scholarly journals Identification of key genes and long non-coding RNA associated ceRNA networks in hepatocellular carcinoma

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e8021 ◽  
Author(s):  
Jun Liu ◽  
Wenli Li ◽  
Jian Zhang ◽  
Zhanzhong Ma ◽  
Xiaoyan Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Molecular Mechanisms ◽  
Microarray Dataset ◽  
Gene Expression Omnibus ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Hepatic Expression ◽  
Limma Package ◽  
Human Protein Atlas

Background Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths worldwide. Although multiple efforts have been made to understand the development of HCC, morbidity, and mortality rates remain high. In this study, we aimed to discover the mRNAs and long non-coding RNAs (lncRNAs) that contribute to the progression of HCC. We constructed a lncRNA-related competitive endogenous RNA (ceRNA) network to elucidate the molecular regulatory mechanism underlying HCC. Methods A microarray dataset (GSE54238) containing information about both mRNAs and lncRNAs was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) and lncRNAs (DElncRNAs) in tumor tissues and non-cancerous tissues were identified using the limma package of the R software. The miRNAs that are targeted by DElncRNAs were predicted using miRcode, while the target mRNAs of miRNAs were retrieved from miRDB, miRTarBas, and TargetScan. Functional annotation and pathway enrichment of DEGs were performed using the EnrichNet website. We constructed a protein–protein interaction (PPI) network of DEGs using STRING, and identified the hub genes using Cytoscape. Survival analysis of the hub genes and DElncRNAs was performed using the gene expression profiling interactive analysis database. The expression of molecules with prognostic values was validated on the UALCAN database. The hepatic expression of hub genes was examined using the Human Protein Atlas. The hub genes and DElncRNAs with prognostic values as well as the predictive miRNAs were selected to construct the ceRNA networks. Results We found that 10 hub genes (KPNA2, MCM7, CKS2, KIF23, HMGB2, ZWINT, E2F1, MCM4, H2AFX, and EZH2) and four lncRNAs (FAM182B, SNHG6, SNHG1, and SNHG3) with prognostic values were overexpressed in the hepatic tumor samples. We also constructed a network containing 10 lncRNA–miRNA–mRNA pathways, which might be responsible for regulating the biological mechanisms underlying HCC. Conclusion We found that the 10 significantly overexpressed hub genes and four lncRNAs were negatively correlated with the prognosis of HCC. Further, we suggest that lncRNA SNHG1 and the SNHG3-related ceRNAs can be potential research targets for exploring the molecular mechanisms of HCC.

scholarly journals FRI0584 SCREENING AND IDENTIFICATION OF BIOMARKERS IN MYOSITIS

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 896-897
Author(s):  
W. Liu ◽  
X. Zhang
Keyword(s):  
Gene Expression ◽  
Interstitial Lung Disease ◽  
Lung Disease ◽  
Molecular Mechanisms ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network ◽  
Geo Database

Background:Myositis, including dermatomyositis and polymyositis, is autoimmune disorders that is characterized by muscle degeneration in the proximal extremities, with the complications of weakness of muscles, interstitial lung disease and vascular lesions, even leading to death in an acute progressive process[1,2]. However, the molecular mechanisms of myositis are rarely understood.Objectives:Identify the candidate genes in myositis.Methods:Microarray datasets GSE128470, GSE48280 and GSE39454 were extracted from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) and function enrichment analyses were conducted. The protein-protein interaction network and the analyses of hub genes were performed with STRING and Cytoscape.Results:There were 98 DEGs, of which the function and pathways enrichment analyses showed defense response, immune response, response to virus, inflammatory response, response to wounding, cell adhesion, cell proliferation, cell death and macromolecule metabolic process. 20 hub genes were identified, of which 7 including IRF9 TRIM22 MX2 IFITM1 IFI6 IFI44 IFI44L had not been reported in the literature, related to the response to virus, immune response, transcription from RNA polymerase II promoter, cell apoptosis, cell death. The verification analysis about the 7 genes in GSE128314 showed significant differences in myositis.Conclusion:In conclusion, DEGs and hub genes identified in our study showed the potential molecular mechanisms in myositis, providing the helpful targets for diagnosis and clinical strategy of myositis.References:[1] Wu H, Geng D, Xu J. An approach to the development of interstitial lung disease in dermatomyositis: a study of 230 cases in China[J]. Journal of International Medical Research. 2013;41(2):493–501.[2] Fathi M, Dastmalchi M, Rasmussen E, Lundberg IE, Tornling G. Interstitial lung disease, a common manifestation of newly diagnosed polymyositis and dermatomyositis[J]. Annals of the Rheumatic Diseases. 2004;63(3):297–301.Figure 1.The protein-protein interaction network of 20 hub genesFigure 2.7 genes in GSE128314 showed significant differences in myositisAcknowledgments:The authors acknowledge the efforts of the Gene Expression Omnibus (GEO) database. The interpretation and reporting of these data are the sole responsibility of the authors.Disclosure of Interests:None declared

