RESUMO
The availability of large-scale repositories and integrated cancer genome efforts have created unprecedented opportunities to study and describe cancer biology. In this sense, the aim of translational researchers is the integration of multiple omics data to achieve a better identification of homogeneous subgroups of patients in order to develop adequate diagnostic and treatment strategies from the personalized medicine perspective. So far, existing integrative methods have grouped together omics data information, leaving out individual omics data phenotypic interpretation. Here, we present the Massive and Integrative Gene Set Analysis (MIGSA) R package. This tool can analyze several high throughput experiments in a comprehensive way through a functional analysis strategy, relating a phenotype to its biological function counterpart defined by means of gene sets. By simultaneously querying different multiple omics data from the same or different groups of patients, common and specific functional patterns for each studied phenotype can be obtained. The usefulness of MIGSA was demonstrated by applying the package to functionally characterize the intrinsic breast cancer PAM50 subtypes. For each subtype, specific functional transcriptomic profiles and gene sets enriched by transcriptomic and proteomic data were identified. To achieve this, transcriptomic and proteomic data from 28 datasets were analyzed using MIGSA. As a result, enriched gene sets and important genes were consistently found as related to a specific subtype across experiments or data types and thus can be used as molecular signature biomarkers.
Assuntos
Neoplasias da Mama/genética , Biomarcadores Tumorais/metabolismo , Neoplasias da Mama/classificação , Neoplasias da Mama/metabolismo , Neoplasias da Mama/patologia , Conjuntos de Dados como Assunto , Feminino , HumanosRESUMO
Transcriptome analysis is essential to understand the mechanisms regulating key biological processes and functions. The first step usually consists of identifying candidate genes; to find out which pathways are affected by those genes, however, functional analysis (FA) is mandatory. The most frequently used strategies for this purpose are Gene Set and Singular Enrichment Analysis (GSEA and SEA) over Gene Ontology. Several statistical methods have been developed and compared in terms of computational efficiency and/or statistical appropriateness. However, whether their results are similar or complementary, the sensitivity to parameter settings, or possible bias in the analyzed terms has not been addressed so far. Here, two GSEA and four SEA methods and their parameter combinations were evaluated in six datasets by comparing two breast cancer subtypes with well-known differences in genetic background and patient outcomes. We show that GSEA and SEA lead to different results depending on the chosen statistic, model and/or parameters. Both approaches provide complementary results from a biological perspective. Hence, an Integrative Functional Analysis (IFA) tool is proposed to improve information retrieval in FA. It provides a common gene expression analytic framework that grants a comprehensive and coherent analysis. Only a minimal user parameter setting is required, since the best SEA/GSEA alternatives are integrated. IFA utility was demonstrated by evaluating four prostate cancer and the TCGA breast cancer microarray datasets, which showed its biological generalization capabilities.