scholarly journals ­ Comprehensive analysis of key genes associated with ceRNA networks in nasopharyngeal carcinoma based on bioinformatics analysis

2020 ◽  
Author(s):  
Yuanji Xu ◽  
Xinyi Huang ◽  
Wangzhong Ye ◽  
Yangfan Zhang ◽  
Changkun Li ◽  
...  
Keyword(s):  
Protein Kinase ◽  
Nasopharyngeal Carcinoma ◽  
Molecular Mechanisms ◽  
Mapk Pathway ◽  
Gene Set Enrichment Analysis ◽  
Prognostic Indicators ◽  
High Morbidity ◽  
Ppi Network ◽  
Hub Genes ◽  
Normal Tissues

Abstract Background. Nasopharyngeal carcinoma (NPC) is an epithelial malignancy with high morbidity rates in the east and southeast Asia. The molecular mechanisms of NPC remain largely unknown. We explored the pathogenesis, potential biomarkers, and prognostic indicators of NPC.Methods. We analyzed mRNAs, long non-coding RNAs (lncRNAs), and microRNAs (miRNAs) in the whole transcriptome sequencing dataset of our hospital (five normal tissues vs. five NPC tissues) and six microarray datasets (62 normal tissues vs. 334 NPC tissues) downloaded from the Gene Expression Omnibus (GSE12452, GSE13597, GSE95166, GSE126683, and GSE70970, GSE43039). Differential expression analyses, gene ontology (GO) enrichment, kyoto encyclopedia of genes and genomes (KEGG) analysis, and gene set enrichment analysis (GSEA) were conducted. The lncRNA-miRNA-mRNA competing endogenous RNA (ceRNA) networks were constructed using the miRanda and TargetScan database, and a protein-protein interaction (PPI) network of differentially expressed genes (DEGs) was built using Search Tool for the Retrieval of Interacting Genes (STRING) software. Hub genes were identified using Molecular Complex Detection (MCODE), NetworkAnalyzer, and CytoHubba.Results. We identified 61 mRNAs, 14miRNAs, and 10 lncRNAs as shared DEGs related to NPC in seven datasets. Changes in NPC were enriched in the chromosomal region, sister chromatid segregation, and nuclear chromosome segregation. GSEA indicated that the mitogen-activated protein kinase (MAPK) pathway, phosphatidylinositol-3 OH kinase/protein kinase B (PI3K-Akt) pathway, apoptotic pathway, and tumor necrosis factor (TNF) were involved in the initiation and development of NPC. Finally, 20 hub genes were screened out via the PPI network.Conclusions. Several DEGs and their biological processes, pathways, and interrelations were found in our current study by bioinformatics analyses. Our findings may offer insights into the biological mechanisms underlying NPC and identify potential therapeutic targets for NPC.

scholarly journals ­ Comprehensive analysis of key genes associated with ceRNA networks in nasopharyngeal carcinoma based on bioinformatics analysis

2020 ◽  
Author(s):  
Yuanji Xu ◽  
Xinyi Huang ◽  
Wangzhong Ye ◽  
Yangfan Zhang ◽  
Changkun Li ◽  
...  
Keyword(s):  
Protein Kinase ◽  
Nasopharyngeal Carcinoma ◽  
Molecular Mechanisms ◽  
Mapk Pathway ◽  
Gene Set Enrichment Analysis ◽  
Prognostic Indicators ◽  
High Morbidity ◽  
Ppi Network ◽  
Hub Genes ◽  
Normal Tissues

