There are whole connecions of adjusable weighs beween inpu nodes and oupu nodes. Thus, Kohonen neural nework is a map from n-dimension oupu space o 2-dimension oupu plae, and keep he same opological feaure. Based on Kohonen neural nework, he added layer (ha is, oupu layer) following he compeiive layer makes he neural nework achieve he goals of predicion, classificaion and so on. The obained nework is named he S-Kohonen neural nework. The adjusmen of learning

4 H.P. Hu e al rae in algorihm immediaely affecs clusering precision and convergence. Oupu of S-Kohonen neural nework is 1 or 2. In his sudy, he learning rae in S-Kohonen neural nework was chosen o make clusering beer and convergence faser, whose formula is he exponenial funcion: α() ( ) T 0e = α (Equaion 1) η() ( ) T 0e = η (Equaion 2) where a() is he learning rae of conneced weighs beween inpu layer and compeiive layer, a 0 is iniial, η () is he learning rae beween compeiive layer, and he oupu layer, η 0 is iniial, is he -h ieraion, T is he oal number of ieraions. a() decreases wih he increasing ieraions. BP neural nework BP (Back Propagaion) neural nework proposed by Rumelhar and McCelland in 1986 is a mulilayer feedforward nework, which used o be he wides neural nework. BP neural nework consiss of hree layers: inpu layer, hidden layer and oupu layer. Each layer influences nex layer. The learning process of BP algorihm is supervised. If he error of he oucome in he oupu layer is larger han he goal-error, he BP algorihm is urned ino he back propagaion. Topological srucure of BP neural nework is shown in Figure 2. Figure 2. Topological srucure of BP neural nework. In Figure 2, X 1, X 2,, Xn are he inpu values of he nework and Y 1, Y 2,, Ym are he predicive values of he nework. There is a relaion of funcion map beween n independen variables and m dependen variables. Suppor vecor machines (SVM) Suppor vecor machine (SVM), firsly proposed by Vapnik, is a new-syle machine learning mehod for paern classificaion and nonlinear regression. In his mehod, classificaion hyperplae is se up as a decision surface, which maximizes edge isolaion beween he posiive samples

5 Cancer classificaion based on gene expression and he negaive samples. The classificaion mehod of SVM is divided ino wo classes: he direc approach and he indirec approach. In his paper, he one-o-many mehod of he second class was adoped o se up models samples so ha k SVM is buil by samples of k classes. SIMULATION EXPERIMENT Daa processing In his sudy, experimenal daa wih he same proporion are used in he above hree mehods. The classificaion predicion models are buil by hree neural neworks such ha he resuls are comparable. A simulaion experimen was carried ou o verify he validaion of hese hree neural neworks. Gene expression daa of UICC II colon paiens were normalized, and classified as eiher no relapse or relapse. Gene feaures ( ) from 53 samples were used in he hree neural neworks menioned above. The resuls are shown in Figure 3. Figure 3. Classificaion accuracy of he hree neural neworks wih he number of feaure genes. EXPERIMENT RESULTS Samples (53) wih 500 chosen gene feaures chosen were used for classificaion predicion by S-Kohonen neural nework, BP neural nework, and SVM neural nework. Some experimens were performed repeaedly in erm of randomness of iniial weighs. Based on he characerisic of he daa, he nodes of inpu layer were 500 and he nodes of oupu layer were 2. The firs, hird, and fifh experimen resuls by S-Kohonen neural nework were aken o obain he classificaion chars, as shown in Figure 4. By analyzing he classificaion chars, he srucure of S-Kohonen neural nework was increasingly sable. The winner nodes of differen caegory were disribued by blocks.

6 H.P. Hu e al Figure 4. Firs, hird, and fifh classificaion chars by use of S-Kohonen neural nework. Nodes (25) in hidden layers were chosen in he BP neural nework. The firs, hird, and fifh experimenal resuls by BP neural nework were aken o obain he classificaion chars, as shown in Figure 5. The resuls show ha he classificaion resuls were arbirary. Some experimenal resuls by SVM neural nework were aken o obain he same classificaion accuracy, as shown in Figure 6. Figure 5. Firs, hird, fifh classificaion chars used by he BP neural nework. Figure 6. Classificaion char of SVM neural nework.

7 Cancer classificaion based on gene expression Resuls from en repeaed experimens were aken, and he righ coun of classificaion for each is lised in Table 2. Prediced classificaion by S-Kohonen neural nework were in accordance wih he expeced classificaion, which reached an accuracy of 91%. Comparing BP neural nework and SVM neural nework wih S-Kohonen neural nework, he classificaion accuracies of BP neural nework and SVM neural nework were 66 and 70%, respecively. These resuls are shown in Table 2. Table 2. Classificaion resuls of S-Kohonen, BP, and SVM neural nework. 1s ime 2nd ime 3rd ime 4h ime 5h ime 6h ime 7h ime 8h ime 9h ime 10h ime Classificaion accuracy (%) S-Kohonen BP SVM DISCUSSION In he paper, S-Kohonen BP, and SVM neural neworks were used o classify sage UICC II colon paiens ino eiher he no relapse group or he relapse group. Trained se and es se chosen were equivalen each ime. However, he classificaion resuls were differen. The resuls showed ha Kohonen neural nework is more suiable for classificaion of colon cancer based on gene expression as compared o BP and SVM neural nework, and mainains beer classificaion accuracy and sabiliy. Thus, i can mee he pracical requiremens of classificaion and predicion. Conflics of ineres The auhors declare no conflic of ineres. ACKNOWLEDGMENTS Research suppored by he Naional Naural Science Foundaion (# ) of China. REFERENCES Bi DB, Ning H, Liu S, Que X, e al. (2015). Gene expression paerns combined wih nework analysis idenify hub genes associaed wih bladder cancer. Compu. Biol. Chem. 56: Biglarian A, Bakhshi E, Gohari MR and Khodabakhshi R (2012). Arificial Neural Nework for Predicion of Disan Measasis in Colorecal Cancer. Asian Pac. J. Cancer Prev. 13: Golub TR, Slonim DK, Tamayo P, Huard C, e al. (1999). Molecular Classificaion of Cancer: Class Discovery and Class Predicion by Gene Expression Monioring. Science 286: Lundin M, Lundin J, Burke HB, Toikkanen S, e al. (1999). Arificial neural neworks applied o survival predicion in breas cancer. Oncology 57: Michies S, Koscielny S and Hill C(2005). Predicion of cancer oucome wih microarrays: a muliple random validaion sraegy. Lance 365: Peerson C and Ringnér M. (2003). Analyzing Tumor Gene expression profiles. Arif. Inell. Med. 28: Poapenko IO, Lüders T, Russnes HG, Helland Å, e al. (2015). Glycan-relaed gene expression signaures in breas cancer subypes; relaion o survival. Mol. Oncol. 9: Valarmahi P and Radhakrishna V (2013). Tumor Predicion in Mammogram using Neural Nework. Global J. Inc. 3: Vene D, Dumon JE and Deours V (2011). Mos Random Gene Expression Signaures Are Significanly Associaed wih Breas Cancer Oucome. PLoS Compu. Biol. 7: e