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


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2013-03-19

AUTHORS

Hsiu-Ling Chou, Chung-Tay Yao, Sui-Lun Su, Chia-Yi Lee, Kuang-Yu Hu, Harn-Jing Terng, Yun-Wen Shih, Yu-Tien Chang, Yu-Fen Lu, Chi-Wen Chang, Mark L Wahlqvist, Thomas Wetter, Chi-Ming Chu

ABSTRACT

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. More... »

PAGES

100-100

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/1471-2105-14-100

DOI

http://dx.doi.org/10.1186/1471-2105-14-100

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1004401539

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/23506640


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48 accuracy
49 algorithm
50 analysis
51 area
52 artificial neural network
53 better extrapolation
54 bootstrap method
55 breast cancer
56 breast cancer dataset
57 breast cancer microarray databases
58 breast cancer recurrence
59 breast cancer relapse
60 breast cancer survivability
61 cDNA microarray analysis
62 cancer
63 cancer datasets
64 cancer microarray database
65 cancer recurrence
66 cancer relapse
67 cancer survivability
68 candidate genes
69 cell cycle G2/M DNA damage checkpoint pathway
70 checkpoint pathway
71 clinical variables
72 collection
73 compilation
74 composite model
75 curves
76 cycle G2/M DNA damage checkpoint pathway
77 damage checkpoint pathway
78 data compilation
79 data mining algorithms
80 database
81 dataset
82 decision tree
83 expression profiles
84 expression profiling
85 extrapolation
86 five-year breast cancer relapse
87 five-year recurrence
88 gene expression profiles
89 gene expression profiling
90 genes
91 genetic information
92 genetic variables
93 good predictive power
94 information
95 integration
96 logistic regression
97 low predictive power
98 method
99 microarray analysis
100 microarray database
101 microarray datasets
102 microarray technology
103 mining algorithms
104 model
105 network
106 neural network
107 oncologists
108 pathway
109 patients
110 performance
111 poor extrapolation
112 power
113 predictive power
114 profile
115 profiling
116 recurrence
117 regression
118 relapse
119 risk
120 samples
121 subjects
122 survivability
123 technology
124 test
125 test samples
126 thousands
127 thousands of genes
128 trees
129 values
130 variables
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