Abstract Background Nasopharyngeal carcinoma (NPC) is an epithelial malignancy with high morbidity rates in the east and southeast Asia. The molecular mechanisms of NPC remain largely unknown. We explored the pathogenesis, potential biomarkers, and prognostic indicators of NPC. Methods We analyzed mRNAs, long non-coding RNAs (lncRNAs), and microRNAs (miRNAs) in the whole transcriptome sequencing dataset of our hospital (five normal tissues vs. five NPC tissues) and six microarray datasets (62 normal tissues vs. 334 NPC tissues) downloaded from the Gene Expression Omnibus (GSE12452, GSE13597, GSE95166, GSE126683, and GSE70970, GSE43039). Differential expression analyses, gene ontology (GO) enrichment, kyoto encyclopedia of genes and genomes (KEGG) analysis, and gene set enrichment analysis (GSEA) were conducted. The lncRNA-miRNA-mRNA competing endogenous RNA (ceRNA) networks were constructed using the miRanda and TargetScan database, and a protein-protein interaction (PPI) network of differentially expressed genes (DEGs) was built using Search Tool for the Retrieval of Interacting Genes (STRING) software. Hub genes were identified using Molecular Complex Detection (MCODE), NetworkAnalyzer, and CytoHubba. Results We identified 61 mRNAs, 14miRNAs, and 10 lncRNAs as DEGs related to NPC. Changes in NPC were enriched in the chromosomal region, sister chromatid segregation, and nuclear chromosome segregation. GSEA indicated that the mitogen-activated protein kinase (MAPK) pathway, phosphatidylinositol-3 OH kinase/protein kinase B (PI3K-Akt) pathway, apoptotic pathway, and tumor necrosis factor (TNF) were involved in the initiation and development of NPC. Finally, 20 hub genes were screened out via the PPI network. Conclusions Our findings could offer insights into the biological mechanisms underlying NPC and identify potential therapeutic targets, biomarkers and prognostic indicators for NPC.

scholarly journals ­ Comprehensive analysis of key genes associated with ceRNA networks in nasopharyngeal carcinoma based on bioinformatics analysis

2020 ◽  
Author(s):  
Yuanji Xu ◽  
Xinyi Huang ◽  
Wangzhong Ye ◽  
Yangfan Zhang ◽  
Changkun Li ◽  
...  
Keyword(s):  
Protein Kinase ◽  
Nasopharyngeal Carcinoma ◽  
Molecular Mechanisms ◽  
Mapk Pathway ◽  
Gene Set Enrichment Analysis ◽  
Prognostic Indicators ◽  
High Morbidity ◽  
Ppi Network ◽  
Hub Genes ◽  
Normal Tissues

Abstract Background. Nasopharyngeal carcinoma (NPC) is an epithelial malignancy with high morbidity rates in the east and southeast Asia. The molecular mechanisms of NPC remain largely unknown. We explored the pathogenesis, potential biomarkers, and prognostic indicators of NPC.Methods. We analyzed mRNAs, long non-coding RNAs (lncRNAs), and microRNAs (miRNAs) in the whole transcriptome sequencing dataset of our hospital (five normal tissues vs. five NPC tissues) and six microarray datasets (62 normal tissues vs. 334 NPC tissues) downloaded from the Gene Expression Omnibus (GSE12452, GSE13597, GSE95166, GSE126683, and GSE70970, GSE43039). Differential expression analyses, gene ontology (GO) enrichment, kyoto encyclopedia of genes and genomes (KEGG) analysis, and gene set enrichment analysis (GSEA) were conducted. The lncRNA-miRNA-mRNA competing endogenous RNA (ceRNA) networks were constructed using the miRanda and TargetScan database, and a protein-protein interaction (PPI) network of differentially expressed genes (DEGs) was built using Search Tool for the Retrieval of Interacting Genes (STRING) software. Hub genes were identified using Molecular Complex Detection (MCODE), NetworkAnalyzer, and CytoHubba.Results. We identified 61 mRNAs, 14miRNAs, and 10 lncRNAs as shared DEGs related to NPC in seven datasets. Changes in NPC were enriched in the chromosomal region, sister chromatid segregation, and nuclear chromosome segregation. GSEA indicated that the mitogen-activated protein kinase (MAPK) pathway, phosphatidylinositol-3 OH kinase/protein kinase B (PI3K-Akt) pathway, apoptotic pathway, and tumor necrosis factor (TNF) were involved in the initiation and development of NPC. Finally, 20 hub genes were screened out via the PPI network.Conclusions. Several DEGs and their biological processes, pathways, and interrelations were found in our current study by bioinformatics analyses. Our findings may offer insights into the biological mechanisms underlying NPC and identify potential therapeutic targets, biomarkers and prognostic indicators for NPC.

scholarly journals Low expression of CHRDL1 and SPARCL1 predicts poor prognosis of lung adenocarcinoma based on comprehensive analysis and immunohistochemical validation

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Huan Deng ◽  
Qingqing Hang ◽  
Dijian Shen ◽  
Yibi Zhang ◽  
Ming Chen
Keyword(s):  
Lung Adenocarcinoma ◽  
Molecular Mechanisms ◽  
Transforming Growth Factor ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Prognostic Indicators ◽  
Hub Genes ◽  
Coexpressed Genes

