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Meta-analysis of mRNA expression profiles to identify differentially expressed genes in lung adenocarcinoma tissue from smokers and non-smokers.

ABSTRACT: Compared to other types of lung cancer, lung adenocarcinoma patients with a history of smoking have a poor prognosis during the treatment of lung cancer. How lung adenocarcinoma-related genes are differentially expressed between smoker and non-smoker patients has yet to be fully elucidated. We performed a meta-analysis of four publicly available microarray datasets related to lung adenocarcinoma tissue in patients with a history of smoking using R statistical software. The top 50 differentially expressed genes (DEGs) in smoking vs. non?smoking patients are shown using heat maps. Additionally, we conducted KEGG and GO analyses. In addition, we performed a PPI network analysis for 8 genes that were selected during a previous analysis. We identified a total of 2,932 DEGs (1,806 upregulated, 1,126 downregulated) and five genes (CDC45, CDC20, ANAPC7, CDC6, ESPL1) that may link lung adenocarcinoma to smoking history. Our study may provide new insights into the complex mechanisms of lung adenocarcinoma in smoking patients, and our novel gene expression signatures will be useful for future clinical studies.

SUBMITTER: He X

PROVIDER: S-EPMC5802042 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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Meta-analysis of mRNA expression profiles to identify differentially expressed genes in lung adenocarcinoma tissue from smokers and non-smokers.

He Xiaona X Zhang Cheng C Shi Chao C Lu Quqin Q

Oncology reports 20180108 3

Compared to other types of lung cancer, lung adenocarcinoma patients with a history of smoking have a poor prognosis during the treatment of lung cancer. How lung adenocarcinoma-related genes are differentially expressed between smoker and non-smoker patients has yet to be fully elucidated. We performed a meta-analysis of four publicly available microarray datasets related to lung adenocarcinoma tissue in patients with a history of smoking using R statistical software. The top 50 differentially ...[more]

PMID: 29328493

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Project description:BackgroundCircular RNAs (circRNAs) may function as the decoys for microRNAs (miRNAs) or proteins, the templates for translation, and the sources of pseudogene generation. The purpose of this study is to determine the diagnostic circRNAs, which are related to lung adenocarcinoma (LUAD), that adsorb miRNAs on the basis of the competing endogenous RNA (ceRNA) hypothesis.MethodsThe differentially expressed circRNAs (DEcircRNAs) in LUAD were revealed by the microarray data (GSE101586 and GSE101684) that were obtained from the Gene Expression Omnibus (GEO) database. The miRNAs that were targeted by the DEcircRNAs were predicted with the CircInteractome, and the target mRNAs of the miRNAs were found by the miRDB and the TargetScan. The ceRNA network was built by the Cytoscape. The potential biological roles and the regulatory mechanisms of the circRNAs were investigated by the Gene Ontology (GO) enrichment analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. The expression of the host genes of circRNAs was examined by the Ualcan. The survival analysis was performed by the Kaplan-Meier plotter.ResultsIn comparison with normal lung tissues, LUAD tissues contained 7 overlapping cancer-specific DEcircRNAs with 294 miRNA response elements (MREs). Among the 7 DEcircRNAs, 3 circRNAs (hsa_circ_0072088, hsa_circ_0003528, and hsa_circ_0008274) were upregulated and 4 circRNAs (hsa_circ_0003162, hsa_circ_0029426, hsa_circ_0049271, and hsa_circ_0043256) were downregulated. A circRNA-miRNA-mRNA regulatory network, which included 33 differentially expressed miRNAs (DEmiRNAs) and 2007 differentially expressed mRNAs (DEmRNAs), was constructed. These mRNAs were enriched in the biological function of cell-cell adhesion, response to hypoxia, and stem cell differentiation and were involved in the PI3K-Akt signaling, HIF-1 signaling, and cAMP signaling pathways.ConclusionOur results indicated that 7 DEcircRNAs could have diagnostic value for LUAD. Additionally, the circRNAs-mediated ceRNA network might provide a novel perspective into unraveling the pathogenesis and progression of LUAD.

