scholarly journals Differences in gene expression between high- and low-grade serous ovarian cancers: implications for diagnosis and prognosis

2022 ◽  
Author(s):  
Jianhao Xu ◽  
Qian Wang ◽  
Fang Cao ◽  
Zhiyong Deng ◽  
Xiaojiao Gao ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Ovarian Tissue ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Low Grade ◽  
Serous Ovarian Cancer ◽  
Hub Genes ◽  
Ki 67 ◽  
Diagnosis And Prognosis

Abstract Background: The clinical presentations of high-grade serous ovarian cancer (HGSOC) and low-grade serous ovarian cancer (LGSOC) differ. In this study, we aimed to identify the essential molecules for the diagnosis and prognosis of these OC subtypes. Methods: Differentially expressed genes between HGSOC and LGSOC were identified using three GEO series. The functional enrichment analysis was performed to investigate different biological processes and pathways. The protein-protein interaction network was constructed, and the discovered hub genes were frequently validated using prognostic correlation and immunohistochemistry (IHC) in GEPIA and HPA databases. Finally, we screened out BIRC5 and used IHC to detect its expression in 20 cases of borderline serous tumors, 20 cases of LGSOC, and 38 cases of HGSOC, and further analyzed its correlation with clinical characteristics.Results: In comparison with LGSOC, 79 upregulated and 85 downregulated genes were identified in HGSOC. The biological roles of these genes were mainly centered on the cell cycle process and chromosomal segregation. Among the 10 hub genes chosen, BIRC5 is positively related to the overall survival of patients with OC (p = 0.014) and can distinguish OC from normal ovarian tissue. In addition to database analysis, we verify BIRC5 through the specimen resources in our case database. According to the IHC results of our specimens, we found that BIRC5 can not only distinguish HGSOC and LGSOC but also positively correlate with the age, preoperative CA125 level, FIGO stage，and TP53/Ki-67 expression in tumor specimens.Conclusions: BIRC5 is a reliable marker that can distinguish HGSOC from LGSOC, guide prognosis, and be utilized in clinical IHC.

scholarly journals Differences in Gene Expression Between High- and Low-Grade Serous Ovarian Cancers: Implications for Diagnosis and Prognosis

2021 ◽  
Author(s):  
Jianhao Xu ◽  
Qian Wang ◽  
Fang Cao ◽  
Zhiyong Deng ◽  
Xiaojiao Gao ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Low Grade ◽  
Serous Ovarian Cancer ◽  
Hub Genes ◽  
Diagnosis And Prognosis ◽  
Potential Marker

Abstract Background The clinical presentations of high-grade serous ovarian cancer (HGSOC) and low-grade serous ovarian cancer (LGSOC) differ. In this study, we aimed to identify the essential molecules for the diagnosis and prognosis of these OC subtypes. Methods Differentially expressed genes (DEGs) between HGSOC and LGSOC were identified using three GEO series. The functional enrichment analysis was performed to investigate different biological processes and pathways. The protein–protein interaction network was constructed, and hub genes were screened to narrow the focus of the study. The discovered hub genes were frequently validated using prognostic correlation, co-expression, and immunohistochemistry (IHC) in GTEx, Oncomine, GEPIA, cBioportal, HPA, and other databases. Results In comparison with LGSOC, 79 upregulated genes and 85 downregulated genes were identified in HGSOC, and the biological roles of these genes were mostly centered on the cell cycle process and chromosomal segregation. Among the 10 hub genes chosen, BIRC5 was favorably linked with overall survival of patients with ovarian cancer (p = 0.014), whereas RRM2 was negatively correlated with the ovarian cancer stage (p = 0.0251). In IHC studies, the intensity of BIRC5 expression in ovarian cancer was greater than that in normal ovarian tissues; however, RRM2 was not substantially expressed in either ovarian cancer tissues or normal ovarian tissues. Conclusions BIRC5 is a potential marker that can distinguish HGSOC from LGSOC, guide prognosis, and be utilized in clinical IHC.

