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pmid:32411535
Identification of key genes associated with progression and prognosis for lung squamous cell carcinoma.
Xiaohan Ma · Huijun Ren · Ruoyu Peng · Yi Li · Liang Ming
PeerJ · 2020 doi:10.7717/peerj.9086
Paper
complete
Experiment
partial
Data
partial
Analysis
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Execution
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Complete file
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Differentially expressed genes (DEGs) between LUSC and normal tissues were identified by GEO2R, and functional analysis was employed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) online tool. Protein-protein interaction (PPI) and hub genes were identified via the Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape software. Hub genes were further validated in The Cancer Genome Atlas (TCGA) database. Subsequently, survival analysis was performed using the Kapla-Meier curve and Cox progression analysis. Based on univariate and multivariate Cox progression analysis, a gene signature was established to predict overall survival. Receiver operating characteristic curve was used to evaluate the prognostic value of the model.\nRESULTS: A total of 116 up-regulated genes and 84 down-regulated genes were identified. These DEGs were mainly enriched in the two pathways: cell cycle and p53 signaling way. According to the degree of protein nodes in the PPI network, 10 hub genes were identified. The mRNA expression levels of the 10 hub genes in LUSC were also significantly up-regulated in the TCGA database. Furthermore, a novel seven-gene signature (FLRT3, PPP2R2C, MMP3, MMP12, CAPN8, FILIP1 and SPP1) from the DEGs was constructed and acted as a significant and independent prognostic signature for LUSC.\nCONCLUSIONS: The 10 hub genes might be tightly correlated with LUSC progression. The seven-gene signature might be an independent biomarker with a significant predictive value in LUSC overall survival.", "claims": [{"id": "claim-method", "statement": "In order to identify the potential prognostic values of the DEGs in LUSC, univariate and multivariate Cox regression were conducted using the R package. 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