scholarly journals Investigation of the Mechanism of Complement System in Diabetic Nephropathy via Bioinformatics Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Bojun Xu ◽  
Lei Wang ◽  
Huakui Zhan ◽  
Liangbin Zhao ◽  
Yuehan Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Diabetic Nephropathy ◽  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Bioinformatics Analysis ◽  
Topological Analysis ◽  
Gene Expression Omnibus ◽  
Complement Cascade ◽  
Hub Genes ◽  
Novel Biomarkers

Objectives. Diabetic nephropathy (DN) is a major cause of end-stage renal disease (ESRD) throughout the world, and the identification of novel biomarkers via bioinformatics analysis could provide research foundation for future experimental verification and large-group cohort in DN models and patients. Methods. GSE30528, GSE47183, and GSE104948 were downloaded from Gene Expression Omnibus (GEO) database to find differentially expressed genes (DEGs). The difference of gene expression between normal renal tissues and DN renal tissues was firstly screened by GEO2R. Then, the protein-protein interactions (PPIs) of DEGs were performed by STRING database, the result was integrated and visualized via applying Cytoscape software, and the hub genes in this PPI network were selected by MCODE and topological analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out to determine the molecular mechanisms of DEGs involved in the progression of DN. Finally, the Nephroseq v5 online platform was used to explore the correlation between hub genes and clinical features of DN. Results. There were 64 DEGs, and 32 hub genes were identified, enriched pathways of hub genes involved in several functions and expression pathways, such as complement binding, extracellular matrix structural constituent, complement cascade related pathways, and ECM proteoglycans. The correlation analysis and subgroup analysis of 7 complement cascade-related hub genes and the clinical characteristics of DN showed that C1QA, C1QB, C3, CFB, ITGB2, VSIG4, and CLU may participate in the development of DN. Conclusions. We confirmed that the complement cascade-related hub genes may be the novel biomarkers for DN early diagnosis and targeted treatment.

scholarly journals Network-Based Genetic Profiling Reveals Cellular Pathway Differences Between Follicular Thyroid Carcinoma and Follicular Thyroid Adenoma

2020 ◽  
Vol 17 (4) ◽  
pp. 1373 ◽  
Author(s):  
Md. Ali Hossain ◽  
Tania Akter Asa ◽  
Md. Mijanur Rahman ◽  
Shahadat Uddin ◽  
Ahmed A. Moustafa ◽  
...  
Keyword(s):  
Gene Expression ◽  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Gene Expression Omnibus ◽  
Thyroid Adenoma ◽  
Hub Genes ◽  
Microarray Gene Expression ◽  
Genetic Profiling ◽  
Follicular Thyroid Adenoma ◽  
Pathway Analyses

Molecular mechanisms underlying the pathogenesis and progression of malignant thyroid cancers, such as follicular thyroid carcinomas (FTCs), and how these differ from benign thyroid lesions, are poorly understood. In this study, we employed network-based integrative analyses of FTC and benign follicular thyroid adenoma (FTA) lesion transcriptomes to identify key genes and pathways that differ between them. We first analysed a microarray gene expression dataset (Gene Expression Omnibus GSE82208, n = 52) obtained from FTC and FTA tissues to identify differentially expressed genes (DEGs). Pathway analyses of these DEGs were then performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) resources to identify potentially important pathways, and protein-protein interactions (PPIs) were examined to identify pathway hub genes. Our data analysis identified 598 DEGs, 133 genes with higher and 465 genes with lower expression in FTCs. We identified four significant pathways (one carbon pool by folate, p53 signalling, progesterone-mediated oocyte maturation signalling, and cell cycle pathways) connected to DEGs with high FTC expression; eight pathways were connected to DEGs with lower relative FTC expression. Ten GO groups were significantly connected with FTC-high expression DEGs and 80 with low-FTC expression DEGs. PPI analysis then identified 12 potential hub genes based on degree and betweenness centrality; namely, TOP2A, JUN, EGFR, CDK1, FOS, CDKN3, EZH2, TYMS, PBK, CDH1, UBE2C, and CCNB2. Moreover, transcription factors (TFs) were identified that may underlie gene expression differences observed between FTC and FTA, including FOXC1, GATA2, YY1, FOXL1, E2F1, NFIC, SRF, TFAP2A, HINFP, and CREB1. We also identified microRNA (miRNAs) that may also affect transcript levels of DEGs; these included hsa-mir-335-5p, -26b-5p, -124-3p, -16-5p, -192-5p, -1-3p, -17-5p, -92a-3p, -215-5p, and -20a-5p. Thus, our study identified DEGs, molecular pathways, TFs, and miRNAs that reflect molecular mechanisms that differ between FTC and benign FTA. Given the general similarities of these lesions and common tissue origin, some of these differences may reflect malignant progression potential, and include useful candidate biomarkers for FTC and identifying factors important for FTC pathogenesis.

