Dataset Information

Diagnostic Utility of Gene Expression Profiles.

ABSTRACT: Two crucial problems arise from a microarray experiment in which the primary objective is to locate differentially expressed genes for the diagnosis of diseases such as cancer and Alzheimer's. The first problem is the detection of a subset of genes which provides an optimum discriminatory power between diseased and normal subjects, and the second problem is the statistical estimation of discriminatory power from the optimum subset of genes between two groups of subjects. We develop a new method to select an optimum subset of discriminatory genes by searching over possible linear combinations of gene expression profiles and locating the one which provides the maximum discriminatory power between two sources of RNA as measured by the area under the receiver operating characteristic (ROC) curve. We further provide an estimate to the optimum discriminatory power between the diseased and the healthy subjects over the selected subsets of genes. The proposed stepwise approach takes in account of the gene-to-gene correlations in the estimation of discriminating power as well as the associated variability and allows the number of genes to be selected based on the increment of the discriminating power. Finally, the proposed methodology is applied to a benchmark microarray experiment and compared to the results obtained through existing approaches in the literature.

SUBMITTER: Xiong C

PROVIDER: S-EPMC4026209 | biostudies-literature | 2013 Jan

REPOSITORIES: biostudies-literature

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Diagnostic Utility of Gene Expression Profiles.

Xiong Chengjie C Yan Yan Y Gao Feng F

Journal of biometrics & biostatistics 20130101 1

Two crucial problems arise from a microarray experiment in which the primary objective is to locate differentially expressed genes for the diagnosis of diseases such as cancer and Alzheimer's. The first problem is the detection of a subset of genes which provides an optimum discriminatory power between diseased and normal subjects, and the second problem is the statistical estimation of discriminatory power from the optimum subset of genes between two groups of subjects. We develop a new method ...[more]

PMID: 24851193

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Project description:ObjectiveTo identify potential diagnostic markers of lupus nephritis (LN) based on bioinformatics and machine learning and to explore the significance of immune cell infiltration in this pathology.MethodsSeven LN gene expression datasets were downloaded from the GEO database, and the larger sample size was used as the training group to obtain differential genes (DEGs) between LN and healthy controls, and to perform gene function, disease ontology (DO), and gene set enrichment analyses (GSEA). Two machine learning algorithms, least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE), were applied to identify candidate biomarkers. The diagnostic value of LN diagnostic gene biomarkers was further evaluated in the area under the ROC curve observed in the validation dataset. CIBERSORT was used to analyze 22 immune cell fractions from LN patients and to analyze their correlation with diagnostic markers.ResultsThirty and twenty-one DEGs were screened in kidney tissue and peripheral blood, respectively. Both of which covered macrophages and interferons. The disease enrichment analysis of DEGs in kidney tissues showed that they were mainly involved in immune and renal diseases, and in peripheral blood it was mainly enriched in cardiovascular system, bone marrow, and oral cavity. The machine learning algorithm combined with external dataset validation revealed that C1QA(AUC = 0.741), C1QB(AUC = 0.758), MX1(AUC = 0.865), RORC(AUC = 0.911), CD177(AUC = 0.855), DEFA4(AUC= 0.843)and HERC5(AUC = 0.880) had high diagnostic value and could be used as diagnostic biomarkers of LN. Compared to controls, pathways such as cell adhesion molecule cam, and systemic lupus erythematosus were activated in kidney tissues; cell cycle, cytoplasmic DNA sensing pathways, NOD-like receptor signaling pathways, proteasome, and RIG-1-like receptors were activated in peripheral blood. Immune cell infiltration analysis showed that diagnostic markers in kidney tissue were associated with T cells CD8 and Dendritic cells resting, and in blood were associated with T cells CD4 memory resting, suggesting that CD4 T cells, CD8 T cells and dendritic cells are closely related to the development and progression of LN.ConclusionC1QA, C1QB, MX1, RORC, CD177, DEFA4 and HERC5 could be used as new candidate molecular markers for LN. It may provide new insights into the diagnosis and molecular treatment of LN in the future.

Project description:Effective management and control of parasitic infections on farms depends on their early detection. Traditional serological diagnostic methods for Fasciola hepatica infection in livestock are specific and sensitive, but currently the earliest detection of the parasite only occurs at approximately three weeks post-infection. At this timepoint, parasites have already entered the liver and caused the tissue damage and immunopathology that results in reduced body weight and loss in productivity. Here, we investigated whether the differential abundance of micro(mi)miRNAs in sera of F. hepatica-infected sheep has potential as a tool for the early diagnosis of infection. Using miRNA sequencing analysis, we discovered specific profiles of sheep miRNAs at both the pre-hepatic and hepatic infection phases in comparison to non-infected sheep. In addition, six F. hepatica-derived miRNAs were specifically identified in sera from infected sheep. Thus, a panel of differentially expressed miRNAs comprising four sheep (miR-3231-3p; miR133-5p; 3957-5p; 1197-3p) and two parasite miRNAs (miR-124-3p; miR-Novel-11-5p) were selected as potential biomarkers. The expression of these candidates in sera samples from longitudinal sheep infection studies collected between 7 days and 23 weeks was quantified using RT-qPCR and compared to samples from age-matched non-infected sheep. We identified oar-miR-133-5p and oar-miR-3957-5p as promising biomarkers of fasciolosis, detecting infection as early as 7 days. The differential expression of the other selected miRNAs was not sufficient to diagnose infection; however, our analysis found that the most abundant forms of fhe-miR-124-3p in sera were sequence variants (IsomiRs) of the canonical miRNA, highlighting the critical importance of primer design for accurate diagnostic RT-qPCR. Accordingly, this investigative study suggests that certain miRNAs are biomarkers of F. hepatica infection and validates miRNA-based diagnostics for the detection of fasciolosis in sheep.

Dataset Information

Diagnostic Utility of Gene Expression Profiles.

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Diagnostic Utility of Gene Expression Profiles.

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