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 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 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.

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.

scholarly journals Based on bioinformatics analysis, NF1, BTRC and MAPK14 are the potential biomarkers in the progression of osteoarthritis synovitis

2021 ◽  
Author(s):  
Mingyi Yang ◽  
Yani Su ◽  
Yao Ma ◽  
Yirixiati Aihaiti ◽  
Peng Xu
Keyword(s):  
Differentially Expressed Genes ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Hub Gene ◽  
Potential Biomarkers

Abstract Objective: To study the potential biomarkers and related pathways in osteoarthritis (OA) synovial lesions, and to provide theoretical basis and research directions for the pathogenesis and treatment of OA. Methods: Download the microarray data sets GSE12021 and GSE82107 from Gene Expression Omnibus. GEO2R recognizes differentially expressed genes. Perform functional enrichment analysis of differentially expressed genes and construct protein-protein interaction network. Cytoscape performs module analysis and enrichment analysis of top-level modules. Further identify the Hub gene and perform functional enrichment analysis. TargetScan, miRDB and miRWalk three databases predict the target miRNAs of Hub gene and identify key miRNAs. Results: Finally, 10 Hub genes and 17 key miRNAs related to the progression of OA synovitis were identified. NF1, BTRC and MAPK14 may play a vital role in OA synovial disease. Conclusion: The Hub genes and key miRNAs discovered in this study may be potential biomarkers in the development of OA synovitis, and provide research methods and target basis for the pathogenesis and treatment of OA.

Identification of lncRNA-associated ceRNA network in high-grade serous ovarian cancer metastasis

Epigenomics ◽  
2020 ◽  
Vol 12 (14) ◽  
pp. 1175-1191
Author(s):  
Xi Li ◽  
Sihui Yu ◽  
Rui Yang ◽  
Qi Wang ◽  
Xiangnan Liu ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Cancer Metastasis ◽  
Expression Patterns ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Sequencing Data ◽  
Expression Levels

Aim: To uncover a novel lncRNA–miRNA–mRNA network associated with high-grade serous ovarian cancer metastasis. Material & methods: The candidate differentially expressed lncRNAs were obtained from RNA-sequencing data and determined by functional experiments. The downstream miRNAs and mRNAs were identified by bioinformatic prediction and subjected to functional enrichment analysis. Results: The expression levels of lncRNA ENTPD1-AS1/PRANCR/NR2F2-AS1 were reduced in omental metastatic tissues. Similar differential expression patterns of these lncRNAs were also found in lnCAR database and we verified their tumor suppressive roles by performing functional experiments. Furthermore, we predicted miRNAs and mRNAs via bioinformatic tools and validated their alteration in expression levels in presence of lncRNA interference. Conclusion: We proposed a potential ceRNA regulatory mechanism in high-grade serous ovarian cancer omental metastasis

HMMR Functions as a Prognostic Biomarker in Uterine Carcinosarcoma Identified by Comprehensive Bioinformatics Analysis

2020 ◽  
Author(s):  
Hui Sun ◽  
Li Ma ◽  
Jie Chen
Keyword(s):  
Cancer Progression ◽  
Poor Prognosis ◽  
Bioinformatics Analysis ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Hub Genes ◽  
Normal Tissues ◽  
Potential Biomarker

Abstract BackgroundUterine carcinosarcoma (UCS) is a rare aggressive tumor with a high metastasis rate and poor prognosis. Bioinformatics analysis has been widely applied to screen and analyze genes in linkage to various types of cancer progression. This study aims to explore the molecular mechanism of UCS. MethodsFirst, transcriptional different expression data between UCS and normal samples were got from the GEPIA database. Subsequently, differentially expressed genes were analyzed through the Metascape with Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Then, the STRING website and Cytoscape software were applied to construct the protein-protein interaction network. Finally, the top 30 genes obtained through the MCC algorithm were selected as hub genes, which was finally validated in TIMER, and UALCAN databases. ResultsA total of 1894 DEGs (579 up-regulated and 1315 down-regulated) were identified, GO and KEGG functional enrichment analysis were performed for the DEGs. The PPI network was constructed based on DEGs, and four clusters were excavated for further analysis and the top 30 genes were identified as hub genes. Our data showed that the expression of HMMR is significantly higher in UCS tissues compared to the paired normal tissues (p<0.05) and the elevated expression of HMMR is related to poor prognosis in patients with UCS (p= 0.0031). TPX2, AURKA, BRCA1 and BARD1 are essential for the function of HMMR. TPX2 and AURKA were found to be significantly higher in UCS compared to the normal tissue (p<0.05), and there was a statistically significant positive correlation between the expression of HMMR and AURKA, TPX2, BRCA1, BARD1 in UCS (p=1.08e-07, p=1.62e-05, p=2.02e-3, p=6.54e-6). ConclusionsOur study suggested that HMMR may be a potential biomarker for predicting the prognosis of UCS patients.

scholarly journals Comprehensive Analysis of Key Genes and Regulatory Elements in Ewing Sarcoma With Prognostic Values

2021 ◽  
Author(s):  
Mi Li ◽  
Hao Li ◽  
Yan Zong ◽  
TingXin Wang ◽  
Sisi Deng ◽  
...  
Keyword(s):  
Candidate Gene ◽  
Ewing Sarcoma ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Regulatory Elements ◽  
Functional Enrichment ◽  
Data Series ◽  
Hub Genes ◽  
Public Data

Abstract Background: Ewing sarcoma (ES) is one of the most common types of round cell mesenchymal neoplasms related to children and young adults. It is characterized by chromosomal translocations. However, that does not completely explain the ES proliferation and development. Methods: Several public data series (GSE45544, GSE68591, and GSE68776) were used to identify differentially expressed genes (DEGs) between ES and normal tissue. Furthermore, functional enrichment analysis and protein-protein interaction network of DEGs were performed. The regulatory network of DEGs and hub genes, survival analysis of hub genes was visualized. In addition, functional predictions of the candidate gene were analyzed. Results: A total of 142 DEGs 10 hub genes were identified. While out of them, only one candidate gene FGF7 was found to be suitable as a candidate gene. Conclusions: In conclusion, the DEGs, hub genes, and their regulatory molecules screened out in this research could contribute to comprehend the mechanisms in ES and provide potential research molecular for further study.

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