Bel'skaya Lyudmila V, Sarf Elena A, Kosenok Victor K, Gundyrev Ivan A
Laboratory of biochemistry, Omsk State Pedagogical University, 14, Tukhachevsky str, Omsk 644043, Russia.
Department of Oncology, Omsk State Medical University, 12, Lenina str, Omsk 644099, Russia.
Diagnostics (Basel). 2020 Mar 27;10(4):186. doi: 10.3390/diagnostics10040186.
The aim of the work is to study the metabolic characteristics of saliva in lung cancer for use in early diagnosis and determining the prognosis of the disease. The patient group included 425 lung cancer patients, 168 patients with non-cancerous lung diseases, and 550 healthy volunteers. Saliva samples were collected from all participants in the experiment before treatment and 34 biochemical saliva parameters were determined. Participants were monitored for six years to assess survival rates. The statistical analysis was performed by means of Statistica 10.0 (StatSoft) program and R package (version 3.2.3). To construct the classifier, the Random Forest method was used; the classification quality was assessed using the cross-validation method. Prognostic factors were analyzed by multivariate analysis using Cox's proportional hazard model in a backward step-wise fashion to adjust for potential confounding factors. A complex of metabolic changes occurring in saliva in lung cancer is described. Seven biochemical parameters were identified (catalase, triene conjugates, Schiff bases, pH, sialic acids, alkaline phosphatase, chlorides), which were used to construct the classifier. The sensitivity and specificity of the method were 69.5% and 87.5%, which is practically not inferior to the diagnostic characteristics of markers routinely used in the diagnosis of lung cancer. Significant independent factors in the poor prognosis of lung cancer are imidazole compounds (ICs) above 0.478 mmol/L and salivary lactate dehydrogenase activity below 545 U/L. Saliva has been shown to have great potential for the development of diagnostic and prognostic tests for lung cancer.
这项工作的目的是研究肺癌患者唾液的代谢特征，以用于早期诊断和确定疾病的预后。患者组包括425例肺癌患者、168例非癌性肺部疾病患者和550名健康志愿者。在治疗前从所有参与实验的人员中采集唾液样本，并测定34种唾液生化参数。对参与者进行了六年的监测以评估生存率。使用Statistica 10.0（StatSoft）程序和R软件包（版本3.2.3）进行统计分析。为构建分类器，使用了随机森林方法；使用交叉验证方法评估分类质量。使用Cox比例风险模型通过多变量分析以向后逐步方式分析预后因素，以调整潜在的混杂因素。描述了肺癌患者唾液中发生的一系列代谢变化。确定了七个生化参数（过氧化氢酶、三烯共轭物、席夫碱、pH值、唾液酸、碱性磷酸酶、氯化物），这些参数用于构建分类器。该方法的敏感性和特异性分别为69.5%和87.5%，实际上并不低于肺癌诊断中常规使用的标志物的诊断特征。肺癌预后不良的显著独立因素是咪唑化合物（IC）高于0.478 mmol/L和唾液乳酸脱氢酶活性低于545 U/L。唾液已被证明在肺癌诊断和预后测试的开发方面具有巨大潜力。
