scholarly journals Identification of Hub Genes related to the Progression of Bladder Cancer by an Integrated Bioinformatics Analysis

2021 ◽  
Author(s):  
Han Wang ◽  
Jieqing Chen ◽  
Xinhui Liao ◽  
Yang Liu ◽  
Aifa Tang ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Differentially Expressed Genes ◽  
Molecular Mechanisms ◽  
Signalling Pathway ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Immune Infiltration ◽  
Ppi Networks ◽  
Normal Bladder

Abstract BACKGROUND and OBJECTIVE: A better understanding of the molecular mechanisms underlying bladder cancer is necessary to identify candidate therapeutic targets. METHODS: We screened for genes associated with bladder cancer progression and prognosis. Publicly available expression data were obtained from TCGA and GEO to identify differentially expressed genes (DEGs) between bladder cancer and normal bladder tissues. Weighted co-expression networks were constructed, and Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Associations between hub genes and immune infiltration and immune therapy were evaluated. RESULTS: 3461 DEGs in TCGA-BC and 1069 DEGs in the GSE dataset were identified, with 87 overlapping differentially expressed genes between the bladder cancer and normal bladder groups. Hub genes in the tumour group were mainly enriched for cell proliferation-related GO terms and KEGG pathways, while hub genes in the normal group were related to the synthesis and secretion of neurotransmitters. PPI networks for the genes identified in the normal and tumour groups were constructed. Based on a survival analysis, CDH19, RELN, PLP1, and TRIB3 were significantly associated with prognosis (P < 0.05). Four hub genes were significantly enriched in the MAPK signalling pathway, VEGF signalling pathway, WNT signalling pathway, cell cycle, and P53 signalling pathway based on a gene set enrichment analysis; these genes were associated with immune infiltration levels in bladder cancer. CONCLUSIONS: CDH19, RELN, PLP1, and TRIB3 may play important roles in the development of bladder cancer and are potential therapeutic and prognostic targets.

A Comprehensive Data Analysis of Differentially Regulated Genes in Melanoma

2020 ◽  
Author(s):  
Yanjie Han ◽  
Xinxin Li ◽  
Jiliang Yan ◽  
Chunyan Ma ◽  
Xin Wang ◽  
...  
Keyword(s):  
Differentially Expressed Genes ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Growth Factor Receptor ◽  
Principal Component ◽  
Distant Metastases ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Ppi Networks

Abstract Background: Melanoma is the most deadly tumor in skin tumors and is prone to distant metastases. The incidence of melanoma has increased rapidly in the past few decades, and current trends indicate that this growth is continuing. This study was aimed to explore the molecular mechanisms of melanoma pathogenesis and discover underlying pathways and genes associated with melanoma.Methods: We used high-throughput expression data to study differential expression profiles of related genes in melanoma. The differentially expressed genes (DEGs) of melanoma in GSE15605, GSE46517, GSE7553 and the Cancer Genome Atlas (TCGA) datasets were analyzed. Differentially expressed genes (DEGs) were identified by paired t-test. Then the DEGs were performed cluster and principal component analyses and protein–protein interaction (PPI) network construction. After that, we analyzed the differential genes through bioinformatics and got hub genes. Finally, the expression of hub genes was confirmed in the TCGA databases and collected patient tissue samples.Results: Total 144 up-regulated DEGs and 16 down-regulated DEGs were identified. A total of 17 gene ontology analysis (GO) terms and 11 pathways were closely related to melanoma. Pathway of pathways in cancer was enriched in 8 DEGs, such as junction plakoglobin (JUP) and epidermal growth factor receptor (EGFR). In the PPI networks, 9 hub genes were obtained, such as loricrin (LOR), filaggrin (FLG), keratin 5 (KRT5), corneodesmosin (CDSN), desmoglein 1 (DSG1), desmoglein 3 (DSG3), keratin 1 (KRT1), involucrin (IVL) and EGFR. The pathway of pathways in cancer and its enriched DEGs may play important roles in the process of melanoma. The hub genes of DEGs may become promising melanoma candidate genes. Five key genes FLG, DSG1, DSG3, IVL and EGFR were identified in the TCGA database and melanoma tissues.Conclusions: The results suggested that FLG, DSG1, DSG3, IVL and EGFR might play important roles and potentially be valuable in the prognosis and treatment of melanoma.

scholarly journals Bioinformatics Analysis of Differentially Expressed Genes Involved in Irritable Bowel Syndrome With Diarrhea

