Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Vafaee F

PROVIDER: S-EPMC5981448 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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

Vafaee Fatemeh F Diakos Connie C Kirschner Michaela B MB Reid Glen G Michael Michael Z MZ Horvath Lisa G LG Alinejad-Rokny Hamid H Cheng Zhangkai Jason ZJ Kuncic Zdenka Z Clarke Stephen S

NPJ systems biology and applications 20180601

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 <i>data-driven</i> and <i>knowledge-based</i> approaches for biomarker identification has been recogni ...[more]

PMID: 29872543

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Project description:BackgroundColorectal cancer (CRC) is the third most lethal and malignant type of cancer in the world. Abnormal expression of human microRNA-200a (hsa-miRNA-200a or miR-200a) has previously been characterized as a clinically noticeable biomarker in several cancers, but its role in CRC is still unclear.MethodsThree CRC miRNA expression datasets were integratively analyzed by Least Absolute Shrinkage and Selector Operation (LASSO) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) algorithms. Nine candidate miRNAs were identified and validated for diagnostic and prognostic capability with the prediction model. The potential roles of the tumor suppressor miR-200a-3p in invasion, migration, and epithelial-mesenchymal transition of CRC cells were elaborated by in vitro studies.ResultsNine miRNAs (miR-492, miR-200a, miR-338, miR-29c, miR-101, miR-148a, miR-92a, miR-424, and miR-210) were identified as potentially useful diagnostic biomarkers in the clinic. The overall accuracy rate of the nine miRNAs in the diagnostic model was 0.94, 0.89, and 0.978 in the testing, validation, and independent validation dataset, respectively. CRC patients in the GSE29622 cohort were separated by the prognostic model into the low-risk score group and the high-risk score group. The area under the receiver operating characteristic curve (AUC) was 0.872 and 0.783 for predicting the 1- to 10-year survival of CRC patients. The performance of the prognostic model was validated by an independent TCGA-Colon Adenocarcinoma (COAD) dataset with AUC values between 0.911 and 0.796 in predicting 1- to 10-year survival. Nomograms comprising risk scores, tumor stage, and TNM staging were generated for predicting 1-, 3-, and 5-year overall survival (OS) in the GSE29622 and TCGA-COAD datasets. Colony formation, invasion, and migration in DLD1 and SW480 cells were suppressed by overexpression of miR-200a-3p. Inhibition of miR-200a-3p function contributed to abnormal colony formation, migration, invasion, and epithelial-mesenchymal transition (EMT). miR-200a-3p binding sites were located within the 3'-untranslated region (3'-UTR) of the Forkhead box protein A1 (FOXA1) mRNA.ConclusionWe developed and validated a diagnostic and prognostic prediction model for CRC. miR-200a-3p was determined to be a potential diagnostic and prognostic biomarker for CRC.

Dataset Information

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

Publications

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

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