scholarly journals SMC4, CCNB1 and CKS1B as potential targets and new critical biomarkers for the prognosis of human bladder cancer

2021 ◽  
Author(s):  
Hongpeng Fang ◽  
Zhansen Huang ◽  
Xianzi Zeng ◽  
Jiaming Wan ◽  
Jieying Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bladder Cancer ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Gene Expression Omnibus ◽  
Transcriptional Level ◽  
Hub Genes ◽  
Kaplan Meier ◽  
High Form

Abstract Background As a common malignant cancer of the urinary system, the precise molecular mechanisms of bladder cancer remain to be illuminated. The purpose of this study was to identify core genes with prognostic value as potential oncogenes for the diagnosis, prognosis or novel therapeutic targets of bladder cancer. Methods The gene expression profiles GSE3167 and GSE7476 were available from the Gene Expression Omnibus (GEO) database. Next, PPI network was built to filter the hub gene through the STRING database and Cytoscape software and GEPIA and Kaplan-Meier plotter were implemented. Frequency and type of hub genes and sub groups analysis were performed in cBioportal and ULCAN database. Finally,We used RT-qPCR to confirm our results. Results Totally, 251 DEGs were excavated from two datasets in our study. We only founded high expression of SMC4, TYMS, CCNB1, CKS1B, NUSAP1 and KPNA2 was associated with worse outcomes in bladder cancer patients and no matter from the type of mutation or at the transcriptional level of hub genes, the tumor showed a high form of expression. However, only the expression of SMC4,CCNB1and CKS1B remained changed between the cancer and the normal samples in our results of RT-qPCR. Conclusion In conclusion,These findings indicate that the SMC4,CCNB1 and CKS1B may serve as critical biomarkers in the development and poor prognosis.

scholarly journals Identification of Potential Key Genes and Molecular Mechanisms of Medulloblastoma Based on Integrated Bioinformatics Approach

2022 ◽  
Vol 2022 ◽  
pp. 1-17
Author(s):  
Md. Rakibul Islam ◽  
Lway Faisal Abdulrazak ◽  
Mohammad Khursheed Alam ◽  
Bikash Kumar Paul ◽  
Kawsar Ahmed ◽  
...  
Keyword(s):  
Gene Expression ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Clinical Information ◽  
Gene Expression Omnibus ◽  
Hub Genes ◽  
New Treatments

Background. Medulloblastoma (MB) is the most occurring brain cancer that mostly happens in childhood age. This cancer starts in the cerebellum part of the brain. This study is designed to screen novel and significant biomarkers, which may perform as potential prognostic biomarkers and therapeutic targets in MB. Methods. A total of 103 MB-related samples from three gene expression profiles of GSE22139, GSE37418, and GSE86574 were downloaded from the Gene Expression Omnibus (GEO). Applying the limma package, all three datasets were analyzed, and 1065 mutual DEGs were identified including 408 overexpressed and 657 underexpressed with the minimum cut-off criteria of ∣ log   fold   change ∣ > 1 and P < 0.05 . The Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and WikiPathways enrichment analyses were executed to discover the internal functions of the mutual DEGs. The outcomes of enrichment analysis showed that the common DEGs were significantly connected with MB progression and development. The Search Tool for Retrieval of Interacting Genes (STRING) database was used to construct the interaction network, and the network was displayed using the Cytoscape tool and applying connectivity and stress value methods of cytoHubba plugin 35 hub genes were identified from the whole network. Results. Four key clusters were identified using the PEWCC 1.0 method. Additionally, the survival analysis of hub genes was brought out based on clinical information of 612 MB patients. This bioinformatics analysis may help to define the pathogenesis and originate new treatments for MB.

