Identification of Core Prognosis-Related Candidate Genes in Cervical Cancer via Integrated Bioinformatical Analysis

Purposes. Cervical cancer (CC) is one of the highest frequently occurred malignant gynecological tumors with high rates of morbidity and mortality. Here, we aimed to identify significant genes associated with poor outcome. Materials and methods. Differentially expressed genes (DEGs) between CC tissues and normal cervical tissues were picked out by GEO2R tool and Venn diagram software. Database for Annotation, Visualization and Integrated Discovery (DAVID) was performed to analyze gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway. The protein-protein interactions (PPIs) of these DEGs were visualized by Cytoscape with Search Tool for the Retrieval of Interacting Genes (STRING). Afterwards, Kaplan-Meier analysis was applied to analyze the overall survival among these genes. The Gene Expression Profiling Interactive Analysis (GEPIA) was applied for further validation of the expression level of these genes. Results. The mRNA expression profile datasets of GSE63514, GSE27678, and GSE6791 were downloaded from the Gene Expression Omnibus database (GEO). In total, 76 CC tissues and 35 normal tissues were collected in the three profile datasets. There were totally 73 consistently expressed genes in the three datasets, including 65 up-regulated genes and 8 down-regulated genes. Of PPI network analyzed by Molecular Complex Detection (MCODE) plug-in, all 65 up-regulated genes and 4 down-regulated genes were selected. The results of the Kaplan-Meier survival analysis showed that 3 of the 65 up-regulated genes had a significantly worse prognosis, while 3 of the 4 down-regulated genes had a significantly better outcome. For validation in GEPIA, 4 of 6 genes (PLOD2, ANLN, AURKA, and AR) were confirmed to be significantly deregulated in CC tissues compared to normal tissues. Conclusion. We have identified three up-regulated (PLOD2, ANLN, and AURKA) and a down-regulated DEGs (AR) with poor prognosis in CC on the basis of integrated bioinformatical methods, which could be regarded as potential therapeutic targets for CC patients.

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Identification and analysis of genes associated with papillary thyroid carcinoma by bioinformatics methods

Bioscience Reports ◽

10.1042/bsr20190083 ◽

2019 ◽

Vol 39 (4) ◽

Cited By ~ 3

Author(s):

Shulong Zhang ◽

Quan Wang ◽

Qi Han ◽

Huazhong Han ◽

Pinxiang Lu

Keyword(s):

Gene Expression ◽

Papillary Thyroid Carcinoma ◽

Thyroid Carcinoma ◽

Protein Interactions ◽

Disease Free Survival ◽

Gene Expression Omnibus ◽

Receptor Interaction ◽

Papillary Thyroid ◽

Protein Protein Interactions ◽

Search Tool

AbstractThe molecular mechanism of the occurrence and development of papillary thyroid carcinoma (PTC) has been widely explored, but has not been completely elucidated. The present study aimed to identify and analyze genes associated with PTC by bioinformatics methods. Two independent datasets were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between PTC tissues and matched non-cancerous tissues were identified using GEO2R tool. The common DEGs in the two datasets were screened out by VennDiagram package, and analyzed by the following tools: KOBAS, Database for Annotation, Visualization, and Integrated Discovery (DAVID), Search tool for the retrieval of interacting genes/proteins (STRING), UALCAN and Gene expression profiling interactive analysis (GEPIA). A total of 513 common DEGs, including 259 common up-regulated and 254 common down-regulated genes in PTC, were screened out. These common up-regulated and down-regulated DEGs were most significantly enriched in cytokine–cytokine receptor interaction and metabolic pathways, respectively. Protein–protein interactions (PPI) network analysis showed that the up-regulated genes: FN1, SDC4, NMU, LPAR5 and the down-regulated genes: BCL2 and CXCL12 were key genes. Survival analysis indicated that the high expression of FN1 and NMU genes significantly decreased disease-free survival of patients with thyroid carcinoma. In conclusion, the genes and pathways identified in the current study will not only contribute to elucidating the pathogenesis of PTC, but also provide prognostic markers and therapeutic targets for PTC.

