Diagnosis of lung cancer using salivary miRNAs expression and clinical characteristics
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 a...
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| Format: | Article |
| Language: | English |
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BMC
2025-01-01
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| Series: | BMC Pulmonary Medicine |
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| Online Access: | https://doi.org/10.1186/s12890-025-03502-6 |
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| author | Negar Alizadeh Hoda Zahedi Maryam Koopaie Mahnaz Fatahzadeh Reza Mousavi Sajad Kolahdooz |
| author_facet | Negar Alizadeh Hoda Zahedi Maryam Koopaie Mahnaz Fatahzadeh Reza Mousavi Sajad Kolahdooz |
| author_sort | Negar Alizadeh |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-f2ac68ab605249c2945a2a30dba8a505 |
| institution | Kabale University |
| issn | 1471-2466 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Pulmonary Medicine |
| spelling | doaj-art-f2ac68ab605249c2945a2a30dba8a5052025-01-26T12:12:56ZengBMCBMC Pulmonary Medicine1471-24662025-01-0125111310.1186/s12890-025-03502-6Diagnosis of lung cancer using salivary miRNAs expression and clinical characteristicsNegar Alizadeh0Hoda Zahedi1Maryam Koopaie2Mahnaz Fatahzadeh3Reza Mousavi4Sajad Kolahdooz5Department of Oral Medicine, School of Dentistry, Tehran University of Medical SciencesDepartment of Oral Medicine, School of Dentistry, Tehran University of Medical SciencesDepartment of Oral Medicine, School of Dentistry, Tehran University of Medical SciencesDivision of Oral Medicine, Department of Oral Medicine, Rutgers School of Dental MedicineDepartment of Medical Genetics, Faculty of Medicine, Tabriz University of Medical SciencesUniversal Scientific Education and Research Network (USERN)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.https://doi.org/10.1186/s12890-025-03502-6Lung cancermicroRNASmall-cell lung carcinomaLung adenocarcinomaSalivaBiomarker |
| spellingShingle | Negar Alizadeh Hoda Zahedi Maryam Koopaie Mahnaz Fatahzadeh Reza Mousavi Sajad Kolahdooz Diagnosis of lung cancer using salivary miRNAs expression and clinical characteristics BMC Pulmonary Medicine Lung cancer microRNA Small-cell lung carcinoma Lung adenocarcinoma Saliva Biomarker |
| title | Diagnosis of lung cancer using salivary miRNAs expression and clinical characteristics |
| title_full | Diagnosis of lung cancer using salivary miRNAs expression and clinical characteristics |
| title_fullStr | Diagnosis of lung cancer using salivary miRNAs expression and clinical characteristics |
| title_full_unstemmed | Diagnosis of lung cancer using salivary miRNAs expression and clinical characteristics |
| title_short | Diagnosis of lung cancer using salivary miRNAs expression and clinical characteristics |
| title_sort | diagnosis of lung cancer using salivary mirnas expression and clinical characteristics |
| topic | Lung cancer microRNA Small-cell lung carcinoma Lung adenocarcinoma Saliva Biomarker |
| url | https://doi.org/10.1186/s12890-025-03502-6 |
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