scholarly journals Integrative analysis of key candidate genes and signaling pathways in ovarian cancer by bioinformatics

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Cuicui Dong ◽  
Xin Tian ◽  
Fucheng He ◽  
Jiayi Zhang ◽  
Xiaojian Cui ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Candidate Genes ◽  
Differentially Expressed Genes ◽  
Expression Profiling ◽  
Overlapping Genes ◽  
Hub Genes ◽  
Gynecological Tumors ◽  
Normal Tissues ◽  
Cancer Tissues

Abstract Background Ovarian cancer is one of the most common gynecological tumors, and among gynecological tumors, its incidence and mortality rates are fairly high. However, the pathogenesis of ovarian cancer is not clear. The present study aimed to investigate the differentially expressed genes and signaling pathways associated with ovarian cancer by bioinformatics analysis. Methods The data from three mRNA expression profiling microarrays (GSE14407, GSE29450, and GSE54388) were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes between ovarian cancer tissues and normal tissues were identified using R software. The overlapping genes from the three GEO datasets were identified, and profound analysis was performed. The overlapping genes were used for pathway and Gene Ontology (GO) functional enrichment analysis using the Metascape online tool. Protein–protein interactions were analyzed with the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING). Subnetwork models were selected using the plugin molecular complex detection (MCODE) application in Cytoscape. Kaplan–Meier curves were used to analyze the univariate survival outcomes of the hub genes. The Human Protein Atlas (HPA) database and Gene Expression Profiling Interactive Analysis (GEPIA) were used to validate hub genes. Results In total, 708 overlapping genes were identified through analyses of the three microarray datasets (GSE14407, GSE29450, and GSE54388). These genes mainly participated in mitotic sister chromatid segregation, regulation of chromosome segregation and regulation of the cell cycle process. High CCNA2 expression was associated with poor overall survival (OS) and tumor stage. The expression of CDK1, CDC20, CCNB1, BUB1B, CCNA2, KIF11, CDCA8, KIF2C, NDC80 and TOP2A was increased in ovarian cancer tissues compared with normal tissues according to the Oncomine database. Higher expression levels of these seven candidate genes in ovarian cancer tissues compared with normal tissues were observed by GEPIA. The protein expression levels of CCNA2, CCNB1, CDC20, CDCA8, CDK1, KIF11 and TOP2A were high in ovarian cancer tissues, which was further confirmed via the HPA database. Conclusion Taken together, our study provided evidence concerning the altered expression of genes in ovarian cancer tissues compared with normal tissues. In vivo and in vitro experiments are required to verify the results of the present study.

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.

scholarly journals Application of Bioinformatics Analysis to Identify Important Pathways and Hub Genes in Ovarian Cancer Affected by WT1

2021 ◽  
Vol 9 ◽  
Author(s):  
Kai Meng ◽  
Jinghe Cao ◽  
Yehao Dong ◽  
Mengchen Zhang ◽  
Chunfeng Ji ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Mrna Expression ◽  
Signaling Pathways ◽  
Differentially Expressed Genes ◽  
Molecular Mechanisms ◽  
Wilms Tumor ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Normal Tissues ◽  
Cancer Tissues

Wilms tumor gene (WT1) is used as a marker for the diagnosis and prognosis of ovarian cancer. However, the molecular mechanisms involving WT1 in ovarian cancer require further study. Herein, we used bioinformatics and other methods to identify important pathways and hub genes in ovarian cancer affected by WT1. The results showed that WT1 is highly expressed in ovarian cancer and is closely related to the overall survival and progression-free survival (PFS) of ovarian cancer. In ovarian cancer cell line SKOV3, WT1 downregulation increased the mRNA expression of 638 genes and decreased the mRNA expression of 512 genes, which were enriched in the FoxO, AMPK, and the Hippo signaling pathways. The STRING online tool and Cytoscape software were used to construct a Protein-protein interaction (PPI) network and for Module analysis, and 18 differentially expressed genes (DEGs) were selected. Kaplan-Meier plotter analysis revealed that 16 of 18 genes were related to prognosis. Analysis of GEPIA datasets indicated that 7 of 16 genes were differentially expressed in ovarian cancer tissues and in normal tissues. The expression of IGFBP1 and FBN1 genes increased significantly after WT1 interference, while the expression of the SERPINA1 gene decreased significantly. The correlation between WT1 expression and that of these three genes was consistent with that of ovarian cancer tissues and normal tissues. According to the GeneMANIA online website analysis, there were complex interactions between WT1, IGFBP1, FBN1, SERPINA1, and 20 other genes. In conclusion, we have identified important signaling pathways involving WT1 that affect ovarian cancer, and distinguished three differentially expressed genes regulated by WT1 associated with the prognosis of ovarian cancer. Our findings provide evidence outlining mechanisms involving WT1 gene expression in ovarian cancer and provides a rational for novel treatment of ovarian cancer.

