scholarly journals Identification of Key Genes Involved in the Pathogenesis of Recurrent Pelvic Organ Prolapse Using Bioinformatics Analysis

2021 ◽  
Author(s):  
Ling Zhu ◽  
Xiaoling Ni ◽  
Shanshan Tang ◽  
Wenhua Liu
Keyword(s):  
Gene Expression ◽  
Pelvic Organ Prolapse ◽  
Immune Cell ◽  
Differential Expression Analysis ◽  
Pelvic Organ ◽  
Functional Enrichment ◽  
Hub Genes ◽  
Network Analyses ◽  
Uterosacral Ligaments ◽  
Key Genes

Abstract Background: The causes of the recurrence of pelvic organ prolapse (POP) are sufficiently understood. However, few studies investigate the key genes of recurrence POP. The present study aimed to screen the hub genes of recurrence POP. Microarray data of 4 recurrent POP and 4 primary POP uterosacral ligaments in the GSE28660 gene expression dataset were used as research objects. we used the online Gene Expression Omnibus (GEO) microarray expression profiling dataset to identify differentially expressed genes (DEGs). Also, functional enrichment and protein-protein interaction (PPI) network analyses were performed, and the key modules were identified. Then, we investigated the differential immune cell infiltration between recurrent POP and primary POP tissues using the CIBERSORT algorithm.Results: In total, 84 upregulated and 32 downregulated genes were identified in the differential expression analysis.Conclusion: This human genome DNA microarrays analysis identified a recurrence POP signature of 116 genes, and 2 hub genes, including cell death-inducing DFFA-like effector (CIDEA) and hemoglobin subunit delta (HBD) may participate in the pathogenesis of recurrence POP, giving them a certain diagnostic and therapeutic value.

scholarly journals Identification of Potentially Therapeutic Target Genes of Hepatocellular Carcinoma

2020 ◽  
Vol 17 (3) ◽  
pp. 1053
Author(s):  
Chengzhang Li ◽  
Jiucheng Xu
Keyword(s):  
Gene Expression ◽  
Target Genes ◽  
Expression Profiles ◽  
Differential Expression Analysis ◽  
Gene Expression Profiles ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Hub Genes ◽  
Gene Differential Expression ◽  
Oncomine Database

Background: Hepatocellular carcinoma (HCC) is a major threat to public health. However, few effective therapeutic strategies exist. We aimed to identify potentially therapeutic target genes of HCC by analyzing three gene expression profiles. Methods: The gene expression profiles were analyzed with GEO2R, an interactive web tool for gene differential expression analysis, to identify common differentially expressed genes (DEGs). Functional enrichment analyses were then conducted followed by a protein-protein interaction (PPI) network construction with the common DEGs. The PPI network was employed to identify hub genes, and the expression level of the hub genes was validated via data mining the Oncomine database. Survival analysis was carried out to assess the prognosis of hub genes in HCC patients. Results: A total of 51 common up-regulated DEGs and 201 down-regulated DEGs were obtained after gene differential expression analysis of the profiles. Functional enrichment analyses indicated that these common DEGs are linked to a series of cancer events. We finally identified 10 hub genes, six of which (OIP5, ASPM, NUSAP1, UBE2C, CCNA2, and KIF20A) are reported as novel HCC hub genes. Data mining the Oncomine database validated that the hub genes have a significant high level of expression in HCC samples compared normal samples (t-test, p < 0.05). Survival analysis indicated that overexpression of the hub genes is associated with a significant reduction (p < 0.05) in survival time in HCC patients. Conclusions: We identified six novel HCC hub genes that might be therapeutic targets for the development of drugs for some HCC patients.

scholarly journals HOXA11 and MMP2 gene expression in uterosacral ligaments of women with pelvic organ prolapse

2014 ◽  
Vol 15 (2) ◽  
pp. 104-108 ◽  
Author(s):  
Nafiye Yilmaz ◽  
Gulnur Ozaksit ◽  
Yunus Kasim Terzi ◽  
Saynur Yilmaz ◽  
Burcu Budak ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pelvic Organ Prolapse ◽  
Pelvic Organ ◽  
Uterosacral Ligaments

scholarly journals Integrated Bioinformatic Analysis of Key Biomarkers and Signaling Pathways in Psoriasis