Project description:Background: Lung adenocarcinoma (LUAD) is the leading cause of deaths worldwide, and metastasis accounts for the vast majority of cancer-related deaths. Driver mutations play important roles in treatment decision making for LUAD patients, while the complicated metastatic progress cannot be explained by genetic aberrations alone. Epigenomic reprogramming is particularly notable as an important signature of the metastasis transition. However, long noncoding RNAs (lncRNAs) hijacked by super-enhancer (SE), vital regulatory elements in epigenome, remain elusive in the progression of metastasis. Methods: SE associated lncRNAs microarray was utilized to identified the dysregulated lncRNAs related to metastasis. ChIP-seq, Hi-C data analysis and luciferase reporter assay were utilized to confirm LINC01977 was hijacked by SE. In vitro and in vivo assays were applied to elucidate effects of LINC01977 on the malignancy of LUAD. Results: In this study, we identified that LINC01977, a cancer-testis lncRNA, was up-regulated in tumor, which was driven by a …kb-long SE upstream of LINC01977 and sensitive to BRD4 inhibitor. LINC01977-antisense oligonucleotides (ASO) dramatically suppresses proliferation and invasion both in vitro and in vivo. Mechanically, LINC01977 promotes phosphorylation of SMAD3 to facilitate its nuclear retention, and it also act as a scaffold to cooperate the interaction between SMAD3 and CBP/P300, the transcriptional co-activators, to regulate the downstream target gene ZEB1. Interestingly, SMAD3 also positively regulated LINC09177 transcription by binding the promoter and active elements of its SE, which was medicated by canonical TGF-β signaling secreted by M2-like tumor-associated macrophages (TAM2). We also revealed that the expression of LINC01977 was positively correlated with TAM2 infiltration especially in early stage. Additionally, stage I LUAD patients with high LIN01977 expression predicated a shorter progression-free survival (PFS). Conclusions: LINC01977 promotes the malignant progression of LUAD through facilitating the interaction between SMAD3 and CBP/P300. The upregulation of LINC01977 was medicated by super-enhancer and TAM2 infiltration, dependent on canonical TGF-β signaling. LINC01977 could be a valuable therapeutic target especially for early stage patients.

Project description:In this study, we introduce and use Efficiency Analysis to compare differences in the apparent internal and external consistency of competing normalization methods and tests for identifying differentially expressed genes. Using publicly available data, two lung adenocarcinoma datasets were analyzed using caGEDA (http://bioinformatics2.pitt.edu/GE2/GEDA.html) to measure the degree of differential expression of genes existing between two populations. The datasets were randomly split into at least two subsets, each analyzed for differentially expressed genes between the two sample groups, and the gene lists compared for overlapping genes. Efficiency Analysis is an intuitive method that compares the differences in the percentage of overlap of genes from two or more data subsets, found by the same test over a range of testing methods. Tests that yield consistent gene lists across independently analyzed splits are preferred to those that yield less consistent inferences. For example, a method that exhibits 50% overlap in the 100 top genes from two studies should be preferred to a method that exhibits 5% overlap in the top 100 genes. The same procedure was performed using all available normalization and transformation methods that are available through caGEDA. The 'best' test was then further evaluated using internal cross-validation to estimate generalizable sample classification errors using a Naïve Bayes classification algorithm. A novel test, termed D1 (a derivative of the J5 test) was found to be the most consistent, and to exhibit the lowest overall classification error, and highest sensitivity and specificity. The D1 test relaxes the assumption that few genes are differentially expressed. Efficiency Analysis can be misleading if the tests exhibit a bias in any particular dimension (e.g. expression intensity); we therefore explored intensity-scaled and segmented J5 tests using data in which all genes are scaled to share the same intensity distribution range. Efficiency Analysis correctly predicted the 'best' test and normalization method using the Beer dataset and also performed well with the Bhattacharjee dataset based on both efficiency and classification accuracy criteria.

Dataset Information

Meta-analysis of mRNA expression profiles to identify differentially expressed genes in lung adenocarcinoma tissue from smokers and non-smokers.

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Meta-analysis of mRNA expression profiles to identify differentially expressed genes in lung adenocarcinoma tissue from smokers and non-smokers.

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