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[Identification of differentially expressed genes between lung adenocarcinoma and squamous cell carcinoma using transcriber signature analysis].

ABSTRACT: OBJECTIVE:To analyze the differentially expressed genes (DEGs) between lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) with bioinformatics analysis and search for potential biomarkers for clinical diagnosis of nonsmall cell lung cancer (NSCLC). METHODS:The gene expression profiling datasets of LUAD and LUSC were acquired. The transcriptome differences between LUAD and LUSC were identified using R language processing and t-test analysis. The differential expressions of the genes were shown by Venn diagram. The DEGs identified by GEO2R were analyzed with DAVID and Ingenuity Pathway Analysis (IPA) to identify the signaling pathways and biomarkers that could be used for differential diagnosis of LUAD and LUSC. The TCGA data and the biomarker expression data from clinical lung cancer samples were used to verify the differential expressions of the Osteoarthritis pathway and LXR/RXR between LUAD and LUSC. We further examined the differential expressions of miR-181 and its two target genes, WNT5A and MBD2, in 23 clinical specimens of lung squamous cell carcinoma and the paired adjacent tissues. RESULTS:GEO data analysis identified 851 DEGs (including 276 up-regulated and 575 down-regulated genes) in LUAD and 885 DEGs (including 406 up-regulated and 479 down-regulated genes) in LUSC. DAVID and IPA analysis revealed that leukocyte migration and inflammatory responses were more abundant in LUAD than in LUSC. Osteoarthritis pathway was inhibited in LUAD and activated in LUSC. IPA analysis showed that transcription factors (GATA4, RELA, YBX1, TP63 and MBD2), cytokines (WNT5A and IL1A) and microRNAs (miR-34a, miR-181b and miR-15a) differed significantly between LUAD and LUSC. miR-34a with IL-1A, miR-15a with YBX1, and miR-181b with WNT5A and MBD2 could serve as the paired microRNA and mRNA targets for differential diagnosis of NSCLC subtypes. Analysis of the clinical samples showed an increased expression of miR-181b-5p and the down-regulation of WNT5A, which could be used as molecular markers for the diagnosis of LUSC. CONCLUSIONS:Through transcriptome analysis, we identified candidate genes, paired microRNAs and pathways for differentiating LUAD and LUSC, and they can provide novel differential diagnosis and therapeutic strategies for LUAD and LUSC.

SUBMITTER: Peng S

PROVIDER: S-EPMC6743921 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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[Identification of differentially expressed genes between lung adenocarcinoma and squamous cell carcinoma using transcriber signature analysis].

Peng Shuxian S Li Xun X Liu Qin Q Zhang Yingheng Y Zou Liming L Gong Xiaoli X Wang Miaomiao M Ma Xiaodong X

Nan fang yi ke da xue xue bao = Journal of Southern Medical University 20190601 6

<h4>Objective</h4>To analyze the differentially expressed genes (DEGs) between lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) with bioinformatics analysis and search for potential biomarkers for clinical diagnosis of nonsmall cell lung cancer (NSCLC).<h4>Methods</h4>The gene expression profiling datasets of LUAD and LUSC were acquired. The transcriptome differences between LUAD and LUSC were identified using R language processing and t-test analysis. The differential expressi ...[more]

PMID: 31270041

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Project description:Aberrant methylated genes (DMGs) play an important role in the etiology and pathogenesis of esophageal squamous cell carcinoma (ESCC). In this study, we aimed to integrate three cohorts profile datasets to ascertain aberrant methylated-differentially expressed genes and pathways associated with ESCC by comprehensive bioinformatics analysis. We downloaded data of gene expression microarrays (GSE20347, GSE38129) and gene methylation microarrays (GSE52826) from the Gene Expression Omnibus (GEO) database. Aberrantly differentially expressed genes (DEGs) were obtained by GEO2R tool. The David database was then used to perform Gene ontology (GO) analysis and Kyoto Encyclopedia of Gene and Genome pathway enrichment analyses on selected genes. STRING and Cytoscape software were used to construct a protein-protein interaction (PPI) network, then the modules in the PPI networks were analyzed with MCODE and the hub genes chose from the PPI networks were verified by Oncomine and TCGA database. In total, 291 hypomethylation-high expression genes and 168 hypermethylation-low expression genes were identified at the screening step, and finally found six mostly changed hub genes including KIF14, CDK1, AURKA, LCN2, TGM1, and DSG1. Pathway analysis indicated that aberrantly methylated DEGs mainly associated with the P13K-AKT signaling, cAMP signaling and cell cycle process. After validation in multiple databases, most hub genes remained significant. Patients with high expression of AURKA were associated with shorter overall survival. To summarize, we have identified six feasible aberrant methylated-differentially expressed genes and pathways in ESCC by bioinformatics analysis, potentially providing valuable information for the molecular mechanisms of ESCC. Our data combined the analysis of gene expression profiling microarrays and gene methylation profiling microarrays, simultaneously, and in this way, it can shed a light for screening and diagnosis of ESCC in future.

Project description:Osteocytes represent the most abundant cellular component of mammalian bones with important functions in bone mass maintenance and remodeling. To elucidate the differential gene expression between osteoblasts and osteocytes we completed a comprehensive analysis of their gene profiles. Selective identification of these two mature populations was achieved by utilization of visual markers of bone lineage cells. We have utilized dual GFP reporter mice in which osteocytes are expressing GFP (topaz) directed by the DMP1 promoter, while osteoblasts are identified by expression of GFP (cyan) driven by 2.3 kb of the Col1a1 promoter. Histological analysis of 7-day-old neonatal calvaria confirmed the expression pattern of DMP1GFP in osteocytes and Col2.3 in osteoblasts and osteocytes. To isolate distinct populations of cells we utilized fluorescent activated cell sorting (FACS). Cell suspensions were subjected to RNA extraction, in vitro transcription and labeling of cDNA and gene expression was analyzed using the Illumina WG-6v1 BeadChip. Following normalization of raw data from four biological replicates, 3444 genes were called present in all three sorted cell populations: GFP negative, Col2.3cyan(+) (osteoblasts), and DMP1topaz(+) (preosteocytes and osteocytes). We present the genes that showed in excess of a 2-fold change for gene expression between DMP1topaz(+) and Col2.3cyan(+) cells. The selected genes were classified and grouped according to their associated gene ontology terms. Genes clustered to osteogenesis and skeletal development such as Bmp4, Bmp8a, Dmp1, Enpp1, Phex and Ank were highly expressed in DMP1topaz(+)cells. Most of the genes encoding extracellular matrix components and secreted proteins had lower expression in DMP1topaz(+) cells, while most of the genes encoding plasma membrane proteins were increased. Interestingly a large number of genes associated with muscle development and function and with neuronal phenotype were increased in DMP1topaz(+) cells, indicating some new aspects of osteocyte biology. Although a large number of genes differentially expressed in DMP1topaz(+) and Col2.3cyan(+) cells in our study have already been assigned to bone development and physiology, for most of them we still lack any substantial data. Therefore, isolation of osteocyte and osteoblast cell populations and their subsequent microarray analysis allowed us to identify a number or genes and pathways with potential roles in regulation of bone mass.

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.

Dataset Information

[Identification of differentially expressed genes between lung adenocarcinoma and squamous cell carcinoma using transcriber signature analysis].

Publications

[Identification of differentially expressed genes between lung adenocarcinoma and squamous cell carcinoma using transcriber signature analysis].

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