Combined sputum hypermethylation and eNose analysis for lung cancer diagnosis

2014 ◽  
Vol 67 (8) ◽  
pp. 707-711 ◽  
Author(s):  
A Jasmijn Hubers ◽  
Paul Brinkman ◽  
Remco J Boksem ◽  
Robert J Rhodius ◽  
Birgit I Witte ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Patients ◽  
Cancer Diagnosis ◽  
Breath Analysis ◽  
Exhaled Breath ◽  
Dna Hypermethylation ◽  
Lung Cancer Patients ◽  
Lung Cancer Diagnosis ◽  
Exhaled Breath Analysis ◽  
Validation Set

AimsThe aim of this study is to explore DNA hypermethylation analysis in sputum and exhaled breath analysis for their complementary, non-invasive diagnostic capacity in lung cancer.MethodsSputum samples and exhaled breath were prospectively collected from 20 lung cancer patients and 31 COPD controls (Set 1). An additional 18 lung cancer patients and 8 controls only collected exhaled breath as validation set (Set 2). DNA hypermethylation of biomarkers RASSF1A, cytoglobin, APC, FAM19A4, PHACTR3, 3OST2 and PRDM14 was considered, and breathprints from exhaled breath samples were created using an electronic nose (eNose).ResultsBoth DNA hypermethylation markers in sputum and eNose were independently able to distinguish lung cancer patients from controls. The combination of RASSF1A and 3OST2 hypermethylation had a sensitivity of 85% with a specificity of 74%. eNose had a sensitivity of 80% with a specificity of 48%. Sensitivity for lung cancer diagnosis increased to 100%, when RASSF1A hypermethylation was combined with eNose, with specificity of 42%. Both methods showed to be complementary to each other (p≤0.011). eNose results were reproducible in Set 2.ConclusionsWhen used in concert, RASSF1A hypermethylation in sputum and exhaled breath analysis are complementary for lung cancer diagnosis, with 100% sensitivity in this series. This finding should be further validated.

scholarly journals VOC biomarkers identification and predictive model construction for lung cancer based on exhaled breath analysis: research protocol for an exploratory study

BMJ Open ◽  
2019 ◽  
Vol 9 (8) ◽  
pp. e028448 ◽  
Author(s):  
Wenwen Li ◽  
Wei Dai ◽  
Mingxin Liu ◽  
Yijing Long ◽  
Chunyan Wang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Early Detection ◽  
Predictive Model ◽  
Cancer Patients ◽  
Healthy Subjects ◽  
Solid Phase ◽  
Breath Analysis ◽  
Exhaled Breath ◽  
Lung Cancer Patients ◽  
Exhaled Breath Analysis

IntroductionLung cancer is the most common cancer and the leading cause of cancer death in China, as well as in the world. Late diagnosis is the main obstacle to improving survival. Currently, early detection methods for lung cancer have many limitations, for example, low specificity, risk of radiation exposure and overdiagnosis. Exhaled breath analysis is one of the most promising non-invasive techniques for early detection of lung cancer. The aim of this study is to identify volatile organic compound (VOC) biomarkers in lung cancer and to construct a predictive model for lung cancer based on exhaled breath analysis.Methods and analysisThe study will recruit 389 lung cancer patients in one cancer centre and 389 healthy subjects in two lung cancer screening centres. Bio-VOC breath sampler and Tedlar bag will be used to collect breath samples. Gas chromatography-mass spectrometry coupled with solid phase microextraction technique will be used to analyse VOCs in exhaled breath. VOC biomarkers with statistical significance and showing abilities to discriminate lung cancer patients from healthy subjects will be selected for the construction of predictive model for lung cancer.Ethics and disseminationThe study was approved by the Ethics Committee of Sichuan Cancer Hospital on 6 April 2017 (No. SCCHEC-02-2017-011). The results of this study will be disseminated in presentations at academic conferences, publications in peer-reviewed journals and the news media.Trial registration numberChiCTR-DOD-17011134; Pre-results.

scholarly journals Exhaled Breath Analysis of Lung Cancer Patients Using a Metal Oxide Sensor

2011 ◽  
Vol 20 (5) ◽  
pp. 300-304 ◽  
Author(s):  
Joon-Boo Yu ◽  
Hyung-Gi Byun ◽  
Sholin Zhang ◽  
Seoung-Hun Do ◽  
Jeong-Ok Lim ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Metal Oxide ◽  
Cancer Patients ◽  
Breath Analysis ◽  
Exhaled Breath ◽  
Lung Cancer Patients ◽  
Exhaled Breath Analysis ◽  
Metal Oxide Sensor

scholarly journals Increased microRNA-30a levels in bronchoalveolar lavage fluid as a diagnostic biomarker for lung cancer

