Dataset Information

Transcriptional profiling analysis predicts potential biomarkers for glaucoma: HGF, AKR1B10 and AKR1C3.

ABSTRACT: Glaucoma results in damage to the optic nerve and vision loss. The aim of this study was to screen more accurate biomarkers and targets for glaucoma. The datasets E-GEOD-7144 and E-MEXP-3427 were screened for differently expressed genes (DEGs) by significance analysis of microarrays. Functional and pathway enrichment analysis were processed. Pathway relationship networks and gene co-expression networks were constructed. DEGs of disease and treatment with the same symbols were of interest. RT-qPCR was processed to verify the expression of key DEGs. A total of 1,019 DEGs of glaucoma were identified and 93 DEGs in transforming growth factor-?1 (TGF-?1) and TGF-?1-2 treatment cases compared with the normal control group. Pathway relationship network of glaucoma was constructed with 25 nodes. The pathway relationship network of TGF-?1 and -2 treatment groups was constructed with 11 nodes. Glaucoma-related DEGs in GO terms and pathways were inserted and 180 common DEGs were obtained. Then, gene co-expression network of glaucoma-related DEGs was constructed with 91 nodes. Furthermore, DEGs of TGF-?1 and -2 treated glaucoma in GO terms and pathways were inserted, and 29 common DEGs were identified. Based on these DEGs, gene co-expression network was constructed with 12 nodes and 16 edges. Finally, a total of 6 important DEGs of disease and treatment were inserted and obtained. They were HGF, AKR1B10, AKR1C3, PPAP2B, INHBA and BCAT1. The expression of HGF, AKR1B10 and AKR1C3 was decreased in glaucoma samples and treatment samples. In conclusion, HGF, AKR1B10 and AKR1C3 may be key genes for glaucoma diagnosis and treatment.

SUBMITTER: Nie Q

PROVIDER: S-EPMC6257105 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Transcriptional profiling analysis predicts potential biomarkers for glaucoma: HGF, AKR1B10 and AKR1C3.

Nie Qiaoli Q Zhang Xiaoyan X

Experimental and therapeutic medicine 20181017 6

Glaucoma results in damage to the optic nerve and vision loss. The aim of this study was to screen more accurate biomarkers and targets for glaucoma. The datasets E-GEOD-7144 and E-MEXP-3427 were screened for differently expressed genes (DEGs) by significance analysis of microarrays. Functional and pathway enrichment analysis were processed. Pathway relationship networks and gene co-expression networks were constructed. DEGs of disease and treatment with the same symbols were of interest. RT-qPC ...[more]

PMID: 30542465

Similar Datasets

Project description:PurposeGlaucoma 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.MethodA 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.ResultsA 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%.ConclusionWe 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.

Project description:The present study aimed to screen potential biomarkers for uterine leiomyomas disease, particularly target genes associated with the mediator of RNA polymerase II transcription subunit 12 (MED12) mutation. The microarray data of GSE30673, including 10 MED12 wild-type myometrium, 8 MED12 mutation leiomyoma and 2 MED12 wild-type leiomyoma samples, were downloaded from the Gene Expression Omnibus database. Compared with myometrium samples, differently-expressed genes (DEGs) in the MED12 mutation and wild-type leiomyoma samples were identified using the Limma package. The two sets of DEGs obtained were intersected to screen common DEGs. The DEGs in the MED12 mutation and wild-type leiomyoma samples, and common DEGs were defined as group A, B and C. Gene Ontology (GO) and pathway enrichment analyses were performed using the Database for Annotation, Visualization and Integrated Discovery online tool. Based on the Kyoto Encyclopedia of Genes and Genomes database, pathway relation networks were constructed. DEGs in GO terms and pathways were intersected to screen important DEGs. Subsequently, a gene co?expression network was constructed and visualized using Cytoscape software. Reverse transcription?quantitative polymerase chain reaction was used to detect the expression levels of important DEGs. A total of 1,258 DEGs in group A were screened, and enriched for extracellular matrix (ECM) organization and ECM?receptor interaction. In addition, a total of 1,571 DEGs in group B were enriched for cell adhesion. Furthermore, 391 DEGs were involved in extracellular matrix organization. Pathway relation networks of group A, B and C were constructed with nodes of 48, 39, and 28, respectively. Finally, 135 important DEGs were obtained, including Acyl?CoA synthetase medium?chain family member 3, protein S (?) (PROS1) and F11 receptor. A gene co?expression network with 68 nodes was constructed. The expression of caspase 1 (CASP1) and aldehyde dehydrogenase 1 family member A1 (ALDH1A1) was significant higher in SK?UT?1 compared with that in PHM1?31 cells, while the expression of PROS1 was significant lower in SK?UT?1 cells. These results that CASP1, ALDH1A1 and PROS1 may be potential biomarkers for uterine leiomyomas. Furthermore, hematopoietic prostaglandin D synthase and carbonyl reductase 3 (CBR3) may be particular genes associated with the MED12 mutation in this disease.

Dataset Information

Transcriptional profiling analysis predicts potential biomarkers for glaucoma: HGF, AKR1B10 and AKR1C3.

Publications

Transcriptional profiling analysis predicts potential biomarkers for glaucoma: HGF, AKR1B10 and AKR1C3.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets