Dataset Information

Exploration of estrogen receptor-associated hub genes and potential molecular mechanisms in non-smoking females with lung adenocarcinoma using integrated bioinformatics analysis.

ABSTRACT: The present study aimed to explore important estrogen receptor-associated genes and to determine the potential pathogenic and prognostic factors for lung adenocarcinoma in non-smoking females. The gene expression profiles of the two datasets (GSE32863 and GSE75037) were downloaded from the Gene Expression Omnibus (GEO) database. Data for non-smoking female patients with lung adenocarcinoma from The Cancer Genome Atlas (TCGA) database were also downloaded. The Linear Models for Microarray Data package in R was used to explore the differentially expressed genes (DEGs) between samples from non-smoking female patients with lung adenocarcinoma and samples of adjacent non-cancerous lung tissue. The Database for Annotation, Visualization and Integrated Discovery was used for functional enrichment of the DEGs. The Search Tool for the Retrieval of Interacting Genes/Proteins and Cytoscape software were used to obtain a protein-protein interaction (PPI) network and to identify the hub genes. In addition, the network between the estrogen receptor and the DEGs was constructed. A Kaplan-Meier survival plot was used to analyze the overall survival (OS). In total, 248 DEGs were identified in the GEO database, and 2,362 DEGs were identified in TCGA database. The intersection of the two datasets (DEGs in GEO and TCGA) revealed 170 DEGs, and these were selected for further investigation. Gene Ontology was used to group the 170 DEGs into biological process, molecular function and cellular component categories. Kyoto Encyclopedia of Genes and Genomes pathway analysis was subsequently performed. A total of 27 hub genes, including caveolin 1 (CAV1), matrix metallopeptidase 9 (MMP9), secreted phosphoprotein 1 (SPP1) and collagen type I ? 1 chain (COL1A1), were closely associated with the estrogen receptor. CAV1 and SPP1 were associated with the OS. However, MMP9 and COL1A1 did not have any significant effect on OS. In summary, the identification of CAV1, MMP9, SPP1 and COL1A1 may provide novel insights into the molecular mechanism of lung adenocarcinoma in non-smoking female patients, and the results obtained in the current study may guide future clinical studies.

SUBMITTER: Wang H

PROVIDER: S-EPMC6781748 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Exploration of estrogen receptor-associated hub genes and potential molecular mechanisms in non-smoking females with lung adenocarcinoma using integrated bioinformatics analysis.

Wang Hao H Zhang Zhihong Z Xu Ke K Wei Song S Li Lailing L Wang Lijun L

Oncology letters 20190910 5

The present study aimed to explore important estrogen receptor-associated genes and to determine the potential pathogenic and prognostic factors for lung adenocarcinoma in non-smoking females. The gene expression profiles of the two datasets (GSE32863 and GSE75037) were downloaded from the Gene Expression Omnibus (GEO) database. Data for non-smoking female patients with lung adenocarcinoma from The Cancer Genome Atlas (TCGA) database were also downloaded. The Linear Models for Microarray Data pa ...[more]

PMID: 31611968

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Project description:BackgroundOne of the most frequent malignancies is lung carcinoma which poses heavy burden on the global health. The link among differentially expressed genes (DEGs) and lung cancer patients' clinical outcomes was still missing. In this study, we integrated transcriptome data with clinical data to investigate the relationship between them in lung carcinoma patients.MethodsTo begin, DEGs were identified using the Gene Expression Omnibus (GEO) gene expression pattern (GSE180347). Then, these DEGs are being searched in the TCGA database using the DEGs collected in the preceding phase. The Kaplan-Meier plotter was then used to assess the predictive value of these DEGs in patients with lung cancer.ResultsOur study revealed a total of 45 DEGs, 15 of which were up-regulated and 30 of which were down-regulated. These DEGs were mostly enriched in cytokine receptor binding and cytokine activity, according to GO enrichment analysis. These DEGs were mostly enriched in cytokine-cytokine receptor interaction, according to KEGG enrichment analysis. Based on the PPI network, which comprises of 12 DEGs, a major module was discovered. They are mostly interested in cytotoxicity mediated by natural killer cells. Among all 45 DEGs, the mutations of NCAM1 account for the most cases in TCGA database with a percentage above 15%. Among the 12 DEGs in the significant module, higher expression of FAS, GPR29, HAVCR2, and NCAM1 exhibits longer survival time with hazard ratio and 95% confident interval of 0.79 (0.69-0.89), 0.80 (0.70-0.90), 0.71 (0.60-0.84), and 0.73 (0.62-0.86), respectively. However, higher expression of FCGR3A and IFNG exhibits shorter survival time with hazard ratio and 95% confident interval of 1.50 (1.32-1.71) and 1.15 (1.02-1.31), respectively.ConclusionOur results demonstrate significant correlation between some DEGs and the survival outcome in lung adenocarcinomas patients, providing a comprehensive bioinformatics study in anticipation of future molecular mechanisms and biomarker studies.

