Detection of ovarian cancer-specific gene by differentially expressed gene polymerase chain reaction prescreening and direct DNA sequencing

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 21106-21106 ◽  
Author(s):  
J. Kim ◽  
J. H. Pak ◽  
W. H. Choi ◽  
J. Y. Kim ◽  
W. D. Joo ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Ribosomal Protein ◽  
Differentially Expressed Genes ◽  
Triosephosphate Isomerase ◽  
Differentially Expressed Gene ◽  
Differentially Expressed ◽  
Specific Gene ◽  
Cancer Tissue ◽  
Normal Ovary ◽  
Ribosomal Protein L11

21106 Background: To detect the genes differentially expressed in the ovarian cancer, we analysed the genes in the ovarian cancer and normal ovary by differentially expressed gene(DEG) PCR using the RNA extracted from the both tissues. We examined the relationship between the specific genes of ovarian cancer and pathogenesis of ovarian cancer. Methods: Differentially expressed genes were screened by ACP-based PCR. Differentially expressed bands were extracted from agarose gel, and then directly sequenced. Finally we determined the clinical importances of differentially expressed genes. Results: Some genes were overexpressed in the ovarian cancer tissue than normal ovary, such as plexin B1(PLXNB1), aminoacylase 1(ACY1), solute carrier family 25 protein(SLC25A5), triosephosphate isomerase 1(TPI 1), poliovirus receptor-related 3 protein(PVRL 3), clusterin, LY6/PLAUR domain containing 1 protein(LYPDC 1). And other five genes were more expressed in the normal ovary than ovarian cancer, such as ribosomal protein L11 and L23, tenascin XB (TNXB), complement component 1 and actin alpha 2. Conclusions: Clusterin was highly expressed in the tissue from ovarian cancer, which was identified with anti- or proapoptotic activity regulated by calcium homeostasis in prostate, breast and colorectal cancers. And it suggests the possibility that regulation of clusterin activity provides the prospect of breaking down cancer cells‘ resistance to apoptosis in the ovarian cancer. Ribosomal protein L11 and L23 was highly expressed in normal ovary, which plays an important role in regulating the stability and function of the p53 tumor suppressor protein. It suggests that suppression of ribosomal protein L11 may act an important role in proliferation of ovarian cancer and over-expression of ribosomal protein L11 may act an important role in cell cycle arrest in the treatment of the ovarian cancer. No significant financial relationships to disclose.

scholarly journals A red herring in zebrafish genetics: allele-specific gene expression can underlie altered transcript abundance in zebrafish mutants

2021 ◽  
Author(s):  
Richard J White ◽  
Eirinn Mackay ◽  
Stephen W Wilson ◽  
Elisabeth M Busch-Nentwich
Keyword(s):  
Differentially Expressed Genes ◽  
Differentially Expressed Gene ◽  
Transcript Abundance ◽  
Differentially Expressed ◽  
Model Organisms ◽  
Specific Gene ◽  
Wild Type ◽  
Specific Expression ◽  
Genomic Location ◽  
Allele Specific

In model organisms, RNA sequencing is frequently used to assess the effect of genetic mutations on cellular and developmental processes. Typically, animals heterozygous for a mutation are crossed to produce offspring with different genotypes. Resultant embryos are grouped by genotype to compare homozygous mutant embryos to heterozygous and wild-type siblings. Genes that are differentially expressed between the groups are assumed to reveal insights into the pathways affected by the mutation. Here we show that in zebrafish, differentially expressed genes are often overrepresented on the same chromosome as the mutation due to different levels of expression of alleles from different genetic backgrounds. Using an incross of haplotype-resolved wild-type fish, we found evidence of widespread allele-specific expression, which appears as differential expression when comparing embryos homozygous for a region of the genome to their siblings. When analysing mutant transcriptomes, this means that differentially expressed genes on the same chromosome as a mutation of interest may not be caused by that mutation. Typically, the genomic location of a differentially expressed gene is not considered when interpreting its importance with respect to the phenotype. This could lead to pathways being erroneously implicated or overlooked due to the noise of spurious differentially expressed genes on the same chromosome as the mutation. These observations have implications for the interpretation of RNA-seq experiments involving outbred animals and non-inbred model organisms.

scholarly journals RNA-Seq Transcriptome Analysis of Peripheral Blood From Cattle Infected With Mycobacterium bovis Across an Experimental Time Course

2021 ◽  
Vol 8 ◽  
Author(s):  
Kirsten E. McLoughlin ◽  
Carolina N. Correia ◽  
John A. Browne ◽  
David A. Magee ◽  
Nicolas C. Nalpas ◽  
...  
Keyword(s):  
Differentially Expressed Genes ◽  
Mycobacterium Bovis ◽  
Peripheral Blood ◽  
Post Infection ◽  
Time Course ◽  
De Novo ◽  
Differentially Expressed Gene ◽  
Differentially Expressed ◽  
Infection Time ◽  
Time Point