Abstract Purpose Exploring the molecular mechanisms of lung adenocarcinoma (LUAD) is beneficial for developing new therapeutic strategies and predicting prognosis. This study was performed to select core genes related to LUAD and to analyze their prognostic value. Methods Microarray datasets from the GEO (GSE75037) and TCGA-LUAD datasets were analyzed to identify differentially coexpressed genes in LUAD using weighted gene coexpression network analysis (WGCNA) and differential gene expression analysis. Functional enrichment analysis was conducted, and a protein–protein interaction (PPI) network was established. Subsequently, hub genes were identified using the CytoHubba plug-in. Overall survival (OS) analyses of hub genes were performed. The Clinical Proteomic Tumor Analysis Consortium (CPTAC) and the Human Protein Atlas (THPA) databases were used to validate our findings. Gene set enrichment analysis (GSEA) of survival-related hub genes were conducted. Immunohistochemistry (IHC) was carried out to validate our findings. Results We identified 486 differentially coexpressed genes. Functional enrichment analysis suggested these genes were primarily enriched in the regulation of epithelial cell proliferation, collagen-containing extracellular matrix, transforming growth factor beta binding, and signaling pathways regulating the pluripotency of stem cells. Ten hub genes were detected using the maximal clique centrality (MCC) algorithm, and four genes were closely associated with OS. The CPTAC and THPA databases revealed that CHRDL1 and SPARCL1 were downregulated at the mRNA and protein expression levels in LUAD, whereas SPP1 was upregulated. GSEA demonstrated that DNA-dependent DNA replication and catalytic activity acting on RNA were correlated with CHRDL1 and SPARCL1 expression, respectively. The IHC results suggested that CHRDL1 and SPARCL1 were significantly downregulated in LUAD. Conclusions Our study revealed that survival-related hub genes closely correlated with the initiation and progression of LUAD. Furthermore, CHRDL1 and SPARCL1 are potential therapeutic and prognostic indicators of LUAD.

scholarly journals Network-Based Expression Analyses and Experimental Verifications Reveal the Involvement of STUB1 in Acute Kidney Injury

2021 ◽  
Vol 8 ◽  
Author(s):  
Yanting Shi ◽  
Genwen Chen ◽  
Jie Teng
Keyword(s):  
Acute Kidney Injury ◽  
Molecular Mechanisms ◽  
Kidney Injury ◽  
Gene Set Enrichment Analysis ◽  
High Morbidity ◽  
Dependent Manner ◽  
Sequencing Data ◽  
Hub Genes ◽  
Ppi Networks ◽  
Renal Tubular

Acute kidney injury (AKI) is a severe and frequently observed condition associated with high morbidity and mortality. The molecular mechanisms underlying AKI have not been elucidated due to the complexity of the pathophysiological processes. Thus, we investigated the key biological molecules contributing to AKI based on the transcriptome profile. We analyzed the RNA sequencing data from 39 native human renal biopsy samples and 9 reference nephrectomies from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) and Gene Ontology (GO) analysis revealed that various GO terms were dysregulated in AKI. Gene set enrichment analysis (GSEA) highlighted dysregulated pathways, including “DNA replication,” “chemokine signaling pathway,” and “metabolic pathways.” Furthermore, the protein-to-protein interaction (PPI) networks of the DEGs were constructed, and the hub genes were identified using Cytoscape. Moreover, weighted gene co-expression network analysis (WGCNA) was performed to validate the DEGs in AKI-related modules. Subsequently, the upregulated hub genes STUB1, SOCS1, and VHL were validated as upregulated in human AKI and a mouse cisplatin-induced AKI model. Moreover, the biological functions of STUB1 were investigated in renal tubular epithelial cells. Cisplatin treatment increased STUB1 expression in a dose-dependent manner at both the mRNA and protein levels. Knockdown of STUB1 by siRNA increased the expression of proapoptotic Bax and cleaved caspase-3 while decreasing antiapoptotic Bcl-2. In addition, silencing STUB1 increased the apoptosis of HK-2 cells and the proinflammatory cytokine production of IL6, TNFα, and IL1β induced by cisplatin. These results indicated that STUB1 may contribute to the initiation and progression of AKI by inducing renal tubular epithelial cell apoptosis and renal inflammation.

scholarly journals High Expression of UBB, RAC1, and ITGB1 Predicts Worse Prognosis among Nonsmoking Patients with Lung Adenocarcinoma through Bioinformatics Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Huan Deng ◽  
Yichao Huang ◽  
Li Wang ◽  
Ming Chen
Keyword(s):  
Extracellular Matrix ◽  
Lung Adenocarcinoma ◽  
Molecular Mechanisms ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Prognostic Indicators ◽  
Hub Genes ◽  
Go Enrichment

