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

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.

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Bioinformatics Analysis to Identify Key Genes and Pathways Associated with Sex Differences in Rheumatoid Arthritis

10.21203/rs.3.rs-687988/v1 ◽

2021 ◽

Author(s):

Siwei Su ◽

Wenjun Jiang ◽

Xiaoying Wang ◽

Sen Du ◽

Lu Zhou ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Gene Expression ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Hub Genes ◽

Protein Protein Interaction ◽

Kegg Pathways ◽

Ppi Networks ◽

Males And Females ◽

Key Genes

Abstract ObjectiveThis study aims to explore the key genes and investigated the different signaling pathways of rheumatoid arthritis (RA) between males and females.Data and MethodsThe gene expression data of GSE55457, GSE55584, and GSE12021 were obtained from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using R software. Then, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis of DEGs were conducted via Database for Annotation, Visualization, and Integrated Discovery (DAVID). The protein-protein interaction (PPI) networks of DEGs were constructed by Cytoscape 3.6.0. ResultsA total of 416 upregulated DEGs and 336 downregulated DEGs were identified in males, and 744 upregulated DEGs and 309 downregulated DEGs were identified in females.IL6, MYC, EGFR, FOS and JUN were considered as hub genes in RA pathogenesis in males, while IL6, ALB, PTPRC, CXCL8 and CCR5 were considered as hub genes in RA pathogenesis in females. ConclusionIdentified DEG may be involved in the different mechanisms of RA disease progression between males and females, and they are treated as prognostic markers or therapeutic targets for males and females. The pathogenesis mechanism of RA is sex-dependent.

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Screening and identification of key biomarkers in alimentary tract cancers: A bioinformatic analysis

Cancer Biomarkers ◽

10.3233/cbm-201580 ◽

2020 ◽

Vol 29 (2) ◽

pp. 221-233

Author(s):

Zeling Cai ◽

Yi Wei ◽

Shuai Chen ◽

Yu Gong ◽

Yue Fu ◽

...

Keyword(s):

Molecular Mechanisms ◽

Alimentary Tract ◽

Bioinformatic Analysis ◽

Gene Expression Omnibus ◽

Hub Genes ◽

Protein Protein Interaction ◽

Cellular Processes ◽

Screening And Identification ◽

Gene Modules ◽

Microarray Datasets

BACKGROUND: Alimentary tract cancers (ATCs) are the most malignant cancers in the world. Numerous studies have revealed the tumorigenesis, diagnosis and treatment of ATCs, but many mechanisms remain to be explored. METHODS: To identify the key genes of ATCs, microarray datasets of oesophageal cancer, gastric cancer and colorectal cancer were obtained from the Gene Expression Omnibus (GEO) database. In total, 207 differentially expressed genes (DEGs) were screened. KEGG and GO function enrichment analyses were conducted, and a protein-protein interaction (PPI) network was generated and gene modules analysis was performed using STRING and Cytoscape. RESULTS: Five hub genes were screened, and the associated biological processes indicated that these genes were mainly enriched in cellular processes, protein binding and metabolic processes. Clinical survival analysis showed that COL10A1 and KIF14 may be significantly associated with the tumorigenesis or pathology grade of ATCs. In addition, relative human ATC cell lines along with blood samples and tumour tissues of ATC patients were obtained. The data proved that high expression of COL10A1 and KIF14 was associated with tumorigenesis and could be detected in blood. CONCLUSION: In conclusion, the identification of hub genes in the present study helped us to elucidate the molecular mechanisms of tumorigenesis and identify potential diagnostic indicators and targeted treatment for ATCs.

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

PeerJ ◽

10.7717/peerj.8021 ◽

2019 ◽

Vol 7 ◽

pp. e8021 ◽

Cited By ~ 4

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.

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Screen Key Genes Associated with Distraction-Induced Osteogenesis of Stem Cells Using Bioinformatics Methods

International Journal of Molecular Sciences ◽

10.3390/ijms22126505 ◽

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.

