A data-driven, knowledge-based approach to biomarker discovery: application to circulating microRNA markers of colorectal cancer prognosis

Autor: Steven Clarke, Michael Michael, Zhangkai Jason Cheng, Fatemeh Vafaee, Connie I. Diakos, Lisa G. Horvath, Zdenka Kuncic, Michaela B. Kirschner, Hamid Alinejad-Rokny, Glen Reid
Jazyk: angličtina
Rok vydání: 2018
Předmět:
Zdroj: npj Systems Biology and Applications, Vol 4, Iss 1, Pp 1-12 (2018)
NPJ Systems Biology and Applications
ISSN: 2056-7189
Popis: Recent advances in high-throughput technologies have provided an unprecedented opportunity to identify molecular markers of disease processes. This plethora of complex-omics data has simultaneously complicated the problem of extracting meaningful molecular signatures and opened up new opportunities for more sophisticated integrative and holistic approaches. In this era, effective integration of data-driven and knowledge-based approaches for biomarker identification has been recognised as key to improving the identification of high-performance biomarkers, and necessary for translational applications. Here, we have evaluated the role of circulating microRNA as a means of predicting the prognosis of patients with colorectal cancer, which is the second leading cause of cancer-related death worldwide. We have developed a multi-objective optimisation method that effectively integrates a data-driven approach with the knowledge obtained from the microRNA-mediated regulatory network to identify robust plasma microRNA signatures which are reliable in terms of predictive power as well as functional relevance. The proposed multi-objective framework has the capacity to adjust for conflicting biomarker objectives and to incorporate heterogeneous information facilitating systems approaches to biomarker discovery. We have found a prognostic signature of colorectal cancer comprising 11 circulating microRNAs. The identified signature predicts the patients’ survival outcome and targets pathways underlying colorectal cancer progression. The altered expression of the identified microRNAs was confirmed in an independent public data set of plasma samples of patients in early stage vs advanced colorectal cancer. Furthermore, the generality of the proposed method was demonstrated across three publicly available miRNA data sets associated with biomarker studies in other diseases.
MiRNA biomarker discovery: a network-based, knowledge-driven approach The identification of robust and reproducible molecular markers is one of the biggest challenges in personalised cancer medicine. The increasing use of systems biology approaches has prompted researchers to integrate heterogeneous data into existing knowledge bases whose incorporation into the biomarker discovery workflow may adjust for data heterogeneity and limitation, and offer more precise, robust and consistent biomarkers. In this study, we have sought to determine network-based miRNA biomarker signatures from the plasma of colorectal cancer patients that hold prognostic utility. We performed miRNA profiling and then constructed an miRNA-mediated gene regulatory network and developed a multi-objective optimisation-based computational framework to identify miRNA biomarkers using both the miRNA expression profile and knowledge from this miRNA-mediated regulatory network. We have demonstrated the ability of the proposed approach in identifying robust, accurate and reproducible biomarkers.
Databáze: OpenAIRE
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