Bioinformatics Methods to Select Prognostic Biomarker Genes from Large Scale Datasets: A Review. Issue 12 (10th December 2018)

• Record Type: Journal Article
• Authors: Jardillier, Rémy; Chatelain, Florent; Guyon, Laurent
• Is Part Of: Biotechnology journal. Volume 13:Issue 12(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-12-10
• Subjects: cancer -- false discovery rate -- prognostic biomarker -- survival data -- variable selection; Biotechnology -- Periodicals; 660.605
• DOI: 10.1002/biot.201800103
• Languages: English
• ISSNs: 1860-6768

Abstract

Abstract : With the increased availability of survival datasets, that comprise both molecular information (e.g., gene expression), and clinical information (e.g., patient survival), numerous genes are proposed as prognostic biomarkers. Despite efforts and money invested, very few of these biomarkers have been clinically validated and are used routinely. A high false discovery rate is assumed to be largely responsible for this, in particular as the number of tested genes is extremely high relative to the number of patients followed. Here, after describing the historical methodologies on which recent developments have often been based, this review describes studies that have been performed in the last few years. The concepts will be illustrated for a renal cancer dataset, and the corresponding scripts are provided (Supporting Information). These new developments belong to three main fields of applications. First, variable selection concerns various improvements to lasso penalization. Second, accurate definition of p ‐values and control of the false discovery rate have also been the subject of many studies. Third, the incorporation of biological knowledge, often through the form of networks or pathways, can be used as an a priori and/or to reduce dimensionality. These new and promising developments deserve benchmarking by independent groups not involved in their development, with various independent datasets. Further work on the methodologies is also still required. Abstract : Review of methods to select gene transcripts correlated with patient survival, from transcriptomic and clinical datasets. Recommended methods are elastic net, adaptive elastic net which ensures more consistency, backtracking including pairwise interactions in the model, and DRWPSurv which consists in selection on KEGG pathways. The methods are advised according to the application, and the coding effort required. This article is part of an AFOB (Asian Federation of Biotechnology) Special issue. To learn more about the AFOB visitwww.afob.org .