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Bioinformatic Analysis: Screening and Identification of Differentially Expressed Genes Modified by m6A in Ovarian Cancer

10.21203/rs.3.rs-111366/v1 ◽

2020 ◽

Author(s):

Huidong Liu ◽

Wen-wen Zhang ◽

Ge Lou

Keyword(s):

Ovarian Cancer ◽

Differentially Expressed Genes ◽

Clinical Sample ◽

Therapeutic Targets ◽

Enrichment Analysis ◽

Underlying Mechanism ◽

Bioinformatic Analysis ◽

Gene Expression Omnibus ◽

Differentially Expressed ◽

Kegg Pathways

Abstract Background: N6-methyladenosine(m6A) is one of the most common RNA modifications that occurs at the nitrogen-6 position of adenine. Emerging evidence has revealed that regulatory functions of m6A play an essential role in the development of cancer. However the study of m6A in ovarian cancer(OC) is still in our infancy. In this work ,we aimed to identify and analysis the differentially expressed genes(DEGs） modified by m6A which can provide new therapeutic targets and key biomarkers in OC.Methods: We downloaded Microarray datasets GSE146553 and GSE124766 from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified by GEO2R analysis tools. Subsequently, The DAVID database was used to construct Enrichment analysis of GO and KEGG pathways. Next, the DEGs modified by m6A were identified by m6AVar database. Finally, the functional analysis and clinical sample validation of these genes were verified by ONCOMINE, GEPIA, cBioPortal online platform and Kaplan-Meier Plotter.Results:152 DEGs were selected ,and the DEGs were mainly enriched in extracellular exosome, spindle microtubule, response to hypoxia and cell cycle .And we identified 15 DEGs which were modified by m6A:MAPK10、MXRA5、CHD7、MECOM、SCN7A、GREB、PRUNE2、MX2、TOP2A、JAM2、DST、LAPTM5、CDKN2A、GATM and ANGPTL1. After statistical analysis, two DEGs (SCN7A and GAMT) were selected for detailed study. We revealed that SCN7A and GAMT were expressed at a low level in OC. Afterwards, Survival analysis showed that SCN7A and GAMT expression were correlated with OC overall survival. And the expression of SCN7A and GAMT mRNA decreasing in different TNM stages. Finally, we presumed that the modification of m6A spongs GAMT via EIF4A3 or FUS to participate in the occcurrence and the development of OC.Conclusion: Altogether, the current study identified and analysised the DEGs modified by m6A in OC. It will help us to investigate the underlying mechanism and progression of OC. In addition, it can provide new diagnostic markers and potential therapeutic targets in OC.

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Identification of key genes and their microRNAs in glioma: Evidence from bioinformatic analysis

10.21203/rs.3.rs-27708/v1 ◽

2020 ◽

Author(s):

Xichao Wen ◽

Meijuan Fu ◽

Wensong Wu ◽

Zhaomu Zeng ◽

Kebin Zheng

Keyword(s):

Survival Analysis ◽

Molecular Mechanisms ◽

Process Analysis ◽

Interaction Network ◽

Tumor Grade ◽

Bioinformatic Analysis ◽

Sh3 Domain ◽

Gene Expression Omnibus ◽

Hub Genes ◽

Condensed Chromosome

Abstract Background Glioma is one of the most common primary intracranial tumors. Although a lot of studies have been conducted to elucidate the pathogeny of glioma, the molecular mechanisms are still unclear because of its complex biological functions. Methods To identify the candidate genes in the carcinogenesis and progression of glioma, microarray datasets GSE4290, GSE122498 and GSE2223 were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified, and performed function enrichment of DEGs by Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The protein-protein interaction network (PPI) was constructed using STRING and Cytoscape to find hub genes. Survival analysis and GEPIA database was conducted to screen and validate critical genes. Analysis of miRNA and genetic alteration was used to explore and predict the molecule mechanism. Results A total of 150 DEGs were identified, consisting of 54 downregulated genes and 96 upregulated genes. The enriched functions and pathways of the DEGs include regulation of transportation, synaptic transmission and SH3 domain binding. Fifteen hub genes were identified and biological process analysis revealed that these genes were mainly enriched in SH3 domain binding, neuron projection terminus, mitotic nuclear division, condensed chromosome and affected the brain development. Survival analysis showed that VAMP2, PPP3CA, DLGAP5, KIF14, REPS2, CENPU, KNTC1 and SMC4, may be involved in the carcinogenesis, invasion or recurrence of glioma. These 8 hub genes, which were related miRNAs and genetic changes were commonly involved in the development of glioma, were closely associated with tumor grade. Conclusion DEGs and hub genes identified in the present study help us understand the molecular mechanisms of carcinogenesis and progression of glioma, and provide candidate targets for diagnosis and treatment of glioma.

