Identification and Validation of Hub Genes Associated with Bladder Cancer by Integrated Bioinformatics and Experimental Assays.
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ABSTRACT:
SUBMITTER: Chen K
PROVIDER: S-EPMC8721040 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Rheumatoid arthritis (RA) is an autoimmune disease characterized by systemic inflammation, especially synovitis, leading to joint damage. It is important to explore potential biomarkers and therapeutic targets to improve the clinical treatment of RA. However, the potential underlying mechanisms of action of available treatments for RA have not yet been fully elucidated. The present study investigated the potential biomarkers of RA and identified specific targets for therapeutic intervention. A comprehensive analysis was performed using mRNA files downloaded from the Gene Expression Omnibus. Differences in gene expression were analyzed and compared between the normal and RA groups. In addition, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed on differentially expressed genes (DEGs). A protein-protein interaction network, Molecular Complex Detection and cytoHubba network were evaluated to identify hub genes. Finally, using an experimental RA rat model induced by Freund's complete adjuvant (FCA), the expression of potential biomarkers or target genes in RA were verified through reverse transcription-quantitative PCR. The results of the mRNA dataset processing revealed 195 DEGs in patients with RA when compared with the healthy controls. Moreover, 10 hub genes were identified in patients with RA and four candidate mRNAs were identified, as follows: Discs large homolog-associated protein 5 (DLGAP5), kinesin family member 20A (KIF20A), maternal embryonic leucine zipper kinase (MELK) and nuclear division cycle 80 (NDC80). Finally, the bioinformatics analysis results were validated by quantifying the expression of the DLGAP5, KIF20A, MELK and NDC80 genes in the FCA-induced experimental RA rat model. The findings of the present study suggested that the treatment of RA may be successful through the inhibition of DLGAP5, KIF20A, MELK and NDC80 expression. Therefore, the targeting of these genes may result in more effective treatments for patients with RA.
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Project description:BackgroundThis study aimed to identify the hub genes associated with prognosis of patients with ovarian cancer by using integrated bioinformatics analysis and experimental validation.MethodsFour microarray datasets (GSE12470, GSE14407, GSE18521 and GSE46169) were analyzed by the GEO2R tool to screen common differentially expressed genes (DEGs). Gene Ontology, the Kyoto Encyclopedia of Genes and Genomes, the (KEGG) pathway and Reactome pathway enrichment analysis, protein-protein interaction (PPI) construction, and the identification of hub genes were performed. Furthermore, we performed the survival and expression analysis of the hub genes. In vitro functional assays were performed to assess the effects of hub genes on ovarian cancer cell proliferation, caspase-3/7 activity and invasion.ResultsA total of 89 common DEGs were identified among these four datasets. The KEGG and Reactome pathway results showed that the DEGs were mainly associated with cell cycle, mitotic and p53 signaling pathway. A total of 20 hub genes were identified from the PPI network by using sub-module analysis. The survival analysis revealed that high expression of six hub genes (AURKA, BUB1B, CENPF, KIF11, KIF23 and TOP2A) were significantly correlated with shorter overall survival and progression-free survival of patients with ovarian cancer. Furthermore, the expression of the six hub genes were validated by the GEPIA database and Human Protein Atlas, and functional studies revealed that knockdown of KIF11 and KIF23 suppressed the SKOV3 cell proliferation, increased caspase-3/7 activity and attenuated invasive potentials of SKOV3 cells. In addition, knockdown of KIF11 and KIF23 up-regulated E-cadherin mRNA expression but down-regulated N-cadherin and vimentin mRNA expression in SKOV3 cells.ConclusionOur results showed that six hub genes were up-regulated in ovarian cancer tissues and may predict poor prognosis of patients with ovarian cancer. KIF11 and KIF23 may play oncogenic roles in ovarian cancer cell progression via promoting ovarian cancer cell proliferation and invasion.