2021 ◽  
Author(s):  
Suwei Tang ◽  
Ping Xu ◽  
Shaoqiong Xie ◽  
Wencheng Jiang ◽  
Jiajing Lu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Signaling Pathways ◽  
Immune Cell ◽  
Cell Types ◽  
Bioinformatic Analysis ◽  
Receptor Interaction ◽  
Ppi Network ◽  
Hub Genes ◽  
Diagnostic Efficacy ◽  
Network Analyses

Abstract Background: Psoriasis is a relatively common autoimmune inflammatory skin disease with a chronic etiology. The present study was designed to detect novel biomarkers and pathways associated with psoriasis incidence. Methods: Differentially expressed genes (DEGs) associated with psoriasis in the Gene Expression Omnibus (GEO) database were identified, and their functional roles and interactions were then annotated and evaluated through GO, KEGG, and gene set variation (GSVA) analyses. In addition, the STRING database was leveraged to construct a protein-protein interaction (PPI) network, and key hub genes from this network were validated as being relevant through receiver operating characteristic (ROC) curve analyses of three additional GEO datasets. The CIBERSORT database was additionally used to assess the relationship between these gene expression-related findings and immune cell infiltration. Results: In total 197 psoriasis-related DEGs were identified and found to primarily be associated with the NOD-like receptor, IL-17, and cytokine-cytokine receptor interaction signaling pathways. GSVA revealed significant differences between normal and lesional groups (P < 0.05), while PPI network analyses identified CXCL10 as the hub gene with the highest degree value, whereas IRF7, IFIT3, OAS1, GBP1, and ISG15 were promising candidate genes for the therapeutic treatment of psoriasis. ROC analyses confirmed that these 6 hub genes exhibited good diagnostic efficacy (AUC > 70%), and were predicted to be associated with increased sensitivity to 10 drugs (P < 0.01). The CIBERSORT database further predicted that these hub genes were associated with infiltration by 22 different immune cell types. Conclusion: These results offer a robust foundation for future studies of the molecular basis for psoriasis, potentially guiding efforts to treat this common and disruptive disease.

scholarly journals Identification of potential crucial genes and pathways associated with vein graft restenosis based on gene expression analysis in experimental rabbits

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e4704 ◽  
Author(s):  
Qiang Liu ◽  
Xiujie Yin ◽  
Mingzhu Li ◽  
Li Wan ◽  
Liqiao Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Throughput ◽  
Vein Graft ◽  
Molecular Mechanisms ◽  
Rapid Development ◽  
Bypass Graft ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Hub Genes ◽  
Network Analyses

Occlusive artery disease (CAD) is the leading cause of death worldwide. Bypass graft surgery remains the most prevalently performed treatment for occlusive arterial disease, and veins are the most frequently used conduits for surgical revascularization. However, the clinical efficacy of bypass graft surgery is highly affected by the long-term potency rates of vein grafts, and no optimal treatments are available for the prevention of vein graft restenosis (VGR) at present. Hence, there is an urgent need to improve our understanding of the molecular mechanisms involved in mediating VGR. The past decade has seen the rapid development of genomic technologies, such as genome sequencing and microarray technologies, which will provide novel insights into potential molecular mechanisms involved in the VGR program. Ironically, high throughput data associated with VGR are extremely scarce. The main goal of the current study was to explore potential crucial genes and pathways associated with VGR and to provide valid biological information for further investigation of VGR. A comprehensive bioinformatics analysis was performed using high throughput gene expression data. Differentially expressed genes (DEGs) were identified using the R and Bioconductor packages. After functional enrichment analysis of the DEGs, protein–protein interaction (PPI) network and sub-PPI network analyses were performed. Finally, nine potential hub genes and fourteen pathways were identified. These hub genes may interact with each other and regulate the VGR program by modulating the cell cycle pathway. Future studies focusing on revealing the specific cellular and molecular mechanisms of these key genes and pathways involved in regulating the VGR program may provide novel therapeutic targets for VGR inhibition.

scholarly journals Significance of hub genes and immune cell infiltration identified by bioinformatics analysis in pelvic organ prolapse

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9773
Author(s):  
Ying Zhao ◽  
Zhijun Xia ◽  
Te Lin ◽  
Yitong Yin
Keyword(s):  
Pelvic Organ Prolapse ◽  
Mast Cells ◽  
Immune Cell ◽  
Pearson Correlation ◽  
Cell Types ◽  
Pelvic Organ ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Differential Analysis ◽  
Hub Genes