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Weighted gene co-expression network analysis to identify key modules and hub genes related to hyperlipidaemia

10.21203/rs.3.rs-54056/v2 ◽

2021 ◽

Author(s):

Fu Jun Liao ◽

Peng-Fei Zheng ◽

Yao-Zong Guan ◽

Hong Wei Pan ◽

Wei Li

Keyword(s):

Molecular Mechanisms ◽

Acid Metabolism ◽

Unsaturated Fatty Acids ◽

Microarray Dataset ◽

Hub Genes ◽

Online Tool ◽

Pathway Enrichment ◽

Protein Protein Interaction ◽

Qpcr Analysis ◽

Blue Module

Abstract Background: The purpose of this study was to explore the potential molecular targets of hyperlipidaemia and the related molecular mechanisms.Methods: The microarray dataset of GSE66676 obtained from patients with hyperlipidaemia was downloaded. Weighted gene co-expression network (WGCNA) analysis was used to analyse the gene expression profile, and the royal blue module was considered to have the highest correlation. Gene Ontology (GO) functional and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were implemented for the identification of genes in the royal blue module using the Database for Annotation, Visualization and Integrated Discovery (DAVID) online tool (version 6.8; http://david.abcc.ncifcrf.gov). A protein-protein interaction (PPI) network was established by using the online STRING tool. Then, several hub genes were identified by the MCODE and cytoHubba plug-ins in Cytoscape software.Results: The significant module (royal blue) identified was associated with TC, TG and non-HDL-C. GO and KEGG enrichment analyses revealed that the genes in the royal blue module were associated with carbon metabolism, steroid biosynthesis, fatty acid metabolism and biosynthesis pathways of unsaturated fatty acids. SQLE (degree = 17) was revealed as a key molecule associated with hypercholesterolaemia (HCH), and SCD was revealed as a key molecule associated with hypertriglyceridaemia (HTG). RT-qPCR analysis also confirmed the above results based on our HCH/HTG samples.Conclusions: SQLE and SCD are related to hyperlipidaemia, and SQLE/SCD may be new targets for cholesterol-lowering or triglyceride-lowering therapy, respectively.

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Prognostic values and prospective pathway signaling of miR-126 in non-small cell lung cancer: a study based on gene expression omnibus and bioinformatics analysis

10.21203/rs.3.rs-30704/v1 ◽

2020 ◽

Author(s):

Zichen Jiao ◽

Ao Yu ◽

Xiaofeng He ◽

Yulong Xuan ◽

He Zhang ◽

...

Keyword(s):

Gene Expression ◽

Target Genes ◽

Gene Expression Omnibus ◽

Data Sets ◽

Hub Genes ◽

Biological Regulation ◽

Protein Protein Interaction ◽

Kegg Pathways ◽

Go Enrichment ◽

Nsclc Patients

Abstract Objective MiRNAs are considered to be crucial for NSCLC’s initiation and development. MiRNAs have been widely identified in NSCLC. However, the role of miR-126 in NSCLC has not been fully explained.Methods miR-126 Expression in NSCLC was evaluated by analyzing the common data sets in Gene Expression Omnibus(GEO) database and reviewing former thesis papers. Three mRNA datasets, GSE18842, GSE19804 and GSE101929, from GEO to indentify the differentially expressed genes (DEG). We prognosed the target genes of hsa-miR-126-5p using TargetScan and analyzed the gene overlap between the target genes of miR-126 and DEG in NSCLC. Subsequently, we analyzed Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. We used STRING and Cytoscape to construct a protein-protein interaction (PPI) network, and analyzed the influence of HUB gene on the prognosis of NSCLC.Results A common pattern of mir-126 downregulation in NSCLC was identified in the literature review. A total of 187 DEGs were identified, both NSCLC-related and miR-126-related. Many DEGs are extendedly enriched in cell membranes, signal receptor binding, and biological regulation. Among the 10 main Hub genes analyzed by PPI, 4 HUB genes (NCAP-G,MELK,KIAA0101,TPX2) were obviously related to the poor recuperation of NSCLC patients. When these genes highly expressed, survival rate of NSCLC patients was low. Furthermore, we identified the recessive miR-126-related genes that may be involved in NSCLC, such as TPX2, HMMR, and ANLN through network analysis.Conclusion this study suggests that mir-126 is radical for the biological processing of NSCLC.

