scholarly journals Pathway analysis of differentially expressed genes in patients with acute aortic dissection

2007 ◽  
Vol 55 (S 1) ◽  
Author(s):  
SA Mohamed ◽  
T Hanke ◽  
AW Erasmi ◽  
D Richardt ◽  
HH Sievers
Keyword(s):  
Aortic Dissection ◽  
Differentially Expressed Genes ◽  
Pathway Analysis ◽  
Acute Aortic Dissection ◽  
Differentially Expressed

scholarly journals Pathway Analysis of Differentially Expressed Genes in Patients with Acute Aortic Dissection

2009 ◽  
Vol 4 ◽  
pp. BMI.S2530 ◽  
Author(s):  
Salah A. Mohamed ◽  
Hans H. Sievers ◽  
Thorsten Hanke ◽  
Doreen Richardt ◽  
Claudia Schmidtke ◽  
...  
Keyword(s):  
Aortic Dissection ◽  
Differentially Expressed Genes ◽  
Pathway Analysis ◽  
Acute Aortic Dissection ◽  
Differentially Expressed ◽  
Control Group ◽  
Pathophysiological Mechanism ◽  
Qrt Pcr ◽  
Starting Point ◽  
Fibrillin 1

Background Acute aortic dissection (AAD) is a life-threatening condition with high mortality and a relatively unclarified pathophysiological mechanism. Although differentially expressed genes in AAD have been recognized, interactions between these genes remain poorly defined. This study was conducted to gain a better understanding of the molecular mechanisms underlying AAD and to support the future development of a clinical test for monitoring patients at high risk. Materials and Methods Aortic tissue was collected from 19 patients with AAD (mean age 61.7 ± 13.1 years), and from eight other patients (mean age 32.9 ± 12.2 years) who carried the mutated gene for Marfan syndrome (MS). Six patients (mean age 56.7 ± 12.3 years) served as the control group. The PIQOR™ Immunology microarray with 1076 probes in quadruplicates was utilized; the differentially expressed genes were analysed in a MedScan search using PathwayAssist software. Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) and protein analysis were performed. Results Interactions of MS fibrillin-1 (FBN1) in the MedScan pathway analysis showed four genes, fibulin-1 (FBLN1), fibulin-2 (FBLN2), decorin (DCN) and microfibrillar associated protein 5 (MFAP5), which were differentially expressed in all tissue from AAD. The validation of these genes by qRT-PCR revealed a minimum of three-fold downregulation of FBLN1 (0.5 ± 0.4 vs. 6.1 ± 2.3 fold, p = 0.003) and of DCN (2.5 ± 1.0 vs. 8.5 ± 4.7 fold, p = 0.04) in AAD compared to MS and control samples. Conclusions Downregulation of fibrillin-1 (FBN1) may weaken extracellular components in the aorta and/or interfer with the transmission of cellular signals and eventually cause AAD. Additional research on these four identified genes can be a starting point to develop a diagnostic tool.

Pathway analysis of differentially expressed genes in Mycobacterium bovis challenged bovine macrophages

2018 ◽  
Vol 115 ◽  
pp. 343-352 ◽  
Author(s):  
Sanjeev Kumar Shukla ◽  
Shubhra Shukla ◽  
Rehan Khan ◽  
Anuj Ahuja ◽  
Lakshya Veer Singh ◽  
...  
Keyword(s):  
Differentially Expressed Genes ◽  
Mycobacterium Bovis ◽  
Pathway Analysis ◽  
Differentially Expressed

scholarly journals Digital Gene Expression Profiling Analysis of Aged Mice under Moxibustion Treatment

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Nan Liu ◽  
Yunyao Jiang ◽  
Min Xing ◽  
Baixiao Zhao ◽  
Jincai Hou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Ontology ◽  
Gene Expression Profiling ◽  
Differentially Expressed Genes ◽  
Pathway Analysis ◽  
Expression Profiling ◽  
Digital Gene Expression ◽  
Differentially Expressed ◽  
Aged Mice ◽  
Digital Gene Expression Profiling