scholarly journals Identification of Genes in Patients for Predicting Ulcerative Colitis-Associated Colorectal Cancer

2020 ◽  
Author(s):  
hongyun wei ◽  
qian zhang ◽  
xiaosa chi ◽  
xiaohui yang ◽  
zibin tian ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Ulcerative Colitis ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Rank Test ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Limma Package ◽  
Public Datasets

Abstract BackgroundUlcerative colitis (UC) has been considered as a risk factor for colorectal cancer (CRC). However, effective biomarkers for predicting UC-associated CRC are lacking. Therefore, it is necessary to screen biomarkers associated with UC-related CRC, which could be used to evaluate UC-associated CRC early, and provide possible mechanisms involved in UC-associated CRC. Efficient bioinformatics analysis could help us to explore potential biomarkers.MethodsTwo public datasets, including 44 UC without CRC samples and 17 UC-associated CRC samples were chosen from Gene Expression Omnibus (GEO) database. Sva package was used to remove batch effects, and then we screened out differentially expressed genes (DEGs) with limma package. STRING and Cytoscape were used to achieve protein-protein interaction (PPI) network analysis. The survival curves between high and low gene expression were performed by log rank test based on the cancer genome atlas (TCGA) program. The expression of three identified hub genes was validated based on Oncomine. To validate the expression of three hub genes, we compared the expression of three hub genes between normal and colorectal cancer based on Oncomine.Results405 DEGs were identified, including 256 down-regulated genes and 149 up-regulated genes in UC-associated CRC tissues. 16 hub genes were identified. And among them, RPL6, RPL7, and RPL35 were related to poor prognosis of patients in survival analysis. Higher expression of RPL6, RPL7, and RPL35 was validated in CRC tissues based on Oncomine.ConclusionsOur study showed that overexpressed RPL6, RPL7, and RPL 35 may be potential tumor oncogenes and could act as a prognostic factor in clinical diagnosis and treatment.

scholarly journals Identification of Key Genes with Clinical Outcome in Benign Meningioma Using Bioinformatic Analysis

2020 ◽  
Author(s):  
Ming Cao ◽  
Chen Shen ◽  
Jie Zhu ◽  
YuHai Wang
Keyword(s):  
Molecular Mechanisms ◽  
Therapeutic Targets ◽  
Microarray Dataset ◽  
Bioinformatic Analysis ◽  
Gene Expression Omnibus ◽  
Hub Genes ◽  
Benign Meningioma ◽  
Protein Protein Interaction ◽  
Kegg Pathways ◽  
Key Genes

Abstract Background: Meningioma is the second most common type of brain neoplasms.However,the underlying molecular mechanisms are still not clear,and the main treatment is mainly surgery plus radiotherapy. Material and method: To explore the key genes in benign meningioma,we downloaded microarray dataset GSE43290 from Gene Expression Omnibus(GEO) database.The differential genes (DEGs) between benign meningioma and normal meninges were identified by GEO2R.The gene ontology (GO) and Kyoto Encyclopedia of Gene and Genomes (KEGG) pathway were performed by the Database for Annotation,Visualization and Integrated Discovery (DAVID).The protein-protein interaction (PPI) network and module analysis were performed and visualized by the Search Tool for the Retrieval of Interacting Gene database (STRING) and Cytoscape.The hub genes were evaluated by the Cytohubba and further explored by MCODE plugin of Cytoscape and Enrichr.The relationship between hub genes and clinical factors were further explored by GSE16581 through R software. Result: A total of 358 DEGs were identified,including 15 upregulated genes and 343 downregulated genes.The main enriched functions were extracellular matrix organization、inflammatory response、cell adhesion、extracellular space and integrin binding.The main KEGG pathways were Malaria and focal adhesion.Among these DEGs,5 overlapping genes(CXCL8、AGT、CXCL2、CXCL12、CXCR4) were selected as hub genes.CXCL2 and CXCL8 were correlated with age and tumor recurrence,which could be clinical therapeutic targets. Conclusion: This study indicates the key genes in benign meningioma which may help us understand the molecular mechanisms and provide the candidate therapeutic targets.