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TTK, CDC25A, and ESPL1 as Prognostic Biomarkers for Endometrial Cancer

BioMed Research International ◽

10.1155/2020/4625123 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Qiannan Yang ◽

Bojun Yu ◽

Jing Sun

Keyword(s):

Gene Expression ◽

Endometrial Cancer ◽

Protein Interactions ◽

Poor Prognosis ◽

Molecular Mechanisms ◽

Gene Expression Omnibus ◽

Venn Diagram ◽

Kegg Pathways ◽

Kaplan Meier ◽

Pathway Analyses

Objective. Endometrial cancer (EC) is one of the most common malignant gynaecological tumours worldwide. This study was aimed at identifying EC prognostic genes and investigating the molecular mechanisms of these genes in EC. Methods. Two mRNA datasets of EC were downloaded from the Gene Expression Omnibus (GEO). The GEO2R tool and Draw Venn Diagram were used to identify differentially expressed genes (DEGs) between normal endometrial tissues and EC tissues. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID). Next, the protein-protein interactions (PPIs) of these DEGs were determined by the Search Tool for the Retrieval of Interacting Genes (STRING) tool and Cytoscape with Molecular Complex Detection (MCODE). Furthermore, Kaplan-Meier survival analysis was performed by UALCAN to verify genes associated with significantly poor prognosis. Next, Gene Expression Profiling Interactive Analysis (GEPIA) was used to verify the expression levels of these selected genes. Additionally, a reanalysis of the KEGG pathways was performed to understand the potential biological functions of selected genes. Finally, the associations between these genes and clinical features were analysed based on TCGA cancer genomic datasets for EC. Results. In EC tissues, compared with normal endometrial tissues, 147 of 249 DEGs were upregulated and 102 were downregulated. A total of 64 upregulated genes were assembled into a PPI network. Next, 14 genes were found to be both associated with significantly poor prognosis and highly expressed in EC tissues. Reanalysis of the KEGG pathways found that three of these genes were enriched in the cell cycle pathway. TTK, CDC25A, and ESPL1 showed higher expression in cancers with late stage and higher tumour grade. Conclusion. In summary, through integrated bioinformatics approaches, we found three significant prognostic genes of EC, which might be potential therapeutic targets for EC patients.

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GJA1 Expression and Its Prognostic Value in Cervical Cancer

BioMed Research International ◽

10.1155/2020/8827920 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Silu Meng ◽

Xinran Fan ◽

Jianwei Zhang ◽

Ran An ◽

Shuang Li

Keyword(s):

Gene Expression ◽

Cervical Cancer ◽

Logistic Regression ◽

Gap Junction ◽

Signaling Pathway ◽

Immune Cells ◽

Immune Cell ◽

Normal Tissues ◽

Kaplan Meier ◽

Chi Squared

Gap Junction Protein Alpha 1 (GJA1) belongs to the gap junction family and has been widely studied in cancers. We evaluated the role of GJA1 in cervical cancer (CC) using public data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. The difference of GJA1 expression level between CC and normal tissues was analyzed by the Gene Expression Profiling Interactive Analysis (GEPIA), six GEO datasets, and the Human Protein Atlas (HPA). The relationship between clinicopathological features and GJA1 expression was analyzed by the chi-squared test and the logistic regression. Kaplan–Meier survival analysis and Cox proportional hazard regression analysis were used to assessing the effect of GJA1 expression on survival. Gene set enrichment analysis (GSEA) was used to screen the signaling pathways regulated by GJA1. Immune Cell Abundance Identifier (ImmuCellAI) was chosen to analyze the immune cells affected by GJA1. The expression of GJA1 in CC was significantly lower than that in normal tissues based on the GEPIA, GEO datasets, and HPA. Both the chi-squared test and the logistic regression showed that high-GJA1 expression was significantly correlated with keratinization, hormone use, tumor size, and FIGO stage. The Kaplan–Meier curves suggested that high-GJA1 expression could indicate poor prognosis ( p = 0.0058 ). Multivariate analysis showed that high-GJA1 expression was an independent predictor of poor overall survival (HR, 4.084; 95% CI, 1.354-12.320; p = 0.013 ). GSEA showed many cancer-related pathways, such as the p53 signaling pathway and the Wnt signaling pathway, were enriched in the high-GJA1-expression group. Immune cell abundance analysis revealed that the abundance of CD8 naive, DC, and neutrophil was significantly increased in the high-GJA1-expression group. In conclusion, GJA1 can be regarded as a potential prognostic marker of poor survival and therapeutic target in CC. Moreover, many cancer-related pathways may be the critical pathways regulated by GJA1. Furthermore, GJA1 can affect the abundance of immune cells.