scholarly journals Co-expression network analysis identifies specific hub genes in association with developmental neuronal remodeling in Drosophila melanogaster

2019 ◽  
Author(s):  
Yunze Liu ◽  
Xiaojie Sun ◽  
Aijun Qu
Keyword(s):  
Drosophila Melanogaster ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Pathway Enrichment Analysis ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Neuronal Remodeling ◽  
Gene Modules

As an evolutionarily conserved mechanism, developmental neuronal remodeling is needed for the proper wiring of the nervous system and is critical for understanding the neurodevelopment mechanisms. Previous studies have shown that during metamorphosis lots of Drosophila melanogaster mushroom body neurons experience stereotypic remodeling. However, the related regulators and downstream executors of pathways are yet unclear, especially studies of transcriptional gene co-expression analysis of nervous systems remain insufficient. In this study, we develop a weighted gene co-expression network (WGCNA) to classify gene modules associated with neuronal remodeling. Moreover, functional and pathway enrichment analysis with protein-protein network construction is applied to detect high informative hub genes in the targeted gene module. Thus, we select a total of five hub genes that play critical roles in neuronal remodeling and identify them with functional enrichment analysis and protein-protein interaction network. Overall, this study provides insight into the underlying molecular mechanism of developmental neuronal remodeling in Drosophila melanogaster.

scholarly journals Co-expression network analysis identifies specific hub genes in association with developmental neuronal remodeling in Drosophila melanogaster

2019 ◽  
Author(s):  
Yunze Liu ◽  
Xiaojie Sun ◽  
Aijun Qu
Keyword(s):  
Drosophila Melanogaster ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Pathway Enrichment Analysis ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Neuronal Remodeling ◽  
Gene Modules

As an evolutionarily conserved mechanism, developmental neuronal remodeling is needed for the proper wiring of the nervous system and is critical for understanding the neurodevelopment mechanisms. Previous studies have shown that during metamorphosis lots of Drosophila melanogaster mushroom body neurons experience stereotypic remodeling. However, the related regulators and downstream executors of pathways are yet unclear, especially studies of transcriptional gene co-expression analysis of nervous systems remain insufficient. In this study, we develop a weighted gene co-expression network (WGCNA) to classify gene modules associated with neuronal remodeling. Moreover, functional and pathway enrichment analysis with protein-protein network construction is applied to detect high informative hub genes in the targeted gene module. Thus, we select a total of five hub genes that play critical roles in neuronal remodeling and identify them with functional enrichment analysis and protein-protein interaction network. Overall, this study provides insight into the underlying molecular mechanism of developmental neuronal remodeling in Drosophila melanogaster.

Prognostic Values and Prospective Pathway Signaling of miR-30a in Ovarian Cancer: A Study Based on Gene Expression Omnibus and Bioinformatics Analysis

2020 ◽  
Author(s):  
Weijia Lu ◽  
Yunyu Wu ◽  
CanXiong Lu ◽  
Ting Zhu ◽  
ZhongLu Ren ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Target Genes ◽  
Ovarian Tissue ◽  
Differentially Expressed Gene ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Public Data

Abstract Objective MicroRNAs (MiRNAs) is considered to play an important role in the occurrence and development of ovarian cancer(OC). Although miRNAs has been widely recognized in ovarian cancer, the role of hsa-miR-30a-5p (miR-30a) in OC has not been fully elucidated. Methods Through the analysis of public data sets in Gene Expression Omnibus (GEO) database and literature review, the significance of miR-30a expression in OC is evaluated. Three mRNA datasets of OC and normal ovarian tissue, GSE14407, GSE18520 and GSE36668, were downloaded from GEO to find the differentially expressed gene (DEG). Then the target genes of hsa-miR-30a-5p were predicted by miRWALK3.0 and TargetScan. Then, the gene overlap between DEG and the predicted target genes of miR-30a in OC was analyzed by Gene Ontology (GO) enrichment analysis. Protein-protein interaction (PPI) network was constructed by STRING and Cytoscape, and the effect of HUB gene on the prognosis of OC was analyzed. Results A common pattern of up-regulation of miR-30a in OC was found. A total of 225 DEG, were identified, both OC-related and miR-30a-related. Many DEG are enriched in the interactions of intracellular matrix tissue, ion binding and biological process regulation. Among the 10 major Hub genes analyzed by PPI, five Hub genes were significantly related to the overall poor survival of OC patients, in which the low expression of ESR1 ,MAPK10, Tp53 and the high expression of YKT ,NSF were related to poor prognosis of OC.

