Suppr超能文献

用于早期检测的候选血清卵巢癌生物标志物的验证。

Validation of candidate serum ovarian cancer biomarkers for early detection.

作者信息

Su Feng, Lang Jennifer, Kumar Ashutosh, Ng Carey, Hsieh Brian, Suchard Marc A, Reddy Srinivasa T, Farias-Eisner Robin

机构信息

Department of Obstetrics and Gynecology, University of California, Los Angeles, CA 90095, U.S.A.

出版信息

Biomark Insights. 2007 Oct 16;2:369-75.

Abstract

OBJECTIVE

We have previously analyzed protein profiles using Surface Enhanced Laser Desorption and Ionization Time-Of-Flight Mass Spectroscopy (SELDI-TOF-MS) [Kozak et al. 2003, Proc. Natl. Acad. Sci. U.S.A. 100:12343-8] and identified 3 differentially expressed serum proteins for the diagnosis of ovarian cancer (OC) [Kozak et al. 2005, Proteomics, 5:4589-96], namely, apolipoprotein A-I (apoA-I), transthyretin (TTR) and transferin (TF). The objective of the present study is to determine the efficacy of the three OC biomarkers for the detection of early stage (ES) OC, in direct comparison to CA125.

METHODS

The levels of CA125, apoA-I, TTR and TF were measured in 392 serum samples [82 women with normal ovaries (N), 24 women with benign ovarian tumors (B), 85 women with ovarian tumors of low malignant potential (LMP), 126 women with early stage ovarian cancer (ESOC), and 75 women with late stage ovarian cancer (LSOC)], obtained through the GOG and Cooperative Human Tissue Network. Following statistical analysis, multivariate regression models were built to evaluate the utility of the three OC markers in early detection.

RESULTS

Multiple logistic regression models (MLRM) utilizing all biomarker values (CA125, TTR, TF and apoA-I) from all histological subtypes (serous, mucinous, and endometrioid adenocarcinoma) distinguished normal samples from LMP with 91% sensitivity (specificity 92%), and normal samples from ESOC with a sensitivity of 89% (specificity 92%). MLRM, utilizing values of all four markers from only the mucinous histological subtype showed that collectively, CA125, TTR, TF and apoA-I, were able to distinguish normal samples from mucinous LMP with 90% sensitivity, and further distinguished normal samples from early stage mucinous ovarian cancer with a sensitivity of 95%. In contrast, in serum samples from patients with mucinous tumors, CA125 alone was able to distinguish normal samples from LMP and early stage ovarian cancer with a sensitivity of only 46% and 47%, respectively. Furthermore, collectively, apoA-I, TTR and TF (excluding CA-125) distinguished i) normal samples from samples representing all histopathologic subtypes of LMP, with a sensitivity of 73%, ii) normal samples from ESOC with a sensitivity of 84% and iii) normal samples from LSOC with a sensitivity of 97%. More strikingly, the sensitivity in distinguishing normal versus mucinous ESOC, utilizing apoA-I, TF and TTR (CA-125 excluded), was 95% (specificity 86%; AUC 95%).

CONCLUSIONS

These results suggest that the biomarker panel consisting of apoA-I, TTR and TF may significantly improve early detection of OC.

摘要

目的

我们之前使用表面增强激光解吸电离飞行时间质谱(SELDI-TOF-MS)分析了蛋白质谱[科扎克等人,2003年,《美国国家科学院院刊》100:12343 - 8],并鉴定出3种差异表达的血清蛋白用于卵巢癌(OC)的诊断[科扎克等人,2005年,《蛋白质组学》,5:4589 - 96],即载脂蛋白A - I(apoA - I)、转甲状腺素蛋白(TTR)和转铁蛋白(TF)。本研究的目的是直接与CA125比较,确定这三种OC生物标志物用于检测早期(ES)OC的效能。

方法

通过妇科肿瘤学组(GOG)和合作人体组织网络获取了392份血清样本[82名卵巢正常的女性(N)、24名患有良性卵巢肿瘤的女性(B)、85名患有低恶性潜能卵巢肿瘤的女性(LMP)、126名患有早期卵巢癌的女性(ESOC)以及75名患有晚期卵巢癌的女性(LSOC)],检测其中CA125、apoA - I、TTR和TF的水平。经过统计分析后,构建多变量回归模型以评估这三种OC标志物在早期检测中的效用。

结果

利用所有组织学亚型(浆液性、黏液性和子宫内膜样腺癌)的所有生物标志物值(CA125、TTR、TF和apoA - I)建立的多元逻辑回归模型(MLRM)区分正常样本与LMP的敏感性为91%(特异性为92%),区分正常样本与ESOC的敏感性为89%(特异性为92%)。仅利用黏液性组织学亚型的所有四个标志物的值建立的MLRM表明,CA125、TTR、TF和apoA - I共同能够以90%的敏感性区分正常样本与黏液性LMP,并且以95%的敏感性进一步区分正常样本与早期黏液性卵巢癌。相比之下,在黏液性肿瘤患者的血清样本中,单独的CA125区分正常样本与LMP和早期卵巢癌的敏感性分别仅为46%和47%。此外,apoA - I、TTR和TF(不包括CA - 125)共同区分:i)正常样本与代表LMP所有组织病理学亚型的样本,敏感性为73%;ii)正常样本与ESOC,敏感性为84%;iii)正常样本与LSOC,敏感性为97%。更显著的是,利用apoA - I、TF和TTR(排除CA - 125)区分正常与黏液性ESOC的敏感性为95%(特异性86%;曲线下面积95%)。

结论

这些结果表明,由apoA - I、TTR和TF组成的生物标志物组合可能显著改善OC的早期检测。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/acdd/2717832/bb768c4fb33f/bmi-2007-369f1.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验