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

10.1101/2021.09.10.459743 ◽

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.

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Integrated Analysis of the Association Between Alzheimer`s Disease (AD) and Cardiovascular Disease (CVD)

10.1101/2021.06.15.21258992 ◽

2021 ◽

Author(s):

Katherine Liu Wei

Keyword(s):

Gene Expression ◽

Cardiovascular Disease ◽

Cause Of Death ◽

Protein Interactions ◽

Brain Regions ◽

Gene Expression Omnibus ◽

Integrated Analysis ◽

Biological Processes ◽

Protein Protein Interactions ◽

The Us

Alzheimer`s Disease (AD), the sixth leading cause of death in the US, and cardiovascular disease (CVD), the first leading cause of death in the US, are frequently associated. Past studies hypothesize that amyloid deposits damage organs, causing this association. Examining how vascular factors can influence AD pathogenesis can help in understanding the link between the blood to the brain, which can provide alternative paths of exploration for disease treatment. This study analyzes gene expression and shared biological processes between AD and CVD, specifically myocardial infarction and heart failure, via bioinformatic approaches and published datasets from the Gene Expression Omnibus (GEO). First, 73 differentially expressed genes (DEGs) were identified among four datasets using blood samples from AD and CVD patients. Panther`s Gene Ontology Analysis validated several biological processes such as xylulose biosynthetic process and toll-like receptor TLR1:TLR2 signaling pathway along with molecular functions, cellular components, and pathways to be significantly enriched in the list of 73 DEGs. Analysis of protein-protein interactions and the associated gene network indicated that from the list of 73 DEGs, only six (MAPK14, TLR2, HCK, GRB2, PRKCD, PTPN6) had eight or more degrees. Next, those six genes were identified in a normalized dataset containing different brain regions of AD and non-AD patients. Two-sample t-tests for differences in mean showed statistically significant differences in GRB2 and PRKCD, supporting a blood-brain relationship in the association between AD and CVD. This study can help in developing new medications to target AD and CVD susceptible genes.

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Identification of Potential Core Genes and Potential Drugs in Vestibular Schwannoma

10.21203/rs.3.rs-715254/v1 ◽

2021 ◽

Author(s):

Junqiang Yan ◽

Anran Liu ◽

Jiarui Huang ◽

Jiannan Wu ◽

Hongxia Ma ◽

...

Keyword(s):

Vestibular Schwannoma ◽

Protein Interactions ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Pathway Enrichment Analysis ◽

