Gene expression profiling of breast cancer survivability by pooled cDNA microarray analysis using logistic regression, artificial neural networks and decision trees

BMC Bioinformatics

Hsiu-Ling Chou⁴  Chung-Tay Yao³  Sui-Lun Su⁵  Chia-Yi Lee⁵  Kuang-Yu Hu²  Harn-Jing Terng⁸  Yun-Wen Shih⁵  Yu-Tien Chang⁵  Yu-Fen Lu⁵  Chi-Wen Chang¹  Mark L Wahlqvist⁷  Thomas Wetter⁶  Chi-Ming Chu⁵ 

[1] School of Nursing, College of Medicine, Chang-Gung University, Taoyuan, Taiwan

[2] Department of Bioinformatics, Chung Hua University, Hsinchu, Taiwan

[3] Department of Surgery, Cathay General Hospital, Taipei, Taiwan

[4] Department of Nursing, Far Eastern Memorial Hospital & Oriental Institute of Technology, New Taipei, Taiwan

[5] Section of Biomedical informatics, School of Public Health, National Defense Medical Center, Taipei, Taiwan

[6] Department of Medical Informatics, University of Heidelberg, Heidelberg, Germany

[7] National Health Research Institute, Chunan, Taiwan

[8] Advpharma, Inc., Taipei, Taiwan

关键词: Decision tree;    Logistic regression;    Artificial neural network;    Microarray;    Breast cancer;   

Others  :  1087935
DOI  :  10.1186/1471-2105-14-100

 received in 2012-07-12, accepted in 2013-02-26,  发布年份 2013

【 摘 要 】

Background

Microarray technology can acquire information about thousands of genes simultaneously. We analyzed published breast cancer microarray databases to predict five-year recurrence and compared the performance of three data mining algorithms of artificial neural networks (ANN), decision trees (DT) and logistic regression (LR) and two composite models of DT-ANN and DT-LR. The collection of microarray datasets from the Gene Expression Omnibus, four breast cancer datasets were pooled for predicting five-year breast cancer relapse. After data compilation, 757 subjects, 5 clinical variables and 13,452 genetic variables were aggregated. The bootstrap method, Mann–Whitney U test and 20-fold cross-validation were performed to investigate candidate genes with 100 most-significant p-values. The predictive powers of DT, LR and ANN models were assessed using accuracy and the area under ROC curve. The associated genes were evaluated using Cox regression.

Results

The DT models exhibited the lowest predictive power and the poorest extrapolation when applied to the test samples. The ANN models displayed the best predictive power and showed the best extrapolation. The 21 most-associated genes, as determined by integration of each model, were analyzed using Cox regression with a 3.53-fold (95% CI: 2.24-5.58) increased risk of breast cancer five-year recurrence…

Conclusions

The 21 selected genes can predict breast cancer recurrence. Among these genes, CCNB1, PLK1 and TOP2A are in the cell cycle G2/M DNA damage checkpoint pathway. Oncologists can offer the genetic information for patients when understanding the gene expression profiles on breast cancer recurrence.