Bovine tuberculosis, caused by infection with members of the Mycobacterium tuberculosis complex, particularly Mycobacterium bovis, is a major endemic disease affecting cattle populations worldwide, despite the implementation of stringent surveillance and control programs in many countries. The development of high-throughput functional genomics technologies, including RNA sequencing, has enabled detailed analysis of the host transcriptome to M. bovis infection, particularly at the macrophage and peripheral blood level. In the present study, we have analysed the transcriptome of bovine whole peripheral blood samples collected at −1 week pre-infection and +1, +2, +6, +10, and +12 weeks post-infection time points. Differentially expressed genes were catalogued and evaluated at each post-infection time point relative to the −1 week pre-infection time point and used for the identification of putative candidate host transcriptional biomarkers for M. bovis infection. Differentially expressed gene sets were also used for examination of cellular pathways associated with the host response to M. bovis infection, construction of de novo gene interaction networks enriched for host differentially expressed genes, and time-series analyses to identify functionally important groups of genes displaying similar patterns of expression across the infection time course. A notable outcome of these analyses was identification of a 19-gene transcriptional biosignature of infection consisting of genes increased in expression across the time course from +1 week to +12 weeks post-infection.

scholarly journals Discriminative feature of cells characterizes cell populations of interest by a small subset of genes

2021 ◽  
Author(s):  
Takeru Fujii ◽  
Kazumitsu Maehara ◽  
Masatoshi Fujita ◽  
Yasuyuki Ohkawa
Keyword(s):  
Gene Expression ◽  
Statistical Methods ◽  
Cell Population ◽  
Differentially Expressed Gene ◽  
Adaptive Lasso ◽  
Differentially Expressed ◽  
Small Subset ◽  
Specific Gene ◽  
Large Sample Size ◽  
Discriminative Feature

ABSTRACTStatistical methods for detecting differences in individual gene expression are indispensable for understanding cell types. However, conventional statistical methods have faced difficulties associated with the inflation of P-values because of both the large sample size and selection bias introduced by exploratory data analysis such as single-cell transcriptomics. Here, we propose the concept of discriminative feature of cells (DFC), an alternative to using differentially expressed gene-based approaches. We implemented DFC using logistic regression with an adaptive LASSO penalty to perform binary classification for the discrimination of a population of interest and variable selection to obtain a small subset of defining genes. We demonstrated that DFC prioritized gene pairs with non-independent expression using artificial data, and that DFC enabled to characterize the muscle satellite cell population. The results revealed that DFC well captured cell-type-specific markers, specific gene expression patterns, and subcategories of this cell population. DFC may complement differentially expressed gene-based methods for interpreting large data sets.

Construction of a chromosome 17 transcriptome in serous ovarian cancer identifies differentially expressed genes

2008 ◽  
Vol 18 (5) ◽  
pp. 963-975 ◽  
Author(s):  
P. M. Wojnarowicz ◽  
A. Breznan ◽  
S. L. Arcand ◽  
A. Filali-Mouhim ◽  
D. M. Provencher ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Differentially Expressed Genes ◽  
Molecular Genetic ◽  
Primary Cultures ◽  
Differentially Expressed ◽  
Chromosome 17 ◽  
Data Set ◽  
Malignant Ovarian Tumor ◽  
Ovarian Surface Epithelial ◽  
Subtype A

Cytogenetic, molecular genetic, and functional analyses have implicated chromosome 17 genes in epithelial ovarian cancer (EOC). To further characterize the contribution of chromosome 17 genes in EOC, the Affymetrix U133A GeneChip was used to perform transcriptome analyses of 15 primary cultures of normal ovarian surface epithelial (NOSE) cells and 17 malignant ovarian tumor (TOV) samples of the serous histopathologic subtype. A two-way comparative analysis of 776 known genes and expressed sequences identified 253 genes that exhibited at least a threefold difference in expression in at least one TOV sample compared to the mean of NOSE samples. Within this data set, 99 of the 253 (39.1%) genes exhibited similar patterns of expression across all tested samples, suggesting a high degree of concordance in the chromosome 17 transcriptome. This observation was supported by hierarchical clustering analysis that segregated the TOV and NOSE samples into two separate groups. There were 77 genes that were differentially expressed in at least 50% of the TOV samples. Five genes (AdoRA2B at 17p12, CCL2 at 17q12, ACLY at 17q21.2, WIPI1 at 17q24.2, and SLC16A3 at 17q25.3) were significantly (P< 5.13E−11) differentially expressed at least threefold in all serous TOV samples, and all five genes were underexpressed in these TOV samples as compared to the NOSE samples. Interestingly, several of these differentially expressed genes have been previously associated with response to hypoxia.