Aging is closely connected with death, progressive physiological decline, and increased risk of diseases, such as cancer, arteriosclerosis, heart disease, hypertension, and neurodegenerative diseases. It is reported that moxibustion can treat more than 300 kinds of diseases including aging related problems and can improve immune function and physiological functions. The digital gene expression profiling of aged mice with or without moxibustion treatment was investigated and the mechanisms of moxibustion in aged mice were speculated by gene ontology and pathway analysis in the study. Almost 145 million raw reads were obtained by digital gene expression analysis and about 140 million (96.55%) were clean reads. Five differentially expressed genes with an adjusted P value < 0.05 and |log⁡2(fold  change)| > 1 were identified between the control and moxibustion groups. They were Gm6563, Gm8116, Rps26-ps1, Nat8f4, and Igkv3-12. Gene ontology analysis was carried out by the GOseq R package and functional annotations of the differentially expressed genes related to translation, mRNA export from nucleus, mRNA transport, nuclear body, acetyltransferase activity, and so on. Kyoto Encyclopedia of Genes and Genomes database was used for pathway analysis and ribosome was the most significantly enriched pathway term.

scholarly journals Bioinformatics analysis for the identification of differentially expressed genes and related signaling pathways in H. pylori-CagA transfected gastric cancer cells

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11203
Author(s):  
Dingyu Chen ◽  
Chao Li ◽  
Yan Zhao ◽  
Jianjiang Zhou ◽  
Qinrong Wang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Helicobacter Pylori ◽  
Differentially Expressed Genes ◽  
Pathway Analysis ◽  
Kegg Pathway ◽  
Differentially Expressed ◽  
Ppi Network ◽  
Ags Cells ◽  
Kegg Pathway Analysis ◽  
Key Genes

Aim Helicobacter pylori cytotoxin-associated protein A (CagA) is an important virulence factor known to induce gastric cancer development. However, the cause and the underlying molecular events of CagA induction remain unclear. Here, we applied integrated bioinformatics to identify the key genes involved in the process of CagA-induced gastric epithelial cell inflammation and can ceration to comprehend the potential molecular mechanisms involved. Materials and Methods AGS cells were transected with pcDNA3.1 and pcDNA3.1::CagA for 24 h. The transfected cells were subjected to transcriptome sequencing to obtain the expressed genes. Differentially expressed genes (DEG) with adjusted P value < 0.05, — logFC —> 2 were screened, and the R package was applied for gene ontology (GO) enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The differential gene protein–protein interaction (PPI) network was constructed using the STRING Cytoscape application, which conducted visual analysis to create the key function networks and identify the key genes. Next, the Kaplan–Meier plotter survival analysis tool was employed to analyze the survival of the key genes derived from the PPI network. Further analysis of the key gene expressions in gastric cancer and normal tissues were performed based on The Cancer Genome Atlas (TCGA) database and RT-qPCR verification. Results After transfection of AGS cells, the cell morphology changes in a hummingbird shape and causes the level of CagA phosphorylation to increase. Transcriptomics identified 6882 DEG, of which 4052 were upregulated and 2830 were downregulated, among which q-value < 0.05, FC > 2, and FC under the condition of ≤2. Accordingly, 1062 DEG were screened, of which 594 were upregulated and 468 were downregulated. The DEG participated in a total of 151 biological processes, 56 cell components, and 40 molecular functions. The KEGG pathway analysis revealed that the DEG were involved in 21 pathways. The PPI network analysis revealed three highly interconnected clusters. In addition, 30 DEG with the highest degree were analyzed in the TCGA database. As a result, 12 DEG were found to be highly expressed in gastric cancer, while seven DEG were related to the poor prognosis of gastric cancer. RT-qPCR verification results showed that Helicobacter pylori CagA caused up-regulation of BPTF, caspase3, CDH1, CTNNB1, and POLR2A expression. Conclusion The current comprehensive analysis provides new insights for exploring the effect of CagA in human gastric cancer, which could help us understand the molecular mechanism underlying the occurrence and development of gastric cancer caused by Helicobacter pylori.

scholarly journals Screening differentially expressed genes of pancreatic cancer between Mongolian and Han people using bioinformatics technology