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11528
Author(s):  
Wen-Jun Zhu ◽  
Bo-Jiang Chen ◽  
Ying-Ying Zhu ◽  
Ling Sun ◽  
Yu-Chen Zhang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Bronchoalveolar Lavage ◽  
Cancer Patients ◽  
Cancer Diagnosis ◽  
Roc Curve ◽  
Lung Diseases ◽  
Bronchoalveolar Lavage Fluid ◽  
Lavage Fluid ◽  
Lung Cancer Patients ◽  
Lung Cancer Diagnosis

Background MicroRNA-30a (miRNA-30a) levels have been shown to increase in the plasma of lung cancer patients. Herein, we evaluated the miRNA-30a levels in the bronchoalveolar lavage fluid (BALF) of lung cancer patients as a potential biomarker for lung cancer diagnosis. Methods BALF miRNA-30a expression of 174 subjects was quantified using quantitative real-time reverse transcription-polymerase chain reaction and compared between lung cancer patients and control patients with benign lung diseases. Moreover, its diagnostic value was evaluated by performing receiver operating characteristic (ROC) curve analysis. Results The relative BALF miRNA-30a expression was significantly higher in the lung cancer patients than in the controls (0.74 ±  0.55 versus 0.07 ±  0.48, respectively, p < 0.001) as well as in lung cancer patients with stage I–IIA disease than in those with stage IIB–IV disease (0.98 ±  0.64 versus 0.66 ±  0.54, respectively, p < 0.05). Additionally, miRNA-30a distinguished benign lung diseases from lung cancers, with an area under the ROC curve (AUC) of 0.822. ROC analysis also revealed an AUC of 0.875 for the Youden index-based optimal cut-off points for stage I–IIA adenocarcinoma. Thus, increased miRNA-30a levels in BALF may be a useful biomarker for non-small-cell lung cancer diagnosis.

scholarly journals Case study of an autoantibody panel for early detection of lung cancer and ground-glass nodules

2020 ◽  
Vol 146 (12) ◽  
pp. 3349-3357
Author(s):  
Yunli Huo ◽  
Zijian Guo ◽  
Xuehui Gao ◽  
Zhongjuan Liu ◽  
Ruili Zhang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Lung Disease ◽  
Cancer Patients ◽  
Cancer Diagnosis ◽  
Predictive Value ◽  
Positive Test ◽  
Test Results ◽  
Newly Diagnosed ◽  
Lung Cancer Patients ◽  
Lung Cancer Diagnosis

Abstract Purpose Increasing lung cancer incidence in China with a high death rate due to late diagnosis highlights the need for biomarkers, such as panels of autoantibodies (AAbs), for prediction and early lung cancer diagnosis. We conducted a study to further evaluate the clinical performance of an AAb diagnostic kit. Methods Using enzyme-linked immunosorbent assay, levels of seven AAbs in serum samples from 121 patients with newly diagnosed lung cancer, 84 controls (34 healthy individuals and 50 patients with benign lung disease), and 100 indeterminate solid nodules, were measured. Participants were followed up until 6 months after a positive test result to confirm lung cancer diagnosis. Results The seven AAb concentration was significantly higher in lung cancer patients than in controls (P < 0.05). The seven AAb sensitivity and specificity for newly diagnosed lung cancer were 45.5% and 85.3%, respectively, while the seven AAb combined area under the curve (in lung cancer patients versus controls) was 0.660. Of the 28 patients with solid nodules with positive test results, 8 and 3 were diagnosed with lung cancer and benign lung disease, respectively, during follow-up. The positive predictive value of the experiment was 72.7%. Conclusion Positive AAb test results were associated with a high risk of lung cancer. The seven-AAb panel also had a high predictive value for detecting lung cancer in patients with solid nodules. Our seven lung cancer autoantibody types can provide an important early warning sign in the clinical setting.

scholarly journals Improving lung cancer diagnosis by combining exhaled-breath data and clinical parameters

2020 ◽  
Vol 6 (1) ◽  
pp. 00221-2019 ◽  
Author(s):  
Sharina Kort ◽  
Marjolein Brusse-Keizer ◽  
Jan Willem Gerritsen ◽  
Hugo Schouwink ◽  
Emanuel Citgez ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Regression Analysis ◽  
Clinical Information ◽  
Breath Analysis ◽  
Multivariate Regression Analysis ◽  
Clinical Parameters ◽  
Exhaled Breath ◽  
Lung Cancer Diagnosis ◽  
Exhaled Breath Analysis ◽  
Improved Performance