scholarly journals Identification of potential hub genes associated with the pathogenesis and prognosis of hepatocellular carcinoma via integrated bioinformatics analysis

2020 ◽  
Vol 48 (7) ◽  
pp. 030006052091001
Author(s):  
Ziqi Meng ◽  
Jiarui Wu ◽  
Xinkui Liu ◽  
Wei Zhou ◽  
Mengwei Ni ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
Molecular Therapy ◽  
Integrated Analysis ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Interactive Analysis ◽  
Kaplan Meier

Objective The objective was to identify potential hub genes associated with the pathogenesis and prognosis of hepatocellular carcinoma (HCC). Methods Gene expression profile datasets were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between HCC and normal samples were identified via an integrated analysis. A protein–protein interaction network was constructed and analyzed using the STRING database and Cytoscape software, and enrichment analyses were carried out through DAVID. Gene Expression Profiling Interactive Analysis and Kaplan–Meier plotter were used to determine expression and prognostic values of hub genes. Results We identified 11 hub genes ( CDK1, CCNB2, CDC20, CCNB1, TOP2A, CCNA2, MELK, PBK, TPX2, KIF20A, and AURKA) that might be closely related to the pathogenesis and prognosis of HCC. Enrichment analyses indicated that the DEGs were significantly enriched in metabolism-associated pathways, and hub genes and module 1 were highly associated with cell cycle pathway. Conclusions In this study, we identified key genes of HCC, which indicated directions for further research into diagnostic and prognostic biomarkers that could facilitate targeted molecular therapy for HCC.

scholarly journals Identification of novel biomarkers and candidate small molecule drugs in rheumatoid arthritis and osteoarthritis based on bioinformatics analysis of high-throughput data

2020 ◽  
Vol 40 (12) ◽  
Author(s):  
Bin Zuo ◽  
JunFeng Zhu ◽  
Fei Xiao ◽  
ChengLong Wang ◽  
Yun Shen ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Gene Expression ◽  
Signaling Pathway ◽  
Small Molecule ◽  
Gene Expression Omnibus ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Chemokine Signaling ◽  
Limma Package ◽  
Chemokine Signaling Pathway

Abstract Background: Rheumatoid arthritis (RA) and osteoarthritis (OA) are two major types of joint diseases. The present study aimed to identify hub genes involved in the pathogenesis and further explore the potential treatment targets of RA and OA. Methods: The gene expression profile of GSE12021 was downloaded from Gene Expression Omnibus (GEO). Total 31 samples (12 RA, 10 OA and 9 NC samples) were used. The differentially expressed genes (DEGs) in RA versus NC, OA versus NC and RA versus OA groups were screened using limma package. We also verified the DEGs in GSE55235 and GSE100786. Functional annotation and protein–protein interaction (PPI) network construction of OA- and RA-specific DEGs were performed. Finally, the candidate small molecules as potential drugs to treat RA and OA were predicted in CMap database. Results: 165 up-regulated and 163 down-regulated DEGs between RA and NC samples, 73 up-regulated and 293 down-regulated DEGs between OA and NC samples, 92 up-regulated and 98 down-regulated DEGs between RA and OA samples were identified. Immune response and TNF signaling pathway were significantly enriched pathways for RA- and OA-specific DEGs, respectively. The hub genes were mainly associated with ‘Primary immunodeficiency’ (RA vs. NC group), ‘Ribosome’ (OA vs. NC group), and ‘Chemokine signaling pathway’ (RA vs. OA group). Arecoline and Cefamandole were the most promising small molecule to reverse the RA and OA gene expression. Conclusion: Our findings suggest new insights into the underlying pathogenesis of RA and OA, which may improve the diagnosis and treatment of these intractable chronic diseases.

