Methods Inf Med - Identification of breast cancer prognosis markers using integrative sparse boosting.

Tópicos

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{ network(2748) neural(1063) input(814) }
{ patient(2315) diseas(1263) diabet(1191) }
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Resumo

JECTIVES: In breast cancer research, it is important to identify genomic markers associated with prognosis. Multiple microarray gene expression profiling studies have been conducted, searching for prognosis markers. Genomic markers identified from the analysis of single datasets often suffer a lack of reproducibility because of small sample sizes. Integrative analysis of data from multiple independent studies has a larger sample size and may provide a cost-effective solution.METHODS: We collect four breast cancer prognosis studies with gene expression measurements. An accelerated failure time (AFT) model with an unknown error distribution is adopted to describe survival. An integrative sparse boosting approach is employed for marker selection. The proposed model and boosting approach can effectively accommodate heterogeneity across multiple studies and identify genes with consistent effects.RESULTS: Simulation study shows that the proposed approach outperforms alternatives including meta-analysis and intensity approaches by identifying the majority or all of the true positives, while having a low false positive rate. In the analysis of breast cancer data, 44 genes are identified as associated with prognosis. Many of the identified genes have been previously suggested as associated with tumorigenesis and cancer prognosis. The identified genes and corresponding predicted risk scores differ from those using alternative approaches. Monte Carlo-based prediction evaluation suggests that the proposed approach has the best prediction performance.CONCLUSIONS: Integrative analysis may provide an effective way of identifying breast cancer prognosis markers. Markers identified using the integrative sparse boosting analysis have sound biological implications and satisfactory prediction performance.

Resumo Limpo

jectiv breast cancer research import identifi genom marker associ prognosi multipl microarray gene express profil studi conduct search prognosi marker genom marker identifi analysi singl dataset often suffer lack reproduc small sampl size integr analysi data multipl independ studi larger sampl size may provid costeffect solutionmethod collect four breast cancer prognosi studi gene express measur acceler failur time aft model unknown error distribut adopt describ surviv integr spars boost approach employ marker select propos model boost approach can effect accommod heterogen across multipl studi identifi gene consist effectsresult simul studi show propos approach outperform altern includ metaanalysi intens approach identifi major true posit low fals posit rate analysi breast cancer data gene identifi associ prognosi mani identifi gene previous suggest associ tumorigenesi cancer prognosi identifi gene correspond predict risk score differ use altern approach mont carlobas predict evalu suggest propos approach best predict performanceconclus integr analysi may provid effect way identifi breast cancer prognosi marker marker identifi use integr spars boost analysi sound biolog implic satisfactori predict perform

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