2019 ◽  
Author(s):  
Jiasheng Xu ◽  
Kaili Liao ◽  
ZHONGHUA FU ◽  
ZHENFANG XIONG
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Gene Ontology ◽  
Differentially Expressed Genes ◽  
Pathway Analysis ◽  
Differentially Expressed ◽  
Cancer Tissue ◽  
Pancreatic Cancer Tissue ◽  
Tissue Samples ◽  
Multiple Difference

Abstract Objective To screen and analyze differentially expressed genes in pancreatic carcinoma tissues taken from Mongolian and Han patients by Affymetrix Genechip. Methods: Pancreatic ductal cell carcinoma tissues were collected from the Mongolian and Han patients undergoing resection in the Second Affiliated Hospital of Nanchang University during March 2015 to May 2018 and the total RNA was extracted. Differentially expressed genes were selected from the total RNA qualified by Nanodrop 2000 and Agilent 2100 using Affymetrix and a cartogram was drawn; The gene ontology (GO) analysis and Pathway analysis were used for the collection and analysis of biological information of these differentially expressed genes. Finally, some differentially expressed genes were verified by real-time PCR. Results Through the microarray analysis of gene expression, 970 differentially expressed genes were detected by comparing pancreatic cancer tissue samples between Mongolian and Han patients. A total of 257 genes were significantly up-regulated in pancreatic cancer tissue samples in Mongolian patients;while a total of 713 genes were down-regulated. In the Gene Ontology database, 815 differentially expressed genes were identified with clear GO classification, and CPB1 gene had the highest multiple of differential expression (difference multiple: 31.76). The Pathway analysis detected 28 signaling pathways that included these differentially expressed genes, involving a total of 178 genes. Among these pathways, the enrichment of differentially expressed genes in the FAK signaling pathway was the highest and COL11A1 gene had the highest multiple difference (multiple difference: 5.02). The expressions of differentially expressed genes CPB1, COL11A1、ITGA4、BIRC3、PAK4、CPA1、CLPS、PIK3CG and HLA-DPA1 determined by real-time PCR were consistent with the results of gene chip analysis. Conclusions The results of microarray analysis of gene expression profiles showed that there are a large number of differentially expressed genes in pancreatic cancer tissue samples compared between Mongolian and Han populations. These genes are closely related to the proliferation, differentiation, invasion and metastasis and multi-drug resistance of pancreatic cancer and are involved in the regulation of multiple important signaling pathways in organisms.

scholarly journals Differential Gene Expression and Pathway Analysis in Juvenile Nasopharyngeal Angiofibroma Using RNA Sequencing

2018 ◽  
Vol 159 (3) ◽  
pp. 572-575
Author(s):  
Joel W. Jones ◽  
Shireen Usman ◽  
Jacob New ◽  
Andrew Holcomb ◽  
Sumedha Gunewardena ◽  
...  
Keyword(s):  
Growth Factor ◽  
Rna Sequencing ◽  
Differentially Expressed Genes ◽  
Pathway Analysis ◽  
Messenger Rna ◽  
Therapeutic Potential ◽  
Growth Factor Receptor ◽  
Differentially Expressed ◽  
Juvenile Nasopharyngeal Angiofibroma ◽  
Nasopharyngeal Angiofibroma

Juvenile nasopharyngeal angiofibroma (JNA) is a highly vascularized and locally aggressive tumor that typically presents in adolescent males. The molecular biology of this tumor remains understudied. We sought to identify differentially expressed genes in the JNA transcriptome through messenger RNA sequencing of primary fibroblasts from 2 tumor explants and tonsil tissue from tumor-free subjects. In total, 1088 significant, differentially expressed genes were identified with 749 upregulated and 339 downregulated. Pathway analysis identified a number of activated signaling pathways, most notably, the vascular endothelial growth factor (VEGF) pathway (adjusted overlap P = .03). VEGF-A showed a 4.4-fold upregulation in JNA samples. In addition, the angiogenic receptor, fibroblast growth factor receptor 2 (FGFR2), was not present in tumor-free samples but increased in JNA. We validate these findings with immunohistochemistry, demonstrating upregulation of VEGF and FGFR2 in patient sections. Inhibition of the VEGF or FGFR signaling axes may have therapeutic potential in the treatment of JNA.