Project description:ObjectiveGastric cancer (GC) is the fourth most common cause of cancer-related deaths in the world. In the current study, we aim to identify the hub genes and uncover the molecular mechanisms of GC.MethodsThe expression profiles of the genes and the miRNAs were extracted from the Gene Expression Omnibus database. The identification of the differentially expressed genes (DEGs), including miRNAs, was performed by the GEO2R. Database for Annotation, Visualization and Integrated Discovery was used to perform GO and KEGG pathway enrichment analysis. The protein-protein interaction (PPI) network and miRNA-gene network were constructed using Cytoscape software. The hub genes were identified by the Molecular Complex Detection (MCODE) plugin, the CytoHubba plugin and miRNA-gene network. Then, the identified genes were verified by Kaplan-Meier plotter database and quantitative real-time PCR (qRT-PCR) in GC tissue samples.ResultsA total of three mRNA expression profiles (GSE13911, GSE79973 and GSE19826) were downloaded from the Gene Expression Omnibus (GEO) database, including 69, 20 and 27cases separately. A total of 120 overlapped upregulated genes and 246 downregulated genes were identified. The majority of the DEGs were enriched in extracellular matrix organization, collagen catabolic process, collagen fibril organization and cell adhesion. In addition, three KEGG pathways were significantly enriched, including ECM-receptor interaction, protein digestion and absorption, and the focal adhesion pathways. In the PPI network, five significant modules were detected, while the genes in the modules were mainly involved in the ECM-receptor interaction and focal adhesion pathways. By combining the results of MCODE, CytoHubba and miRNA-gene network, a total of six hub genes including COL1A2, COL1A1, COL4A1, COL5A2, THBS2 and ITGA5 were chosen. The Kaplan-Meier plotter database confirmed that higher expression levels of these genes were related to lower overall survival, except for COL5A2. Experimental validation showed that the rest of the five genes had the same expression trend as predicted.ConclusionIn conclusion, COL1A2, COL1A1, COL4A1, THBS2 and ITGA5 may be potential biomarkers and therapeutic targets for GC. Moreover, ECM-receptor interaction and focal adhesion pathways play significant roles in the progression of GC.

Project description:Smoking is one of the most important factors associated with the development of lung cancer. However, the signaling pathways and driver genes in smoking-associated lung adenocarcinoma remain unknown. The present study analyzed 433 samples of smoking-associated lung adenocarcinoma and 75 samples of non-smoking lung adenocarcinoma from the Cancer Genome Atlas database. Gene Ontology (GO) analysis was performed using the Database for Annotation, Visualization and Integrated Discovery and the ggplot2 R/Bioconductor package. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was performed using the R packages RSQLite and org.Hs.eg.db. Multivariate Cox regression analysis was performed to screen factors associated with patient survival. Kaplan-Meier and receiver operating characteristic curves were used to analyze the potential clinical significance of the identified biomarkers as molecular prognostic markers for the five-year overall survival time. A total of 373 differentially expressed genes (DEGs; |log2-fold change|≥2.0 and P<0.01) were identified, of which 71 were downregulated and 302 were upregulated. These DEGs were associated with 28 significant GO functions and 11 significant KEGG pathways (false discovery rate <0.05). Two hundred thirty-eight proteins were associated with the 373 differentially expressed genes, and a protein-protein interaction network was constructed. Multivariate regression analysis revealed that 7 mRNAs, cytochrome P450 family 17 subfamily A member 1, PKHD1 like 1, retinoid isomerohydrolase RPE65, neurotensin receptor 1, fetuin B, insulin-like growth factor binding protein 1 and glucose-6-phosphatase catalytic subunit, significantly distinguished between non-smoking and smoking-associated adenocarcinomas. Kaplan-Meier analysis demonstrated that patients in the 7 mRNAs-high-risk group had a significantly worse prognosis than those of the low-risk group. The data obtained in the current study suggested that these genes may serve as potential novel prognostic biomarkers of smoking-associated lung adenocarcinoma.

Project description:Immunoglobulin A (IgA) nephropathy (IgAN) is the most common glomerular disease. The major pathological changes associated with it affect cell proliferation, fibrosis, apoptosis, inflammation and extracellular matrix (ECM) organization. However, the molecular events underlying IgAN remain to be fully elucidated. In the present study, an integrated bioinformatics analysis was applied to further explore novel potential gene targets for IgAN. The mRNA expression profile datasets GSE93798 and GSE37460 were downloaded from the Gene Expression Omnibus database. After data preprocessing, differentially expressed genes (DEGs) were identified. Gene Ontology (GO) enrichment analysis of DEGs was performed. Protein-protein interaction (PPI) networks of the DEGs were built with the STRING online search tool and visualized by using Cytoscape, and hub genes were identified through the degree of connectivity in the PPI. The hub genes were subjected to Kyoto Encyclopedia of Genes and Genomes pathway analysis, and co-expression analysis was performed. A total of 298 DEGs between IgAN and control groups were identified, and 148 and 150 of these DEGs were upregulated and downregulated, respectively. The DEGs were enriched in distinct GO terms for Biological Process, including cell growth, epithelial cell proliferation, ERK1 and ERK2 cascades, regulation of apoptotic signaling pathway and ECM organization. The top 10 hub genes were then screened from the PPI network by Cytoscape. As novel hub genes, Fos proto-oncogene, AP-1 transcription factor subunit and early growth response 1 were determined to be closely associated with apoptosis and cell proliferation in IgAN. Tumor protein 53, integrin subunit β2 and fibronectin 1 may also be involved in the occurrence and development of IgAN. Co-expression analysis suggested that these hub genes were closely linked with each other. In conclusion, the present integrated bioinformatics analysis provided novel insight into the molecular events and novel candidate gene targets of IgAN.

Project description:ObjectivesLung adenocarcinoma (LUAD) is recognized as one of the most prevalent and deadliest malignancies around the globe. The molecular mechanisms behind LUAD have not been fully elucidated. This study was launched to explore LUAD-associated hub genes and their enriched pathways using bioinformatics methods.MethodsInformation on GSE10072 was retrieved from the Gene Expression Omnibus (GEO) database and analyzed via the Limma package-based GEO2R tool to obtain the top 100 differentially expressed genes (DEGs) in LUAD. The protein-protein interaction (PPI) network of the DEGs was drawn using the STRING website and was shifted into Cytoscape to screen the top 6 hub genes via the CytoHubba application. Furthermore, the expression analysis and validation of hub genes in LUAD samples and cell lines were done using UALCAN, OncoDB, and GENT2 databases. Moreover, OncoDB was also used for analyzing hub gene DNA methylation levels. In addition, cBioPortal, GSEA tool, Kaplan-Meier (KM) plotter, Enrichr, CancerSEA, and DGIdb were performed to explore some other important aspects of hub genes in LUAD.ResultsWe identified Interleukin 6 (IL6), Collagen, type I, alpha 1 (COL1A1), TIMP metallopeptidase inhibitor 1 (TIMP1), CD34 molecule (CD34), Decorin (DCN), and Secreted Phosphoprotein 1 (SPP1) genes as the hub genes in LUAD, out of which IL6, CD34, and DCN were significantly down-regulated while COL1A1, TIMP1, and SPP1 were significantly up-regulated in LUAD cell lines and samples of diverse clinical variables. In this study, we also documented some important correlations between hub genes and other parameters such as DNA methylation, genetic alterations, Overall Survival (OS), and 14 important states at the single cell level. Lastly, we also identified hub genes associated with the ceRNA network and 11 important chemotherapeutic drugs.ConclusionWe identified 6 hub genes involved in the development and progression of LUAD. These hub genes can also be helpful in the accurate detection of LUAD and provide novel ideas for treatment.

Dataset Information

Exploration of estrogen receptor-associated hub genes and potential molecular mechanisms in non-smoking females with lung adenocarcinoma using integrated bioinformatics analysis.

Publications

Exploration of estrogen receptor-associated hub genes and potential molecular mechanisms in non-smoking females with lung adenocarcinoma using integrated bioinformatics analysis.

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