检验医学 ›› 2024, Vol. 39 ›› Issue (12): 1181-1189.DOI: 10.3969/j.issn.1673-8640.2024.12.009

• 论著 • 上一篇    下一篇

基于ProteomeXchange数据库建立结直肠癌筛查模型

邹琛1, 徐润灏2, 丁毅3, 张洁2, 翁文浩1, 王震华4, 曹芸2()   

  1. 1.上海交通大学医学院附属儿童医院检验科,上海 200062
    2.上海交通大学医学院附属仁济医院检验科,上海 200001
    3.上海健康医学院医学技术学院,上海 201318
    4.上海交通大学医学院附属仁济医院消化内科,上海 200001
  • 收稿日期:2024-05-06 修回日期:2024-08-27 出版日期:2024-12-30 发布日期:2025-01-06
  • 通讯作者: 曹 芸,E-mail:caoyun@renji.com。
  • 作者简介:邹 琛,女,1992年生,硕士,主管技师,主要从事临床免疫学检验工作。

Colorectal cancer screening model based on ProteomeXchange database

ZOU Chen1, XU Runhao2, DING Yi3, ZHANG Jie2, WENG Wenhao1, WANG Zhenhua4, CAO Yun2()   

  1. 1. Department of Clinical Laboratory,Children's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine,Shanghai 200062,China
    2. Department of Clinical Laboratory,Renji Hospital,Shanghai Jiao Tong University School of Medicine,Shanghai 200001,China
    3. College of Medical Technology,Shanghai University of Medicine and Health Sciences,Shanghai 201318,China
    4. Department of Gastroenterology,Renji Hospital,Shanghai Jiao Tong University School of Medicine,Shanghai 200001,China
  • Received:2024-05-06 Revised:2024-08-27 Online:2024-12-30 Published:2025-01-06

摘要:

目的 在ProteomeXchange数据库中筛选结直肠癌(CRC)的血液蛋白标志物,并建立筛查模型,评估其对CRC的诊断价值。方法 在ProteomeXchange数据库的PXD018304数据集中筛选CRC的差异表达蛋白,并进行生物信息学分析。选取2021年5月—2022年1月上海交通大学医学院附属仁济医院CRC初诊患者108例(CRC组)和健康志愿者100名(正常对照组),按8∶2的比例分为训练集和验证集。检测所有研究对象的差异表达蛋白和5种肿瘤标志物[癌胚抗原(CEA)、糖类抗原(CA)19-9、CA242、CA50、CA72-4]。采用逐步法二元Logistic回归分析(向后似然比法)建立CRC的筛查模型。采用受试者工作特征(ROC)曲线评价生物标志物单项检测和联合检测模型诊断CRC的效能。结果 从PXD018304数据集中筛选出差异表达蛋白350种,其中上调蛋白214种、下调蛋白136种。根据差异表达蛋白的基因本体(GO)富集分析、京都基因与基因组数据库(KEGG)通路分析、蛋白质-蛋白质相互作用(PPI)网络等生物信息学分析结果,兼顾临床普及度,筛选出10种候选蛋白,其中下调蛋白为载脂蛋白A1(apo A1)、纤维连接蛋白(FN)、谷胱甘肽还原酶(GR)、转铁蛋白(TRF),上调蛋白为载脂蛋白C3(apo C3)、铜蓝蛋白(CER),C反应蛋白(CRP)、补体4(C4)、纤维蛋白原(Fib)、β2-微球蛋白(β2-MG)。与正常对照组比较,CRC组血清apo A1、apo C3、FN、GR、TRF水平降低(P<0.001);血清CER、CRP、C4、β2-MG、CEA、CA19-9水平和血浆Fib水平升高(P<0.001);2个组之间血清CA242、CA50、CA72-4水平差异均无统计学意义(P>0.05)。诊断CRC的曲线下面积(AUC)>0.7的生物标志物为apo A1、apo C3、CRP、FN、GR、TRF、β2-MG、CEA。单项检测效能最高的是apo A1,AUC为0.898,敏感性为81.48%,特异性为86.00%。由apo A1、CRP、FN、TRF和CEA构成的筛查模型诊断CRC的AUC为0.959,敏感性为87.21%,特异性为92.50%,准确率为89.76%。结论 apo A1等10种蛋白或可作为CRC筛查的生物标志物。由apo A1、CRP、FN、TRF和CEA构成的筛查模型对CRC有较高的诊断效能,可为临床CRC筛查提供参考。

关键词: 筛查模型, 蛋白质组学, 生物标志物, 结直肠癌

Abstract:

ObjectiveTo screen blood protein markers of colorectal cancer(CRC) in ProteomeXchange database,and to establish a screening model for evaluating its diagnostic value for CRC. Methods The differentially expressed protein of CRC was screened in PXD018304 dataset of ProteomeXchange database,and bioinformatics analysis was performed. From May 2021 to January 2022,108 newly diagnosed CRC patients(CRC group) and 100 healthy subjects(healthy control group) in Renji Hospital Affiliated to Shanghai Jiao Tong University School of Medicine were enrolled and classified into training set and validation set according to the ratio of 8∶2. The differentially expressed proteins and 5 tumor markers [carcinoembryonic antigen(CEA),carbohydrate antigen(CA) 19-9,CA242,CA50,CA72-4] were determined. Stepwise binary Logistic regression analysis(backward likelihood ratio method) was used to establish a CRC screening model. Receiver operating characteristic(ROC) curve was used to evaluate the efficacy of single and combined determinations in the diagnosis of CRC. Results A total of 350 differentially expressed proteins were screened from the PXD018304 dataset,which included 214 up-regulated proteins and 136 down-regulated proteins. According to the results of Gene Ontology(GO) enrichment analysis,Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway analysis,protein-protein interaction(PPI) network and other bioinformatics analysis for differentially expressed proteins,taking into account clinical popularity,10 candidate proteins were screened out. The down-regulated proteins were apolipoprotein A1(apo A1),fibronectin(FN),glutathione reductase(GR) and transferrin(TRF),and the up-regulated proteins were apolipoprotein C3(apo C3),ceruloplasmin(CER),C-reactive protein(CRP),complement 4(C4),fibrinogen(Fib)and beta2-microglobulin(β2-MG). Compared with healthy control group,CRC group had lower serum levels of apo A1,apo C3,FN,GR and TRF(P<0.001). The levels of serum CER,CRP,C4,β2-MG,CEA,CA19-9 and plasma Fib were increased(P<0.001). There was no statistical significance in serum CA242,CA50 and CA72-4 levels between the 2 groups(P>0.05). The markers with areas under curves(AUC) >0.7 for CRC diagnosis were apo A1,apo C3,CRP,FN,GR,TRF,β2-MG and CEA. The highest single determination efficiency was apo A1(AUC=0.898),with a sensitivity of 81.48% and a specificity of 86.00%. The AUC of the screening model combined with apo A1,CRP,FN,TRF and CEA was 0.959,the sensitivity was 87.21%,the specificity was 92.50%,and the accuracy was 89.76%. Conclusions Apo A1 and other 10 proteins may be used as biomarkers for CRC screening. The screening model combined with apo A1,CRP,FN,TRF and CEA has a high diagnostic efficacy for CRC,which can provide a reference for clinical CRC screening.

Key words: Screening model, Proteomics, Biomarker, Colorectal cancer

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