Diagnosis of lung cancer using salivary miRNAs expression and clinical characteristics.

IF 2.8 3区 医学 Q2 RESPIRATORY SYSTEM BMC Pulmonary Medicine Pub Date : 2025-01-25 DOI:10.1186/s12890-025-03502-6
Negar Alizadeh, Hoda Zahedi, Maryam Koopaie, Mahnaz Fatahzadeh, Reza Mousavi, Sajad Kolahdooz
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Abstract

Objective: Lung cancer (LC), the primary cause for cancer-related death globally is a diverse illness with various characteristics. Saliva is a readily available biofluid and a rich source of miRNA. It can be collected non-invasively as well as transported and stored easily. The process is also reproducible and cost-effective. The aim of this study was to evaluate the salivary expression of microRNAs let-7a-2, miR-221, and miR-20a in saliva and evaluate their efficacy, using multiple logistic regression (MLR) model, in diagnosis of lung cancer.

Materials: Samples of saliva were obtained from 40 lung cancer patients (20 lung adenocarcinoma and 20 lung squamous cell carcinoma) and 20 healthy controls. The levels of let-7a-2, miR-221, and miR-20a expression in saliva were assessed by RT-qPCR. Receiver operating characteristic (ROC) curve was utilized to assess the potential significance of miRNAs in saliva for lung cancer diagnosis with the use of multiple logistic regression (MLR), principal component analysis, and machine learning methods.

Results: Diagnostic odds ratio (DOR) of miR-20a in lung adenocarcinoma diagnosis versus healthy control was higher than miR-221, and DOR of miR-221 was higher than let-7a-2. miR-20a demonstrated a higher DOR for small cell lung carcinoma versus healthy control compared to let-7a-2, which in turn exhibited a higher DOR than miR-221. MLR of miR-221, let-7a-2, miR-20a, and smoking habit using main effects led to accuracy of 0.725 (sensitivity: 0.80, specificity: 0.65) and AUC = 0.795 for differentiation of small-cell lung carcinoma from lung adenocarcinoma. Our results showed that MLR based on salivary miRNAs could diagnose LUAD and SCLC from healthy control using main effects and two-way interactions with the accuracy of 0.90 (sensitivity = 0.95 and specificity = 0.85).

Conclusion: A salivary miRNA-based MLR model is a promising diagnostic tool for lung cancer, offering a non-invasive screening option for high-risk asymptomatic individuals.

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涎腺miRNAs表达及临床特征对肺癌的诊断价值。
目的:肺癌(LC)是一种具有多种特征的多样化疾病,是全球癌症相关死亡的主要原因。唾液是一种现成的生物流体,也是miRNA的丰富来源。它可以无创收集,并且易于运输和储存。该过程还具有可重复性和成本效益。本研究的目的是评估唾液中let-7a-2、miR-221和miR-20a的表达,并评估其在肺癌诊断中的作用,采用多元logistic回归(MLR)模型。材料:40例肺癌患者(20例肺腺癌和20例肺鳞状细胞癌)和20例健康对照者的唾液样本。RT-qPCR检测唾液中let-7a-2、miR-221和miR-20a的表达水平。利用受试者工作特征(ROC)曲线,采用多元逻辑回归(MLR)、主成分分析和机器学习方法,评估唾液中mirna对肺癌诊断的潜在意义。结果:miR-20a在肺腺癌诊断中的诊断优势比(DOR)高于miR-221, miR-221的DOR高于let-7a-2。与let-7a-2相比,miR-20a在小细胞肺癌中的DOR高于健康对照组,而let-7a-2的DOR又高于miR-221。使用主效应对miR-221、let-7a-2、miR-20a的MLR和吸烟习惯进行区分小细胞肺癌与肺腺癌的准确度为0.725(灵敏度为0.80,特异性为0.65),AUC = 0.795。我们的研究结果显示,基于唾液mirna的MLR可以通过主效应和双向相互作用诊断健康对照中的LUAD和SCLC,准确率为0.90(灵敏度= 0.95,特异性= 0.85)。结论:基于唾液mirna的MLR模型是一种很有前景的肺癌诊断工具,为高风险无症状个体提供了一种无创筛查选择。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
BMC Pulmonary Medicine
BMC Pulmonary Medicine RESPIRATORY SYSTEM-
CiteScore
4.40
自引率
3.20%
发文量
423
审稿时长
6-12 weeks
期刊介绍: BMC Pulmonary Medicine is an open access, peer-reviewed journal that considers articles on all aspects of the prevention, diagnosis and management of pulmonary and associated disorders, as well as related molecular genetics, pathophysiology, and epidemiology.
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