scholarly journals Identify key genes and pathways in pancreatic ductal adenocarcinoma via bioinformatics analysis

2020 ◽  
Author(s):  
Huatian Luo ◽  
Da-qiu Chen ◽  
Jing-jing Pan ◽  
Zhang-wei Wu ◽  
Can Yang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Bioinformatics Analysis ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Gene Expression Omnibus ◽  
Ductal Adenocarcinoma ◽  
Hub Genes ◽  
Expression Levels

Abstract Background: Pancreatic cancer has many pathologic types, among which pancreatic ductal adenocarcinoma (PDAC) is the most common one. Bioinformatics has become a very common tool for the selection of potentially pathogenic genes. Methods: Three data sets containing the gene expression profiles of PDAC were downloaded from the gene expression omnibus (GEO) database. The limma package of R language was utilized to explore the differentially expressed genes (DEGs). To analyze functions and signaling pathways, the Database Visualization and Integrated Discovery (DAVID) was used. To visualize the protein-protein interaction (PPI) of the DEGs ,Cytoscape was performed under the utilization of Search Tool for the Retrieval of Interacting Genes (STRING). With the usage of the plug-in cytoHubba in cytoscape software, the hub genes were found out. To verify the expression levels of hub genes, Gene Expression Profiling Interactive Analysis (GEPIA) was performed. Last but not least, UALCAN analysis online tool was implemented to analyze the overall survival. Results: The 376 DEGs were highly enriched in biological processes including signal transduction, apoptotic process and several pathways, mainly associated with Protein digestion and absorption and Pancreatic secretion pathway. The expression levels of nucleolar and spindle associated protein 1 (NUSAP1) and SHC binding and spindle associated 1 (SHCBP1) were discovered highly expressed in pancreatic ductal adenocarcinoma tissues. NUSAP1 and SHCBP1 had a high correlation with prognosis. Conclusions: The findings of this bioinformatics analysis indicate that NUSAP1 and SHCBP1 may be key factors in the prognosis and treatment of pancreatic cancer.

scholarly journals Identification of hub genes and pathways in nonalcoholic steatohepatitis by integrated bioinformatics analysis

2021 ◽  
Author(s):  
Pegah Einaliyan ◽  
Ali Owfi ◽  
Mohammadamin Mahmanzar ◽  
Taha Aghajanzadeh ◽  
Morteza Hadizadeh ◽  
...  
Keyword(s):  
Gene Expression ◽  
Protein Interactions ◽  
Expression Profiles ◽  
Principal Component ◽  
Gene Expression Omnibus ◽  
P Value ◽  
Protein Protein Interactions ◽  
Hub Genes ◽  
Alcoholic Fatty Liver ◽  
Critical Pathways

