使用蛋白质组微阵列和基因表达综合数据库筛选肿瘤相关抗原构建食管鳞癌最优诊断模型。
Using Proteome Microarray and Gene Expression Omnibus Database to Screen Tumour-Associated Antigens to Construct the Optimal Diagnostic Model of Oesophageal Squamous Cell Carcinoma.
机构信息
Department of Epidemiology and Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan Province, China; Henan Key Laboratory of Tumor Epidemiology and State Key Laboratory of Esophageal Cancer Prevention & Treatment, Zhengzhou University, Zhengzhou, Henan Province, China.
Henan Key Laboratory of Tumor Epidemiology and State Key Laboratory of Esophageal Cancer Prevention & Treatment, Zhengzhou University, Zhengzhou, Henan Province, China.
出版信息
Clin Oncol (R Coll Radiol). 2023 Oct;35(10):e582-e592. doi: 10.1016/j.clon.2023.06.014. Epub 2023 Jul 5.
AIMS
Autoantibodies against tumour-associated antigens (TAAs) are promising biomarkers for early immunodiagnosis of cancers. This study was designed to screen and verify autoantibodies against TAAs in sera as diagnostic biomarkers for oesophageal squamous cell carcinoma (ESCC).
MATERIALS AND METHODS
The customised proteome microarray based on cancer driver genes and the Gene Expression Omnibus database were used to identify potential TAAs. The expression levels of the corresponding autoantibodies in serum samples obtained from 243 ESCC patients and 243 healthy controls were investigated by enzyme-linked immunosorbent assay (ELISA). In total, 486 serum samples were randomly divided into the training set and the validation set in the ratio of 2:1. Logistic regression analysis, recursive partition analysis and support vector machine were performed to establish different diagnostic models.
RESULTS
Five and nine candidate TAAs were screened out by proteome microarray and bioinformatics analysis, respectively. Among these 14 anti-TAAs autoantibodies, the expression level of nine (p53, PTEN, GNA11, SRSF2, CXCL8, MMP1, MSH6, LAMC2 and SLC2A1) anti-TAAs autoantibodies in the cancer patient group was higher than that in the healthy control group based on the results from ELISA. In the three constructed models, a logistic regression model including four anti-TAA autoantibodies (p53, SLC2A1, GNA11 and MMP1) was considered to be the optimal diagnosis model. The sensitivity and specificity of the model in the training set and the validation set were 70.4%, 72.8% and 67.9%, 67.9%, respectively. The area under the receiver operating characteristic curve for detecting early patients in the training set and the validation set were 0.84 and 0.85, respectively.
CONCLUSIONS
This approach to screen novel TAAs is feasible, and the model including four autoantibodies could pave the way for the diagnosis of ESCC.
目的
针对肿瘤相关抗原（TAA）的自身抗体是癌症早期免疫诊断有前途的生物标志物。本研究旨在筛选和验证血清中针对 TAA 的自身抗体作为食管鳞状细胞癌（ESCC）的诊断生物标志物。
材料和方法
使用基于癌症驱动基因和基因表达综合数据库的定制蛋白质组微阵列来鉴定潜在的 TAA。通过酶联免疫吸附试验（ELISA）检测 243 例 ESCC 患者和 243 例健康对照者血清样本中相应自身抗体的表达水平。总共将 486 份血清样本随机分为训练集和验证集，比例为 2:1。使用逻辑回归分析、递归分区分析和支持向量机建立不同的诊断模型。
结果
蛋白质组微阵列和生物信息学分析分别筛选出 5 个和 9 个候选 TAA。在这 14 种抗 TAA 自身抗体中，ELISA 结果显示 9 种（p53、PTEN、GNA11、SRSF2、CXCL8、MMP1、MSH6、LAMC2 和 SLC2A1）抗 TAA 自身抗体在癌症患者组中的表达水平高于健康对照组。在构建的三个模型中，包含四个抗 TAA 自身抗体（p53、SLC2A1、GNA11 和 MMP1）的逻辑回归模型被认为是最佳诊断模型。该模型在训练集和验证集中的灵敏度和特异性分别为 70.4%、72.8%和 67.9%、67.9%。在训练集和验证集中，用于检测早期患者的接收者操作特征曲线下面积分别为 0.84 和 0.85。
结论
这种筛选新 TAA 的方法是可行的，包含四个自身抗体的模型为 ESCC 的诊断铺平了道路。
Zotero 插件