Purpose. The molecular mechanism underlying the tumorigenesis and progression of lung adenocarcinoma (LUAD) in nonsmoking patients remains unclear. This study was conducted to select crucial therapeutic and prognostic biomarkers for nonsmoking patients with LUAD. Methods. Microarray datasets from the Gene Expression Omnibus (GSE32863 and GSE75037) were analyzed for differentially expressed genes (DEGs). Gene Ontology (GO) enrichment analysis of DEGs was performed, and protein-protein interaction network was then constructed using the Search Tool for the Retrieval of Interacting Genes and Cytoscape. Hub genes were then identified by the rank of degree. Overall survival (OS) analyses of hub genes were performed among nonsmoking patients with LUAD in Kaplan-Meier plotter. The Cancer Genome Atlas (TCGA) and The Human Protein Atlas (THPA) databases were applied to verify hub genes. In addition, we performed Gene Set Enrichment Analysis (GSEA) of hub genes. Results. We identified 1283 DEGs, including 743 downregulated and 540 upregulated genes. GO enrichment analyses showed that DEGs were significantly enriched in collagen-containing extracellular matrix and extracellular matrix organization. Moreover, 19 hub genes were identified, and 12 hub genes were closely associated with OS. Although no obvious difference was detected in ITGB1, the downregulation of UBB and upregulation of RAC1 were observed in LUAD tissues of nonsmoking patients. Immunohistochemistry in THPA database confirmed that UBB and ITGB1 were downregulated, while RAC1 was upregulated in LUAD. GSEA suggested that ribosome, B cell receptor signaling pathway, and cell cycle were associated with UBB, RAC1, and ITGB1 expression, respectively. Conclusions. Our study provides insights into the underlying molecular mechanisms of the carcinogenesis and progression of LUAD in nonsmoking patients and demonstrated UBB, RAC1, and ITGB1 as therapeutic and prognostic indicators for nonsmoking LUAD. This is the first study to report the crucial role of UBB in nonsmoking LUAD.

scholarly journals Screen Key Genes Associated with Distraction-Induced Osteogenesis of Stem Cells Using Bioinformatics Methods

2021 ◽  
Vol 22 (12) ◽  
pp. 6505
Author(s):  
Jishizhan Chen ◽  
Jia Hua ◽  
Wenhui Song
Keyword(s):  
Stem Cells ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Negative Control ◽  
Gene Set Enrichment Analysis ◽  
Venn Diagram ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Two Samples ◽  
Key Genes

Applying mesenchymal stem cells (MSCs), together with the distraction osteogenesis (DO) process, displayed enhanced bone quality and shorter treatment periods. The DO guides the differentiation of MSCs by providing mechanical clues. However, the underlying key genes and pathways are largely unknown. The aim of this study was to screen and identify hub genes involved in distraction-induced osteogenesis of MSCs and potential molecular mechanisms. Material and Methods: The datasets were downloaded from the ArrayExpress database. Three samples of negative control and two samples subjected to 5% cyclic sinusoidal distraction at 0.25 Hz for 6 h were selected for screening differentially expressed genes (DEGs) and then analysed via bioinformatics methods. The Gene Ontology (GO) terms and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment were investigated. The protein–protein interaction (PPI) network was visualised through the Cytoscape software. Gene set enrichment analysis (GSEA) was conducted to verify the enrichment of a self-defined osteogenic gene sets collection and identify osteogenic hub genes. Results: Three hub genes (IL6, MMP2, and EP300) that were highly associated with distraction-induced osteogenesis of MSCs were identified via the Venn diagram. These hub genes could provide a new understanding of distraction-induced osteogenic differentiation of MSCs and serve as potential gene targets for optimising DO via targeted therapies.

