Dataset Information

A Pathway Based Classification Method for Analyzing Gene Expression for Alzheimer's Disease Diagnosis.

ABSTRACT: Recent studies indicate that gene expression levels in blood may be able to differentiate subjects with Alzheimer's disease (AD) from normal elderly controls and mild cognitively impaired (MCI) subjects. However, there is limited replicability at the single marker level. A pathway-based interpretation of gene expression may prove more robust.This study aimed to investigate whether a case/control classification model built on pathway level data was more robust than a gene level model and may consequently perform better in test data. The study used two batches of gene expression data from the AddNeuroMed (ANM) and Dementia Case Registry (DCR) cohorts.Our study used Illumina Human HT-12 Expression BeadChips to collect gene expression from blood samples. Random forest modeling with recursive feature elimination was used to predict case/control status. Age and APOE ?4 status were used as covariates for all analysis.Gene and pathway level models performed similarly to each other and to a model based on demographic information only.Any potential increase in concordance from the novel pathway level approach used here has not lead to a greater predictive ability in these datasets. However, we have only tested one method for creating pathway level scores. Further, we have been able to benchmark pathways against genes in datasets that had been extensively harmonized. Further work should focus on the use of alternative methods for creating pathway level scores, in particular those that incorporate pathway topology, and the use of an endophenotype based approach.

SUBMITTER: Voyle N

PROVIDER: S-EPMC4927941 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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A Pathway Based Classification Method for Analyzing Gene Expression for Alzheimer's Disease Diagnosis.

Voyle Nicola N Keohane Aoife A Newhouse Stephen S Lunnon Katie K Johnston Caroline C Soininen Hilkka H Kloszewska Iwona I Mecocci Patrizia P Tsolaki Magda M Vellas Bruno B Lovestone Simon S Hodges Angela A Kiddle Steven S Dobson Richard Jb RJ

Journal of Alzheimer's disease : JAD 20160101 3

<h4>Background</h4>Recent studies indicate that gene expression levels in blood may be able to differentiate subjects with Alzheimer's disease (AD) from normal elderly controls and mild cognitively impaired (MCI) subjects. However, there is limited replicability at the single marker level. A pathway-based interpretation of gene expression may prove more robust.<h4>Objectives</h4>This study aimed to investigate whether a case/control classification model built on pathway level data was more robus ...[more]

PMID: 26484910

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Project description:BackgroundOur understanding of the molecular mechanisms underlying Alzheimer's disease (AD) remains incomplete. Previous studies have revealed that genetic factors provide a significant contribution to the pathogenesis and development of AD. In the past years, numerous genes implicated in this disease have been identified via genetic association studies on candidate genes or at the genome-wide level. However, in many cases, the roles of these genes and their interactions in AD are still unclear. A comprehensive and systematic analysis focusing on the biological function and interactions of these genes in the context of AD will therefore provide valuable insights to understand the molecular features of the disease.MethodIn this study, we collected genes potentially associated with AD by screening publications on genetic association studies deposited in PubMed. The major biological themes linked with these genes were then revealed by function and biochemical pathway enrichment analysis, and the relation between the pathways was explored by pathway crosstalk analysis. Furthermore, the network features of these AD-related genes were analyzed in the context of human interactome and an AD-specific network was inferred using the Steiner minimal tree algorithm.ResultsWe compiled 430 human genes reported to be associated with AD from 823 publications. Biological theme analysis indicated that the biological processes and biochemical pathways related to neurodevelopment, metabolism, cell growth and/or survival, and immunology were enriched in these genes. Pathway crosstalk analysis then revealed that the significantly enriched pathways could be grouped into three interlinked modules-neuronal and metabolic module, cell growth/survival and neuroendocrine pathway module, and immune response-related module-indicating an AD-specific immune-endocrine-neuronal regulatory network. Furthermore, an AD-specific protein network was inferred and novel genes potentially associated with AD were identified.ConclusionBy means of network and pathway-based methodology, we explored the pathogenetic mechanism underlying AD at a systems biology level. Results from our work could provide valuable clues for understanding the molecular mechanism underlying AD. In addition, the framework proposed in this study could be used to investigate the pathological molecular network and genes relevant to other complex diseases or phenotypes.