AbstractBackgroundCurrently, non-alcoholic fatty liver disease (NAFLD) is one of the most common chronic liver diseases in the world. Forecasting the short-term, up to 2025, NASH due to fibrosis is one of the leading causes of liver transplantation. Cohort studies revealed that non-alcoholic steatohepatitis (NASH) has a higher risk of fibrosis progression among NAFLD patients. Identifying differentially expressed genes helps to determine NASH pathogenic pathways, make more accurate diagnoses, and prescribe appropriate treatment.Methods and ResultsIn this study, we found 11 NASH datasets by searching in the Gene Expression Omnibus (GEO) database. Subsequently, NASH datasets with low-quality control scores were excluded. Four datasets were analyzed with packages of R/Bioconductor. Then, all integrated genes were Imported into Cytoscape to illustrate the protein-protein interactions network. All hubs and nodes degree has been calculated to determine the hub genes with critical roles in networks.Possible correlations between expression profiles of mutual DEGs were identified employing Principal Component Analysis (PCA). Primary analyzed data were filtered based on gene expression (logFC > 1, logFC < −1) and adj-P-value (<0.05). Ultimately, among 379 DEGs, we selected the top 10 genes (MYC, JUN, EGR1, FOS, CCL2, IL1B, CXCL8, PTGS2, IL6, SERPINE1) as candidates among up and down regulated genes, and critical pathways such as IL-6, IL-17, TGF β, and TNFα were identified.ConclusionThe present study suggests an important DEGs, biological processes, and critical pathways involved in the pathogenesis of NASH disease. Further investigations are needed to clarify the exact mechanisms underlying the development and progression of NASH disease.

scholarly journals Identification of potential core genes in Kawasaki disease using bioinformatics analysis

2019 ◽  
Vol 47 (9) ◽  
pp. 4051-4058
Author(s):  
Wenbin Wu ◽  
Keyou You ◽  
Jinchan Zhong ◽  
Zhanwei Ruan ◽  
Bubu Wang
Keyword(s):  
Gene Expression ◽  
Gene Ontology ◽  
Kawasaki Disease ◽  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Humoral Response ◽  
Hub Genes ◽  
Potential Core

Objective The present study aimed to elucidate the underlying pathogenesis of Kawasaki disease (KD) and to identify potential biomarkers for KD. Methods Gene expression profiles for the GSE68004 dataset were downloaded from the Gene Expression Omnibus database. The pathways and functional annotations of differentially expressed genes (DEGs) in KD were examined by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses using the Database for Annotation, Visualization and Integrated Discovery (DAVID) tool. Protein–protein interactions of the above-described DEGs were investigated using the Search Tool for the Retrieval of Interacting Genes (STRING). Results Gene Ontology analysis revealed that DEGs in KD were significantly enriched in biological processes, including the inflammatory response, innate immune response, defense response to Gram-positive bacteria, and antibacterial humoral response. In addition, 10 hub genes with high connectivity were selected from among these DEGs ( ITGAM, MPO, MAPK14, SLC11A1, HIST2H2BE, ELANE, CAMP, MMP9, NTS, and HIST2H2AC). Conclusion The identification of several novel hub genes in KD enhances our understanding of the molecular mechanisms underlying the progression of this disease. These genes may be potential diagnostic biomarkers and/or therapeutic molecular targets in patients with KD. ITGAM inhibitors in particular may be potential targets for KD therapy.

scholarly journals Identification of Hub Genes and Key Pathways of Paraquat Induced Human Embryonic Pulmonary Fibrosis by Bioinformatics Analysis and Vitro Experiments

2020 ◽  
Author(s):  
Yue Fu ◽  
Xiang Xia Zeng ◽  
Jin Lun Hu ◽  
Mei Yan ◽  
CHun Ming Xie ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pulmonary Fibrosis ◽  
Lung Fibrosis ◽  
Bioinformatics Analysis ◽  
Core Promoter ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
Functional Enrichment ◽  
Hub Genes

Abstract Background: Paraquat is highly toxic pesticide, which usually led to acute lung injury and subsequently develop pulmonary fibrosis, the exact mechanisms of PQ-induced lung fibrosis remain largely unclear and no specific drugs for this disease have been approved. Methods: Our study aimed to identify its potential mechanism though modeling study in vitro and bioinformatics analysis. Gene expression datasets associated with PQ-induced lung fibrosis were obtained from the Gene Expression Omnibus and differentially expressed genes (DEGs) were identified using GEO2R. Functional enrichment analyses were performed using the Database for Annotation. Results: The DEGs in the two datasets, of which 92 overlapping genes were found in two microarray datasets. Functional analysis demonstrated that the 92 DEGs were enriched in the ‘TNF signaling pathway’, ‘CXCR chemokine receptor binding’, and ‘core promoter binding’. Moreover, nine hub genes were identified from a protein‑protein interaction network. Conclusions: This integrative analysis firstly identified candidate genes and pathways in PQ-induced lung fibrosis, as well as benefit to research novel approaches for treating for control of PQ-induced pulmonary fibrosis.