scholarly journals Gene Expression Profile of HCAEC Cells Exposed to Serum From Chronic Kidney Disease Patients: Role of MAPK Signaling Pathway

2021 ◽  
Author(s):  
Angélica Rangel-López ◽  
Oscar Pérez-González ◽  
Sergio Juárez-Méndez ◽  
Ricardo López-Romero ◽  
Minerva Mata-Rocha ◽  
...  
Keyword(s):  
Gene Expression ◽  
Endothelial Dysfunction ◽  
Signaling Pathway ◽  
Gene Expression Profile ◽  
Differentially Expressed Genes ◽  
Pathway Analysis ◽  
Expression Profile ◽  
Differentially Expressed ◽  
Uremic Serum ◽  
Esrd Patients

Abstract End-stage renal disease (ESRD) patients have an elevated risk of cardiovascular (CV) complications including acute myocardial infarction (AMI); endothelial dysfunction and accumulation of uremic toxins have been associated with such CV-events. To explore which molecular pathways are involved in this CV-complication and the effects of the uremic serum on gene expression, an endothelial dysfunction model was studied through microarrays and pathway analysis. mRNA was isolated of human coronary arterial endothelial cells (HCAEC) primary cultures supplemented with 20% uremic serum from two groups of patients, USI: ESRD-patients; UCI: ESRD-AMI-patients. Affymetrix GeneChip® microarray and the LIMMA-package (Linear Models for Microarray Data) of the Bioconductor sofware17 was implemented to identify relevant DEGs between the two groups of uremic patients. Protein-protein interaction networks and pathway analysis were made to analyze the interaction and expression tendency of differentially expressed genes. 100 differentially expressed genes were identified from two data sets triggered by uremic state using bioinformatics, from 16,607. After in a new cohort, 30 genes were overexpressed in UCI group, which we identified 500 ontological genetic terms and one KEGG-pathway with p < 0.05. The metabolic pathway significantly represented was the MAPK signaling pathway. Network analysis showed six genes (PTGS2, SELE, ICAM1, HMOX1, EGR1, and TLR2) that represent potential markers for ESRD with AMI, as an approximation to their underlying mechanisms. The results obtained suggest that uremic toxins in patients with ESRD can alter HCAEC and modify the gene expression profile, which could have an impact on the development of cardiovascular complications in these patients.

scholarly journals Transcriptomic Profiling Identifies Differentially Expressed Genes in Palbociclib-Resistant ER+ MCF7 Breast Cancer Cells

Genes ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 467 ◽  
Author(s):  
Lilibeth Lanceta ◽  
Conor O'Neill ◽  
Nadiia Lypova ◽  
Xiahong Li ◽  
Eric Rouchka ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Differentially Expressed Genes ◽  
Pathway Analysis ◽  
Metabolic Pathways ◽  
Acquired Resistance ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Kegg Database ◽  
Cyclin Dependent Kinases ◽  
Estrogen Receptor Positive

Acquired resistance to cyclin-dependent kinases 4 and 6 (CDK4/6) inhibition in estrogen receptor-positive (ER+) breast cancer remains a significant clinical challenge. Efforts to uncover the mechanisms underlying resistance are needed to establish clinically actionable targets effective against resistant tumors. In this study, we sought to identify differentially expressed genes (DEGs) associated with acquired resistance to palbociclib in ER+ breast cancer. We performed next-generation transcriptomic RNA sequencing (RNA-seq) and pathway analysis in ER+ MCF7 palbociclib-sensitive (MCF7/pS) and MCF7 palbociclib-resistant (MCF7/pR) cells. We identified 2183 up-regulated and 1548 down-regulated transcripts in MCF7/pR compared to MCF7/pS cells. Functional analysis of the DEGs using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database identified several pathways associated with breast cancer, including ‘cell cycle’, ‘DNA replication’, ‘DNA repair’ and ‘autophagy’. Additionally, Ingenuity Pathway Analysis (IPA) revealed that resistance to palbociclib is closely associated with deregulation of several key canonical and metabolic pathways. Further studies are needed to determine the utility of these DEGs and pathways as therapeutics targets against ER+ palbociclib-resistant breast cancer.

Sign in / Sign up

Export Citation Format

Share Document