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首页> 外文期刊>Journal of Cellular Physiology >A prognostic 4-gene expression signature for squamous cell lung carcinoma
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A prognostic 4-gene expression signature for squamous cell lung carcinoma

机译:预后4等位基因表达特征肺鳞状细胞癌

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Squamous cell lung carcinoma (SQCLC), a common and fatal subtype of lung cancer, caused lots of mortalities and showed different outcomes in prognosis. This study was to screen key genes and to figure a prognostic signature to cluster the patients with SQCLC. RNA-Seq data from 550 patients with SQCLC were downloaded from The Cancer Genome Atlas (TCGA). Genetically changed genes were identified and analyzed in univariate survival analysis. Genes significantly influencing prognosis were selected with frequency higher than 100 in lasso regression. Meanwhile, area under the curve (AUC) values and hazard ratios (HR) for seed genes were obtained with R Language. Functional enrichment analysis was performed and clustering effectiveness of the selected common gene set was analyzed with Kaplan-Meier. Finally, the stability and validity of the optimal clustering model were verified. A total of 7,222 genetically changed genes were screened, including 1,045 ones with p<0.05, 1,746, p<0.1, and 2,758, p<0.2. The common gene sets with more than 100 frequencies were 14-Genes, 10-Genes and 6-Genes. Genes with p<0.05 participated in positive regulation of ERK1 and ERK2 cascade, angiogenesis, platelet degranulation, cell-matrix adhesion, extracellular matrix organization, macrophage activation, and so on. A four-gene clustering model in 14-Genes (DPPA, TTTY16, TRIM58, HKDC1, ZNF589, ALDH7A1, LINC01426, IL19, LOC101928358, TMEM92, HRASLS, JPH1, LOC100288778, GCGR) was verified as the optimal. The discovery of four-gene clustering model in 14-Genes can cluster the patient samples effectively. This model would help predict the outcomes of patients with SQCLC then improve the treatment strategies.
机译:肺鳞状细胞癌(SQCLC),一个共同的和致命的肺癌亚型,造成很多死亡率和显示不同的结果预后。图一个预后签名集群SQCLC患者。患者SQCLC下载的癌症基因组图谱(TCGA)。基因识别和分析了单变量生存分析。选择影响预后拉索频率高于100年回归。与此同时,曲线下的面积(AUC)值风险比率(人力资源)种子基因R语言。进行聚类的有效性选定的常见基因设置进行了分析kaplan meier。最优聚类模型的验证。共有7222个基因改变基因筛选,其中包括1045的p < 0.05,2758年1746,p < 0.1, p < 0.2。集100多频率14-Genes,十个基因和6-Genes。参与积极ERK1和监管ERK2级联、血管生成、血小板脱粒,cell-matrix粘连,细胞外基质组织巨噬细胞激活,等等。模型14-Genes (DPPA TTTY16、TRIM58 HKDC1,ZNF589, ALDH7A1、LINC01426, 19, LOC101928358TMEM92、HRASLS JPH1, LOC100288778 GCGR)验证最优。基因研究的四个14-Genes可以聚类模型有效的集群患者样本。模型将有助于预测病人的结果SQCLC然后改进治疗策略。

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