scholarly journals In-silico analysis of differentially expressed genes and their regulating microRNA involved in lymph node metastasis in invasive breast carcinoma

2020 ◽  
Author(s):  
Anupama Modi ◽  
Purvi Purohit ◽  
Ashita Gadwal ◽  
Shweta Ukey ◽  
Dipayan Roy ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Candidate Genes ◽  
Differentially Expressed Genes ◽  
In Silico ◽  
Nodal Metastasis ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Node Metastasis

AbstractIntroductionAxillary nodal metastasis is related to poor prognosis in breast cancer (BC). The metastatic progression in BC is related to molecular signatures. The currently popular methods to evaluate nodal status may give false negatives or give rise to secondary complications. In this study, key candidate genes in BC lymph node metastasis have been identified from publicly available microarray datasets and their roles in BC have been explored through survival analysis and target prediction.MethodsGene Expression Omnibus datasets have been analyzed for differentially expressed genes (DEGs) in lymph node-positive BC patients compared to nodal-negative and healthy tissues. The functional enrichment analysis was done in database for annotation, visualization and integrated discovery (DAVID). Protein-protein interaction (PPI) network was constructed in Search Tool for the Retrieval of Interacting Genes and proteins (STRING) and visualized on Cytoscape. The candidate hub genes were identified and their expression analyzed for overall survival (OS) in Gene Expression Profiling Interactive Analysis (GEPIA). The target miRNA and transcription factors were analyzed through miRNet.ResultsA total of 102 overlapping DEGs were found. Gene Ontology revealed eleven, seventeen, and three significant terms for cellular component, biological process, and molecular function respectively. Six candidate genes, DSC3, KRT5, KRT6B, KRT17, KRT81, and SERPINB5 were significantly associated with nodal metastasis and OS in BC patients. A total of 83 targeting miRNA were identified through miRNet and hsa-miR-155-5p was found to be the most significant miRNA which was targeting five out of six hub genes.ConclusionIn-silico survival and expression analyses revealed six candidate genes and 83 miRNAs, which may be potential diagnostic markers and therapeutic targets in BC patients and miR-155-5p shows promise as it targeted five important hub genes related to lymph-node metastasis.

scholarly journals High SLC7A11 Expression Is Correlated With Poor Prognosis and Associated With Ferroptosis in Ovarian Cancer 

2021 ◽  
Author(s):  
Mengqi Deng ◽  
Yanqin Zhang ◽  
Xiangyu Chang ◽  
Di Wu ◽  
Chunyu Xu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Protein Expression ◽  
Differentially Expressed Genes ◽  
Poor Prognosis ◽  
Bioinformatic Analysis ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Normal Tissues ◽  
And Control

Abstract The current treatments of ovarian cancer (OC) do not yield satisfactory outcomes. Hence, it is necessary to find new treatment targets for OC. In this study, a comprehensive bioinformatic analysis was conducted to identify differentially expressed genes (DEGs) between OC and control tissues. Five datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were screened by comparing gene expression between OC and control tissues. Module analysis of DEGs was performed on the STRING database and GEPIA. Kaplan Meier plotter and GEPIA database analysis the overall survival. Finally, SLC7A11 was found to be is the hubgene. And we confirm that the protein expression of SLC7A11 was increased in OC tissues. Analysis of a variety of tumor gene databases showed that SLC7A11 gene regulated the processes of OC. The low mutation rate of the gene (which were of amplified type) and high mRNA expression were associated with poor prognosis of OC patients.Using erastin-treated ovarian cancer (OC) cell lines, we examined the relationship between ferroptosis and OC. Results showed that OC tissues contained higher malondialdehyde (MDA) levels than normal tissues. Unlike normal ovarian epithelial cells which are not sensitive to erastin, the OC cell line, ES-2 is very sensitive to erastin. Here, we found that ferrostatin-1 treatment increased levels of reactive oxygen species (ROS), malondialdehyde, and SLC7A11 protein expression. These results provide an important theoretical basis for further studies into the role of SLC7A11, the effective biomarker and potential drug target, in the occurrence and development of OC.

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 &lt; 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.

