scholarly journals Identification of diagnostic genes and vital microRNAs involved in rheumatoid arthritis: based on data mining and experimental verification

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11427
Author(s):  
Conglin Ren ◽  
Mingshuang Li ◽  
Yang Zheng ◽  
Fengqing Wu ◽  
Weibin Du ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Western Blot ◽  
Experimental Verification ◽  
Gene Function ◽  
Molecular Mechanisms ◽  
Differential Expression Analysis ◽  
Gene Set Enrichment Analysis ◽  
Hub Genes ◽  
Function Annotation ◽  
Gene Function Annotation

Background The pathogenesis of rheumatoid arthritis (RA) is complex. This study aimed to identify diagnostic biomarkers and transcriptional regulators that underlie RA based on bioinformatics analysis and experimental verification. Material and Methods We applied weighted gene co-expression network analysis (WGCNA) to analyze dataset GSE55457 and obtained the key module most relevant to the RA phenotype. We then conducted gene function annotation, gene set enrichment analysis (GSEA) and immunocytes quantitative analysis (CIBERSORT). Moreover, the intersection of differentially expressed genes (DEGs) and genes within the key module were entered into the STRING database to construct an interaction network and to mine hub genes. We predicted microRNA (miRNA) using a web-based tool (miRDB). Finally, hub genes and vital miRNAs were validated with independent GEO datasets, RT-qPCR and Western blot. Results A total of 367 DEGs were characterized by differential expression analysis. The WGCNA method divided genes into 14 modules, and we focused on the turquoise module containing 845 genes. Gene function annotation and GSEA suggested that immune response and inflammatory signaling pathways are the molecular mechanisms behind RA. Nine hub genes were screened from the network and seven vital regulators were obtained using miRNA prediction. CIBERSORT analysis identified five cell types enriched in RA samples, which were closely related to the expression of hub genes. Through ROC curve and RT-qPCR validation, we confirmed five genes that were specific for RA, including CCL25, CXCL9, CXCL10, CXCL11, and CXCL13. Moreover, we selected a representative gene (CXCL10) for Western blot validation. Vital miRNAs verification showed that only the differences in has-miR-573 and has-miR-34a were statistically significant. Conclusion Our study reveals diagnostic genes and vital microRNAs highly related to RA, which could help improve our understanding of the molecular mechanisms underlying the disorder and provide theoretical support for the future exploration of innovative therapeutic approaches.

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.

scholarly journals Establishment of a Ciliogenesis-Associated Signaling Model for Polycystic Kidney Disease

2021 ◽  
pp. 1-9
Author(s):  
Ling Lu ◽  
Qiuling Liu ◽  
Lei Zhi ◽  
Xuchun Che ◽  
Bo Xiao ◽  
...  
Keyword(s):  
Kidney Disease ◽  
Polycystic Kidney Disease ◽  
Molecular Mechanisms ◽  
Differential Expression Analysis ◽  
Valuable Insight ◽  
Polycystic Kidney ◽  
Hub Genes ◽  
Zebrafish Model

<b><i>Background:</i></b> Polycystic kidney disease (PKD) represents the most prevalent inherited progressive kidney disorder in humans. Due to complexity of the genetic network behind the disease, the molecular mechanisms of PKD are still poorly understood yet. <b><i>Objectives:</i></b> This study aimed to develop a ciliogenesis-associated gene network for PKD patients and comprehensively understand the molecular mechanisms underlying the disease. <b><i>Method:</i></b> The potential hub genes were selected based on the differential expression analysis from the GEO database. Meanwhile, the primary hub genes were further elucidated by both in vivo and in vitro experiments. <b><i>Results:</i></b> In this study, we established a comprehensive differentially expressed genes profile (including <i>GNAS, PI4KB, UMOD, SLC7A13,</i> and <i>MIOX</i>) for PKD patients compared with the control specimen. At the same time, enrichment analysis was utilized to demonstrate that the G-protein-related signaling and cilia assembling signaling pathways were closely associated with PKD development. The further investigations of the interaction between 2 genes (<i>GNAS</i> and <i>PI4KB</i>) with in vivo and in vitro analyses revealed that PI4KB functioned as a downstream factor for GNAS and spontaneously activated the phosphorylation of Akt into p-Akt for ciliogenesis in PKD formation. The <i>PI4KB</i> depletion mutant zebrafish model displayed a PKD phenotype as well as absence of primary cilia in the kidney<i>.</i> <b><i>Conclusions:</i></b> Collectively, our work discovered an innovative potential signaling pathway model for PKD formation, which provided a valuable insight for future study of the mechanism of this disease.

scholarly journals Exosomal microRNAs are novel circulating biomarkers in cigarette, waterpipe smokers, E-cigarette users and dual smokers

