预测胃癌总生存期的7细胞周期特征的鉴定

Lianzhu Zhang, S. Zhou, Junkuo Li, Peinan Chen, X. Zhao, Li-Dong Wang, Xiuling Li, F. Zhou
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引用次数: 5

摘要

虽然一些细胞周期调节因子的遗传改变对胃癌发生过程有重大影响,但它们的预后作用仍有待进一步阐明。下载TCGA-STAD训练集,提取细胞周期基因mRNA表达矩阵,与GSE84437数据集交叉后进行校正,进一步分析。在TCGA-STAD的肿瘤和正常组织样本中发现了差异表达的mrna。单因素Cox回归分析和lasso Cox回归模型建立了新的7基因细胞周期特征(包括GADD45B、TFDP1、CDC6、CDC25A、CDC7、SMC1A和MCM3)用于胃癌预后预测。高危组患者的生存率明显低于低危组患者。该特征被发现是胃癌生存的独立预后因素。包括签名的Nomogram显示了总体生存预测的一些临床净收益。在GSE84437数据集中进一步验证了该签名。在组织芯片中,CDC6和MCM3蛋白在肿瘤组织和癌旁组织之间的表达通过免疫组织化学h评分有显著差异,CDC6是胃癌的独立预后因素。有趣的是,我们的GSEA显示,低风险患者与细胞周期通路更相关,可能从靶向细胞周期的治疗中获益更多。我们的研究确定了一种新的强大的七基因细胞周期特征,可用于胃癌预后预测,可作为临床病理分期的有益补充。该标记可能为细胞周期调节剂在治疗和治疗反应预测中的应用提供潜在的生物标志物。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identification of a seven-cell cycle signature predicting overall survival for gastric cancer
While genetic alterations in several regulators of the cell cycle have a significant impact on the gastric carcinogenesis process, the prognostic role of them remains to be further elucidated. The TCGA-STAD training set were downloaded and the mRNA expression matrix of cell cycle genes was extracted and corrected for further analysis after taking the intersection with GSE84437 dataset. Differentially expressed mRNAs were identified between tumor and normal tissue samples in TCGA-STAD. Univariate Cox regression analysis and lasso Cox regression model established a novel seven-gene cell cycle signature (including GADD45B, TFDP1, CDC6, CDC25A, CDC7, SMC1A and MCM3) for GC prognosis prediction. Patients in the high-risk group shown significantly poorer survival than patients in the low-risk group. The signature was found to be an independent prognostic factor for GC survival. Nomogram including the signature shown some clinical net benefit for overall survival prediction. The signature was further validated in the GSE84437 dataset. In tissue microarray, CDC6 and MCM3 protein expression were significant differences by the immunohistochemistry-based H-score between tumor tissues and adjacent tissues, and CDC6 is an independent prognostic factor for GC. Interestingly, our GSEA revealed that low-risk patients were more related to cell cycle pathways and might benefit more from therapies targeting cell cycle. Our study identified a novel robust seven-gene cell cycle signature for GC prognosis prediction that may serve as a beneficial complement to clinicopathological staging. The signature might provide potential biomarkers for the application of cell cycle regulators to therapies and treatment response prediction.
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