IntroductionExhaled-breath analysis of volatile organic compounds could detect lung cancer earlier, possibly leading to improved outcomes. Combining exhaled-breath data with clinical parameters may improve lung cancer diagnosis.MethodsBased on data from a previous multi-centre study, this article reports additional analyses. 138 subjects with non-small cell lung cancer (NSCLC) and 143 controls without NSCLC breathed into the Aeonose. The diagnostic accuracy, presented as area under the receiver operating characteristic curve (AUC-ROC), of the Aeonose itself was compared with 1) performing a multivariate logistic regression analysis of the distinct clinical parameters obtained, and 2) using this clinical information beforehand in the training process of the artificial neural network (ANN) for the breath analysis.ResultsNSCLC patients (mean±sd age 67.1±9.1 years, 58% male) were compared with controls (62.1±7.0 years, 40.6% male). The AUC-ROC of the classification value of the Aeonose itself was 0.75 (95% CI 0.69–0.81). Adding age, number of pack-years and presence of COPD to this value in a multivariate regression analysis resulted in an improved performance with an AUC-ROC of 0.86 (95% CI 0.81–0.90). Adding these clinical variables beforehand to the ANN for classifying the breath print also led to an improved performance with an AUC-ROC of 0.84 (95% CI 0.79–0.89).ConclusionsAdding readily available clinical information to the classification value of exhaled-breath analysis with the Aeonose, either post hoc in a multivariate regression analysis or a priori to the ANN, significantly improves the diagnostic accuracy to detect the presence or absence of lung cancer.

Concentration and integrity indexes of urine cell-free DNA as promising biomarkers for early lung cancer diagnosis

2021 ◽  
Vol 18 (2) ◽  
pp. 129-139
Author(s):  
Sai Ren ◽  
Xiaodong Ren ◽  
Haiqin Guo ◽  
Lan Liang ◽  
Kun Wei ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Patients ◽  
Cancer Diagnosis ◽  
Characteristic Curve ◽  
Healthy Controls ◽  
Cell Free Dna ◽  
Lung Cancer Patients ◽  
Lung Cancer Diagnosis ◽  
Free Dna ◽  
Pcr Targeting

Aim: To explore the role of urine cell-free DNA (ucfDNA) concentration and integrity indexes as potential biomarkers for lung cancer diagnosis. Materials & methods: Quantitative real-time PCR targeting Arthrobacter luteus ( ALU) repeats at three size fragments ( ALU-60, 115 and 247 bp) was performed in 55 lung cancer patients and 35 healthy individuals. Results: ucfDNA concentration and integrity indexes were significantly higher in lung cancer patients than in healthy controls. The area under the receiver operating characteristic curve for differentiating patients with stage I/II from healthy controls by ALU fragments concentration were 0.856, 0.909 and 0.932, respectively. In addition, the ucfDNA integrity indexes in patients with lymph node metastasis were significantly higher than in patients with non-metastatic. Conclusion: ucfDNA concentration and integrity indexes could serve as promising biomarkers for lung cancer diagnosis.

scholarly journals Effect of Comorbidity on Lung Cancer Diagnosis Timing and Mortality: A Nationwide Population-Based Cohort Study in Taiwan

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Shinechimeg Dima ◽  
Kun-Huang Chen ◽  
Kung-Jeng Wang ◽  
Kung-Min Wang ◽  
Nai-Chia Teng
Keyword(s):  
Lung Cancer ◽  
Overall Survival ◽  
Cancer Patients ◽  
Cancer Diagnosis ◽  
Population Based ◽  
Physician Visits ◽  
Lung Cancer Patients ◽  
Lung Cancer Diagnosis ◽  
Kaplan Meier ◽  
Diagnosis Of Lung Cancer

The effect of comorbidity on lung cancer patients’ survival has been widely reported. The aim of this study was to investigate the effects of comorbidity on the establishment of the diagnosis of lung cancer and survival in lung cancer patients in Taiwan by using a nationwide population-based study design. This study collected various comorbidity patients and analyzed data regarding the lung cancer diagnosis and survival during a 16-year follow-up period (1995–2010). In total, 101,776 lung cancer patients were included, comprising 44,770 with and 57,006 without comorbidity. The Kaplan–Meier analyses were used to compare overall survival between lung cancer patients with and without comorbidity. In our cohort, chronic bronchitis patients who developed lung cancer had the lowest overall survival in one (45%), five (28.6%), and ten years (26.2%) since lung cancer diagnosis. Among lung cancer patients with nonpulmonary comorbidities, patients with hypertension had the lowest overall survival in one (47.9%), five (30.5%), and ten (28.2%) years since lung cancer diagnosis. In 2010, patients with and without comorbidity had 14.86 and 9.31 clinical visits, respectively. Lung cancer patients with preexisting comorbidity had higher frequency of physician visits. The presence of comorbid conditions was associated with early diagnosis of lung cancer.

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