Objective Pelvic organ prolapse (POP) refers to the decline of pelvic organ position and dysfunction caused by weak pelvic floor support. The aim of the present study was to screen the hub genes and immune cell infiltration related to POP disease. Methods Microarray data of 34 POP tissues in the GSE12852 gene expression dataset were used as research objects. Weighted gene co-expression network analysis (WGCNA) was performed to elucidate the hub module and hub genes related to POP occurrence. Gene function annotation was performed using the DAVID tool. Differential analysis based on the GSE12852 dataset was carried out to explore the expression of the selected hub genes in POP and non-POP tissues, and RT-qPCR was used to validate the results. The differential immune cell infiltration between POP and non-POP tissues was investigated using the CIBERSORT algorithm. Results WGCNA revealed the module that possessed the highest correlation with POP occurrence. Functional annotation indicated that the genes in this module were mainly involved in immunity. ZNF331, THBS1, IFRD1, FLJ20533, CXCR4, GEM, SOD2, and SAT were identified as the hub genes. Differential analysis and RT-qPCR demonstrated that the selected hub genes were overexpressed in POP tissues as compared with non-POP tissues. The CIBERSORT algorithm was employed to evaluate the infiltration of 22 immune cell types in POP tissues and non-POP tissues. We found greater infiltration of activated mast cells and neutrophils in POP tissues than non-POP tissues, while the infiltration of resting mast cells was lower in POP tissues. Moreover, we investigated the relationship between the type of immune cell infiltration and hub genes by Pearson correlation analysis. The results indicate that activated mast cells and neutrophils had a positive correlation with the hub genes, while resting mast cells had a negative correlation with the hub genes. Conclusions Our research identified eight hub genes and the infiltration of three types of immune cells related to POP occurrence. These hub genes may participate in the pathogenesis of POP through the immune system, giving them a certain diagnostic and therapeutic value.

scholarly journals Identification of Hub Genes Associated with Immune Cells in Dilated Cardiomyopathy via Weighted Gene Co-expression Network Analysis

2021 ◽  
Author(s):  
Jielin Deng ◽  
Yunqiu Jiang ◽  
Changjin Deng ◽  
hong jiang
Keyword(s):  
Network Analysis ◽  
Dilated Cardiomyopathy ◽  
Immune Cells ◽  
Bioinformatics Analysis ◽  
Immune Cell ◽  
Gene Profiling ◽  
Functional Enrichment ◽  
Pathophysiological Mechanism ◽  
Hub Genes ◽  
Key Genes

Abstract Background: Dilated cardiomyopathy (DCM) is the most common cardiomyopathy which account for a majority of heart failure. Although massive clinic experiments and gene profiling analyses on DCM have been conducted, the molecular mechanism of DCM associated with immune cells has not been fully elucidated. This study was designed to discover the immune mechanism of DCM using integrative bioinformatics analysis and provide new insights into the pathophysiology of DCM. Methods: The GSE29819 dataset was downloaded, and Cibersort was applied to estimate the relative expression of 22 kinds of immune cells based on 14 samples of 7 DCM patients. Weighted gene co-expression network analysis (WGCNA) was performed to cluster the 2500 genes with the highest average expression into different modules and explore relationships between modules and immune cell types. Functional enrichment analysis was performed on key genes in significant modules identified by WGCNA and Cibersort. Key genes were then applied to Cytoscape to construct protein-protein interaction (PPI) network. Differentially expressed genes (DEGs) were identified based on DCM and normal controls in GSE29819 through R language. Hub genes were selected based on the DEGs and the genes identified by PPI and then verified via public GEO databases. Results: The yellow and tan modules with 163 genes were identified as the key modules based on top 2500 DCM microarrays, significantly correlated with M1 and M2 macrophages. The intersection of newly screened 17 genes based on 163 key genes through Cytoscape and 2682 DEGs were defined as hub genes including CCT2, CCL2, and TXN. The results were finally verified via GSE116250 datasets.Conclusions: The three hub genes associated with two immune cells identified by comprehensive bioinformatics analysis may play crucial roles in the pathophysiological mechanism of DCM, which provided potential immunological therapeutic targets and new insights into the treatment of DCM.

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