scholarly journals Identification of Hub Genes Associated with Lung Adenocarcinoma Based on Bioinformatics Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Shuaiqun Wang ◽  
Xiaoling Xu ◽  
Wei Kong
Keyword(s):  
Gene Expression ◽  
Lung Adenocarcinoma ◽  
Differentially Expressed Genes ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Limma Package ◽  
Differential Genes

Lung adenocarcinoma (LUAD) is one of the malignant lung tumors. However, its pathology has not been fully understood. The purpose of this study is to identify the hub genes associated with LUAD by bioinformatics methods. Three gene expression datasets including GSE116959, GSE74706, and GSE85841 downloaded from the Gene Expression Omnibus (GEO) database were used in this study. The differentially expressed genes (DEGs) related to LUAD were screened by using the limma package. Gene Ontology (GO) and KEGG analysis of DEGs were carried out through the DAVID website. The protein-protein interaction (PPI) of differentially expressed genes was drawn by the STRING website, and the results were imported into Cytoscape for visualization. Then, the PPI network was analyzed by using MCODE, and the modules with a score greater than 5 were found by using cytoHubba. Finally, the GEPIA database and UALCAN database were used to verify and analyze the survival of hub genes. We identified 67 upregulated genes and 277 downregulated genes from three LUAD datasets. The results of GO analysis showed that the downregulated genes were significantly enriched in matrix adhesion and angiogenesis and upregulated differential genes were significantly enriched in cell adhesion and vascular development. KEGG pathway analysis showed that the differential genes of LUAD were significantly enriched in viral carcinogenesis and adhesion spots. The PPI network of differentially expressed genes consists of 269 nodes and 625 interactions. In addition, three modules with scores greater than 5 and seven hub genes, namely, MCM4, BIRC5, CDC20, CDC25C, FOXM1, GTSE1, and RFC4, playing an important role in the PPI network were screened out. In this study, we obtained the hub genes and pathways related to LUAD, revealing the molecular mechanism and pathogenesis of LUAD, which is helpful for the early detection of LUAD and provides a new idea for the treatment of LUAD.

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 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 Novel Cancer Stem Cell Markers in Glioblastoma by Comparing Tumor Cells with Stem-cell-like Cell Lines

2021 ◽  
Vol 11 (2) ◽  
pp. 1567-1583
Author(s):  
Divya G.
Keyword(s):  
Gene Expression ◽  
Stem Cell ◽  
Tumor Cells ◽  
Cell Lines ◽  
Molecular Mechanisms ◽  
Cell Attachment ◽  
Microarray Dataset ◽  
Gene Expression Omnibus ◽  
Stem Cell Markers ◽  
Cancer Stem Cell Markers

Aim. The aim of this study is to identify differential gene expression for glioblastoma tumor cells, normoxic and hypoxic glioblastoma stem-like cell lines. Finding the upregulated and downregulated gene and pathway interactions. Analysis to find the differential expression genes and pathway interactions. Materials and methods. The gene expression profiling data from the microarray dataset GSE45117 from the Gene Expression Omnibus (GEO) database, as well as differentially expressed genes (DEGs) between the 2 categories, are used in this analysis. 4 Samples of Glioblastoma tumors were considered as group 1 and 4 samples of normoxic and Hypoxic glioblastoma stem-like cell lines were considered as group 2 in the GEO2R web tool that has been used to screen them. Results. The gene-gene interactions among the DEGs and the GGI network with 37 nodes and 13 edges. The stem-cell-like cell lines showed lower expression of endothelin-related genes such as EDN3 and EDNRA along with dysregulation of enzymes such as PDK1, PGK1 which points to dysregulation of cellular respiratory pathways. This effect in consensus with under expression of cell attachment genes such as COL2A1, COL5A2, COL15A1 denotes a strong shift toward metastasis. Conclusion. Thus, a computational pipeline for identifying the significant genes and pathways involved in the glioblastoma tumors and glioblastoma stem-like cell lines. This study provides a path towards discovering potential leads for the treatment of glioblastoma and aids in comprehending the underlying novel molecular mechanisms.

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.

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