Enrichment analysis applied to disease prognosis

Abstract

Enrichment analysis is well established in the field of transcriptomics, where it is used to identify relevant biological features that characterize a set of genes obtained in an experiment.This article proposes the application of enrichment analysis as a first step in a disease prognosis methodology, in particular of diseases with a strong genetic component. With this analysis the objective is to identify clinical and biological features that characterize groups of patients with a common disease, and that can be used to distinguish between groups of patients associated with disease-related events. Data mining methodologies can then be used to exploit those features, and assist medical doctors in the evaluation of the patients in respect to their predisposition for a specific event.In this work the disease hypertrophic cardiomyopathy (HCM) is used as a case-study, as a first test to assess the feasibility of the application of an enrichment analysis to disease prognosis. To perform this assessment, two groups of patients have been considered: patients that have suffered a sudden cardiac death episode and patients that have not.The results presented were obtained with genetic data and the Gene Ontology, in two enrichment analyses: an enrichment profiling aiming at characterizing a group of patients (e.g. that suffered a disease-related event) based on their mutations; and a differential enrichment aiming at identifying differentiating features between a sub-group of patients and all the patients with the disease. These analyses correspond to an adaptation of the standard enrichment analysis, since multiple sets of genes are being considered, one for each patient.The preliminary results are promising, as the sets of terms obtained reflect the current knowledge about the gene functions commonly altered in HCM patients, thus allowing their characterization. Nevertheless, some factors need to be taken into consideration before the full potential of the enrichment analysis in the prognosis methodology can be evaluated. One of such factors is the need to test the enrichment analysis with clinical data, in addition to genetic data, since both types of data are expected to be necessary for prognosis purposes.

High expression of GALNT7 promotes invasion and proliferation of glioma cells

“…Therefore, we speculate that GALNT7 may affect the progression of glioma by upregulating the expression of target genes through relevant signaling pathways. Based on this hypothesis, GSEA ( 28 , 29 ) was used to identify such relevant signaling pathways. As a result, 5 signaling pathways associated with GALNT7 and glioma were identified, including ‘regulation of the actin cytoskeleton’, ‘natural killer cell-mediated cytotoxicity’, ‘JAK-STAT signaling’, ‘CAMs’ and ‘ECM-receptor interaction’ pathways.…”

Knowledge Representation and Management: a Linked Data Perspective

“…Another popular technique is enrichment analysis that exploits the semantic annotations to identify clinical and biological characteristics that may better describe the outcome of a group of patients with a common disease. For example, recently this technique was effectively applied to improve the disease prognosis of the hypertrophic cardiomyopathy [48].…”