Comprehensive analyses reveal three-gene signatures in ovarian cancer

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

Journal of Ovarian Research ◽

10.1186/s13048-021-00837-6 ◽

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.

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Overcoming chemotherapy resistance of ovarian cancer cells by liposomal cisplatin: Molecular mechanisms unveiled by gene expression profiling

Biochemical Pharmacology ◽

10.1016/j.bcp.2013.01.028 ◽

2013 ◽

Vol 85 (8) ◽

pp. 1077-1090 ◽

Cited By ~ 21

Author(s):

Martin Koch ◽

Michaela L. Krieger ◽

Daniel Stölting ◽

Norbert Brenner ◽

Manfred Beier ◽

...

Keyword(s):

Gene Expression ◽

Ovarian Cancer ◽

Gene Expression Profiling ◽

Cancer Cells ◽

Expression Profiling ◽

Molecular Mechanisms ◽

Chemotherapy Resistance ◽

Ovarian Cancer Cells

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

Bioscience Reports ◽

10.1042/bsr20202911 ◽

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.

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