scholarly journals Comprehensive Bioinformatics Analysis Identifies POLR2I as a Key Gene in the Pathogenesis of Hypertensive Nephropathy

2021 ◽  
Vol 12 ◽  
Author(s):  
Shilong You ◽  
Jiaqi Xu ◽  
Boquan Wu ◽  
Shaojun Wu ◽  
Ying Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Gene Expression Omnibus ◽  
Chronic Hypertension ◽  
Hub Genes ◽  
Hypertensive Nephropathy ◽  
Protein Levels

Hypertensive nephropathy (HN), mainly caused by chronic hypertension, is one of the major causes of end-stage renal disease. However, the pathogenesis of HN remains unclarified, and there is an urgent need for improved treatments. Gene expression profiles for HN and normal tissue were obtained from the Gene Expression Omnibus database. A total of 229 differentially co-expressed genes were identified by weighted gene co-expression network analysis and differential gene expression analysis. These genes were used to construct protein–protein interaction networks to search for hub genes. Following validation in an independent external dataset and in a clinical database, POLR2I, one of the hub genes, was identified as a key gene related to the pathogenesis of HN. The expression level of POLR2I is upregulated in HN, and the up-regulation of POLR2I is positively correlated with renal function in HN. Finally, we verified the protein levels of POLR2I in vivo to confirm the accuracy of our analysis. In conclusion, our study identified POLR2I as a key gene related to the pathogenesis of HN, providing new insights into the molecular mechanisms underlying HN.

scholarly journals Identification of Hub Genes Associated With Sensitivity of 5-Fluorouracil Based Chemotherapy for Colorectal Cancer by Integrated Bioinformatics Analysis

2021 ◽  
Vol 11 ◽  
Author(s):  
Ya Wang ◽  
Qunhui Wei ◽  
Yuqiao Chen ◽  
Shichao Long ◽  
Yuanbing Yao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Colorectal Adenocarcinoma ◽  
Malignant Tumors ◽  
Gene Expression Omnibus ◽  
Chemotherapy Response ◽  
Intrinsic Resistance ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Novel Biomarkers

Colorectal cancer (CRC) is one of the most common malignant tumors. 5-fluorouracil (5-FU) has been used for the standard first-line treatment for CRC patients for several decades. Although 5-FU based chemotherapy has increased overall survival (OS) of CRC patients, the resistance of CRC to 5-FU based chemotherapy is the principal cause for treatment failure. Thus, identifying novel biomarkers to predict response to 5-FU based chemotherapy is urgently needed. In the present study, the gene expression profile of GSE3964 from the Gene Expression Omnibus database was used to explore the potential genes related to intrinsic resistance to 5-FU. A gene module containing 81 genes was found to have the highest correlation with chemotherapy response using Weighted Gene Co-expression Network Analysis (WGCNA). Then a protein-protein interaction (PPI) network was constructed and ten hub genes (TGFBI, NID, LEPREL2, COL11A1, CYR61, PCOLCE, IGFBP7, COL4A2, CSPG2, and VTN) were identified using the CytoHubba plugin of Cytoscape. Seven of these hub genes showed significant differences in expression between chemotherapy-sensitive and chemotherapy-resistant samples. The prognostic value of these seven genes was evaluated using TCGA COAD (Colorectal Adenocarcinoma) data. The results showed that TGFBI was highly expressed in chemotherapy-sensitive patients, and patients with high TGFBI expression have better survival.

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