scholarly journals Integrated Weighted Gene Co-expression Network Analysis Reveals ALB Associated With Prognosis of High-grade Serous Ovarian Cancer

2021 ◽  
Author(s):  
Bo Wang ◽  
Shan Chao ◽  
Bo Guo
Keyword(s):  
Ovarian Cancer ◽  
Survival Analysis ◽  
Network Analysis ◽  
Single Gene ◽  
Enrichment Analysis ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Hub Genes ◽  
Hub Gene ◽  
Proteomic Data

Abstract Background: Ovarian cancer is the gynecologic tumor with the highest fatality rate, and high-grade serous ovarian cancer (HGSOC) is the most common and malignant type of ovarian cancer. One important reason for the poor prognosis of HGSOC is the lack of effective diagnostic and prognostic biomarkers. New biomarkers are necessary for improvement of treatment strategies and to ensure appropriate healthcare decisions.Methods: To construct the co-expression network of HGSOC samples, we applied weighted gene co-expression network analysis (WGCNA) to assess the proteomic data downloaded from Clinical Proteomic Tumor Analysis Consortium (CPTAC), and module-trait relationship was then analyzed and plotted in a heat map to choose key module associated with HGSOC. Enrichment analysis was performed on the genes in the key modules to explore the functional information in which these genes participate. Hub genes with high connectivity in key module were identified by Cytoscape software. Furthermore, the true hub gene was selected through survival analysis, followed by expression verification with transcriptome dataset from TCGA and GTEx. Finally, the potential biological functions of hub gene were analyzed via single-gene Gene Set Enrichment Analysis (GSEA).Results: After merging similar modules, a total of 9 modules were identified. Module-trait analysis revealed that the brown module (cor = 0.7) was significantly associated with HGSOC. The results of enrichment analysis of the genes in the brown module show that these genes were related to the functions of the extracellular matrix, the complement system and the blood system. Ten hub genes with the highest connectivity were selected by protein-protein interaction analysis. After survival analysis and expression verification of hub genes, only ALB was confirmed to be the true hub gene and positively correlated with HGSOC prognosis. Single gene GSEA revealed that ALB was associated with cell material degradation.Conclusion: We conducted the first gene co-expression analysis based on proteomic data from HGSOC samples, and found that ALB had prognostic value, which could be applied in the treatment of HGSOC in the future.

Bioinformatics Analysis of Prognostic value and prospective Pathway signal of miR-30a in Ovarian Cancer

2020 ◽  
Author(s):  
Weijia Lu ◽  
Yunyu Wu ◽  
CanXiong Lu ◽  
Ting Zhu ◽  
ZhongLu Ren ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Target Genes ◽  
Ovarian Tissue ◽  
Critical Role ◽  
Differentially Expressed Gene ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Go Enrichment

Abstract Objective: MicroRNAs (MiRNAs) is thought to play an critical role in the initiation and progress of ovarian cancer(OC). Although miRNAs has been widely recognized in ovarian cancer, the role of hsa-miR-30a-5p (miR-30a) in OC has not been fully elucidated.Methods:Three mRNA datasets of normal ovarian tissue and OC, GSE18520 ,GSE14407 and GSE36668, were downloaded from Gene Expression Omnibus(GEO) to find the differentially expressed gene (DEG). Then the target genes of hsa-miR-30a-5p were predicted by miRWALK3.0 and TargetScan. Then, the gene overlap between DEG and the predicted target genes of miR-30a in OC was analyzed by Gene Ontology (GO) enrichment analysis. Protein-protein interaction (PPI) network was conducted by STRING and Cytoscape, and the effect of HUB gene on the outcome of OC was analyzed.Results：A common pattern of up-regulation of miR-30a in OC was found. A total of 225 DEG, were identified, both OC-related and miR-30a-related. Many DEG are enriched in the interactions of intracellular matrix tissue, ion binding and biological process regulation. Among the 10 major Hub genes analyzed by PPI, five Hub genes were significantly related to the overall poor survival of OC patients, in which the low expression of ESR1 ,MAPK10, Tp53 and the high expression of YKT ,NSF were related to poor prognosis of OC.Conclusion：Our results indicate that miR-30a is of significance for the biological progress of OC.