| S-EPMC7851396 | biostudies-literature
Project description:PurposeSmoking is recognized as a risk factor for Chronic Obstructive Pulmonary Disease (COPD), yet only 20-25% of smokers eventually develop COPD. Since its molecular pathogenesis remains unclear, there is an important need to further understand genetic differences between smokers with COPD and healthy smokers, screen out high-risk and susceptible groups among smokers, and find effective therapeutic targets.MethodsBioinformatics tools were used to screen biomarkers that were significantly associated with COPD smokers and healthy smokers. qRT-PCR and Western blotting analysis were used to detect hub gene expression in CSE-treated BEAS-2B cells and lung tissue of COPD mouse models.ResultsOur study identified 132 DEGs. The GO and KEGG analyses suggested that the ECM-receptor interaction, MAPK signaling pathway, Chemokine signaling pathway, PI3K-Akt signaling pathway, extracellular matrix organization and collagen fibril organization were associated with the occurrence and development of COPD. In addition, WGCNA analysis of GSE1650 showed that the brown module was most correlated with COPD. The intersection between the brown module and DEGs was used to identify 9 HUB genes (COL14A1, SULF1, MOXD1, CXCL12, CHRNA1, COMP, POU2AF1, MMP11, THBS2) that showed consistent expression and upregulation. Both the mRNA and protein expression levels of the Hub genes (except that of MMP11) were significantly upregulated in tobacco smoke exposed mouse emphysema models and CSE treated BEAS-2B cells.ConclusionOur results suggest that COL14A1, SULF1, MOXD1, CXCL12, CHRNA1, COMP, POU2AF1, and THBS2 may be potentially useful biomarkers for identifying smokers with a risk of developing COPD. The GO and KEGG functional enrichment analyses further confirmed the significant role played by ECM in the pathogenesis of COPD. The results of this study may provide further insights into the pathogenetic mechanisms involved in COPD.
| S-EPMC8901430 | biostudies-literature
Project description:Improving the understanding of the molecular mechanism of tuberculous pleurisy is required to develop diagnosis and new therapy strategies of targeted genes. The purpose of this study is to identify important genes related to tuberculous pleurisy. In this study, the expression profile obtained by sequencing the surgically resected pleural tissue was used to explore the differentially co-expressed genes between tuberculous pleurisy tissue and normal tissue. 29 differentially co-expressed genes were screened by weighted gene co-expression network analysis (WGCNA) and differential gene expression analysis methods. According to the functional annotation analysis of R clusterProfiler software package, these genes are mainly enriched in nucleotide-sugar biosynthetic process (biological process), ficolin-1-rich granule lumen (cell component), and electron transfer activity (molecular function). In addition, in the protein-protein interaction (PPI) network, 20 hub genes of DEGs and WCGNA genes were identified using the CytoHubba plug-in of Cytoscape. In the end, RPL17 was identified as a gene that can be the biomarker of tuberculous pleurisy. At the same time, there are seven genes that may have relationship with the disease (UBA7, NDUFB8, UQCRFS1, JUNB, PSMC4, PHPT1, and MAPK11).
| S-EPMC8678451 | biostudies-literature
Project description:BackgroundGastric cancer (GC) is one of the most common solid malignant tumors worldwide with a high-recurrence-rate. Identifying the molecular signatures and specific biomarkers of GC might provide novel clues for GC prognosis and targeted therapy.MethodsGene expression profiles were obtained from the ArrayExpress and Gene Expression Omnibus database. Differentially expressed genes (DEGs) were picked out by R software. The hub genes were screened by cytohubba plugin. Their prognostic values were assessed by Kaplan-Meier survival analyses and the gene expression profiling interactive analysis (GEPIA). Finally, qRT-PCR in GC tissue samples was established to validate these DEGs.ResultsTotal of 295 DEGs were identified between GC and their corresponding normal adjacent tissue samples in E-MTAB-1440, GSE79973, GSE19826, GSE13911, GSE27342, GSE33335 and GSE56807 datasets, including 117 up-regulated and 178 down-regulated genes. Among them, 7 vital upregulated genes (HMMR, SPP1, FN1, CCNB1, CXCL8, MAD2L1 and CCNA2) were selected. Most of them had a significantly worse prognosis except SPP1. Using qRT-PCR, we validated that their transcriptions in our GC tumor tissue were upregulated except SPP1 and FN1, which correlated with tumor relapse and predicts poorer prognosis in GC patients.ConclusionsWe have identified 5 upregulated DEGs (HMMR, CCNB1, CXCL8, MAD2L1, and CCNA2) in GC patients with poor prognosis using integrated bioinformatical methods, which could be potential biomarkers and therapeutic targets for GC treatment.
