Background: Lung squamous cell carcinoma (LUSC) is the second most common histological subtype of lung cancer (LC), and the prognoses of most LUSC patients are so far still very poor. The present study aimed at integrating lncRNA, miRNA and mRNA expression data to identify lncRNA signature in competitive endogenous RNA (ceRNA) network as a potentially prognostic biomarker for LUSC patients.Methods: Gene expression data and clinical characteristics of LUSC patients were retrieved from The Cancer Genome Atlas (TCGA) database, and were integratedly analyzed using bioinformatics methods including Differentially Expressed Gene Analysis (DEGA), Weighted Gene Co-expression Network Analysis (WGCNA), Protein and Protein Interaction (PPI) network analysis and ceRNA network construction.Subsequently, univariate and multivariate Cox regression analyses of differentially expressed lncRNAs (DElncRNAs) in ceRNA network were performed to predict the overall survival (OS) in LUSC patients.Receiver operating characteristic (ROC) analysis was used to evaluate the performance of multivariate Cox regression model. Gene expression profiling interactive analysis (GEPIA) was used to validate key genes.Results: WGCNA showed that turquoise module including 1,694 DElncRNAs, 2,654 DEmRNAs as well as 113 DEmiRNAs was identified as the most significant modules (cor=0.99, P<1e-200), and differentially expressed RNAs in the module were used to subsequently analyze. PPI network analysis identified FPR2, GNG11 and ADCY4 as critical genes in LUSC, and survival analysis revealed that low mRNA expression of FPR2 and GNG11 resulted in a higher OS rate of LUSC patients. A lncRNA-miRNA-mRNA ceRNA network including 121 DElncRNAs, 18 DEmiRNAs and 3 DEmRNAs was established, and univariate and multivariate Cox regression analysis of those 121 DElncRNAs showed a group of 3 DElncRNAs (TTTY16, POU6F2-AS2 and CACNA2D3-AS1) had significantly prognostic value in OS of LUSC patients. ROC analysis showed that the area under the curve (AUC) of the 3-lncRNA signature associated with 3-year survival was 0.629. Conclusions:The current study provides novel insights into the lncRNA-related regulatory mechanisms underlying LUSC, and identifying 3-lncRNA signature may serve as a potentially prognostic biomarker in predicting the OS of LUSC patients.
Background: Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, which accounts for about 85% of all lung cancer types. However, critical biological pathways and key genes implicated in NSCLC remain ambiguous. Objectives: The present study aimed at identifying the critical biological pathways and key genes implicated in NSCLC, and providing insight in the molecular mechanism underlying NSCLC. Methods: In this case-control bioinformatics study, the researchers used four microarray data of NSCLC from public gene expression omnibus (GEO) database at the national center for biotechnology information (NCBI) website. The microarray data came from studies of American, Spanish, and Taiwanese NSCLC patients, and in total contained 190 NSCLC tissue and 180 normal lung tissue. A standardized microarray preprocessing and gene set enrichment analysis (GSEA) were used to analyze each microarray data and obtained significantly regulated pathways. Venn analysis was used to identify the common significantly regulated biological pathways. Protein and protein interaction (PPI) network analysis was used to identify the key genes within common significantly regulated pathways. The PPI information was retrieved from STRING database, and cytoscape software was used to construct and visualize the PPI network. Results: Through integrating GSEA results of four microarray data, finally, the researchers identified 22 common up-regulated and 85 common down-regulated pathways. Many genes within 107 common significantly regulated pathways were significantly enriched within cell cycle pathway (P value of 2.58e-79) and focal adhesion pathway (P value of 2.44e-81). The PPI network showed that up-regulated CDK1 (P value = 1.33e-18 and logFC = 1.41) and down-regulated PIK3R1 (P value = 5.09e-22 and logFC = -1.13) genes shared the most abundant edges, and were associated with NSCLC. Conclusions: This cross-sectional study showed increased concordance between gene expression profiling data. These identified pathways and genes provide some insight in the molecular mechanisms of NSCLC, and the genes may serve as candidate diagnostic and therapeutic targets of NSCLC.
This paper explores the sustainability factors affecting market governance innovation initiatives via the seven-day unconditional return offline shopping initiative introduced by the Suzhou Municipal Administration of Market Supervision, which is also combined with market governance innovation cases from other regions. With that, this study conducted a qualitative comparative analysis using the computer software fs/QCA3.0, so as to determine the sustainability factors affecting market governance innovation initiatives. The data analysis concludes that the case is systematic, institutionalized, and informational in character.
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