scholarly journals Renal Gene Expression Database (RGED): a relational database of gene expression profiles in kidney disease

Database ◽  
2014 ◽  
Vol 2014 (0) ◽  
pp. bau092-bau092 ◽  
Author(s):  
Q. Zhang ◽  
B. Yang ◽  
X. Chen ◽  
J. Xu ◽  
C. Mei ◽  
...  
Keyword(s):  
Gene Expression ◽  
Kidney Disease ◽  
Relational Database ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Gene Expression Database

A Novel Approach for Analyzing Gene Expression Profiles Defines Two Distinct Subgroups of t(4;11) Positive Infant Acute Lymphoblastic Leukemia Patients.

Blood ◽  
2007 ◽  
Vol 110 (11) ◽  
pp. 4282-4282
Author(s):  
Ronald W. Stam ◽  
Stefan Bohlander ◽  
Renate Kirschner-Schwabe ◽  
Susan Arentsen-Peters ◽  
Rob Pieters ◽  
...  
Keyword(s):  
Gene Expression ◽  
Acute Lymphoblastic Leukemia ◽  
Relational Database ◽  
Target Genes ◽  
Expression Profiles ◽  
Lymphoblastic Leukemia ◽  
Gene Expression Profiles ◽  
Biological Properties ◽  
Database Program ◽  
Infant Acute Lymphoblastic Leukemia

Abstract Gene expression profiles (GEPs) are becoming increasingly more important for diagnostic procedures, allowing clinical predictions including treatment response and outcome for various types of cancer. However, established gene signatures not always appear helpful in understanding underlying disease mechanisms. Therefore, we explored an alternative approach for analyzing GEPs of a group of t(4;11) positive infant acute lymphoblastic leukemia patients (n=15), a highly aggressive type of MLL rearranged leukemia. We developed a method that uses a relational database program in combination with a normalization approach and specific discriminators. For normalization, every GEP of a given t(4;11) positive ALL sample was compared with 3 GEPs of normal bone marrow aspirates derived from healthy donors. Using the GeneChip Analysis Suite 5.0 program for single comparison analysis, the resulting gene lists were then compared with each other and only consistently identified genes (present on all three gene lists) were used for further analysis. This noise reduction decreased the amount of potentially deregulated target genes to about 30–40%. The remaining gene lists represented highly significant target genes that were then incorporated into a relational database program using specific discriminators. These discriminators were: upregulation of HOXA9 and HOXA10, presence/absence of the AF4-MLL fusion transcripts in addition to the MLL-AF4 fusion, and the localization of the genomic breakpoint within the MLL gene. This pilot study led to promising results, surprisingly classifying individual t(4;11) positive ALL patients into two distinct subgroups. Both subgroups share about 80 target genes, but also display particular sets of subgroup specific target genes. Importantly, these identified target genes can directly be linked to biological properties of t(4;11) positive leukemia cells, and therefore, allow important novel insights into this aggressive type of leukemia in infants.

scholarly journals Identification of VCAN as Hub Gene for Diabetic Kidney Disease Immune Injury Using Integrated Bioinformatics Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Qiannan Xu ◽  
Binjue Li ◽  
Yucheng Wang ◽  
Cuili Wang ◽  
Shi Feng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Kidney Disease ◽  
Diabetic Kidney Disease ◽  
Expression Profiles ◽  
Kidney Tissue ◽  
Gene Expression Profiles ◽  
Online Database ◽  
Hub Gene ◽  
Diabetic Kidney ◽  
Immune Injury

