Liu Yang, Wang Miaomiao, Lu Yang, Zhang Shuyan, Kang Lin, Zheng Guona, Ren Yanan, Guo Xiaowan, Zhao Huanfen, Hao Han
Department of Pathology, Hebei General Hospital, Shijiazhuang, China.
Basic Medical College, Hebei Medical University, Shijiazhuang, China.
Front Genet. 2022 Nov 15;13:1055202. doi: 10.3389/fgene.2022.1055202. eCollection 2022.
Esophageal cancer (EC) is recognized as one of the most common malignant tumors in the word. Based on the biological process of EC occurrence and development, exploring molecular biomarkers can provide a good guidance for predicting the risk, prognosis and treatment response of EC. Proteomics has been widely used as a technology that identifies, analyzes and quantitatively acquires the composition of all proteins in the target tissues. Proteomics characterization applied to construct a prognostic signature will help to explore effective biomarkers and discover new therapeutic targets for EC. This study showed that we established a 8 proteins risk model composed of ASNS, b-Catenin_pT41_S45, ARAF_pS299, SFRP1, Vinculin, MERIT40, BAK and Atg4B multivariate Cox regression analysis of the proteome data in the Cancer Genome Atlas (TCGA) to predict the prognosis power of EC patients. The risk model had the best discrimination ability and could distinguish patients in the high- and low-risk groups by principal component analysis (PCA) analysis, and the high-risk patients had a poor survival status compared with the low-risk patients. It was confirmed as one independent and superior prognostic predictor by the receiver operating characteristic (ROC) curve and nomogram. K-M survival analysis was performed to investigate the relationship between the 8 proteins expressions and the overall survival. GSEA analysis showed KEGG and GO pathways enriched in the risk model, such as metabolic and cancer-related pathways. The high-risk group presented upregulation of dendritic cells resting, macrophages M2 and NK cells activated, downregulation of plasma cells, and multiple activated immune checkpoints. Most of the potential therapeutic drugs were more appropriate treatment for the low-risk patients. Through adequate analysis and verification, this 8 proteins risk model could act as a great prognostic evaluation for EC patients and provide new insight into the diagnosis and treatment of EC.
食管癌（EC）被认为是世界上最常见的恶性肿瘤之一。基于EC发生发展的生物学过程，探索分子生物标志物可为预测EC的风险、预后及治疗反应提供良好指导。蛋白质组学作为一种识别、分析和定量获取靶组织中所有蛋白质组成的技术已被广泛应用。应用蛋白质组学特征构建预后特征有助于探索有效的生物标志物并发现EC的新治疗靶点。本研究表明，我们建立了一个由天冬酰胺合成酶（ASNS）、β-连环蛋白磷酸化位点pT41_S45、ARAF磷酸化位点pS299、分泌型卷曲相关蛋白1（SFRP1）、纽蛋白、MERIT40、凋亡诱导因子BAK和自噬相关蛋白4B（Atg4B）组成的8蛋白风险模型，通过对癌症基因组图谱（TCGA）中的蛋白质组数据进行多变量Cox回归分析来预测EC患者的预后能力。该风险模型具有最佳的区分能力，通过主成分分析（PCA）可区分高风险组和低风险组患者，且高风险患者与低风险患者相比生存状况较差。通过受试者工作特征（ROC）曲线和列线图证实其为一个独立且优越的预后预测指标。进行K-M生存分析以研究这8种蛋白表达与总生存期之间的关系。基因集富集分析（GSEA）显示风险模型中富集了KEGG和GO通路，如代谢和癌症相关通路。高风险组表现为静息树突状细胞、M2型巨噬细胞和活化自然杀伤细胞上调，浆细胞下调，以及多个活化的免疫检查点。大多数潜在治疗药物对低风险患者更适合。通过充分的分析和验证，这个8蛋白风险模型可为EC患者提供良好的预后评估，并为EC的诊断和治疗提供新的见解。