2021 ◽  
Author(s):  
Yuan-Mei Lou ◽  
Yan-Zhi Ge ◽  
Wen Chen ◽  
Lin Su ◽  
Jia-Qi Zhang ◽  
...  
Keyword(s):  
Irritable Bowel Syndrome ◽  
Differentially Expressed Genes ◽  
Molecular Mechanisms ◽  
Bioinformatics Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Ppi Networks ◽  
Black Module ◽  
Irritable Bowel

Abstract Purpose: Irritable bowel syndrome with diarrhea (IBS-D) is a common functional gastrointestinal disorder around the world. However, the molecular mechanisms of IBS-D are still not well understood. This study was designed to identify key biomarkers and immune infiltration in the rectal mucosa of IBS-D by bioinformatics analysis. Methods: The gene expression profiles of GSE36701 were downloaded from the GEO database. The differentially expressed genes (DEGs) were identified and functional enrichment and pathway analyses were performed. Using STRING and Cytoscape, protein-protein interaction (PPI) networks were constructed and core genes were identified. Subsequently, 22 immune cell types of IBS-D tissues were explored by the Cell type Identification by Estimating Relative Subsets of RNA Transcripts. Finally, the co-expression network of DEGs was estimated by the weigh gene co-expression network analysis method to identify IBS-D-related modules and deeply hub genes. Results: 224 up-regulated and 171 down-regulated genes in IBS-D patients: Our analysis indicated that several DEGs might play crucial roles in IBS-D, such as CDC20, UBE2C, AURKA, CDC26, CKS1B and PSMB3. Later, we found that immune infiltrating cells such as T cells CD4 memory resting, M2 macrophages are crucial in IBS-D progression. In the end, a total of 9 co-expression gene modules were calculated and the black module was found to have the highest correlation. 15 hub genes were identified both in DEGs and the black module. Conclusions: This study identified molecular mechanisms and a series of candidate genes as well as significant pathways from the bioinformatics network, which may provide a diagnostic method and therapeutic targets for IBS-D.

Identification of differentially expressed genes, biological pathways and prognostic signature in bladder cancer

2021 ◽  
Author(s):  
Fucai Tang ◽  
Xiayan Qian ◽  
Zeguang Lu ◽  
Yongchang Lai ◽  
Zhibiao Li ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Differentially Expressed Genes ◽  
Pathway Analysis ◽  
Kegg Pathway ◽  
Biological Pathways ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Go Analysis ◽  
Ppi Networks ◽  
Kegg Pathway Analysis

Abstract Background Bladder cancer (BC) is one of the most common malignant cancer of urinary system in the worldwide. The purpose of the present study was to analysis differentially expressed genes (DEGs), biological pathways and prognostic significance BC by bioinformatics analysis. Methods The gene expression dataset GSE7476 and the mRNA Seq sequencing data were downloaded respectively from GEO and TCGA. A total of 220 DEGs were obtained in BC. GO analysis and KEGG pathway analysis were performed for up- and down-regulated DEGs. Then, a protein-protein interaction (PPI) networks and module were constructed by Cytoscape software. Survival analysis of hub genes was performed. Results The result of GO analysis revealed that the up-regulated DEGs were enriched mainly in sister chromatid segregation, while the down-regulated DEGs were enriched mainly in muscle contraction. The result of KEGG pathway analysis showed that up-regulated DEGs were enriched mainly in cell cycle, while down-regulated DEGs enriched in IL-17 signaling pathway. 41 hub gene and 3 crucial modules were identified in the PPI network. 15 genes significantly associated with patient prognosis in BC were obtained by Kaplan-Meier analysis. Conclusions In summary, the present study identified hub genes, crucial pathways and provide possible the molecular targets and prognostic biomarkers for targeted therapy and prognostic assessment of BC.

scholarly journals Bioinformatics analysis of differentially expressed genes and identification of an miRNA–mRNA network associated with entorhinal cortex and hippocampus in Alzheimer’s disease

Hereditas ◽  
2021 ◽  
Vol 158 (1) ◽  
Author(s):  
Haoming Li ◽  
Linqing Zou ◽  
Jinhong Shi ◽  
Xiao Han
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Differentially Expressed Genes ◽  
Entorhinal Cortex ◽  
Molecular Mechanisms ◽  
Kegg Pathway ◽  
Neurodegenerative Disorder ◽  
Early Stage ◽  
Differentially Expressed ◽  
Hub Genes

