Department of Hepatobiliary Surgery, The First Affiliated Hospital, Wenzhou Medical University , Wenzhou, China.
Bioengineered. 2020 Dec;11(1):1368-1381. doi: 10.1080/21655979.2020.1847398.
Utilizing genomic data to predict cancer prognosis was insufficient. Proteomics can improve our understanding of the etiology and progression of cancer and improve the assessment of cancer prognosis. And the Clinical Proteomic Tumor Analysis Consortium (CPTAC) has generated extensive proteomics data of the vast majority of tumors. Based on CPTAC, we can perform a proteomic pan-carcinoma analysis. We collected the proteomics data and clinical features of cancer patients from CPTAC. Then, we screened 69 differentially expressed proteins (DEPs) with R software in five cancers: hepatocellular carcinoma (HCC), children's brain tumor tissue consortium (CBTTC), clear cell renal cell carcinoma (CCRC), lung adenocarcinoma (LUAD) and uterine corpus endometrial carcinoma (UCEC). GO and KEGG analysis were performed to clarify the function of these proteins. We also identified their interactions. The DEPs-based prognostic model for predicting over survival was identified by least absolute shrinkage and selection operator (LASSO)-Cox regression model in training cohort. Then, we used the time-dependent receiver operating characteristics analysis to evaluate the ability of the prognostic model to predict overall survival and validated it in validation cohort. The results showed that the DEPs-based prognostic model could accurately and effectively predict the survival rate of most cancers.
利用基因组数据预测癌症预后是不够的。蛋白质组学可以帮助我们更好地理解癌症的病因和进展，并改善对癌症预后的评估。临床蛋白质组肿瘤分析联盟 (CPTAC) 已经生成了绝大多数肿瘤的广泛蛋白质组学数据。基于 CPTAC，我们可以进行蛋白质组泛癌分析。我们从 CPTAC 收集了癌症患者的蛋白质组学数据和临床特征。然后，我们使用 R 软件在五种癌症中筛选出 69 个差异表达蛋白 (DEPs)：肝细胞癌 (HCC)、儿童脑肿瘤组织联盟 (CBTTC)、透明细胞肾细胞癌 (CCRC)、肺腺癌 (LUAD) 和子宫体子宫内膜癌 (UCEC)。进行 GO 和 KEGG 分析以阐明这些蛋白质的功能。我们还鉴定了它们的相互作用。通过训练队列中的最小绝对收缩和选择算子 (LASSO)-Cox 回归模型确定基于 DEPs 的预测总生存期的预后模型。然后，我们使用时间依赖性接收者操作特征分析来评估预后模型预测总生存期的能力，并在验证队列中进行验证。结果表明，基于 DEPs 的预后模型可以准确有效地预测大多数癌症的生存率。