scholarly journals Bioinformatics analysis of prognostic value and prospective pathway signal of miR-30a in ovarian cancer

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Weijia Lu ◽  
Yunyu Wu ◽  
Can Xiong Lu ◽  
Ting Zhu ◽  
Zhong Lu Ren ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Target Genes ◽  
Ovarian Tissue ◽  
Critical Role ◽  
Differentially Expressed Gene ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Go Enrichment

Abstract Objective MicroRNAs (MiRNAs) is thought to play a critical role in the initiation and progress of ovarian cancer (OC). Although miRNAs has been widely recognized in ovarian cancer, the role of hsa-miR-30a-5p (miR-30a) in OC has not been fully elucidated. Methods Three mRNA datasets of normal ovarian tissue and OC, GSE18520,GSE14407 and GSE36668, were downloaded from Gene Expression Omnibus (GEO) to find the differentially expressed gene (DEG). Then the target genes of hsa-miR-30a-5p were predicted by miRWALK3.0 and TargetScan. Then, the gene overlap between DEG and the predicted target genes of miR-30a in OC was analyzed by Gene Ontology (GO) enrichment analysis. Protein-protein interaction (PPI) network was conducted by STRING and Cytoscape, and the effect of HUB gene on the outcome of OC was analyzed. Results A common pattern of up-regulation of miR-30a in OC was found. A total of 225 DEG, were identified, both OC-related and miR-30a-related. Many DEG are enriched in the interactions of intracellular matrix tissue, ion binding and biological process regulation. Among the 10 major Hub genes analyzed by PPI, five Hub genes were significantly related to the overall poor survival of OC patients, in which the low expression of ESR1,MAPK10, Tp53 and the high expression of YKT,NSF were related to poor prognosis of OC. Conclusion Our results indicate that miR-30a is of significance for the biological progress of OC.

scholarly journals Bioinformatics-Based Analysis of lncRNA-mRNA Interaction Network of Mild Hepatic Encephalopathy in Cirrhosis

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Ke Wang ◽  
Yanzhen Lu ◽  
Zhifeng Zhao ◽  
Chihao Zhang
Keyword(s):  
Hepatic Encephalopathy ◽  
Inflammatory Responses ◽  
Critical Role ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Messenger Rnas ◽  
Hub Genes ◽  
Regulatory Pathways

Backgrounds. Serum long noncoding RNAs (lncRNAs) and messenger RNAs (mRNAs) interaction network was discovered to exert an important role in liver cirrhosis while little is known in mild hepatic encephalopathy (MHE). Therefore, we aim to systematically evaluate the serum lncRNA-mRNA network and its regulatory mechanism in MHE. Methods. The data of serum mRNAs and lncRNAs were derived from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were calculated between 11 cirrhotic patients with and without MHE. Next, the biological functions and underlined pathways of DEGs were determined through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Finally, an interactive network between lncRNAs and mRNAs was built, and hub genes were identified, respectively. Results. A total of 64 differentially expressed lncRNAs (dif-lncRNAs) were found between patients with and without MHE, including 30 up- and 34 downregulated genes. 187 differentially expressed mRNAs (dif-mRNAs) were identified, including 84 up- and 103 downregulated genes. Functional enrichment analysis suggested that the regulatory pathways involved in MHE mainly consisted of a series of immune and inflammatory responses. Several hub mRNAs involved in regulatory network were identified, including CCL5, CCR5, CXCR3, CD274, STAT1, CXCR6, and EOMES. In addition, lnc-FAM84B-8 and lnc-SAMD3-1 were found to regulate these above hub genes through building a lncRNA-mRNA network. Conclusion. This is the first study to construct the serum lncRNA-mRNA network in MHE, demonstrating the critical role of lncRNAs in regulating inflammatory and immunological profiles in the developing of MHE, suggesting a latent mechanism in this pathophysiological process.

