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 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 ◽  
Microarray 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 from 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 showed the highest increase in expression level (multiple difference: 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 strongest and COL11A1 gene showed the highest multiple difference (multiple difference: 5.02). The expression 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 microarray 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 comparing Mongolian and Han population. These genes are closely related to the cell proliferation, differentiation, invasion, metastasis and multi-drug resistance in pancreatic cancer. They are also involved in the regulation of multiple important signaling pathways in organisms.

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

2020 ◽  
Author(s):  
Jiasheng Xu ◽  
Kaili Liao ◽  
Zhonghua Fu ◽  
Zhenfang Xiong
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Gene Ontology ◽  
Differentially Expressed Genes ◽  
Microarray 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 from 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 showed the highest increase in expression level (multiple difference: 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 strongest and COL11A1 gene showed the highest multiple difference (multiple difference: 5.02). The expression 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 microarray 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 comparing Mongolian and Han population. These genes are closely related to the cell proliferation, differentiation, invasion, metastasis and multi-drug resistance in pancreatic cancer. They are also involved in the regulation of multiple important signaling pathways in organisms.

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

2020 ◽  
Author(s):  
Jiasheng Xu ◽  
Kaili Liao ◽  
ZHONGHUA FU ◽  
ZHENFANG XIONG
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Gene Ontology ◽  
Differentially Expressed Genes ◽  
Microarray 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 from 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 showed the highest increase in expression level (multiple difference: 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 strongest and COL11A1 gene showed the highest multiple difference (multiple difference: 5.02). The expression 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 microarray 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 comparing Mongolian and Han population. These genes are closely related to the cell proliferation, differentiation, invasion, metastasis and multi-drug resistance in pancreatic cancer. They are also involved in the regulation of multiple important signaling pathways in organisms.

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 ◽  
Microarray 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 from 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 showed the highest increase in expression level (multiple difference: 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 strongest and COL11A1 gene showed the highest multiple difference (multiple difference: 5.02). The expression 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 microarray 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 comparing Mongolian and Han population. These genes are closely related to the cell proliferation, differentiation, invasion, metastasis and multi-drug resistance in pancreatic cancer. They are also involved in the regulation of multiple important signaling pathways in organisms.

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 Selenium Regulates Gene Expression for Glucosinolate and Carotenoid Biosynthesis in Arabidopsis

2011 ◽  
Vol 136 (1) ◽  
pp. 23-34 ◽  
Author(s):  
Carl E. Sams ◽  
Dilip R. Panthee ◽  
Craig S. Charron ◽  
Dean A. Kopsell ◽  
Joshua S. Yuan
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Plant Species ◽  
Chlorophyll Biosynthesis ◽  
Plant Secondary Metabolites ◽  
Carotenoid Biosynthesis ◽  
Differentially Expressed ◽  
Biosynthesis Pathway ◽  
Tissue Samples ◽  
Expression Of Genes

Glucosinolates (GSs) and carotenoids are important plant secondary metabolites present in several plant species, including arabidopsis (Arabidopsis thaliana). Although genotypic and environmental regulation of GSs and carotenoid compounds has been reported, few studies present data on their regulation at the molecular level. Therefore, the objective of this study was to explore differential expression of genes associated with GSs and carotenoids in arabidopsis in response to selenium fertilization, shown previously to impact accumulations of both classes of metabolites in Brassica species. Arabidopsis was grown under 0.0 or 10.0 μM Na2SeO4 in hydroponic culture. Shoot and root tissue samples were collected before anthesis to measure GSs and carotenoid compounds and conduct gene expression analysis. Gene expression was determined using arabidopsis oligonucleotide chips containing more than 31,000 genes. There were 1274 differentially expressed genes in response to selenium (Se), of which 516 genes were upregulated. Ontology analysis partitioned differentially expressed genes into 20 classes. Biosynthesis pathway analysis using AraCyc revealed that four GSs, one carotenoid, and one chlorophyll biosynthesis pathways were invoked by the differentially expressed genes. Involvement of the same gene in more than one biosynthesis pathway indicated that the same enzyme may be involved in multiple GS biosynthesis pathways. The decrease in carotenoid biosynthesis under Se treatment occurred through the downregulation of phytoene synthase at the beginning of the carotenoid biosynthesis pathway. These findings may be useful to modify the GS and carotenoid levels in arabidopsis and may lead to modification in agriculturally important plant species.

scholarly journals Investigation of Aberrantly Methylated Differentially Expressed Genes in Pancreatic Cancer by Integrated Bioinformatics Analysis

2021 ◽  
Author(s):  
Cailin xue ◽  
Peng gao ◽  
Xudong zhang ◽  
Xiaohan cui ◽  
Lei jin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Signaling Pathways ◽  
Differentially Expressed Genes ◽  
Dna Sequences ◽  
Differentially Expressed ◽  
Receptor Interaction ◽  
Biological Processes ◽  
Ppi Network ◽  
Hub Genes

