Dataset Information

Systematic analysis using a bioinformatics strategy identifies SFTA1P and LINC00519 as potential prognostic biomarkers for lung squamous cell carcinoma.

ABSTRACT: Lung cancer has high incidence and mortality rates, in which lung squamous cell carcinoma (LUSC) is a primary type of non-small cell lung carcinoma (NSCLC). The aim of our study was to discover long non-coding RNAs (lncRNAs) associated with diagnose and prognosis for LUSC. RNA sequencing data obtained from LUSC samples were extracted from The Cancer Genome Atlas database (TCGA). Two prognosis-associated lncRNAs (including SFTA1P and LINC00519) were selected from LUSC samples, and the expression levels were also verified to be associated abnormal in LUSC clinical samples. Our findings demonstrate that lncRNAs SFTA1P and LINC00519 exert important functions in human LUSC and may serve as new targets for LUSC diagnosis and therapy.

SUBMITTER: Yin YZ

PROVIDER: S-EPMC7847518 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Systematic analysis using a bioinformatics strategy identifies SFTA1P and LINC00519 as potential prognostic biomarkers for lung squamous cell carcinoma.

Yin Yu-Zhen YZ Yao Shi-Hua SH Li Chun-Guang CG Ma Yu-Shui YS Kang Zhou-Jun ZJ Zhang Jia-Jia JJ Jia Cheng-You CY Hou Li-Kun LK Qin Shan-Shan SS Fan Xin X Zhang Han H Yang Meng-Die MD Zhang Dan-Dan DD Lu Gai-Xia GX Wang Hui-Min HM Gu Li-Peng LP Tian Lin-Lin LL Wang Pei-Yao PY Cao Ping-Sheng PS Wu Wei W Cao Zi-Yang ZY Lv Zhong-Wei ZW Shi Bo-Wen BW Wu Chun-Yan CY Jiang Geng-Xi GX Fu Da D Yu Fei F

American journal of translational research 20210115 1

Lung cancer has high incidence and mortality rates, in which lung squamous cell carcinoma (LUSC) is a primary type of non-small cell lung carcinoma (NSCLC). The aim of our study was to discover long non-coding RNAs (lncRNAs) associated with diagnose and prognosis for LUSC. RNA sequencing data obtained from LUSC samples were extracted from The Cancer Genome Atlas database (TCGA). Two prognosis-associated lncRNAs (including SFTA1P and LINC00519) were selected from LUSC samples, and the expression ...[more]

PMID: 33527016

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Project description: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.

Project description:Background: Oral cancer is a frequently encountered neoplasm of the head and neck region, being the eighth most common type of human malignancy worldwide. Despite improvement in its control, morbidity and mortality, rates have improved little in the past decades. The present investigations about gene interaction and pathways still could not clear the appearance and development of oral squamous cell carcinoma (OSCC), completely. The aim of this study is to investigate the key genes and microRNAs interaction in OSCC. Materials and Methods: The microarray datasets GSE13601 and GSE98463, including mRNA and miRNA profiles, were extracted from the GEO database and were analyzed using GEO2R. Functional and pathway enrichment analyses were performed by using the DAVID database. The protein-protein interaction (PPI) network was constructed and analyzed using STRING database and Cytoscape software, respectively. Finally, miRDB was applied to predict the targets of the differentially expressed miRNAs (DEMs). Results: Totally, 97 differentially expressed genes (DEGs) were found in OSCC, including 66 up-regulated and 31 down-regulated genes. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses showed that up-regulated genes were significantly enriched in movement of cell or subcellular component, cell adhesion, biological adhesion, cellular localization, apoptotic signaling pathway, while the downregulated genes were enriched in muscle system process and oxidation-reduction process. From the PPI network, the top 10 nodes with the highest degree were detected as hub genes. In addition, 18 DEMs were screened, which included 7 up-regulated and 11 down-regulated miRNAs. STAT1 was potentially targeted by three miRNAs, including has-miR- 6825-5P, has-miR-4495, and has-miR-5580-3P. Conclusion: The roles of DEMs such as hsa-mir-5580-3p in OSCC through interactions with DEGs CD44, ACLY, ACTR3, STAT1, LAMC2 and YWHAZ may offer a suitable candidate biomarker pattern for diagnosis, prognosis and treatment processes in OSCC.

Dataset Information

Systematic analysis using a bioinformatics strategy identifies SFTA1P and LINC00519 as potential prognostic biomarkers for lung squamous cell carcinoma.

Publications

Systematic analysis using a bioinformatics strategy identifies SFTA1P and LINC00519 as potential prognostic biomarkers for lung squamous cell carcinoma.

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