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 Bioinformatics Study on the Response of Human Endothelial Cells to Different Strains of Staphylococcus Aureus

2020 ◽  
Author(s):  
Tian-ao Xie ◽  
Ke-ying Fang ◽  
Wen-chao Cao ◽  
Jie Lv ◽  
Jia-xin Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Staphylococcus Aureus ◽  
Endothelial Cells ◽  
Differentially Expressed Genes ◽  
Expression Patterns ◽  
Differentially Expressed ◽  
Specific Gene ◽  
Human Endothelial Cells ◽  
Different Strains ◽  
Differential Genes

Abstract BackgroundStaphylococcus aureus-induced bacteremia has an impact on human health due to its high mortality rate of 20–30%. To better study the invasion process of staphylococcus aureus, we conducted a study in human endothelial cells to try to find a link between the infection process and bacteremia at the molecular level.MethodsIn this study, the datasets GSE13736, GSE82036 were analyzed using R software to identify differentially expressed genes. Only the infection samples of four different strains had differential gene expression compared to the control samples. Then the GO analysis and KEGG analysis were conducted to construct a protein-protein interaction (PPI) network which shows the interaction and influence relationship between these differential genes. Finally, the central gene of the selected CytoHubba plug-in was verified using GraphPad Prism 8.ResultsThere were 421 differential genes in the Strain 6850, including 64 up-regulated and 357 down-regulated; There were 319 differential genes in the Strain 8325-4, including 14 up-regulated and 305 down-regulated. There were 90 differential genes in the Strain K70058396, including 12 up-regulated and 78 down-regulated. There were 876 differential genes in the Strain K1801/10, accompanied by 261 up-regulated and 615 down-regulated. An analysis of GO and KEGG revealed that these differentially expressed genes were significantly enriched in pathways associated with immune response and cytokines; Verification of the hub gene can provide a molecular basis for studying the relationship between invasive endothelial infection and bacteremia.ConclusionsWe found specific gene expression patterns in endothelial cells in response to infection with Strain K70058396, and these central genes and their expression products (RSAD2, DDX58, IFITT3, and IFIH1) play a key role in this process of infection.

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 Bioinformatic Analysis: Screening and Identification of Differentially Expressed Genes Modified by m6A in Ovarian Cancer

2020 ◽  
Author(s):  
Huidong Liu ◽  
Wen-wen Zhang ◽  
Ge Lou
Keyword(s):  
Ovarian Cancer ◽  
Differentially Expressed Genes ◽  
Clinical Sample ◽  
Therapeutic Targets ◽  
Enrichment Analysis ◽  
Underlying Mechanism ◽  
Bioinformatic Analysis ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Kegg Pathways

Abstract Background: N6-methyladenosine(m6A) is one of the most common RNA modifications that occurs at the nitrogen-6 position of adenine. Emerging evidence has revealed that regulatory functions of m6A play an essential role in the development of cancer. However the study of m6A in ovarian cancer(OC) is still in our infancy. In this work ,we aimed to identify and analysis the differentially expressed genes(DEGs） modified by m6A which can provide new therapeutic targets and key biomarkers in OC.Methods: We downloaded Microarray datasets GSE146553 and GSE124766 from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified by GEO2R analysis tools. Subsequently, The DAVID database was used to construct Enrichment analysis of GO and KEGG pathways. Next, the DEGs modified by m6A were identified by m6AVar database. Finally, the functional analysis and clinical sample validation of these genes were verified by ONCOMINE, GEPIA, cBioPortal online platform and Kaplan-Meier Plotter.Results:152 DEGs were selected ,and the DEGs were mainly enriched in extracellular exosome, spindle microtubule, response to hypoxia and cell cycle .And we identified 15 DEGs which were modified by m6A:MAPK10、MXRA5、CHD7、MECOM、SCN7A、GREB、PRUNE2、MX2、TOP2A、JAM2、DST、LAPTM5、CDKN2A、GATM and ANGPTL1. After statistical analysis, two DEGs (SCN7A and GAMT) were selected for detailed study. We revealed that SCN7A and GAMT were expressed at a low level in OC. Afterwards, Survival analysis showed that SCN7A and GAMT expression were correlated with OC overall survival. And the expression of SCN7A and GAMT mRNA decreasing in different TNM stages. Finally, we presumed that the modification of m6A spongs GAMT via EIF4A3 or FUS to participate in the occcurrence and the development of OC.Conclusion: Altogether, the current study identified and analysised the DEGs modified by m6A in OC. It will help us to investigate the underlying mechanism and progression of OC. In addition, it can provide new diagnostic markers and potential therapeutic targets in OC.

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