Abstract Background Alzheimer’s disease (AD) is a fatal neurodegenerative disorder, and the lesions originate in the entorhinal cortex (EC) and hippocampus (HIP) at the early stage of AD progression. Gaining insight into the molecular mechanisms underlying AD is critical for the diagnosis and treatment of this disorder. Recent discoveries have uncovered the essential roles of microRNAs (miRNAs) in aging and have identified the potential of miRNAs serving as biomarkers in AD diagnosis. Methods We sought to apply bioinformatics tools to investigate microarray profiles and characterize differentially expressed genes (DEGs) in both EC and HIP and identify specific candidate genes and pathways that might be implicated in AD for further analysis. Furthermore, we considered that DEGs might be dysregulated by miRNAs. Therefore, we investigated patients with AD and healthy controls by studying the gene profiling of their brain and blood samples to identify AD-related DEGs, differentially expressed miRNAs (DEmiRNAs), along with gene ontology (GO) analysis, KEGG pathway analysis, and construction of an AD-specific miRNA–mRNA interaction network. Results Our analysis identified 10 key hub genes in the EC and HIP of patients with AD, and these hub genes were focused on energy metabolism, suggesting that metabolic dyshomeostasis contributed to the progression of the early AD pathology. Moreover, after the construction of an miRNA–mRNA network, we identified 9 blood-related DEmiRNAs, which regulated 10 target genes in the KEGG pathway. Conclusions Our findings indicated these DEmiRNAs having the potential to act as diagnostic biomarkers at an early stage of AD.

scholarly journals Screening the Hub Genes and Analyzing the Mechanisms in Discharged COVID-19 Patients Retesting Positive through Bioinformatics Analysis

2021 ◽  
Author(s):  
Ke-Ying Fang ◽  
Gui-Ning Liang ◽  
Zhuo-Qing Zhuang ◽  
Yong-Xin Fang ◽  
Yu-Qian Dong ◽  
...  
Keyword(s):  
Differentially Expressed Genes ◽  
Social Order ◽  
Expression System ◽  
Enrichment Analysis ◽  
Differentially Expressed ◽  
Virus Reactivation ◽  
Hub Genes ◽  
Messenger Ribonucleic Acid ◽  
Ppi Networks ◽  
Significant Enrichment

Abstract Background: With the worldwide spread of COVID-19, people’s health and social order have been exposed to enormous risks. After encountering patients who test positive again after discharge, our study analyzed the pathogenesis to further assess the risk and possibility of virus reactivation.Methods: A separate microarray was acquired from the Integrated Gene Expression System (GEO), and its samples were divided into two groups: a “convalescent-RTP” group consisting of recovery and “retesting-positive” (RTP) patients (group CR) and a “health-RTP” group consisting of healthy control and RTP patients (group HR). The enrichment analysis was performed with R software, obtaining the gene ontology (GO) and Kyoto pluripotent stem cells (KEGG) of the genes and genomes. Subsequently, the protein–protein interaction (PPI) networks of each group were established and the hub genes were discovered using the cytoHubba plug-in.Results: In this study, 20 differentially expressed genes were identified, and 6622 genes were identified in the group CR, consisting of 5003 up-regulated and 1619 down-regulated genes. Meanwhile, 7335 genes were screened in the group HR, including 4323 up-regulated and 3012 down-regulated ones. The GO and KEGG analysis of the two groups revealed significant enrichment of these differentially expressed genes in pathways associated with immune response and apoptosis. In the PPI network constructed, 10 hub genes in group CR were identified, including TP53BP1, SNRPD1, SNRPD2, SF3B1, SNRNP200, MRPS16, MRPS9, CALM1, PPP2R1A, YWHAZ. Similarly, TP53BP1, RPS15, EFTUD2, MRPL16, MRPL17, MRPS14, RPL35A, MRPL32, MRPS6, POLR2G were selected as hub genes.Conclusions: Using the messenger ribonucleic acid (mRNA) expression data from GSE166253, we explore the pathogenesis of retesting positive in COVID-19 from the immune mechanism and molecular level. We found TP53BP1, SNRPD1 and SNRPD2 as hub genes in RTP patients. Hence, their regulatory pathway is vital to the management and prognostic prediction of RTP patients, rendering the further study of these hub genes necessary.

scholarly journals A study on regional differences in decidualization of the mouse uterus

Reproduction ◽  
2017 ◽  
Vol 153 (5) ◽  
pp. 645-653 ◽  
Author(s):  
Miao Zhao ◽  
Wen-Qian Zhang ◽  
Ji-Long Liu
Keyword(s):  
Differentially Expressed Genes ◽  
Regional Differences ◽  
Molecular Mechanisms ◽  
Cell Metabolism ◽  
Gene Expression Pattern ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Promoter Regions ◽  
Mouse Uterus ◽  
Region Gene

