scholarly journals A Prediction Model Based on DNA Methylation Biomarkers and Radiological Characteristics for Identifying Malignant From Benign Pulmonary Nodules

2020 ◽  
Author(s):  
Wenqun Xing ◽  
Haibo Sun ◽  
Chi Yan ◽  
Chengzhi Zhao ◽  
Dongqing Wang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Dna Methylation ◽  
Prediction Model ◽  
Characteristic Curve ◽  
Multivariate Logistic Regression Analysis ◽  
Pulmonary Nodules ◽  
Diagnostic Value ◽  
Receiver Operator Characteristic Curve ◽  
Model Based ◽  
Diagnosis Of Lung Cancer

Abstract BackgroundLung cancer remains the leading cause of cancer deaths across the world. Early detection of lung cancer by low-dose computed tomography (LDCT) can reduce the mortality rate. However, making a definitive preoperative diagnosis of malignant pulmonary nodules (PNs) found by LDCT is a clinical challenge. This study aimed to develop a prediction model based on DNA methylation biomarkers and radiological characteristics for identifying malignant pulmonary nodules from benign PNs. MethodsWe assessed three DNA methylation biomarkers (PTGER4, RASSF1A, and SHOX2) in a training cohort of 110 individuals with PNs. Using univariate and multivariate logistic regression analysis, we developed a prediction model based on the three DNA methylation biomarkers and one radiological characteristic for identifying malignant from benign PNs. The performance of the prediction model with that of the methylation biomarkers and the Mayo Clinic model were compared using the non-parametric approach of DeLong et al. with the area under a receiver operator characteristic curve (AUC) analysis. ResultsThe developed prediction model achieved a sensitivity of 87.3% and a specificity of 95.7% with an AUC value of 0.951 in malignant PNs diagnosis, being significantly higher than the three DNA methylation biomarkers (84.1% sensitivity and 89.4% specificity, p=0.013) or clinical/radiological characteristics (76.2% sensitivity and 87.2% specificity, p=0.001) alone. Validation of the prediction model in the testing cohort of 100 subjects with PNs confirmed the diagnostic value.ConclusionWe have shown that integrating DNA methylation biomarkers and radiological characteristics could more accurately identify lung cancer in subjects with CT-found PNs. The prediction model developed in our study may provide clinical utility in combination with LDCT to improve the over-all diagnosis of lung cancer.

scholarly journals A prediction model based on DNA methylation biomarkers and radiological characteristics for identifying malignant from benign pulmonary nodules

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Wenqun Xing ◽  
Haibo Sun ◽  
Chi Yan ◽  
Chengzhi Zhao ◽  
Dongqing Wang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Dna Methylation ◽  
Prediction Model ◽  
Mayo Clinic ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Pulmonary Nodules ◽  
Receiver Operator Characteristic Curve ◽  
Support Vector ◽  
Model Based

Abstract Background Lung cancer remains the leading cause of cancer deaths across the world. Early detection of lung cancer by low-dose computed tomography (LDCT) can reduce the mortality rate. However, making a definitive preoperative diagnosis of malignant pulmonary nodules (PNs) found by LDCT is a clinical challenge. This study aimed to develop a prediction model based on DNA methylation biomarkers and radiological characteristics for identifying malignant pulmonary nodules from benign PNs. Methods We assessed three DNA methylation biomarkers (PTGER4, RASSF1A, and SHOX2) and clinically-relevant variables in a training cohort of 110 individuals with PNs. Four machine-learning-based prediction models were established and compared, including the K-nearest neighbors (KNN), random forest (RF), support vector machine (SVM), and logistic regression (LR) algorithms. Variables of the best-performing algorithm (LR) were selected through stepwise use of Akaike’s information criterion (AIC). The constructed prediction model was compared with the methylation biomarkers and the Mayo Clinic model using the non-parametric approach of DeLong et al. with the area under a receiver operator characteristic curve (AUC) analysis. Results A prediction model was finally constructed based on three DNA methylation biomarkers and one radiological characteristic for identifying malignant from benign PNs. The developed prediction model achieved an AUC value of 0.951 in malignant PNs diagnosis, significantly higher than the three DNA methylation biomarkers (0.912, 95% CI:0.843–0.958, p = 0.013) or Mayo Clinic model (0.823, 95% CI:0.739–0.890, p = 0.001). Validation of the prediction model in the testing cohort of 100 subjects with PNs confirmed the diagnostic value. Conclusion We have shown that integrating DNA methylation biomarkers and radiological characteristics could more accurately identify lung cancer in subjects with CT-found PNs. The prediction model developed in our study may provide clinical utility in combination with LDCT to improve the over-all diagnosis of lung cancer.

scholarly journals Autoantibody Signatures Discovered by HuProt Protein Microarray to Enhance the Diagnosis of Lung Cancer