scholarly journals Screening and Identification of Potential Biomarkers Associated with Cutaneous Squamous Cell Carcinoma

2021 ◽  
Author(s):  
Wenxing Su ◽  
Biao Huang ◽  
Qingyi Zhang ◽  
Wei Han ◽  
Lu An ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Bioinformatic Analysis ◽  
Cutaneous Squamous Cell Carcinoma ◽  
Functional Enrichment ◽  
Hub Genes

Abstract Cutaneous squamous cell carcinoma (cSCC) is the leading cause of death in patients with non-melanoma skin cancers (NMSC). However, unclear pathogenesis of cSCC limits the application of molecular targeted therapy. We downloaded three microarray data (GSE2503, GSE45164 and GSE66359) from Gene Expression Omnibus (GEO) and screened out their common difference genes between tumor and non-tumor tissues. Functional enrichment analysis was performed using DAVID. The STRING online website was used to construct a protein-protein interaction network (PPI), and then Cytoscape performed module analysis and degree calculation.A total of 146 DEGs was identified with significant differences, including 113 up-regulated genes and 33 down-regulated genes. The enriched functions and pathways of the DEGs include microtubule-based movement, ATP binding, cell cycle, p53 signaling pathway, oocyte meiosis and PLK1 signaling events. Nine hub genes were identified, namely CDK1, AURKA, RRM2, CENPE, CCNB1, KIAA0101, ZWINT, TOP2A, ASPM. The differential expression of these genes has been verified in other data sets. By integrated bioinformatic analysis, the hub genes identified in this study elucidated the molecular mechanism of the pathogenesis and progression of cSCC, and are expected to become future biomarkers or therapeutic targets.

scholarly journals Screening and Identification of Novel Biomarkers Associated with Cutaneous Squamous Cell Carcinoma

2020 ◽  
Author(s):  
Wenxing Su ◽  
Biao Huang ◽  
Wei Han ◽  
Lu An ◽  
Yi Guan ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Cutaneous Squamous Cell Carcinoma ◽  
Functional Enrichment ◽  
Data Sets ◽  
Hub Genes

Abstract Background: Cutaneous squamous cell carcinoma (cSCC) is the leading cause of death in patients with non-melanoma skin cancers (NMSC). However, unclear pathogenesis of cSCC limits the application of molecular targeted therapy. Results: To identify the hub genes in the pathogenesis and progression of cSCC, we downloaded the microarray data sets GSE2503, GSE45164 and GSE66359 from the Gene Expression Omnibus (GEO) database, and identified differentially expressed genes (DEGs) between tumor and non-tumor tissues. Functional enrichment analysis was performed using DAVID. The STRING online website was used to construct a protein-protein interaction network (PPI), and then Cytoscape performed module analysis and degree calculation. 146 DEGs were identified with significant differences, including 113 up-regulated genes and 33 down-regulated genes. The enriched functions and pathways of the DEGs include microtubule-based movement, ATP binding, cell cycle, p53 signaling pathway, oocyte meiosis and PLK1 signaling events. Nine hub genes were identified, namely CDK1, AURKA, RRM2, CENPE, CCNB1, KIAA0101, ZWINT, TOP2A, ASPM. The differential expression of these genes has been verified in other data sets. In addition, the ROC curve also confirmed their ability to predict disease. Conclusion: By integrated bioinformatic analysis, the DEGs and hub genes identified in this study elucidated the molecular mechanism of the pathogenesis and progression of cSCC, and are expected to become future biomarkers or therapeutic targets.

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