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

IF 2.6 3区 医学 Q2 RESPIRATORY SYSTEM
Negar Alizadeh, Hoda Zahedi, Maryam Koopaie, Mahnaz Fatahzadeh, Reza Mousavi, Sajad Kolahdooz
{"title":"Diagnosis of lung cancer using salivary miRNAs expression and clinical characteristics.","authors":"Negar Alizadeh, Hoda Zahedi, Maryam Koopaie, Mahnaz Fatahzadeh, Reza Mousavi, Sajad Kolahdooz","doi":"10.1186/s12890-025-03502-6","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>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.</p><p><strong>Materials: </strong>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.</p><p><strong>Results: </strong>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).</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":9148,"journal":{"name":"BMC Pulmonary Medicine","volume":"25 1","pages":"41"},"PeriodicalIF":2.6000,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11765895/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMC Pulmonary Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s12890-025-03502-6","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"RESPIRATORY SYSTEM","Score":null,"Total":0}
引用次数: 0

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.

求助全文
约1分钟内获得全文 求助全文
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.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信