2021 ◽  
Author(s):  
Yulin Wang ◽  
Jiaqi Li ◽  
Xue Zhang ◽  
Man Liu ◽  
Longtao Ji ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Validation Cohort ◽  
Mediastinal Lymph Node ◽  
Protein Microarray ◽  
Characteristic Curve ◽  
Pulmonary Nodules ◽  
Diagnostic Value ◽  
Enzyme Linked Immunosorbent Assay ◽  
Significant Difference ◽  
Diagnosis Of Lung Cancer

Abstract Background: This study aims to comprehensively discover novel autoantibodies (TAAbs) against tumor-associated antigens (TAAs) and establish diagnostic models for assisting in the diagnosis of lung cancer (LC) and discrimination of pulmonary nodules (PN).Methods: HuProt human microarray was used to discover the candidate TAAs and Enzyme-linked immunosorbent assay (ELISA) was performed to detect the level of TAAbs in 634 participants of two independent validation cohorts. Logistic regression analysis was used to construct models. Receiver operating characteristic curve (ROC) analysis was utilized to assess the diagnostic value of models.Results: Eleven TAAs were discovered by means of protein microarray and data analysis. The level of ten TAAbs (anti-SARS, anti-ZPR1, anti-FAM131A, anti-GGA3, anti-PRKCZ, anti-HDAC1, anti-GOLPH3, anti-NSG1, anti-CD84 and anti-EEA1) was higher in LC patients than that in NC of validation cohort 1 (P<0.05). The model 1 comprising 4 TAAbs (anti-ZPR1, anti-PRKCZ, anti-NSG1 and anti-CD84) and CEA reached an AUC of 0.813 (95%CI: 0.762-0.864) for diagnosing LC from normal individuals. 5 of 10 TAAbs (anti-SARS, anti-GOLPH3, anti-NSG1, anti-CD84 and anti-EEA1) existed a significant difference between malignant pulmonary nodules (MPN) and benign pulmonary nodules (BPN) patients in validation cohort 2 (P<0.05). Model 2 consisting of anti-EEA1, traditional biomarkers (CEA, CYFRA211 and CA125) and 3 CT characteristics (vascular notch sign, lobulation sign, mediastinal lymph node enlargement) could distinguish MPN from BPN patients with an AUC of 0.845 (sensitivity: 58.3%, specificity: 96.6%).Conclusions: High-throughput protein microarray is an efficient approach to discovering novel TAAbs which could increase the accuracy of lung cancer diagnosis in the clinic.

scholarly journals A Prediction Model Based on Biomarkers and Clinical Characteristics for Detection of Lung Cancer in Pulmonary Nodules

2017 ◽  
Vol 10 (1) ◽  
pp. 40-45 ◽  
Author(s):  
Jie Ma ◽  
Maria A. Guarnera ◽  
Wenxian Zhou ◽  
HongBin Fang ◽  
Feng Jiang
Keyword(s):  
Lung Cancer ◽  
Prediction Model ◽  
Clinical Characteristics ◽  
Pulmonary Nodules ◽  
Model Based

scholarly journals A panel of DNA methylation biomarkers for detection and improving diagnostic efficiency of lung cancer

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bing Wei ◽  
Fengxin Wu ◽  
Wenqun Xing ◽  
Haibo Sun ◽  
Chi Yan ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Dna Methylation ◽  
Imaging Techniques ◽  
High Sensitivity ◽  
Clinical Problem ◽  
Diagnostic Value ◽  
Prostaglandin E ◽  
High Risk Population ◽  
Diagnostic Efficiency ◽  
Diagnosis Of Lung Cancer

AbstractLung cancer remains the leading cause of cancer deaths worldwide. Although low-dose spiral computed tomography (LDCT) screening is used for the detection of lung cancer in a high-risk population, false-positive results of LDCT remain a clinical problem. Here, we developed a blood test of a novel panel of three established lung cancer methylation biomarkers for lung cancer detection. Short stature homeobox 2 gene (SHOX2), ras association domain family 1A gene (RASSF1A), and prostaglandin E receptor 4 gene (PTGER4) methylation was analyzed in a training cohort of 351 individuals (197 controls, 154 cases) and validated from an independent cohort of 149 subjects (89 controls, 60 cases). The novel panel biomarkers distinguished between malignant and benign lung disease at high sensitivity and specificity: 87.0% sensitivity [95% CI 80.2–91.5%], 98.0% specificity [95% CI 94.9–99.4%]. Sensitivity in adenocarcinoma, squamous cell carcinoma, small cell lung cancer, and other lung cancer was 89.0%, 87.5%, 85.7%, and 77.8%, respectively. Notably, cancer patients in stage I and II showed high diagnostic sensitivity at 82.5% and 90.5%, respectively. