scholarly journals Meta-analysis based gene expression profiling reveals functional genes in ovarian cancer

2020 ◽  
Vol 40 (11) ◽  
Author(s):  
Lin Zhao ◽  
Yuhui Li ◽  
Zhen Zhang ◽  
Jing Zou ◽  
Jianfu Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Gene Expression Profiling ◽  
Differentially Expressed Genes ◽  
Expression Profiling ◽  
Molecular Mechanisms ◽  
Meta Analysis ◽  
Differentially Expressed ◽  
Functional Genes ◽  
P Value

Abstract Background: Ovarian cancer causes high mortality rate worldwide, and despite numerous attempts, the outcome for patients with ovarian cancer are still not well improved. Microarray-based gene expressional analysis provides with valuable information for discriminating functional genes in ovarian cancer development and progression. However, due to the differences in experimental design, the results varied significantly across individual datasets. Methods: In the present study, the data of gene expression in ovarian cancer were downloaded from Gene Expression Omnibus (GEO) and 16 studies were included. A meta-analysis based gene expression analysis was performed to identify differentially expressed genes (DEGs). The most differentially expressed genes in our meta-analysis were selected for gene expression and gene function validation. Results: A total of 972 DEGs with P-value < 0.001 were identified in ovarian cancer, including 541 up-regulated genes and 431 down-regulated genes, among which 92 additional DEGs were found as gained DEGs. Top five up- and down-regulated genes were selected for the validation of gene expression profiling. Among these genes, up-regulated CD24 molecule (CD24), SRY (sex determining region Y)-box transcription factor 17 (SOX17), WFDC2, epithelial cell adhesion molecule (EPCAM), innate immunity activator (INAVA), and down-regulated aldehyde oxidase 1 (AOX1) were revealed to be with consistent expressional patterns in clinical patient samples of ovarian cancer. Gene functional analysis demonstrated that up-regulated WFDC2 and INAVA promoted ovarian cancer cell migration, WFDC2 enhanced cell proliferation, while down-regulated AOX1 was functional in inducing cell apoptosis of ovarian cancer. Conclusion: Our study shed light on the molecular mechanisms underlying the development of ovarian cancer, and facilitated the understanding of novel diagnostic and therapeutic targets in ovarian cancer.

scholarly journals Meta-analysis based gene expressional profiling reveals functional genes in ovarian cancer

2020 ◽  
Author(s):  
Lin Zhao ◽  
Yuhui Li ◽  
Zhen Zhang ◽  
Hong Guo ◽  
Jianfu Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Differentially Expressed Genes ◽  
Molecular Mechanisms ◽  
Meta Analysis ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Functional Genes ◽  
P Value ◽  
Clinical Patient

Abstract Background Ovarian cancer causes high mortality rate worldwide, and despite numerous attempts, the outcome for patients with ovarian cancer are still not well improved. Microarray based gene expressional analysis provides with valuable information for discriminating functional genes in ovarian cancer development and progression. However, due to the differences in experimental design, the results varied significantly across individual datasets. Methods In the present study, the data of gene expression in ovarian cancer was downloaded from Gene Expression Omnibus and 16 studies were included. A meta-analysis based gene expression analysis was performed to identify differentially expressed genes (DEGs). The most differentially expressed genes in our meta-analysis were selected for gene expression and gene function validation. Results A total of 972 DEGs with P -value<0.001 were identified in ovarian cancer, including 541 up-regulated genes and 431 down-regulated genes, among which 92 additional DEGs were found as gained DEGs. Top five up-and down-regulated genes were selected for the validation of gene expressional profiling. Among these genes, up-regulated CD24 , SOX17 , WFDC2 , EPCAM , INAVA , and down-regulated AOX1 were revealed to be with consistent expressional patterns in clinical patient samples of ovarian cancer. Gene functional analysis demonstrated that up-regulated WFCD2 and INAVA promoted ovarian cancer cell migration, WFDC2 enhanced cell proliferation, while down-regulated AOX1 was functional in inducing cell apoptosis of ovarian cancer. Interestingly. Conclusion Our study shed light on the molecular mechanisms underlying the development of ovarian cancer, and facilitated the understanding of novel diagnostic and therapeutic targets in ovarian cancer.

Gene Expression Profiling Reveals Differentially Expressed Genes in Ovarian Cancer of the Hen: Support for Oviductal Origin?

2010 ◽  
Vol 1 (4) ◽  
pp. 177-186 ◽  
Author(s):  
Lindsey S. Treviño ◽  
James R. Giles ◽  
Wei Wang ◽  
Mary Ellen Urick ◽  
Patricia Ann Johnson
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Gene Expression Profiling ◽  
Differentially Expressed Genes ◽  
Expression Profiling ◽  
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 PO-046 Gene expression profiling of peripheral blood mononuclear cells in young male trampoline athletes

2018 ◽  
Vol 1 (3) ◽  
Author(s):  
Li Gao ◽  
Yong Jie Yang ◽  
En Qi Li ◽  
Jia Ning Mao
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Bone Health ◽  
Expression Profiling ◽  
Molecular Mechanisms ◽  
Interaction Network ◽  
Differentially Expressed ◽  
Signal Pathways ◽  
Healthy Controls ◽  
Mineral Density

Objective Evidence indicates that physical activity influence bone health. However, the molecular mechanisms mediating the beneficial adaptations to exercise are not well understood. The purpose of this study was to examine the differentially expressed genes in PBMC between athletes and healthy controls, and to analyze the important functional genes and signal pathways that cause increased bone mineral density in athletes, in order to further reveal the molecular mechanisms of exercise promoting bone health. Methods Five professional trampoline athletes and five age-matched untrained college students participated in this study. Used the human expression Microarray V4.0 expression profiling chip to detect differentially expressed genes in the two groups, and performed KEGG Pathway analysis and application of STRING database to construct protein interaction Network; Real-Time PCR technology was used to verify the expression of some differential genes.  Results Compared with healthy controls, there were significant improvement in lumbar spine bone mineral density, and 236 up-regulated as well as 265 down-regulated in serum samples of athletes. The differentially expressed genes involved 28 signal pathways, such as cell adhesion molecules. Protein interaction network showed that MYC was at the core node position. Real-time PCR results showed that the expression levels of CD40 and ITGα6 genes in the athletes were up-regulated compared with the healthy controls, the detection results were consistent with that of the gene chip. Conclusions The findings highlight that long-term high-intensity trampoline training could induce transcriptional changes in PBMC of the athletes. These data suggest that gene expression fingerprints can serve as a powerful research tool to design novel strategies for monitoring exercise. The findings of the study also provide support for the notion that PBMC could be used as a substitute to study exercise training that affects bone health.

scholarly journals Gene Expression Profiling of Sorted Peripheral Blood Cells Using Microarray and Next Generation Sequencing Reveals Distinct Molecular Signatures in the Polymorphonuclear and Mononuclear Cells of Patients with Polycythemia Vera and Primary Myelofibrosis

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 5201-5201
Author(s):  
Chieh Lee Wong ◽  
Baoshan Ma ◽  
Gareth Gerrard ◽  
Martyna Adamowicz-Brice ◽  
Zainul Abidin Norziha ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
Gene Expression Profiling ◽  
Differential Expression ◽  
Differentially Expressed Genes ◽  
Expression Profiling ◽  
Differentially Expressed ◽  
Cell Type ◽  
Cell Type Specific ◽  
Generation Sequencing

Abstract Background The past decade has witnessed a significant progress in the understanding of the molecular pathogenesis of myeloproliferative neoplasms (MPN). A large number of genes have now been implicated in the pathogenesis of MPN but their relative importance, the mechanisms by which they cause different cell types to predominate and their implications for prognosis remain unknown. We hypothesized that there are other genes which may contribute to the pathogenesis of the different disease subtypes detectable only by cell-type specific analysis. Aim The aim of this study was to perform gene expression profiling on different cell types from patients with MPN in order to identify novel variants and driver mutations, to elucidate the pathogenesis and to identify predictors of survival in patients with MPN in a multiracial country. Methods We performed gene expression profiling on normal controls (NC) and patients with MPN from 3 different races (Malay, Chinese and Indian) in Malaysia who were diagnosed with essential thrombocythemia (ET), polycythemia vera (PV) and primary myelofibrosis (PMF) according to the 2008 WHO diagnostic criteria for MPN. Two cohorts of patients, the patient and validation cohorts, from 3 tertiary-level hospitals were recruited prospectively over 3 years and informed consents were obtained. Peripheral blood samples were taken and sorted into polymorphonuclear cells (PMNs), mononuclear cells (MNCs) and T cells. RNA was extracted from each cell population. Gene expression profiling was performed using the Illumina HumanHT-12 Expression Beadchip for microarray and the Illumina Nextera XT DNA Sample Preparation Kit for next generation sequencing on the patient and validation cohorts respectively. Results Twenty-eight patients (10 ET, 11 PV and 7 PMF) and 11 NC were recruited into the patient cohort. Twelve patients (4 ET, 4 PV and 4 PMF) and 4 NC were recruited into the validation cohort. Gene expression levels for each cell type in each disease were compared with NC. In the patient cohort, the number of differentially expressed genes in ET, PV and PMF was 0, 141 and 15 respectively for PMNs (p < 0.05 after multiple testing correction) and 5, 170 and 562 respectively for MNCs (p < 0.05). No differentially expressed genes were identified for T cells in any of the three disease groups. RNA-seq analysis of samples from the validation cohort was used to corroborate these findings. After combination, we were able to confirm differential expression of 0, 14 and 7 genes in ET, PV and PMF respectively for PMNs (p < 0.05) and 51 genes in only PMF for MNCs (p < 0.05). The validated differentially expressed genes for PMNs and MNCs were mutually exclusive except for one gene. The differentially expressed genes in PV and PMF for PMNs were involved in cellular processes and metabolic pathways whereas the differentially expressed genes for PMF in MNCs were involved in regulation of cytoskeleton, focal adhesion and cell signaling pathways. Conclusion This is the first study to use microarray and next generation sequencing techniques to compare cell type-specific expression of genes between different subtypes of MPN. The lack of differential expression in T cells validates the techniques used and indicates that they are not part of the neoplastic clone. Differential expression of genes for MNCs was seen only in PMF which may be related to their more severe phenotype. Interestingly, there were fewer differentially expressed genes in PMF compared to PV for PMNs. The lack of differential expression in ET may either reflect the relatively milder phenotype of the disease or that differential expression is limited to megakaryocytes-platelets which were not studied. The lists of mutually exclusive cell type-specific differentially expressed genes for PMNs and MNCs provide further insight into the pathogenesis of MPN and into the differences between its different forms. The identified genes also indicate further routes for investigation of pathogenesis and possible disease-specific targets for therapy. Disclosures Aitman: Illumina: Honoraria.

scholarly journals Comprehensive analyses reveal three-gene signatures in ovarian cancer

2020 ◽  
Author(s):  
Chuang Li ◽  
Yuan Lyu ◽  
Caixia Liu
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Molecular Mechanisms ◽  
Hub Genes ◽  
Clinical Prognosis ◽  
Gene Signatures ◽  
Cancer Tissues ◽  
Differently Expressed Genes

Abstract Background: Ovarian cancer is a common cancer that affects the quality of women’s life. With the limitation of the early diagnosis of the disease, ovarian cancer has a high mortality rate worldwide. However, the molecular mechanisms underlying tumor invasion, proliferation, and metastasis in ovarian cancer remain unclear. We aimed to identify, using bioinformatics, important genes and pathways that may serve crucial roles in the prevention, diagnosis, and treatment of ovarian cancer. Methods: Three microarray datasets (GSE14407, GSE36668, and GSE26712) were selected for whole-genome gene expression profiling , and differentially expressed genes were identified between normal and ovarian cancer tissues. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed using DAVID. Additionally, a protein-protein interaction network was constructed to reveal possible interactions among the differently expressed genes. The prognostic values of the hub genes were investigated using Gene Expression Profiling Interactive Analysis (GEPIA) and the KM plotter database. Meanwhile, the mRNA expression analysis of the hub genes was performed using the GEPIA database. Results: We obtained 247 upregulated and 530 downregulated differently expressed genes, and 52 hub genes in the significant gene modules. Enrichment analysis revealed that the hub genes were significantly ( P < 0.05) associated with proliferation. Additionally, BIRC5, CXCL13, and PBK were revealed to be significantly associated with the clinical prognosis of patients with ovarian cancer. Immunohistochemical staining results obtained from the Human Protein Atlas revealed that BIRC5, PBK, and CXCL13 were highly expressed in ovaria cancer tissues. Conclusion Three-gene signatures ( BIRC5, CXCL13 , and PBK ) are associated with the occurrence, development, and prognosis of OC, and may therefore serve as biological markers of the disease.

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