Although regional differences in mouse decidualization have been recognized for decades, the molecular mechanisms remain understudied. In the present study, by using RNA-seq, we compared transcriptomic differences between the anti-mesometrial (AM) region and the mesometrial (M) region of mouse uterus on day 8 of pregnancy. A total of 1423 differentially expressed genes were identified, of which 811 genes were upregulated and 612 genes were downregulated in the AM region compared to those in the M region. Gene ontology analysis showed that upregulated genes were generally involved in cell metabolism and differentiation, whereas downregulated genes were associated with lymphocyte themes and immune response. Through network analysis, we identified a total of 6 hub genes. These hub genes are likely more important than other genes due to their key positions in the network. We also examined the promoter regions of differentially expressed genes for the enrichment of transcription factor-binding sites. In the end, we demonstrated that a similar regional gene expression pattern can be observed in the artificial decidualization model. Our study contributes to an increase in the knowledge on the molecular mechanisms underlying regional decidualization in mice.

scholarly journals Wfs1 and Related Molecules as Key Candidate Genes in the Hippocampus of Depression

2021 ◽  
Vol 11 ◽  
Author(s):  
Jing Yang ◽  
Chaoqin Chen ◽  
Xiaoyuan Jin ◽  
Lu Liu ◽  
Jiajia Lin ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Mouse Model ◽  
Differentially Expressed Genes ◽  
Molecular Mechanisms ◽  
Visual Analysis ◽  
Pathological Process ◽  
Differentially Expressed ◽  
Mild Stress ◽  
Ppi Networks ◽  
Chemokine Signaling

BackgroundDepression is a prevalent mental disorder, which is difficult to diagnose and treat due to its unclear pathogenic mechanisms. The discovery of novel and effective therapeutic targets for depression is urgently needed. The hippocampus is a crucial region involved in depression and has been a therapeutic target for many antidepressants. Thus, it is beneficial for comprehensive research to be carried out on the molecular mechanisms of the hippocampus involved in the pathogenesis of depression. This study aims to investigate the differentially expressed genes (DEG) in the hippocampus in a chronic unpredictable mild stress (CUMS) mouse model.MethodThe study obtained GSE84183 from the GEO database. The R language screened the differential expression genes (DEG) in the hippocampus tissue of depressed mice, and the enrichment pathways of DEGs were analyzed. A protein-protein interaction (PPI) network was constructed in the STRING database and visualized in Cytoscape software. MicroRNAs for these DEGs were obtained from TarBase and mortar base databases, and transcription factors (TF) related to DEG were predicted from the ENCODE database. Both networks used the visual analysis platform NetworkAnalyst. Finally, the microRNA-TF network was integrated based on the above two networks and imported into Cytoscape for further analysis.ResultsThis study screened 325 differentially expressed genes, containing 42 downregulated genes and 283 upregulated genes. Most of these genes are enriched in the cell cycle and the chemokine signaling pathway. Meanwhile, Wfs1, one of the top ten DEGs, was identified as the key regulator of the cell cycle and the participator in the highest number of modules screened out in PPI networks. Wfs1-related molecules, including UBTF, mmu-mir-17-5p, and mmu-mir-7b-5p, were therefore screened out. Furthermore, we confirmed the downregulation of Wfs1 and upregulation of UBTF/mmu-mir-17-5p/mmu-mir-7b-5p in the hippocampus of the CUMS mouse model. Our data indicate that Wfs1 and related molecules were predicted to be associated with the pathological process of depression. This research provided potential new molecular targets of stress-induced depression.

scholarly journals Identification of Hub Genes in Anaplastic Thyroid Carcinoma: Evidence From Bioinformatics Analysis

2020 ◽  
Vol 19 ◽  
pp. 153303382096213
Author(s):  
Liqi Li ◽  
Mingjie Zhu ◽  
Hu Huang ◽  
Junqiang Wu ◽  
Dong Meng
Keyword(s):  
Thyroid Carcinoma ◽  
Differentially Expressed Genes ◽  
Molecular Mechanisms ◽  
Quantitative Polymerase Chain Reaction ◽  
Interaction Network ◽  
Anaplastic Thyroid Carcinoma ◽  
Rank Aggregation ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Rare Type