| S-EPMC8201699 | biostudies-literature
Project description:Uterine Corpus Endometrial Carcinoma (UCEC) is one of the most common malignancies of the female genital tract and there remains a major public health problem. Although significant progress has been made in explaining the progression of UCEC, it is still warranted that molecular mechanisms underlying the tumorigenesis of UCEC are to be elucidated. The aim of the current study was to investigate key modules and hub genes related to UCEC pathogenesis, and to explore potential biomarkers and therapeutic targets for UCEC. The RNA-seq dataset and corresponding clinical information for UCEC patients were obtained from the Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were screened between 23 paired UCEC tissues and adjacent non-cancerous tissues. Subsequently, the co-expression network of DEGs was determined via weighted gene co-expression network analysis (WGCNA). The Blue and Brown modules were identified to be significantly positively associated with neoplasm histologic grade. The highly connected genes of the two modules were then investigated as potential key factors related to tumor differentiation. Additionally, a protein-protein interaction (PPI) network for all genes in the two modules was constructed to obtain key modules and nodes. 10 genes were identified by both WGCNA and PPI analyses, and it was shown by Kaplan-Meier curve analysis that 6 out of the 10 genes were significantly negatively related to the 5-year overall survival (OS) in patients (AURKA, BUB1, CDCA8, DLGAP5, KIF2C, TPX2). Besides, according to the DEGs from the two modules, lncRNA-miRNA-mRNA and lncRNA-TF-mRNA networks were constructed to explore the molecular mechanism of UCEC-related lncRNAs. 3 lncRNAs were identified as being significantly negatively related to the 5-year OS (AC015849.16, DUXAP8 and DGCR5), with higher expression in UCEC tissues compared to non-tumor tissues. Finally, quantitative Real-time PCR was applied to validate the expression patterns of hub genes. Cell proliferation and colony formation assays, as well as cell cycle distribution and apoptosis analysis, were performed to test the effects of representative hub genes. Altogether, this study not only promotes our understanding of the molecular mechanisms for the pathogenesis of UCEC but also identifies several promising biomarkers in UCEC development, providing potential therapeutic targets for UCEC.
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Project description:BackgroundIgA nephropathy (IgAN) is the most common primary glomerular disease and the leading cause of the end-stage renal disease in the world. The pathogenesis of IgAN has not been well elucidated, and yet treatment is limited. High-throughput microarray has been applied for elucidating molecular biomarkers and potential mechanisms involved in IgAN. This study aimed to identify the potential key genes and therapeutics associated with IgAN using integrative bioinformatics and transcriptome-based computational drug repurposing approach.MethodsThree datasets of mRNA expression profile were obtained from the gene expression omnibus database and differentially expressed genes (DEGs) between IgAN glomeruli and normal tissue were identified by integrated analysis. Gene ontology and pathway enrichment analyses of the DEGs were performed by R software, and protein-protein interaction networks were constructed using the STRING online search tool. External dataset and immunohistochemical assessment of kidney biopsy specimens were used for hub gene validation. Potential compounds for IgAN therapy were obtained by Connectivity Map (CMap) analysis and preliminarily verified in vitro. Stimulated human mesangial cells were collected for cell proliferation and cell cycle analysis using cell counting kit 8 and flow cytometry, respectively.Results134 DEGs genes were differentially expressed across kidney transcriptomic data from IgAN patients and healthy living donors. Enrichment analysis showed that the glomerular compartments underwent a wide range of interesting pathological changes during kidney injury, focused on anion transmembrane transporter activity and protein digestion and absorption mostly. Hub genes (ITGB2, FCER1G, CSF1R) were identified and verified to be significantly upregulated in IgAN patients, and associated with severity of renal lesions. Computational drug repurposing with the CMap identified tetrandrine as a candidate treatment to reverse IgAN hub gene expression. Tetrandrine administration significantly reversed mesangial cell proliferation and cell cycle transition.ConclusionThe identification of DEGs and related therapeutic strategies of IgAN through this integrated bioinformatics analysis provides a valuable resource of therapeutic targets and agents of IgAN. Especially, our findings suggest that tetrandrine might be beneficial for IgAN, which deserves future research.