Screening of cervical cancer-related hub genes based on comprehensive bioinformatics analysis

2021 ◽  
pp. 1-13
Author(s):  
Simei Tu ◽  
Hao Zhang ◽  
Xiaocheng Yang ◽  
Wen Wen ◽  
Kangjing Song ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cervical Cancer ◽  
Molecular Mechanisms ◽  
Disease Free Survival ◽  
The Cancer Genome Atlas ◽  
Mrna Levels ◽  
Hub Genes ◽  
Normal Tissues ◽  
Significant Difference ◽  
Core Genes

BACKGROUND: Since the molecular mechanisms of cervical cancer (CC) have not been completely discovered, it is of great significance to identify the hub genes and pathways of this disease to reveal the molecular mechanisms of cervical cancer. OBJECTIVE: The study aimed to identify the biological functions and prognostic value of hub genes in cervical cancer. METHODS: The gene expression data of CC patients were downloaded from the Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) database. The core genes were screened out by differential gene expression analysis and weighted gene co-expression network analysis (WGCNA). R software, the STRING online tool and Cytoscape software were used to screen out the hub genes. The GEPIA public database was used to further verify the expression levels of the hub genes in normal tissues and tumour tissues and determine the disease-free survival (DFS) rates of the hub genes. The protein expression of the survival-related hub genes was identified with the Human Protein Atlas (HPA) database. RESULTS: A total of 64 core genes were screened, and 10 genes, including RFC5, POLE3, RAD51, RMI1, PALB2, HDAC1, MCM4, ESR1, FOS and E2F1, were identified as hub genes. Compared with that in normal tissues, RFC5, POLE3, RAD51,RMI1, PALB2, MCM4 and E2F1 were all significantly upregulated in cervical cancer, ESR1 was significantly downregulated in cervical cancer, and high RFC5 expression in CC patients was significantly related to OS. In the DFS analysis, no significant difference was observed in the expression level of RFC5 in cervical cancer patients. Finally, RFC5 protein levels verified by the HPA database were consistently upregulated with mRNA levels in CC samples. CONCLUSIONS: RFC5 may play important roles in the occurrence and prognosis of CC. It could be further explored and validated as a potential predictor and therapeutic target for CC.

scholarly journals Identification of Novel Hub Genes Through Expression Profiles Analysis in Adrenocortical Carcinoma

2020 ◽  
Author(s):  
Chenhe Yao ◽  
Xiaoling Zhao ◽  
Xuemeng Shang ◽  
Binghan Jia ◽  
Shuaijie Dou ◽  
...  
Keyword(s):  
Adrenocortical Carcinoma ◽  
Expression Profiling ◽  
Molecular Mechanisms ◽  
Cross Validation ◽  
Expression Profiles ◽  
Ppi Network ◽  
Hub Genes ◽  
Tumor Association ◽  
Prediction Of Prognosis ◽  
Geo Database

Abstract Background: Adrenocortical carcinoma (ACC) is a heterogeneous and rare malignant tumor associated with a poor prognosis. The molecular mechanisms of ACC remain elusive and more accurate biomarkers for the prediction of prognosis are needed.Methods: In this study, integrative profiling analyses were performed to identify novel hub genes in ACC to provide promising targets for future investigation. Three gene expression profiling datasets in the GEO database were used for the identification of overlapped differentially expressed genes (DEGs) following the criteria of adj.P.Value<0.05 and |log2 FC|>0.5 in ACC. Novel hub genes were screened out following a series of processes: the retrieval of DEGs with no known associations with ACC on Pubmed, then the cross-validation of expression values and significant associations with overall survival in the GEPIA2 and starBase databases, and finally the prediction of gene-tumor association in the GeneCards database.Results: Four novel hub genes were identified and two of them, TPX2 and RACGAP1, were positively correlated with the staging. Interestingly, co-expression analysis revealed that the association between TPX2 and RACGAP1 was the strongest and that the expression of HOXA5 was almost completely independent of that of RACGAP1 and TPX2. Furthermore, the PPI network consisting of four novel genes and seed genes in ACC revealed that HOXA5, TPX2, and RACGAP1 were all associated with TP53. Conclusions: This study identified four novel hub genes (TPX2, RACHAP1, HXOA5 and FMO2) that may play crucial roles in the tumorigenesis and the prediction of prognosis of ACC.

scholarly journals Identification of key genes and associated pathways in neuroendocrine tumors through bioinformatics analysis and predictions of small drug molecules

2020 ◽  
Author(s):  
Praveenkumar Devarbhavi ◽  
Basavaraj Vastrad ◽  
Anandkumar Tengli ◽  
Chanabasayya Vastrad ◽  
Iranna Kotturshetti
Keyword(s):  
Drug Target ◽  
Target Gene ◽  
Molecular Mechanisms ◽  
Target Genes ◽  
Regulatory Elements ◽  
Future Research ◽  
High Morbidity ◽  
Hub Genes ◽  
Go Enrichment ◽  
Significant Difference