2020 ◽  
Author(s):  
Kameshwar P. Singh ◽  
Krishna P. Maremanda ◽  
Dongmei Li ◽  
Irfan Rahman
Keyword(s):  
Differential Expression ◽  
Health Effects ◽  
Molecular Mechanisms ◽  
Electronic Cigarettes ◽  
Differential Expression Analysis ◽  
Pairwise Comparison ◽  
Gene Set Enrichment Analysis ◽  
Cigarette Smoke Exposure ◽  
Tobacco Exposure ◽  
Non Coding Rnas

Abstract Background Electronic cigarettes (e-cigs) produce aerosolized substances by heating a liquid, which contains large number of chemicals. The aerosol generated by E-cig may produce serious health effects. Cigarette smoke exposure may causes various diseases including COPD, atherosclerosis, and lung cancer. Waterpipe tobacco smoking also causes various acute and chronic health effects including cardiopulmonary diseases. MicroRNAs are present in higher concentration in exosomes that play a major role in various normal physiological functions and diseases. We hypothesized that the non-coding RNAs transcript may serve as susceptibility to disease biomarkers by smoking and vaping. Results Our data show the enrichment of various non-coding RNAs that include microRNAs, tRNAs, piRNAs, snoRNAs, snRNAs, Mt-tRNAs, and other biotypes in exosomes. The detailed differential expression analysis of microRNAs, tRNAs and piRNA showed significant changes between pairwise comparisons of different groups. The common changes in differential expression of 8 microRNAs that are hsa-let-7a-5p, hsa-miR-21-5p, hsa-miR-29b-3p, hsa-let-7f-5p, hsa-miR-143-3p, hsa-miR-30a-5p, hsa-let-7i-5p, and hsa-let-7g-5p were found when compared with all smoking and vaping groups with non-smoking group. The e-cig group has differentially expressed 7 microRNAs (hsa-miR-224-5p, hsa-let-7c-5p, hsa-miR-193b-3p, hsa-miR-30e-5p, hsa-miR-423-3p, hsa-miR-500b-3p, hsa-miR-365a-3p|hsa-miR-365b-3p) that is specific for this group, not expressed in other three groups. Gene set enrichment analysis of microRNA showed significant changes in the top six enriched functions that consisted of biological pathway, biological process, molecular function, cellular component, site of expression and transcription factor in all groups. Further, the pairwise comparison of tRNAs and piRNA in all groups also revealed significant changes in differential expression. Conclusions Plasma exosomes of cigarette smokers, waterpipe smokers, e-cig users and dual smokers have common differential expression of microRNAs (hsa-let-7a-5p, hsa-miR-21-5p, hsa-miR-29b-3p, hsa-let-7f-5p, hsa-miR-143-3p, hsa-miR-30a-5p, hsa-let-7i-5p, and hsa-let-7g-5p), may be biomarker for tobacco exposure. Additionally, the e-cig users have also differential expressed microRNAs (hsa-miR-224-5p, hsa-let-7c-5p, hsa-miR-193b-3p, hsa-miR-30e-5p, hsa-miR-423-3p, hsa-miR-500b-3p, and hsa-miR-365a-3p|hsa-miR-365b-3p) that is specific for this group. This study will help to better understand molecular mechanisms of plasma exosome non-coding RNAs and in developing biomarkers that may be useful in diagnosis and therapy of pulmonary injury and disease by smoking and vaping.

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 FBLN5 Is an Underlying Common Tumor Suppressor in Breast Cancer and Thyroid Cancer

2022 ◽  
Author(s):  
Zhao-min XIE ◽  
Ying-sheng XIAO ◽  
Chun-yan XU ◽  
Qin XIE ◽  
Wen-de WANG ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Thyroid Cancer ◽  
Tumor Suppressor ◽  
Molecular Mechanisms ◽  
Differential Expression Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Prognostic Gene ◽  
Hormone Biosynthesis ◽  
Key Genes

Abstract Background: Breast cancer (BC) patients have a greater risk of developing thyroid cancer (TC) than the general population. Similarly, TC patients are more likely to develop BC, suggesting an underlying common etiology. In this study, we sought to identify the potential cross-talking pathway and related molecular mechanisms conferring to the sequential development of BC and TC.Methods: We first used Multiple Primary-Standardized Incidence Ratios (MP-SIR) Program of SEER*Stat to calculate SIR to confirm the relationship between BC and TC. Then the RNA-seq was downloaded from The Cancer Genome Atlas (TCGA). And we built a co-expression network via Weighted Gene Co-expression Network Analysis (WGCNA) and obtained the most significant modules. The key genes were obtained by differential gene expression (DGE) analysis and WGCNA analysis. Furthermore, String database and Cytoscape software were used to construct protein-protein interactions (PPI), and defined the maximum Maximal Clique Centrality (MCC) value as hub gene.Then we performed prognosis analysis on the hub genes and obtained the prognostic genes of BC and TC. Finally, gene set enrichment analysis (GSEA) was used to investigate the molecular pathways associated with prognostic gene expressed both in BC and TC.Results: From the SEER database, we found that the risk of developing BC in TC patients was SIR 1.12, 95% CI [1.07, 1.18], and the risk of developing BC in TC patients was SIR 1.29, 95% CI [1.23, 1.26]. Fifty-nine key genes obtained by differential expression analysis and WGCNA identify that PI3K/AKT was the most enriched pathway in BC and TC. In addition, the Recombinant Fibulin 5 (FBLN5) was shown to be of significant prognostic value for both BC and TC and was down-regulated in BC and TC tissues. GSEA demonstrated that FBLN5 enrichment pathways associated with BC and TC mainly included: B cell receptor signaling pathway, steroid hormone biosynthesis, and pathways in cancer.Conclusions: The PI3K/AKT signaling is most co-enriched pathway in BC and TC. FBLN5 is the most relevant prognostic gene and an underlying common tumor suppressor in both BC and TC, with down-stream pathways involving immunity, hormone biosynthesis and carcinogenesis.