Project description:Alzheimer's disease (AD) is a neurodegenerative and progressive disorder that results in brain malfunctions. Resting-state (RS) functional magnetic resonance imaging (fMRI) techniques have been successfully applied for quantifying brain activities of both Alzheimer's disease (AD) and amnestic mild cognitive impairment (aMCI) patients. Region-based approaches are widely utilized to classify patients from cognitively normal subjects (CN). Nevertheless, region-based approaches have a few limitations, reproducibility owing to selection of disease-specific brain regions, and heterogeneity of brain activities during disease progression. For coping with these issues, network-based approaches have been suggested in the field of molecular bioinformatics. In comparison with individual gene-based approaches, they acquired more accurate results in diverse disease classification, and reproducibility was confirmed by replication studies. In our work, we applied a similar methodology integrating brain pathway information into pathway activity inference, and permitting classification of both aMCI and AD patients based on pathway activities rather than single region activities.After aggregating the 59 brain pathways from literature, we estimated brain pathway activities by using exhaustive search algorithms between patients and cognitively normal subjects, and identified discriminatory pathways according to disease progression. We used three different data sets and each data set consists of two different groups. Our results show that the pathway-based approach (AUC = 0.89, 0.9, 0.75) outperformed the region-based approach (AUC = 0.69, 0.8, 0.68). Also, our approach provided enhanced diagnostic power achieving higher accuracy, sensitivity, and specificity (pathway-based approach: accuracy = 83%; sensitivity = 86%; specificity = 78%, region-based approach: accuracy = 74%; sensitivity = 78%; specificity = 76%).We proposed a novel method inferring brain pathway activities for disease classification. Our approach shows better classification performance than region-based approach in four classification models. We expect that brain pathway-based approach would be helpful for precise classification of brain disorders, and provide new opportunities for uncovering disrupted brain pathways caused by disease. Moreover, discriminatory pathways between patients and cognitively normal subjects may facilitate the interpretation of functional alterations during disease progression.

Project description:MotivationSelecting the most relevant genes for sample classification is a common process in gene expression studies. Moreover, determining the smallest set of relevant genes that can achieve the required classification performance is particularly important in diagnosing cancer and improving treatment.ResultsIn this study, I propose a novel method to eliminate irrelevant and redundant genes, and thus determine the smallest set of relevant genes for breast cancer diagnosis. The method is based on random forest models, gene set enrichment analysis (GSEA), and my developed Sort Difference Backward Elimination (SDBE) algorithm; hence, the method is named GSEA-SDBE. Using this method, genes are filtered according to their importance following random forest training and GSEA is used to select genes by core enrichment of Kyoto Encyclopedia of Genes and Genomes pathways that are strongly related to breast cancer. Subsequently, the SDBE algorithm is applied to eliminate redundant genes and identify the most relevant genes for breast cancer diagnosis. In the SDBE algorithm, the differences in the Matthews correlation coefficients (MCCs) of performing random forest models are computed before and after the deletion of each gene to indicate the degree of redundancy of the corresponding deleted gene on the remaining genes during backward elimination. Next, the obtained MCC difference list is divided into two parts from a set position and each part is respectively sorted. By continuously iterating and changing the set position, the most relevant genes are stably assembled on the left side of the gene list, facilitating their identification, and the redundant genes are gathered on the right side of the gene list for easy elimination. A cross-comparison of the SDBE algorithm was performed by respectively computing differences between MCCs and ROC_AUC_score and then respectively using 10-fold classification models, e.g., random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), extreme gradient boosting (XGBoost), and extremely randomized trees (ExtraTrees). Finally, the classification performance of the proposed method was compared with that of three advanced algorithms for five cancer datasets. Results showed that analyzing MCC differences and using random forest models was the optimal solution for the SDBE algorithm. Accordingly, three consistently relevant genes (i.e., VEGFD, TSLP, and PKMYT1) were selected for the diagnosis of breast cancer. The performance metrics (MCC and ROC_AUC_score, respectively) of the random forest models based on 10-fold verification reached 95.28% and 98.75%. In addition, survival analysis showed that VEGFD and TSLP could be used to predict the prognosis of patients with breast cancer. Moreover, the proposed method significantly outperformed the other methods tested as it allowed selecting a smaller number of genes while maintaining the required classification accuracy.

Dataset Information

A Pathway Based Classification Method for Analyzing Gene Expression for Alzheimer's Disease Diagnosis.

Publications

A Pathway Based Classification Method for Analyzing Gene Expression for Alzheimer's Disease Diagnosis.

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