| S-EPMC9273898 | biostudies-literature
Project description:BackgroundIntratumoral oxidative stress (OS) has been associated with the progression of various tumors. However, OS has not been considered a candidate therapeutic target for pancreatic cancer (PC) owing to the lack of validated biomarkers.MethodsWe compared gene expression profiles of PC samples and the transcriptome data of normal pancreas tissues from The Cancer Genome Atlas (TCGA) and Genome Tissue Expression (GTEx) databases to identify differentially expressed OS genes in PC. PC patients' gene profile from the Gene Expression Omnibus (GEO) database was used as a validation cohort.ResultsA total of 148 differentially expressed OS-related genes in PC were used to construct a protein-protein interaction network. Univariate Cox regression analysis, least absolute shrinkage, selection operator analysis revealed seven hub prognosis-associated OS genes that served to construct a prognostic risk model. Based on integrated bioinformatics analyses, our prognostic model, whose diagnostic accuracy was validated in both cohorts, reliably predicted the overall survival of patients with PC and cancer progression. Further analysis revealed significant associations between seven hub gene expression levels and patient outcomes, which were validated at the protein level using the Human Protein Atlas database. A nomogram based on the expression of these seven hub genes exhibited prognostic value in PC.ConclusionOur study provides novel insights into PC pathogenesis and provides new genetic markers for prognosis prediction and clinical treatment personalization for PC patients.
| S-EPMC7753072 | biostudies-literature
Project description:BackgroundAt present, the pathogenesis of intracranial aneurysms (IA) remains unclear, which significantly hinders the development of novel strategies for the clinical treatment. In this study, bioinformatics methods were used to identify the potential hub genes and pathways associated with the pathogenesis of IA.MethodsThe gene expression datasets of patients with intracranial aneurysm were downloaded from the Gene Expression Database (GEO), and the different data sets were integrated by the robust rank aggregation (RRA) method to identify the differentially expressed genes between patients with intracranial aneurysm and the controls. The functional enrichment analyses of the significant differentially expressed genes (DEGs) were performed and the protein-protein interaction (PPI) network was constructed; thereafter, the hub genes were screened by cytoHubba plug-in of Cytoscape, and finally sequencing dataset GSE122897 was used to verify the hub genes.ResultsThe GSE15629, GSE75436, GSE26969, and GSE6551 expression profiles have been included in this study, including 34 intracranial aneurysm samples and 26 control samples. The four datasets obtained 136 significant DEGs (45 up-regulated, 91 down-regulated). Enrichment analysis showed that the extracellular matrix structural constituent and the ECM-receptor interaction were closely related to the occurrence of IA. It was finally determined that eight hub genes associated with the development of IA, including VCAN, COL1A1, COL11A1, COL5A1, COL5A2, POSTN, THBS2, and CDH2.ConclusionThe discovery of potential hub genes and pathways could enhance the understanding of the molecular mechanisms associated with the development of IA. These hub genes may be potential therapeutic targets for the management and new biomarker for the diagnosis of IA.
| S-EPMC8331219 | biostudies-literature
Project description:BackgroundClear cell renal cell carcinoma (ccRCC) is a common genitourinary cancer type with a high mortality rate. Due to a diverse range of biochemical alterations and a high level of tumor heterogeneity, it is crucial to select highly validated prognostic biomarkers to be able to identify subtypes of ccRCC early and apply precision medicine approaches.MethodsTranscriptome data of ccRCC and clinical traits of patients were obtained from the GSE126964 dataset of Gene Expression Omnibus and The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) database. Weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) screening were applied to detect common differentially co-expressed genes. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes analysis, survival analysis, prognostic model establishment, and gene set enrichment analysis were also performed. Immunohistochemical analysis results of the expression levels of prognostic genes were obtained from The Human Protein Atlas. Single-gene RNA sequencing data were obtained from the GSE131685 and GSE171306 datasets.ResultsIn the present study, a total of 2,492 DEGs identified between ccRCC and healthy controls were filtered, revealing 1,300 upregulated genes and 1,192 downregulated genes. Using WGCNA, the turquoise module was identified to be closely associated with ccRCC. Hub genes were identified using the maximal clique centrality algorithm. After having intersected the hub genes and the DEGs in GSE126964 and TCGA-KIRC dataset, and after performing univariate, least absolute shrinkage and selection operator, and multivariate Cox regression analyses, ALDOB, EFHD1, and ESRRG were identified as significant prognostic factors in patients diagnosed with ccRCC. Single-gene RNA sequencing analysis revealed the expression profile of ALDOB, EFHD1, and ESRRG in different cell types of ccRCC.ConclusionsThe present results demonstrated that ALDOB, EFHD1, and ESRRG may act as potential targets for medical therapy and could serve as diagnostic biomarkers for ccRCC.
| S-EPMC8516333 | biostudies-literature