Background: Diabetic kidney disease (DKD) is a leading cause of chronic kidney disease in China. Tubular injury contributes to the progression of DKD. Our study was conducted to explore the differential gene expression profiles between kidneys from patients with DKD and kidney living donors (LDs).Methods: In total, seven DKD and eighteen LD gene expression profiles from the GSE104954 dataset were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were analyzed in R with the limma package. DEGs were uploaded to the g:Profiler online database to explore the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Ingenuity pathway analysis (IPA) was carried out using online IPA software. Weighted gene co-expression network analysis (WGCNA) was performed using the WGCNA R package. By integrating DEGs and genes from the top 1 phenotype-gene associated module, we determined the hub gene. We next tested the hub gene, VCAN, in the GSE30122 dataset. We also validated the versican levels in human kidney tissues, explored immune cell type enrichment using an online database xCell, and investigated the correlation between cell types and VCAN expression.Results: A total of 563 DEGs was identified. A large number of pathways were involved in the immune response process according to the results of GO, KEGG, and IPA. Using WGCNA, we selected the lightcyan module in which genes showed the strongest correlation with the phenotype and smallest P-value. We also identified VCAN as a hub gene by integrating DEG analysis and WGCNA. Versican expression was upregulated in human diabetic kidney tissue. Moreover, versican was speculated to play a role in immune injury according to the enrichment of functions and signaling pathways. VCAN transcript levels correlate with the assembly of immune cells in the kidney.Conclusion: Immune processes played an essential role in DKD tubulointerstitium injury. The hub gene VCAN contributed to this process.

scholarly journals Development and Application of a Rat Ovarian Gene Expression Database

Endocrinology ◽  
2004 ◽  
Vol 145 (11) ◽  
pp. 5384-5396 ◽  
Author(s):  
Misung Jo ◽  
Mary C. Gieske ◽  
Charles E. Payne ◽  
Sarah E. Wheeler-Price ◽  
Joseph B. Gieske ◽  
...  
Keyword(s):  
Gene Expression ◽  
Chorionic Gonadotropin ◽  
Granulosa Cells ◽  
Human Chorionic Gonadotropin ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Gene Expression Database ◽  
Total Rna ◽  
A Genome ◽  
Six Genes

Abstract The pituitary gonadotropins play a key role in follicular development and ovulation through the induction of specific genes. To identify these genes, we have constructed a genome-wide rat ovarian gene expression database (rOGED). The database was constructed from total RNA isolated from intact ovaries, granulosa cells, or residual ovarian tissues collected from immature pregnant mare serum gonadotropin (PMSG)/human chorionic gonadotropin-treated rats at 0 h (no PMSG), 12 h, and 48 h post PMSG, as well as 6 and 12 h post human chorionic gonadotropin. The total RNA was used for DNA microarray analysis using Affymetrix Rat Expression Arrays 230A and 230B (Affymetrix, Santa Clara, CA). The microarray data were compiled and used for display of individual gene expression profiles through specially developed software. The final rOGED provides immediate analysis of temporal gene expression profiles for over 28,000 genes in intact ovaries, granulosa cells, and residual ovarian tissue during follicular growth and the preovulatory period. The accuracy of the rOGED was validated against the gene profiles for over 20 known genes. The utility of the rOGED was demonstrated by identifying six genes that have not been described in the rat periovulatory ovary. The mRNA expression patterns and cellular localization for each of these six genes (estrogen sulfotransferase, synaptosomal-associated protein 25 kDa, runt-related transcription factor, calgranulin B, α1-macroglobulin, and MAPK phosphotase-3) were confirmed by Northern blot analyses and in situ hybridization, respectively. The current findings demonstrate that the rOGED can be used as an instant reference for ovarian gene expression profiles, as well as a reliable resource for identifying important yet, to date, unknown ovarian genes.

1327: Gene Expression Profiles in Benign Prostatic Hyperplasia

2004 ◽  
Vol 171 (4S) ◽  
pp. 349-350
Author(s):  
Gaelle Fromont ◽  
Michel Vidaud ◽  
Alain Latil ◽  
Guy Vallancien ◽  
Pierre Validire ◽  
...  
Keyword(s):  
Gene Expression ◽  
Benign Prostatic Hyperplasia ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Prostatic Hyperplasia
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