Department of Gastrointestinal Surgery, The First Hospital of Jilin University, Changchun, 130021, Jilin, China.
Jilin Province Key Laboratory of Bioinformatics for Gastrointestinal Tumor, Changchun, Jilin, China.
Biol Res. 2019 Aug 9;52(1):42. doi: 10.1186/s40659-019-0249-0.
Prognosis remains one of most crucial determinants of gastric cancer (GC) treatment, but current methods do not predict prognosis accurately. Identification of additional biomarkers is urgently required to identify patients at risk of poor prognoses.
Tissue microarrays were used to measure expression of nine GC-associated proteins in GC tissue and normal gastric tissue samples. Hierarchical cluster analysis of microarray data and feature selection for factors associated with survival were performed. Based on these data, prognostic scoring models were established to predict clinical outcomes. Finally, ingenuity pathway analysis (IPA) was used to identify a biological GC network.
Eight proteins were upregulated in GC tissues versus normal gastric tissues. Hierarchical cluster analysis and feature selection showed that overall survival was worse in cyclin dependent kinase (CDK)2, Akt1, X-linked inhibitor of apoptosis protein (XIAP), Notch4, and phosphorylated (p)-protein kinase C (PKC) α/β2 immunopositive patients than in patients that were immunonegative for these proteins. Risk score models based on these five proteins and clinicopathological characteristics were established to determine prognoses of GC patients. These proteins were found to be involved in cancer related-signaling pathways and upstream regulators were identified.
This study identified proteins that can be used as clinical biomarkers and established a risk score model based on these proteins and clinicopathological characteristics to assess GC prognosis.
预后仍然是胃癌（GC）治疗中最重要的决定因素之一，但目前的方法并不能准确预测预后。迫切需要识别其他生物标志物，以确定预后不良的风险患者。
使用组织微阵列测量 GC 组织和正常胃组织样本中九种与 GC 相关的蛋白质的表达。对微阵列数据进行层次聚类分析，并对与生存相关的因素进行特征选择。基于这些数据，建立预后评分模型以预测临床结局。最后，使用Ingenuity Pathway Analysis（IPA）鉴定 GC 相关的生物学网络。
与正常胃组织相比，GC 组织中有 8 种蛋白质上调。层次聚类分析和特征选择显示，与这些蛋白免疫阴性的患者相比，CDK2、Akt1、X 连锁凋亡抑制剂蛋白（XIAP）、Notch4 和磷酸化蛋白激酶 C（PKC）α/β2 免疫阳性的患者总体生存率更差。基于这五种蛋白和临床病理特征建立了风险评分模型，以确定 GC 患者的预后。这些蛋白被发现参与了癌症相关的信号通路，并确定了上游调节因子。
本研究鉴定了可作为临床生物标志物的蛋白，并基于这些蛋白和临床病理特征建立了风险评分模型，以评估 GC 的预后。
