Dataset Information

Bioinformatics and functional analyses of key genes in smoking-associated lung adenocarcinoma.

ABSTRACT: 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.

SUBMITTER: Zhou D

PROVIDER: S-EPMC6732981 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Bioinformatics and functional analyses of key genes in smoking-associated lung adenocarcinoma.

Zhou Dajie D Sun Yilin Y Jia Yanfei Y Liu Duanrui D Wang Jing J Chen Xiaowei X Zhang Yujie Y Ma Xiaoli X

Oncology letters 20190807 4

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 ggplot ...[more]

PMID: 31516576

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Project description:BackgroundLung cancer is the leading cause of cancer-related mortality worldwide. Although cigarette smoking is an established risk factor for lung cancer, few reliable smoking-related biomarkers for non-small-cell lung cancer (NSCLC) are available. An improved understanding of these biomarkers would further the development of new biomarker-targeted therapies and lead to improvements in overall patient survival.MethodsWe performed bioinformatic analysis to screened potential target genes, then quantitative PCR, western, siRNA, CCK-8, flow cytometry, tumorigenicity assays in nude mice were performed to validated the function.ResultsIn this study, we identified 83 smoking-related genes (SRGs) based on an integration analysis of two Gene Expression Omnibus (GEO) datasets, and 27 hub SRGs with potential carcinogenic effects by analyzing a dataset of smokers with NSCLC in The Cancer Genome Atlas (TCGA) database. A survival analysis revealed three genes with potential prognostic value, namely SRXN1, KRT6A and JAKMIP3. A univariate Cox analysis revealed significant associations of elevated SRXN1 and KRT6A expression with prognosis. A receiver operating characteristic (ROC) curve analysis indicated the high diagnostic value of SRXN1 and KRT6A for smoking and cancer. Quantitative PCR and western blotting validated the increased expression of SRXN1 and KRT6A mRNA and protein, respectively, in lung cancer cell lines and NSCLC tissues. In patients with NSCLC, SRXN1 and KRT6A expression was associated with the tumor-node-metastasis (TNM) stage, presence of metastasis, history of smoking and daily smoking consumption. Furthermore, inhibition of SRXN1 or KRT6A suppressed viability and enhanced apoptosis in the A549 human lung carcinoma cell line. Tumorigenicity assays in nude mice confirmed that the siRNA-mediated downregulation of SRXN1 and KRT6A expression inhibited tumor growth in vivo.ConclusionsIn summary, SRXN1 and KRT6A act as oncogenes in NSCLC and might be potential biomarkers of smoking exposure and the early diagnosis and prognosis of NSCLC in smokers, which is vital for lung cancer therapy.

Project description:BackgroundEsophageal adenocarcinoma (EAC) is an aggressive malignancy and accounts for the majority of cancer-related death worldwide. It is often diagnosed at an advanced stage and entails a poor prognosis for those afflicted. The mechanisms of its pathogenesis and progress remain unclear and require urgent elucidation. This study aimed to identify specific genes and potential pathways associated with the progression and prognosis of EAC using bioinformatics analyses.MethodsEAC microarray datasets from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases were analyzed to identify differentially expressed genes (DEGs) using bioinformatics analysis. The DEGs in TCGA were then analyzed to construct a co-expression network by weighted correlation network analysis (WGCNA), and module-clinical trait relationships were analyzed to explore the genes that associated with clinicopathological parameters of EAC. Gene ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analyses were performed for the cancer-related genes, and a DEG-based protein-protein interaction (PPI) network was used to extract hub genes through Cytoscape plugins. The consensus survival analysis for EAC (OSeac) was performed to identify the prognosis-related genes. The immune infiltration was evaluated by tumor immune estimation resource (TIMER) algorithms, and a risk score prognostic model was established using univariate, multivariate Cox proportional hazards regression, and lasso regression analysis.ResultsUltimately, 190 cancer-related DEGs were identified, 6 of which were found to play vital roles in the progression of EAC, including ACTA2, BGN, CALD1, COL1A1, COL4A1, and DCN. The risk score prognostic model consisted of 6 other genes that had an important impact on the prognosis of EAC, including CLDN3, EPB41L4A, ESM1, MT1X, PAQR5, and PLAU. The area under the curve of the prognostic model for predicting the survival of patients at 1, 2, and 3 years was 0.707, 0.702, and 0.726, respectively.ConclusionsThis study identified several genes with the potential to become useful targets for the diagnosis and treatment of EAC. The 6-gene-related risk score prognostic model and nomogram based on these genes may be a reliable tool for predicting the prognosis of patients with EAC.

Project description: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.

Dataset Information

Bioinformatics and functional analyses of key genes in smoking-associated lung adenocarcinoma.

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Bioinformatics and functional analyses of key genes in smoking-associated lung adenocarcinoma.

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