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.

Cation Lipid-Assisted PEG6-PLGA Polymer Nanoparticles Encapsulated Knocking Down Long ncRNAs Reverse Non-Coding RNA of Xist Through the Support Vector Machine Model to Regulate the Molecular Mechanisms of Gastric Cancer Cell Apoptosis

2021 ◽  
Vol 17 (7) ◽  
pp. 1305-1319
Author(s):  
Zhengwang Sun ◽  
Zirui He ◽  
Rujiao Liu ◽  
Zhe Zhang
Keyword(s):  
Gastric Cancer ◽  
Support Vector Machine ◽  
Molecular Mechanisms ◽  
Drug Carrier ◽  
Differential Expression Analysis ◽  
Gene Set Enrichment Analysis ◽  
Support Vector ◽  
Machine Model ◽  
Knock Out ◽  
Training Support

Gastric adenocarcinoma (GAC) is one kind of gastric cancer with a high incidence rate and mortality. It is essential to study the etiology of GAC and provide theoretical guidance for the prevention and treatment of GAC. Bioinformatics was used via differential expression analysis, weighted gene co-expression network analysis, gene set enrichment analysis, and a training support vector machine (SVM) model to construct a TSIX/mir-320a/Rad51 network as the research index of GAC disease. On the basis of CRISPR/Cas9 gene editing technology, the present study utilizes the Cation lipid-assisted PEG-6-PLGA polymer nanoparticle (CLAN) drug carrier system to prepare the target knock-out TSIX drug with CRISPR/CaS9 nucleic acid. Knocking down lncRNA TSIX restored the suppression role of miR-320a on Rad51 and inhibited the Rad51 expression. Simultaneously, this ceRNA network activated the ATF6 signaling pathway after endoplasmic reticulum stress to promote GAC cells’ apoptosis and inhibit the disease. TSIX/miR-320a/Rad51 network may be a potential biological target of GAC disease and provides a new strategy for treating GAC disease.

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 Exploring microRNA target genes and identifying hub genes in bladder cancer based on bioinformatic analysis

BMC Urology ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hongjian Wu ◽  
Wubing Jiang ◽  
Guanghua Ji ◽  
Rong Xu ◽  
Gaobo Zhou ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Differential Expression ◽  
Expression Analysis ◽  
Target Genes ◽  
Cancer Genomics ◽  
Differential Expression Analysis ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Tumour Suppressor Genes ◽  
Hub Genes

Abstract Background Bladder cancer (BC) is the second most frequent malignancy of the urinary system. The aim of this study was to identify key microRNAs (miRNAs) and hub genes associated with BC as well as analyse their targeted relationships. Methods According to the microRNA dataset GSE112264 and gene microarray dataset GSE52519, differentially expressed microRNAs (DEMs) and differentially expressed genes (DEGs) were obtained using the R limma software package. The FunRich software database was used to predict the miRNA-targeted genes. The overlapping common genes (OCGs) between miRNA-targeted genes and DEGs were screened to construct the PPI network. Then, gene ontology (GO) analysis was performed through the “cluster Profiler” and “org.Hs.eg.db” R packages. The differential expression analysis and hierarchical clustering of these hub genes were analysed through the GEPIA and UCSC Cancer Genomics Browser databases, respectively. KEGG pathway enrichment analyses of hub genes were performed through gene set enrichment analysis (GSEA). Results A total of 12 DEMs and 10 hub genes were identified. Differential expression analysis of the hub genes using the GEPIA database was consistent with the results for the UCSC Cancer Genomics Browser database. The results indicated that these hub genes were oncogenes, but VCL, TPM2, and TPM1 were tumour suppressor genes. The GSEA also showed that hub genes were most enriched in those pathways that were closely associated with tumour proliferation and apoptosis. Conclusions In this study, we built a miRNA-mRNA regulatory targeted network, which explores an understanding of the pathogenesis of cancer development and provides key evidence for novel targeted treatments for BC.

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.

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