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Identifying Potential Prognostic Biomarkers Associated With Clinicopathologic Characteristics of Hepatocellular Carcinoma by Bioinformatics Analysis

10.21203/rs.3.rs-146400/v1 ◽

2021 ◽

Author(s):

Shaojie Huang ◽

Jia Yao ◽

Xiaofan Lai ◽

Lu Yang ◽

Fang Ye ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Molecular Mechanisms ◽

Malignant Tumors ◽

Progression Free Survival ◽

Gene Expression Omnibus ◽

Rank Aggregation ◽

Hub Genes ◽

Clinicopathologic Characteristics ◽

Kegg Pathways ◽

Diagnosis And Prognosis

Abstract Background: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors worldwide. However, the molecular mechanisms of HCC remain largely unknown so far. Methods: To unravel the underlying carcinogenic mechanisms, we utilized Robust Rank Aggregation analysis (RRA) to identify a set of overlapping differentially expressed genes (DEGs) from 5 microarray datasets on Gene Expression Omnibus (GEO) database. Enriched Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of DEGs were conducted. The protein‐protein interaction (PPI) network was constructed and Cytoscape V3.8.0 was used for selecting hub genes. The expression of hub genes was validated in TCGA datasets and HCC samples in our center by qPCR and immunohistochemistry analysis. Results: Totally 126 DEGs were identified. GO and KEGG pathways of DEGs mostly associated with “organelle fission”, “nuclear division” and “caffeine metabolism. Ten hub genes (BUB1B, CDKN3, CCNB1, CCNB2, CDK1, TOP2A, CDC20, MELK, NUSAP1, AURKA) were selected. Overall survival (OS) and progression-free survival (PFS) analysis suggested the good value of these genes for HCC diagnosis and prognosis. These genes were upregulated in HCC samples from TCGA, which were associated with higher tumor grades and possibly resulted from hypomethylation. Moreover, these hub genes were markedly dysregulated in HCC samples in our center and significantly associated with clinicopathologic characteristics of HCC patients. Conclusions: In conclusion, our study identified several hub genes as novel candidate biomarkers for diagnosis and prognosis of HCC, which may provide new insight into HCC pathogenesis in order to search for better treatments.

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Screening and Interaction Analysis of Key Genes in miR-542-3p Over- Expressed Osteosarcoma Cells by Bioinformatics

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207323666200401103353 ◽

2020 ◽

Vol 23 (5) ◽

pp. 411-418

Author(s):

Zhongqiu Li ◽

Peng Zhang ◽

Feifei Feng ◽

Qiao Zhang

Keyword(s):

Gene Expression ◽

Interaction Analysis ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Hub Genes ◽

Protein Protein Interaction ◽

P53 Signaling ◽

Cancer Genome Atlas ◽

Key Genes ◽

Gene Expression Profile Data

Background: Osteosarcoma is one of the most serious primary malignant bone tumors that threaten the lives of children and adolescents. However, the mechanism underlying and how to prevent or treat the disease have not been well understood. Aims & Objective: This aim of the present study was to identify the key genes and explore novel insights into the molecular mechanism of miR-542-3p over-expressed Osteosarcoma. Materials & Methods: Gene expression profile data GDS5367 was downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were screened using GEO2R, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using the DAVID database. And protein-protein interaction (PPI) network was constructed by the STRING database. In addition, the most highly connected module was screened by plugin MCODE and hub genes by plugin CytoHubba. Furthermore, UALCAN and The Cancer Genome Atlas were performed for survival analysis. Result: In total, 1421 DEGs were identified, including 598 genes were up-regulated and 823 genes were down-regulated. GO analysis showed that DEGs were classified into three groups and DEGs mainly enriched in Steroid biosynthesis, Ubiquitin mediated proteolysis and p53 signaling pathway. Six hub genes (UBA52, RNF114, UBE2H, TRIP12, HNRNPC, and PTBP1) may be key genes with the progression of osteosarcoma. Conclusion: The results could better understand the mechanism of osteosarcoma, which may facilitate a novel insight into treatment targets.

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