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 Gene expression profiling reveals candidate genes for defining spider silk gland types

2021 ◽  
pp. 103594
Author(s):  
R. Crystal Chaw ◽  
Thomas H. Clarke ◽  
Peter Arensburger ◽  
Nadia A. Ayoub ◽  
Cheryl Y. Hayashi
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Candidate Genes ◽  
Expression Profiling ◽  
Spider Silk ◽  
Silk Gland

scholarly journals Study on potential differentially expressed genes in stroke by bioinformatics analysis

2021 ◽  
Author(s):  
Xitong Yang ◽  
Pengyu Wang ◽  
Shanquan Yan ◽  
Guangming Wang
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Normal Control ◽  
Enrichment Analysis ◽  
Normal Control Group ◽  
Control Group ◽  
Ppi Network ◽  
Hub Genes ◽  
Pathway Enrichment ◽  
Key Genes

AbstractStroke is a sudden cerebrovascular circulatory disorder with high morbidity, disability, mortality, and recurrence rate, but its pathogenesis and key genes are still unclear. In this study, bioinformatics was used to deeply analyze the pathogenesis of stroke and related key genes, so as to study the potential pathogenesis of stroke and provide guidance for clinical treatment. Gene Expression profiles of GSE58294 and GSE16561 were obtained from Gene Expression Omnibus (GEO), the differentially expressed genes (DEGs) were identified between IS and normal control group. The different expression genes (DEGs) between IS and normal control group were screened with the GEO2R online tool. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the DEGs were performed. Using the Database for Annotation, Visualization and Integrated Discovery (DAVID) and gene set enrichment analysis (GSEA), the function and pathway enrichment analysis of DEGS were performed. Then, a protein–protein interaction (PPI) network was constructed via the Search Tool for the Retrieval of Interacting Genes (STRING) database. Cytoscape with CytoHubba were used to identify the hub genes. Finally, NetworkAnalyst was used to construct the targeted microRNAs (miRNAs) of the hub genes. A total of 85 DEGs were screened out in this study, including 65 upward genes and 20 downward genes. In addition, 3 KEGG pathways, cytokine − cytokine receptor interaction, hematopoietic cell lineage, B cell receptor signaling pathway, were significantly enriched using a database for labeling, visualization, and synthetic discovery. In combination with the results of the PPI network and CytoHubba, 10 hub genes including CEACAM8, CD19, MMP9, ARG1, CKAP4, CCR7, MGAM, CD79A, CD79B, and CLEC4D were selected. Combined with DEG-miRNAs visualization, 5 miRNAs, including hsa-mir-146a-5p, hsa-mir-7-5p, hsa-mir-335-5p, and hsa-mir-27a- 3p, were predicted as possibly the key miRNAs. Our findings will contribute to identification of potential biomarkers and novel strategies for the treatment of ischemic stroke, and provide a new strategy for clinical therapy.

scholarly journals Bioinformatics analysis of differentially expressed genes and pathways in the development of cervical cancer

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Baojie Wu ◽  
Shuyi Xi
Keyword(s):  
Gene Expression ◽  
Cervical Cancer ◽  
Differentially Expressed Genes ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Molecular Targets ◽  
Enrichment Analysis ◽  
Ppi Network ◽  
Hub Genes ◽  
Kegg Pathways

Abstract Background This study aimed to explore and identify key genes and signaling pathways that contribute to the progression of cervical cancer to improve prognosis. Methods Three gene expression profiles (GSE63514, GSE64217 and GSE138080) were screened and downloaded from the Gene Expression Omnibus database (GEO). Differentially expressed genes (DEGs) were screened using the GEO2R and Venn diagram tools. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Gene set enrichment analysis (GSEA) was performed to analyze the three gene expression profiles. Moreover, a protein–protein interaction (PPI) network of the DEGs was constructed, and functional enrichment analysis was performed. On this basis, hub genes from critical PPI subnetworks were explored with Cytoscape software. The expression of these genes in tumors was verified, and survival analysis of potential prognostic genes from critical subnetworks was conducted. Functional annotation, multiple gene comparison and dimensionality reduction in candidate genes indicated the clinical significance of potential targets. Results A total of 476 DEGs were screened: 253 upregulated genes and 223 downregulated genes. DEGs were enriched in 22 biological processes, 16 cellular components and 9 molecular functions in precancerous lesions and cervical cancer. DEGs were mainly enriched in 10 KEGG pathways. Through intersection analysis and data mining, 3 key KEGG pathways and related core genes were revealed by GSEA. Moreover, a PPI network of 476 DEGs was constructed, hub genes from 12 critical subnetworks were explored, and a total of 14 potential molecular targets were obtained. Conclusions These findings promote the understanding of the molecular mechanism of and clinically related molecular targets for cervical cancer.

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