AbstractNeuroendocrine tumor (NET) is one of malignant cancer and is identified with high morbidity and mortality rates around the world. With indigent clinical outcomes, potential biomarkers for diagnosis, prognosis and drug target are crucial to explore. The aim of this study is to examine the gene expression module of NET and to identify potential diagnostic and prognostic biomarkers as well as to find out new drug target. The differentially expressed genes (DEGs) identified from GSE65286 dataset was used for pathway enrichment analyses and gene ontology (GO) enrichment analyses and protein - protein interaction (PPI) analysis and module analysis. Moreover, miRNAs and transcription factors (TFs) that regulated the up and down regulated genes were predicted. Furthermore, validation of hub genes was performed. Finally, molecular docking studies were performed. DEGs were identified, including 453 down regulated and 459 up regulated genes. Pathway and GO enrichment analysis revealed that DEGs were enriched in sucrose degradation, creatine biosynthesis, anion transport and modulation of chemical synaptic transmission. Important hub genes and target genes were identified through PPI network, modules, target gene - miRNA network and target gene - TF network. Finally, survival analyses, receiver operating characteristic (ROC) curve and RT-PCR validated the significant difference of ATP1A1, LGALS3, LDHA, SYK, VDR, OBSL1, KRT40, WWOX, NINL and PPP2R2B between metastatic NET and normal controls. In conclusion, the DEGs and hub genes with their regulatory elements identified in this study will help us understand the molecular mechanisms underlying NET and provide candidate targets for future research.

Gene expression profiles following active HE4 stimulation in epithelial ovarian cancer cells: microarray study and comprehensive bioinformatics analysis

2020 ◽  
Author(s):  
Liancheng Zhu ◽  
Mingzi Tan ◽  
Haoya Xu ◽  
Bei Lin
Keyword(s):  
Ovarian Cancer ◽  
Epithelial Ovarian Cancer ◽  
Molecular Mechanisms ◽  
Bioinformatics Analysis ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Coexpression Analysis ◽  
Pathway Enrichment

Abstract Background: Human epididymis protein 4 (HE4) is a novel serum biomarker for diagnosing epithelial ovarian cancer (EOC) with high specificity and sensitivity, compared with CA125. Recent studies have focused on the roles of HE4 in promoting carcinogenesis and chemoresistance in EOC; however, the molecular mechanisms underlying its action remain poorly understood. This study was conducted to determine the molecular mechanisms underlying HE4 stimulation and identifying key genes and pathways mediating carcinogenesis in EOC by microarray and bioinformatics analysis.Methods: We established a stable HE4-silenced ES-2 ovarian cancer cell line labeled as “S”; the S cells were stimulated with the active HE4 protein, yielding cells labeled as “S4”. Human whole-genome microarray analysis was used to identify differentially expressed genes (DEGs) in S4 and S cells. The “clusterProfiler” package in R, DAVID, Metascape, and Gene Set Enrichment Analysis were used to perform gene ontology (GO) and pathway enrichment analysis, and cBioPortal was used for WFDC2 coexpression analysis. The GEO dataset (GSE51088) and quantitative real-time polymerase chain reaction were used to validate the results. Protein–protein interaction (PPI) network and modular analyses were performed using Metascape and Cytoscape, respectively. Results: In total, 713 DEGs were identified (164 upregulated and 549 downregulated) and further analyzed by GO, pathway enrichment, and PPI analyses. We found that the MAPK pathway accounted for a significant large number of the enriched terms. WFDC2 coexpression analysis revealed ten WFDC2-coexpressed genes (TMEM220A, SEC23A, FRMD6, PMP22, APBB2, DNAJB4, ERLIN1, ZEB1, RAB6B, and PLEKHF1) whose expression levels were dramatically altered in S4 cells; this was validated using the GSE51088 dataset. Kaplan–Meier survival statistics revealed that all 10 target genes were clinically significant. Finally, in the PPI network, 16 hub genes and 8 molecular complex detections (MCODEs) were identified; the seeds of the five most significant MCODEs were subjected to GO and KEGG enrichment analyses and their clinical relevance was evaluated.Conclusions: Through microarray and bioinformatics analyses, we identified DEGs and determined a comprehensive gene network following active HE4 stimulation in EOC cells. We proposed several possible mechanisms underlying the action of HE4 and identified the therapeutic and prognostic targets of HE4 in EOC.

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