Dataset Information

Expression profile analysis to predict potential biomarkers for glaucoma: BMP1, DMD and GEM.

ABSTRACT: Purpose:Glaucoma is the second commonest cause of blindness. We assessed the gene expression profile of astrocytes in the optic nerve head to identify possible prognostic biomarkers for glaucoma. Method:A total of 20 patient and nine normal control subject samples were derived from the GSE9944 (six normal samples and 13 patient samples) and GSE2378 (three normal samples and seven patient samples) datasets, screened by microarray-tested optic nerve head tissues, were obtained from the Gene Expression Omnibus (GEO) database. We used a weighted gene coexpression network analysis (WGCNA) to identify coexpressed gene modules. We also performed a functional enrichment analysis and least absolute shrinkage and selection operator (LASSO) regression analysis. Genes expression was represented by boxplots, functional geneset enrichment analyses (GSEA) were used to profile the expression patterns of all the key genes. Then the key genes were validated by the external dataset. Results:A total 8,606 genes and 19 human optic nerve head samples taken from glaucoma patients in the GSE9944 were compared with normal control samples to construct the co-expression gene modules. After selecting the most common clinical traits of glaucoma, their association with gene expression was established, which sorted two modules showing greatest correlations. One with the correlation coefficient is 0.56 (P = 0.01) and the other with the correlation coefficient is -0.56 (P = 0.01). Hub genes of these modules were identified using scatterplots of gene significance versus module membership. A functional enrichment analysis showed that the former module was mainly enriched in genes involved in cellular inflammation and injury, whereas the latter was mainly enriched in genes involved in tissue homeostasis and physiological processes. This suggests that genes in the green-yellow module may play critical roles in the onset and development of glaucoma. A LASSO regression analysis identified three hub genes: Recombinant Bone Morphogenetic Protein 1 gene (BMP1), Duchenne muscular dystrophy gene (DMD) and mitogens induced GTP-binding protein gene (GEM). The expression levels of the three genes in the glaucoma group were significantly lower than those in the normal group. GSEA further illuminated that BMP1, DMD and GEM participated in the occurrence and development of some important metabolic progresses. Using the GSE2378 dataset, we confirmed the high validity of the model, with an area under the receiver operator characteristic curve of 85%. Conclusion:We identified several key genes, including BMP1, DMD and GEM, that may be involved in the pathogenesis of glaucoma. Our results may help to determine the prognosis of glaucoma and/or to design gene- or molecule-targeted drugs.

SUBMITTER: Zhang DW

PROVIDER: S-EPMC7474882 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Expression profile analysis to predict potential biomarkers for glaucoma: BMP1, DMD and GEM.

Zhang Dao Wei DW Zhang Shenghai S Wu Jihong J

PeerJ 20200903

<h4>Purpose</h4>Glaucoma is the second commonest cause of blindness. We assessed the gene expression profile of astrocytes in the optic nerve head to identify possible prognostic biomarkers for glaucoma.<h4>Method</h4>A total of 20 patient and nine normal control subject samples were derived from the GSE9944 (six normal samples and 13 patient samples) and GSE2378 (three normal samples and seven patient samples) datasets, screened by microarray-tested optic nerve head tissues, were obtained from ...[more]

PMID: 32953253

Similar Datasets

Project description:BackgroundRecent studies have proposed several gene signatures as biomarkers for different grades of gliomas from various perspectives. However, most of these genes can only be used appropriately for patients with specific grades of gliomas.MethodsIn this study, we aimed to identify survival-relevant genes shared between glioblastoma multiforme (GBM) and lower-grade glioma (LGG), which could be used as potential biomarkers to classify patients into different risk groups. Cox proportional hazard regression model (Cox model) was used to extract relative genes, and effectiveness of genes was estimated against random forest regression. Finally, risk models were constructed with logistic regression.ResultsWe identified 104 key genes that were shared between GBM and LGG, which could be significantly correlated with patients' survival based on next-generation sequencing data obtained from The Cancer Genome Atlas for gene expression analysis. The effectiveness of these genes in the survival prediction of GBM and LGG was evaluated, and the average receiver operating characteristic curve (ROC) area under the curve values ranged from 0.7 to 0.8. Gene set enrichment analysis revealed that these genes were involved in eight significant pathways and 23 molecular functions. Moreover, the expressions of ten (CTSZ, EFEMP2, ITGA5, KDELR2, MDK, MICALL2, MAP 2 K3, PLAUR, SERPINE1, and SOCS3) of these genes were significantly higher in GBM than in LGG, and comparing their expression levels to those of the proposed control genes (TBP, IPO8, and SDHA) could have the potential capability to classify patients into high- and low- risk groups, which differ significantly in the overall survival. Signatures of candidate genes were validated, by multiple microarray datasets from Gene Expression Omnibus, to increase the robustness of using these potential prognostic factors. In both the GBM and LGG cohort study, most of the patients in the high-risk group had the IDH1 wild-type gene, and those in the low-risk group had IDH1 mutations. Moreover, most of the high-risk patients with LGG possessed a 1p/19q-noncodeletion.ConclusionIn this study, we identified survival relevant genes which were shared between GBM and LGG, and those enabled to classify patients into high- and low-risk groups based on expression level analysis. Both the risk groups could be correlated with the well-known genetic variants, thus suggesting their potential prognostic value in clinical application.

Project description:PurposeIncreasing evidence suggests myopia is not a simple refractive error, many other factors might also be involved. Here, we assessed myopic and normal corneas' gene expression profiles to identify possible diagnostic and therapeutic biomarkers for myopia.Materials and methodsWe obtained the expression profile of ten patients and seven normal control samples from the GSE112155 and GSE151631 datasets based on the Gene Expression Omnibus (GEO) database. We used the "limma" R package to determine the differentially expressed genes (DEGs) between myopic and normal corneas. Weighted gene co-expression network analysis (WGCNA) was used to identify critical co-expressed modules related to myopia, and enrichment analyses were used to annotate the function of genes encompassed in the compulsory module. We also validated these findings in two external datasets (GSE24641 and GSE136701).ResultsWe identified that the DEGs were significantly enriched in ultraviolet (UV) response, TNF-α signaling via NFκB, Angiogenesis, Myogenesis pathways, etc. We used 2095 genes to construct the co-expression gene modules and found five interesting modules because the eigengene expression of these modules was significantly differentially expressed between myopic and normal corneas. Notably, the enrichment analysis found that the genes encompassed in lightgreen module were significantly enriched in immune-related pathways. These findings were proved by subsequent analysis based on Xcell software. We found the component of B cells, CD4+ memory T cells, CD8+ central memory T cells, plasmacytoid dendritic cells, T helper 2 (Th2) cells, regulatory T cells (Tregs), etc. were significantly increased in myopic corneas, while CD8+ T cells, CD4+ T central memory cells, natural killer T (NKT) cells, and T helper 1 (Th1) cells were significantly decreased.ConclusionOur findings identified some markers that might detect diagnosis and treatment for myopia from cornea aspect. Future studies are warranted to verify the functional role of immune-related pathways in cornea during the pathogenesis or progression of myopia.

Dataset Information

Expression profile analysis to predict potential biomarkers for glaucoma: BMP1, DMD and GEM.

Publications

Expression profile analysis to predict potential biomarkers for glaucoma: BMP1, DMD and GEM.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets