Dataset Information

Identification and validation of key genes with prognostic value in non-small-cell lung cancer via integrated bioinformatics analysis.

ABSTRACT: BACKGROUND:Lung cancer is the most common cause of cancer-related death among all human cancers and the five-year survival rates are only 23%. The precise molecular mechanisms of non-small cell lung cancer (NSCLC) are still unknown. The aim of this study was to identify and validate the key genes with prognostic value in lung tumorigenesis. METHODS:Four GEO datasets were obtained from the Gene Expression Omnibus (GEO) database. Common differentially expressed genes (DEGs) were selected for Kyoto Encyclopedia of Genes and Genomes pathway analysis and Gene Ontology enrichment analysis. Protein-protein interaction (PPI) networks were constructed using the STRING database and visualized by Cytoscape software and Molecular Complex Detection (MCODE) were utilized to PPI network to pick out meaningful DEGs. Hub genes, filtered from the CytoHubba, were validated using the Gene Expression Profiling Interactive Analysis database. The expressions and prognostic values of hub genes were carried out through Gene Expression Profiling Interactive Analysis (GEPIA) and Kaplan-Meier plotter. Finally, quantitative PCR and the Oncomine database were used to verify the differences in the expression of hub genes in lung cancer cells and tissues. RESULTS:A total of 121 DEGs (49 upregulated and 72 downregulated) were identified from four datasets. The PPI network was established with 121 nodes and 588 protein pairs. Finally, AURKA, KIAA0101, CDC20, MKI67, CHEK1, HJURP, and OIP5 were selected by Cytohubba, and they all correlated with worse overall survival (OS) in NSCLC. CONCLUSION:The results showed that AURKA, KIAA0101, CDC20, MKI67, CHEK1, HJURP, and OIP5 may be critical genes in the development and prognosis of NSCLC. KEY POINTS:Our results indicated that AURKA, KIAA0101, CDC20, MKI67, CHEK1, HJURP, and OIP5 may be critical genes in the development and prognosis of NSCLC. Our methods showed a new way to explore the key genes in cancer development.

SUBMITTER: Wang L

PROVIDER: S-EPMC7113067 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Identification and validation of key genes with prognostic value in non-small-cell lung cancer via integrated bioinformatics analysis.

Wang Li L Qu Jialin J Liang Yu Y Zhao Deze D Rehman Faisal Ul FU Qin Kang K Zhang Xiaochun X

Thoracic cancer 20200214 4

<h4>Background</h4>Lung cancer is the most common cause of cancer-related death among all human cancers and the five-year survival rates are only 23%. The precise molecular mechanisms of non-small cell lung cancer (NSCLC) are still unknown. The aim of this study was to identify and validate the key genes with prognostic value in lung tumorigenesis.<h4>Methods</h4>Four GEO datasets were obtained from the Gene Expression Omnibus (GEO) database. Common differentially expressed genes (DEGs) were sel ...[more]

PMID: 32059076

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Project description:BackgroundGastric cancer is one of the most common malignancies worldwide. Although the diagnosis and treatment of this disease have substantially improved in recent years, the five-year survival rate of gastric cancer is still low due to local recurrence and distant metastasis. An in-depth study of the molecular pathogenesis of gastric cancer and related prognostic markers will help improve the quality of life and prognosis of patients with this disease. The purpose of this study was to identify and verify key SNPs in genes with prognostic value for gastric cancer.MethodsSNP-related data from gastric cancer patients were obtained from The Cancer Genome Atlas (TCGA) database, and the functions and pathways of the mutated genes were analyzed using DAVID software. A protein-protein interaction (PPI) network was constructed using the STRING database and visualized by Cytoscape software, and molecular complex detection (MCODE) was used to screen the PPI network to extract important mutated genes. Ten hub genes were identified using cytoHubba, and the expression levels and the prognostic value of the central genes were determined by UALCAN and Kaplan-Meier Plotter. Finally, quantitative PCR and Western blotting were used to verify the expression of the hub genes in gastric cancer cells.ResultsFrom the database, 945 genes with mutations in more than 25 samples were identified. The PPI network had 360 nodes and 1616 edges. Finally, cytoHubba identified six key genes (TP53, HRAS, BRCA1, PIK3CA, AKT1, and SMARCA4), and their expression levels were closely related to the survival rate of gastric cancer patients.ConclusionOur results indicate that TP53, HRAS, BRCA1, PIK3CA, AKT1, and SMARCA4 may be key genes for the development and prognosis of gastric cancer. Our research provides an important bioinformatics foundation and related theoretical foundation for further exploring the molecular pathogenesis of gastric cancer and evaluating the prognosis of patients.

Project description:BackgroundThe objectives of this study were to identify hub genes and biological pathways involved in lung adenocarcinoma (LUAD) via bioinformatics analysis, and investigate potential therapeutic targets.AimTo determine reliable prognostic biomarkers for early diagnosis and treatment of LUAD.MethodsTo identify potential therapeutic targets for LUAD, two microarray datasets derived from the Gene Expression Omnibus (GEO) database were analyzed, GSE3116959 and GSE118370. Differentially expressed genes (DEGs) in LUAD and normal tissues were identified using the GEO2R tool. The Hiplot database was then used to generate a volcanic map of the DEGs. Weighted gene co-expression network analysis was conducted to cluster the genes in GSE116959 and GSE118370 into different modules, and identify immune genes shared between them. A protein-protein interaction network was established using the Search Tool for the Retrieval of Interacting Genes database, then the CytoNCA and CytoHubba components of Cytoscape software were used to visualize the genes. Hub genes with high scores and co-expression were identified, and the Database for Annotation, Visualization and Integrated Discovery was used to perform enrichment analysis of these genes. The diagnostic and prognostic values of the hub genes were calculated using receiver operating characteristic curves and Kaplan-Meier survival analysis, and gene-set enrichment analysis was conducted. The University of Alabama at Birmingham Cancer data analysis portal was used to analyze relationships between the hub genes and normal specimens, as well as their expression during tumor progression. Lastly, validation of protein expression was conducted on the identified hub genes via the Human Protein Atlas database.ResultsThree hub genes with high connectivity were identified; cellular retinoic acid binding protein 2 (CRABP2), matrix metallopeptidase 12 (MMP12), and DNA topoisomerase II alpha (TOP2A). High expression of these genes was associated with a poor LUAD prognosis, and the genes exhibited high diagnostic value.ConclusionExpression levels of CRABP2, MMP12, and TOP2A in LUAD were higher than those in normal lung tissue. This observation has diagnostic value, and is linked to poor LUAD prognosis. These genes may be biomarkers and therapeutic targets in LUAD, but further research is warranted to investigate their usefulness in these respects.

Dataset Information

Identification and validation of key genes with prognostic value in non-small-cell lung cancer via integrated bioinformatics analysis.

Publications

Identification and validation of key genes with prognostic value in non-small-cell lung cancer via integrated bioinformatics analysis.

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