【 授权许可】

   
2013 Chou et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

【 参考文献 】

• [1]American Cancer Society. 2011. http://www.cancer.org/docroot/home/index.asp webcite
• [2]Eifel P, Axelson JA, Costa J, Crowley J, Curran WJ Jr, Deshler A, Fulton S, Hendricks CB, Kemeny M, Kornblith AB: National Institutes of Health Consensus Development Conference Statement: adjuvant therapy for breast cancer: 1–3 November 2000. J Natl Cancer Inst 2001, 93:979-989.
• [3]McGuire WL: Breast cancer prognostic factors: evaluation guidelines. J Natl Cancer Inst 1991, 83:154-155.
• [4]Davis CA, Gerick F, Hintermair V, Friedel CC, Fundel K, Kuffner R, Zimmer R: Reliable gene signatures for microarray classification: assessment of stability and performance. Bioinformatics 2006, 22:2356-2363.
• [5]Gemignani F, Perra C, Landi S, Canzian F, Kurg A, Tonisson N, Galanello R, Cao A, Metspalu A, Romeo G: Reliable detection of beta-thalassemia and G6PD mutations by a DNA microarray. Clin Chem 2002, 48:2051-2054.
• [6]Gutmann O, Kuehlewein R, Reinbold S, Niekrawietz R, Steinert CP, de Heij B, Zengerle R, Daub M: Fast and reliable protein microarray production by a new drop-in-drop technique. Lab Chip 2005, 5:675-681.
• [7]Lassmann S, Kreutz C, Schoepflin A, Hopt U, Timmer J, Werner M: A novel approach for reliable microarray analysis of microdissected tumor cells from formalin-fixed and paraffin-embedded colorectal cancer resection specimens. J Mol Med 2009, 87:211-224.
• [8]Shi L, Perkins RG, Fang H, Tong W: Reproducible and reliable microarray results through quality control: good laboratory proficiency and appropriate data analysis practices are essential. Curr Opin Biotechnol 2008, 19:10-18.
• [9]Stirewalt DL, Pogosova-Agadjanyan EL, Khalid N, Hare DR, Ladne PA, Sala-Torra O, Zhao LP, Radich JP: Single-stranded linear amplification protocol results in reproducible and reliable microarray data from nanogram amounts of starting RNA. Genomics 2004, 83:321-331.
• [10]van der Spek PJ, Kremer A, Murry L, Walker MG: Are gene expression microarray analyses reliable? A review of studies of retinoic acid responsive genes. Genomics Proteomics Bioinformatics 2003, 1:9-14.
• [11]Xu X, Li Y, Zhao H, Wen SY, Wang SQ, Huang J, Huang KL, Luo YB: Rapid and reliable detection and identification of GM events using multiplex PCR coupled with oligonucleotide microarray. J Agric Food Chem 2005, 53:3789-3794.
• [12]Desmedt C, Piette F, Loi S, Wang Y, Lallemand F, Haibe-Kains B, Viale G, Delorenzi M, Zhang Y, d'Assignies MS: Strong time dependence of the 76-gene prognostic signature for node-negative breast cancer patients in the TRANSBIG multicenter independent validation series. Clin Cancer Res 2007, 13:3207-3214.
• [13]Ivshina AV, George J, Senko O, Mow B, Putti TC, Smeds J, Lindahl T, Pawitan Y, Hall P, Nordgren H: Genetic reclassification of histologic grade delineates new clinical subtypes of breast cancer. Cancer Res 2006, 66:10292-10301.
• [14]Sotiriou C, Wirapati P, Loi S, Harris A, Fox S, Smeds J, Nordgren H, Farmer P, Praz V, Haibe-Kains B: Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. J Natl Cancer Inst 2006, 98:262-272.
• [15]Wang Y, Klijn JG, Zhang Y, Sieuwerts AM, Look MP, Yang F, Talantov D, Timmermans M, Meijer-van Gelder ME, Yu J: Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet 2005, 365:671-679.
• [16]Wang Y, Sun G, Ji Z, Xing C, Liang Y: Weighted change-point method for detecting differential gene expression in breast cancer microarray data. PLoS One 2012, 7:e29860.
• [17]Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES: Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 2005, 102:15545-15550.
• [18]Padoan A: Net Reclassification Improvement (NRI) has been proposed as an alternative to the area under the curve of the the ROC. The MathWorks, Inc; 2010. http://www.mathworks.com/matlabcentral/fileexchange/28579-net-reclassification-improvement&watching=28579 webcite. Accessed 22. November 2012
• [19]Pencina MJ, D'Agostino RB Sr, D'Agostino RB Jr, Vasan RS: Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med 2008, 27:157-172. discussion 207–212
• [20]Pencina MJ, D'Agostino RB Sr, Demler OV: Novel metrics for evaluating improvement in discrimination: net reclassification and integrated discrimination improvement for normal variables and nested models. Stat Med 2012, 31:101-113.
• [21]Pencina MJ, D'Agostino RB Sr, Steyerberg EW: Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Stat Med 2011, 30:11-21.
• [22]Beyer SJ, Zhang X, Jimenez RE, Lee ML, Richardson AL, Huang K, Jhiang SM: Microarray analysis of genes associated with cell surface NIS protein levels in breast cancer. BMC Res Notes 2011, 4:397. BioMed Central Full Text
• [23]Delen D, Walker G, Kadam A: Predicting breast cancer survivability: a comparison of three data mining methods. Artif Intell Med 2005, 34:113-127.
• [24]Kumar R, Sharma A, Tiwari RK: Application of microarray in breast cancer: An overview. J Pharm Bioallied Sci 2012, 4:21-26.
• [25]Snow PB, Kerr DJ, Brandt JM, Rodvold DM: Neural network and regression predictions of 5-year survival after colon carcinoma treatment. Cancer 2001, 91:1673-1678.
• [26]Hsu YH: Investigating the Models of Logistic Regression, Decision Tree, Artificial Neural Network and Hybrid Analysis for Predicting Coronary Artery Disease. Taipei, Taiwan: Master Thesis of National Defense Medical Center; 2007.
• [27]Xu L, Tan AC, Winslow RL, Geman D: Merging microarray data from separate breast cancer studies provides a robust prognostic test. BMC Bioinformatics 2008, 9:125. BioMed Central Full Text
• [28]Pawitan Y, Bjohle J, Amler L, Borg AL, Egyhazi S, Hall P, Han X, Holmberg L, Huang F, Klaar S: Gene expression profiling spares early breast cancer patients from adjuvant therapy: derived and validated in two population-based cohorts. Breast Cancer Res 2005, 7:R953-R964. BioMed Central Full Text