Data Sets ◽

Protein Protein Interactions ◽

Potential Core ◽

Normal Tissues ◽

Therapeutic Drugs

Abstract Vestibular schwannoma is a common intracranial benign tumor, but the current drug treatment effect is not obvious. Surgical treatment can usually lead to residual problems such as nerve damage. Therefore, there is no clear molecular target to facilitate better clinical treatment. We analyzed three microarray data sets (GSE39645, GSE54934 and GSE108524) derived from the Gene Expression Omnibus database (GEO). The GEO2R was used to screen for the differentially expressed genes (DEG) between vestibular schwannomas and normal tissues. The ontology function of genes and genome pathway enrichment analysis were performed using annotation, visualizative and comprehensive discovery databases to identify the pathways and functional annotation of DEGs. The protein-protein interactions of these DEGs were analyzed by searching the interaction gene database and visualized by Cytoscape software. The potential therapeutic drugs for vestibular schwannoma were searched by online gene drug interaction analysis.A total of 226 up-regulated and 148 down-regulated DEGs were identified. Among them, ten hub genes with high connectivity (EGFR, PPARG, CD86, CSF1R, SPP1, CDH2, CCND1, CAV1, CYBB and NCAM1) were selected as the central genes that may be closely related to the pathogenesis of vestibular schwannoma, which can be potential treatment targets of vestibular schwannoma. Afatinib and osimerinib may be potential therapeutic drugs.

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EFNB1 Acts as a Novel Prognosis Marker in Glioblastoma through Bioinformatics Methods and Experimental Validation

Journal of Oncology ◽

10.1155/2021/4701680 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Yaohong Shi ◽

Yuanyuan Sun ◽

Hongyan Cheng ◽

Chen Wang

Keyword(s):

Survival Analysis ◽

Protein Interactions ◽

Protein Protein Interactions ◽

Hub Genes ◽

Poor Overall Survival ◽

Online Tool ◽

Interactive Analysis ◽

Kaplan Meier ◽

Maximum Cluster ◽

And Migration

Purpose. Ephrin B1 (EFNB1), the Eph-associated receptor tyrosine kinase ligand, is suggested to have an important function in neurodevelopment. However, its contribution to glioblastoma multiforme (GBM) remains uncertain. This study aimed to determine the prognostic power and immune implication of EFNB1 in GBM. Methods. We first identified differentially coexpressed genes within GBM relative to noncarcinoma samples from GEO and TCGA databases by WGCNA. The STRING online database and the maximum cluster centrality (MCC) algorithm in Cytoscape software were used to design for predicting protein-protein interactions (PPI) and calculating pivot nodes, respectively. The expression of hub genes in cancer and noncancer tissues was verified by an online tool gene expression profile interactive analysis (GEPIA). Thereafter, the TISIDB online tool with Cox correlation regression method was employed to screen for immunomodulators associated with EFNB1 and to model the risk associated with immunomodulators. Results. Altogether 201 differentially expressed genes (DEGs) were discovered. After that, 10 hub genes (CALB2, EFNB1, ENO2, EPHB4, NES, OBSCN, RAB9B, RPL23A, STMN2, and THY1) were incorporated to construct the PPI network. As revealed by survival analysis, EFNB1 upregulation predicted poor overall survival (OS) for GBM cases. Furthermore, we developed a prognostic risk signature according to the EFNB1-associated immunomodulators. Kaplan–Meier survival analysis and receiver operating characteristic method were adopted for analysis, which revealed that our signature showed favorable accuracy of prognosis prediction. Finally, EFNB1 inhibition was found to block cell proliferation and migration in GBM cells. Conclusion. The above results indicate that EFNB1 participates in cancer immunity and progression, which is the candidate biomarker for GBM.

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FURIN correlated with immune infiltration serves as a potential biomarker in SARS-CoV-2 infection-related lung adenocarcinoma

10.21203/rs.3.rs-629424/v2 ◽

2021 ◽

Author(s):

Lianxiang Luo ◽

Manshan Li ◽

Jiating Su ◽

Xinyue Yao ◽

Hui Luo

Keyword(s):

Gene Expression ◽

Lung Adenocarcinoma ◽

Gene Expression Omnibus ◽

Poor Overall Survival ◽

Immune Infiltration ◽

Interactive Analysis ◽

Kaplan Meier ◽

Potential Biomarker ◽

Human Protein Atlas

Abstract FURIN, as a proprotein convertase, has been found to be expressed in a variety of cancers and plays an important role in cancer. In addition, SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) requires FURIN to enter human cells. However, the role of FURIN in lung adenocarcinoma remains unclear. And the expression of SARS-CoV-2 related gene in lung adenocarcinoma has not been clarified. Therefore, in order to explore the prognostic value and mechanism of FURIN in lung adenocarcinoma, we performed bioinformatics analysis with Oncomine, TIMER (Tumor Immune Estimation Resource), GEPIA (Gene Expression Profiling Interactive Analysis), HPA (human protein atlas), UALCAN, PrognoScan, Kaplan-Meier plotter, cBioPortal, and LinkedOmics databases. And then We used GSE44274 in the GEO (Gene Expression Omnibus) database to analyze the expression of FURIN in LUAD patients who infected with SARS-CoV. FURIN was highly expressed in lung adenocarcinoma and was significantly associated with poor overall survival. FURIN expression was found to be correlated with six major permeable immune cells and with macrophage immune marker in LUAD patients. In addition, SARS-CoV-2 infection might affect the expression of FURIN. FURIN can be used as a promising biomarker for determining prognosis and immune infiltration in LUAD patients.

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Identification of the Key Genes of Autism Spectrum Disorder Through Protein-Protein Interaction Network

10.31661/gmj.v8i0.1367 ◽

2019 ◽

Vol 8 ◽

Cited By ~ 1

Author(s):

Mona Zamanian Azodi ◽

Mostafa Rezaei-Tavirani ◽

Majid Rezaei-Tavirani

Keyword(s):

Gene Expression ◽

Autism Spectrum Disorder ◽

Protein Interactions ◽

Interaction Network ◽

Gene Expression Omnibus ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Protein Protein Interactions ◽

Protein Protein Interaction ◽

The Given

Background: Currently, the prevalence of autism spectrum disorder (ASD) is increasing, which widely spurs the interest in the molecular investigation. Thereby, a better understanding of the given disorder mechanisms is likely to be achieved. Bioinformatics suiting protein-protein interactions analysis via the application of high-throughput studies, such as protein array, is one of these achievements.Materials and Methods: The gene expression data from Gene Expression Omnibus (GEO) database were downloaded, and the expression profile of patients with developmental delay and autistic features were analyzed via Cytoscape and its relevant plug-ins.Results: Our findings indicated that EGFR, ACTB, RHOA, CALM1, MAPK1, and JUN genes as the hub-bottlenecks and their related terms could be important in ASD risk. In other words, any expression modification in these genes could trigger dysfunctions in the corresponding biological processes.Conclusion: We suggest that differentially expressed genes could be used as suitable targets for ASD after being validated.[GMJ.2019;8:e1367]

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Construction of ceRNA co-expression network and screening of molecular targets in colorectal cancer

10.21203/rs.2.17910/v1 ◽

2019 ◽

Author(s):

Hui Zhao ◽

Zhanwei Wang ◽

Xi Yang ◽

Jin Liu ◽

Jing Zhuang ◽

...

Keyword(s):

Colorectal Cancer ◽

Protein Interactions ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Differentially Expressed ◽

Protein Protein Interactions ◽

Normal Tissues ◽

Cerna Network ◽

Using Data ◽

Cancer Tissues

Abstract Objective to screen some RNAs that correlated with colorectal cancer (CRC).Methods Differentially expressed miRNAs, lncRNAs, and mRNAs between cancer tissues and normal tissues in CRC were identified using data from the Gene Expression Omnibus (GEO) database. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and protein-protein interactions (PPIs) were performed to do the functioal enrichment analysis. And a lncRNA-miRNA-mRNA network was constructed wich correlated with CRC. RNAs in this network were subjecte to analyze the relationship with the patient prognosis.Results A total of 688, 241, and 103 differentially expressed genes (diff-mRNA), diff-lncRNA, and diff-miRNA were obtained. between cancer tissues and normal tissues. A total of 315 edges were obtained in the ceRNA network. lncRNA RP11-108K3.2 and mRNA ONECUT2 correlated with prognosis.Conclusion The identified RNAs and constructed ceRNA network could provide great sources for the reasearches of therapy the CRC. And the lncRNA RP11-108K3.2 and mRNA ONECUT2 may serve novel prognostic predictor of CRC.

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