Salivary markers and risk factor data: A multivariate modeling approach for head and neck squamous cell carcinoma detection
Salivary markers and risk factor data: A multivariate modeling approach for head and neck squamous cell carcinoma detection
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DOI：
10.3233/cbm-2012-0252
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发表时间：
2011-01-01
影响因子：
3.1
通讯作者：
Franzmann, Elizabeth J.
中科院分区：
文献类型：
--
作者：
Pereira, Lutecia H. Mateus;Adebisi, Islamiyat Nancy;Franzmann, Elizabeth J.
背景：头颈部鳞状细胞癌（HNSCC）是一种使人衰弱和致命的疾病，主要是由于晚期诊断。先前的工作表明可溶性CD 44（solCD 44）和总蛋白可能是HNSCC的有用的诊断标志物。在这项研究中，我们结合联合收割机的标志物solCD 44，IL-8，HA和总蛋白与人口和危险因素的数据，以获得一个多变量的逻辑模型，提高HNSCC检测相比，我们以前的数据，使用生物标志物单独。方法：我们进行了solCD 44，IL-8，HA和总蛋白测定的口腔冲洗液从40 HNSCC患者和39对照使用ELISA检测。对照组有良性上呼吸消化道疾病和吸烟或饮酒史。所有受试者完成了一份问卷，包括人口统计学和危险因素datas.Results：根据癌症子网站，病例和对照组之间的差异，发现所有标志物。多变量logistic模型包括solCD 44、总蛋白和与吸烟、口腔健康和教育相关的变量，其AUC为0.853，比单变量模型有显著改善。根据预测概率临界点，敏感性范围为75-82.5%，特异性范围为69.2-82.1%。结论：一个多变量模型，包括简单而廉价的分子检测与风险因素相结合，是区分HNSCC患者与对照组的一个有前途的工具。在这项病例对照研究中，由此产生的观察结果导致了一个前所未有的多变量模型，该模型将HNSCC病例与对照组区分开来，其准确性高于目前的金标准，检查后进行组织活检。由于这些成分简单、无创且获取成本低廉，因此这种结合生物标志物、风险因素和人口统计数据的模型可以作为未来癌症检测测试的有希望的原型。
Background: Head and neck squamous cell carcinoma (HNSCC) is a debilitating and deadly disease largely due to late stage diagnosis. Prior work indicates that soluble CD44 (solCD44) and total protein may be useful diagnostic markers for HNSCC. In this study we combine the markers solCD44, IL-8, HA, and total protein with demographic and risk factor data to derive a multivariate logistic model that improves HNSCC detection as compared to our previous data using biomarkers alone.Methods: We performed the solCD44, IL-8, HA, and total protein assays on oral rinses from 40 HNSCC patients and 39 controls using ELISA assays. Controls had benign diseases of the upper aerodigestive tract and a history of tobacco or alcohol use. All subjects completed a questionnaire including demographic and risk factor data.Results: Depending on cancer subsite, differences between cases and controls were found for all markers. A multivariate logistic model including solCD44, total protein and variables related to smoking, oral health and education offered a significant improvement over the univariate models with an AUC of 0.853. Sensitivity ranged from 75-82.5% and specificity from 69.2-82.1% depending on predictive probability cut points.Conclusion: A multivariate model, including simple and inexpensive molecular tests in combination with risk factors, results in a promising tool for distinguishing HNSCC patients from controls.Impact: In this case-control study, the resulting observations led to an unprecedented multivariate model that distinguished HNSCC cases from controls with better accuracy than the current gold standard which includes oral examination followed by tissue biopsy. Since the components are simple, noninvasive, and inexpensive to obtain, this model combining biomarkers, risk factor and demographic data serves as a promising prototype for future cancer detection tests.