Diagnostic performance of anti-MAGEA family protein autoantibodies in esophageal squamous cell carcinoma

自身抗体 免疫球蛋白G 免疫学 接收机工作特性 食管鳞状细胞癌 医学 细胞角蛋白 抗体 生物 内科学 胃肠病学 免疫组织化学
作者
Guiying Sun,Huili Chen,Junfen Xia,Tiandong Li,Hua Ye,Jiaxin Li,Xiaoyue Zhang,Yifan Cheng,Keyan Wang,Jianxiang Shi,Peng Wang
出处
期刊:International Immunopharmacology [Elsevier]
卷期号:125: 111041-111041
标识
DOI:10.1016/j.intimp.2023.111041
摘要

MAGEA family proteins are immunogenic and can produce corresponding autoantibodies, and we aim to evaluate the diagnostic value of anti-MAGEA family protein autoantibodies in esophageal squamous cell carcinoma (ESCC). Protein chip was used to detect the expression level of anti-MAGEA autoantibodies (IgG and IgM) in 20 mixed serum samples. Enzyme linked immunosorbent assay was adopted to determine the expression level of autoantibodies in 1019 serum samples (423 ESCC, 423 healthy control (HC), 173 benign esophageal disease (BED)), and stepwise logistic regression analysis was used for developing a diagnostic model. Eight anti-MAGEA autoantibodies were screened out based on the protein chip. The levels of 7 autoantibodies (MAGEA1-IgG, MAGEA3-IgG, MAGEA3-IgM, MAGEA4-IgG, MAGEA6-IgG, MAGEA10-IgG, MAGEA12-IgG) in ESCC were significantly higher than that in HC, and the levels of anti-MAGEA1 IgG, anti-MAGEA3-IgG, anti-MAGEA4-IgG, anti-MAGEA10-IgG and anti-MAGEA12-IgG autoantibodies in ESCC group were significantly higher than those in BED group. The area under curve (AUC), sensitivity and specificity of the logistic regression model (MAGEA1-IgG, MAGEA4-IgG, MAGEA6-IgG, MAGEA12-IgG) in the training set and the validation set were 0.725 and 0.698, 55.2% and 51.8%, 80.4% and 84.5%, respectively, in distinguishing ESCC and HC. The model also could distinguish between ESCC and BED, with the AUC of 0.743, sensitivity of 55.4% and specificity of 89.0%. The positive rate of the model combined with cytokeratin 19 fragment to diagnose ESCC reached 78.0%. The study identified anti-MAGEA autoantibodies with potential diagnostic value for ESCC, which may provide new promising for the detection of the disease.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
李健的小迷弟应助Jia采纳,获得10
刚刚
香蕉乐菱完成签到,获得积分10
刚刚
LHL关闭了LHL文献求助
刚刚
西红柿炒番茄应助韦老虎采纳,获得10
1秒前
罗布林卡应助斯人采纳,获得20
2秒前
北海发布了新的文献求助10
4秒前
5秒前
5秒前
6秒前
character577完成签到 ,获得积分10
7秒前
ephore应助xiao采纳,获得30
8秒前
孙同学完成签到,获得积分10
8秒前
10秒前
泡泡发布了新的文献求助10
10秒前
11秒前
SOLOMON应助孙同学采纳,获得10
12秒前
12秒前
满意的青寒完成签到,获得积分10
12秒前
13秒前
科研通AI2S应助韦老虎采纳,获得10
13秒前
dreamlife发布了新的文献求助10
13秒前
寂寞的寄松应助灵巧尔蓉采纳,获得10
14秒前
萨摩耶发布了新的文献求助10
16秒前
lxy完成签到,获得积分10
16秒前
16秒前
xiao完成签到 ,获得积分10
18秒前
缥缈冰之发布了新的文献求助10
19秒前
LHL关闭了LHL文献求助
19秒前
科研通AI2S应助conzzz采纳,获得10
19秒前
沙漠水发布了新的文献求助10
19秒前
17发布了新的文献求助20
20秒前
22秒前
外向安珊发布了新的文献求助10
22秒前
传奇3应助缥缈冰之采纳,获得10
25秒前
bisongyu发布了新的文献求助10
25秒前
小花猫发布了新的文献求助20
25秒前
丘比特应助是否采纳,获得10
26秒前
26秒前
俏皮书白发布了新的文献求助10
28秒前
研飞完成签到 ,获得积分10
30秒前
高分求助中
【本贴是提醒信息,请勿应助】请在求助之前详细阅读求助说明!!!! 20000
One Man Talking: Selected Essays of Shao Xunmei, 1929–1939 1000
The Three Stars Each: The Astrolabes and Related Texts 900
Yuwu Song, Biographical Dictionary of the People's Republic of China 800
Multifunctional Agriculture, A New Paradigm for European Agriculture and Rural Development 600
Challenges, Strategies, and Resiliency in Disaster and Risk Management 500
Bernd Ziesemer - Maos deutscher Topagent: Wie China die Bundesrepublik eroberte 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 有机化学 工程类 生物化学 纳米技术 物理 内科学 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 电极 光电子学 量子力学
热门帖子
关注 科研通微信公众号,转发送积分 2482554
求助须知:如何正确求助?哪些是违规求助? 2144906
关于积分的说明 5471723
捐赠科研通 1867316
什么是DOI,文献DOI怎么找? 928172
版权声明 563073
科研通“疑难数据库(出版商)”最低求助积分说明 496557