Anaplastic thyroid carcinoma (ATC) is a rare type of thyroid cancer that results in fatal clinical outcomes; the pathogenesis of this life-threatening disease has yet to be fully elucidated. This study aims to identify the hub genes of ATC that may play key roles in ATC development and could serve as prognostic biomarkers or therapeutic targets. Two microarray datasets (GSE33630 and GSE53072) were obtained from the Gene Expression Omnibus database; these sets included 16 ATC and 49 normal thyroid samples. Differential expression analyses were performed for each dataset, and 420 genes were screened as common differentially expressed genes using the robust rank aggregation method. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were conducted to explore the potential bio-functions of these differentially expressed genes (DEGs). The terms and enriched pathways were primarily associated with cell cycle, cell adhesion, and cancer-related signaling pathways. Furthermore, a protein-protein interaction network of DEG expression products was constructed using Cytoscape. Based on the whole network, we identified 7 hub genes that included CDK1, TOP2A, CDC20, KIF11, CCNA2, NUSAP1, and KIF2C. The expression levels of these hub genes were validated using quantitative polymerase chain reaction analyses of clinical specimens. In conclusion, the present study identified several key genes that are involved in ATC development and provides novel insights into the understanding of the molecular mechanisms of ATC development.

scholarly journals Comprehensive analysis of DNA methylation and gene expression profiles in cholangiocarcinoma

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Cheng Zhang ◽  
Bingye Zhang ◽  
Di Meng ◽  
Chunlin Ge
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Differentially Expressed Genes ◽  
Expression Profiles ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Ppi Networks ◽  
Differentially Methylated Genes ◽  
Tumorigenesis And Progression

Abstract Background The incidence of cholangiocarcinoma (CCA) has risen in recent years, and it has become a significant health burden worldwide. However, the mechanisms underlying tumorigenesis and progression of this disease remain largely unknown. An increasing number of studies have demonstrated crucial biological functions of epigenetic modifications, especially DNA methylation, in CCA. The present study aimed to identify and analyze methylation-regulated differentially expressed genes (MeDEGs) involved in CCA tumorigenesis and progression by bioinformatics analysis. Methods The gene expression profiling dataset (GSE119336) and gene methylation profiling dataset (GSE38860) were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) and differentially methylated genes (DMGs) were identified using the limma packages of R and GEO2R, respectively. The MeDEGs were obtained by overlapping the DEGs and DMGs. Functional enrichment analyses of these genes were then carried out. Protein–protein interaction (PPI) networks were constructed using STRING and visualized in Cytoscape to determine hub genes. Finally, the results were verified based on The Cancer Genome Atlas (TCGA) database. Results We identified 98 hypermethylated, downregulated genes and 93 hypomethylated, upregulated genes after overlapping the DEGs and DMGs. These genes were mainly enriched in the biological processes of the cell cycle, nuclear division, xenobiotic metabolism, drug catabolism, and negative regulation of proteolysis. The top nine hub genes of the PPI network were F2, AHSG, RRM2, AURKB, CCNA2, TOP2A, BIRC5, PLK1, and ASPM. Moreover, the expression and methylation status of the hub genes were significantly altered in TCGA. Conclusions Our study identified novel methylation-regulated differentially expressed genes (MeDEGs) and explored their related pathways and functions in CCA, which may provide novel insights into a further understanding of methylation-mediated regulatory mechanisms in CCA.

scholarly journals Exploring the Key Genes and Pathways in the Formation of Corneal Scar Using Bioinformatics Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Binfeng Liu ◽  
Ang Li ◽  
Hongbo Wang ◽  
Jialin Wang ◽  
Gongwei Zhai ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Protein Interaction ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Signal Pathways ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Corneal Scar

The Corneal wound healing results in the formation of opaque corneal scar. In fact, millions of people around the world suffer from corneal scars, leading to loss of vision. This study aimed to identify the key changes of gene expression in the formation of opaque corneal scar and provided potential biomarker candidates for clinical treatment and drug target discovery. We downloaded Gene expression dataset GSE6676 from NCBI-GEO, and analyzed the Differentially Expressed Genes (DEGs), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment pathway analyses, and protein-protein interaction (PPI) network. A total of 1377 differentially expressed genes were identified and the result of Functional enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) identification and protein-protein interaction (PPI) networks were performed. In total, 7 hub genes IL6 (interleukin-6), MMP9 (matrix metallopeptidase 9), CXCL10 (C-X-C motif chemokine ligand 10), MAPK8 (mitogen-activated protein kinase 8), TLR4 (toll-like receptor 4), HGF (hepatocyte growth factor), EDN1 (endothelin 1) were selected. In conclusion, the DEGS, Hub genes and signal pathways identified in this study can help us understand the molecular mechanism of corneal scar formation and provide candidate targets for the diagnosis and treatment of corneal scar.

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