Abstract Background: Abnormal methylation of DNA sequences plays an important role in the development and progression of pancreatic cancer (PC). The purpose of this study was to identify abnormal methylation genes and related signaling pathways in PC by comprehensive bioinformatic analysis of three datasets in the Gene Expression Omnibus (GEO). Methods: Datasets of gene expression microarrays (GSE91035, GSE15471) and gene methylation microarrays (GSE37480) were downloaded from the GEO database. Aberrantly methylated-differentially expressed genes (DEGs) were analysis by GEO2R software. GO and KEGG enrichment analyses of selected genes were performed using DAVID database. A protein–protein interaction (PPI) network was constructed by STRING and visualized in Cytoscape. Core module analysis was performed by Mcode in Cytoscape. Hub genes were obtained by CytoHubba app. in Cytoscape software. Results: A total of 267 hypomethylation-high expression genes, which were enriched in biological processes of cell adhesion, biological adhesion and regulation of signaling were obtained. KEGG pathway enrichment showed ECM-receptor interaction, Focal adhesion and PI3K-Akt signaling pathway. The top 5 hub genes of PPI network were EZH2, CCNA2, CDC20, KIF11, UBE2C. As for hypermethylation-low expression genes, 202 genes were identified, which were enriched in biological processes of cellular amino acid biosynthesis process and positive regulation of PI3K activity, etc. The pathways enriched were the pancreatic secretion and biosynthesis of amino acids pathways, etc. The five significant hub genes were DLG3, GPT2, PLCB1, CXCL12 and GNG7. In addition, five genes, including CCNA2, KIF11, UBE2C, PLCB1 and GNG7, significantly associated with patient's prognosis were also identified. Conclusion: Novel genes with abnormal expression were identified, which will help us further understand the molecular mechanism and related signaling pathways of PC, and these aberrant genes could possibly serve as biomarkers for precise diagnosis and treatment of PC.

AKT1 (E17K) Mutation in Pancreatic Cancer

2008 ◽  
Vol 7 (5) ◽  
pp. 407-408 ◽  
Author(s):  
Azim Mohamedali ◽  
Nicholas C. Lea ◽  
Roger M. Feakins ◽  
Kavita Raj ◽  
Ghulam J. Mufti ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Cell Line ◽  
Somatic Mutation ◽  
Cancer Progression ◽  
Pleckstrin Homology Domain ◽  
Cancer Tissue ◽  
Pancreatic Cancer Tissue ◽  
Tissue Samples ◽  
Pancreatic Cell ◽  
Ras Mutation

The prevalence of a novel somatic mutation (E17K) in the pleckstrin homology domain of AKT1 was investigated in pancreatic cancer using a quantitative pyrosequencing assay. This mutation was un-detectable in pancreatic cancer tissue samples (n=65) and pancreatic cell line (n=10) DNA suggesting that pancreatic cancer progression is mainly dependent on the K-Ras mutation.

Identification of key biomarkers for thyroid cancer by integrative gene expression profiles

2021 ◽  
pp. 153537022110088
Author(s):  
Jinyi Tian ◽  
Yizhou Bai ◽  
Anyang Liu ◽  
Bin Luo
Keyword(s):  
Gene Expression ◽  
Thyroid Cancer ◽  
Differentially Expressed Genes ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Thyroid Tissue ◽  
Gene Signature ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Tissue Samples

Thyroid cancer is a frequently diagnosed malignancy and the incidence has been increased rapidly in recent years. Despite the favorable prognosis of most thyroid cancer patients, advanced patients with metastasis and recurrence still have poor prognosis. Therefore, the molecular mechanisms of progression and targeted biomarkers were investigated for developing effective targets for treating thyroid cancer. Eight chip datasets from the gene expression omnibus database were selected and the inSilicoDb and inSilicoMerging R/Bioconductor packages were used to integrate and normalize them across platforms. After merging the eight gene expression omnibus datasets, we obtained one dataset that contained the expression profiles of 319 samples (188 tumor samples plus 131 normal thyroid tissue samples). After screening, we identified 594 significantly differentially expressed genes (277 up-regulated genes plus 317 down-regulated genes) between the tumor and normal tissue samples. The differentially expressed genes exhibited enrichment in multiple signaling pathways, such as p53 signaling. By building a protein–protein interaction network and module analysis, we confirmed seven hub genes, and they were all differentially expressed at all the clinical stages of thyroid cancer. A diagnostic seven-gene signature was established using a logistic regression model with the area under the receiver operating characteristic curve (AUC) of 0.967. Seven robust candidate biomarkers predictive of thyroid cancer were identified, and the obtained seven-gene signature may serve as a useful marker for thyroid cancer diagnosis and prognosis.

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.

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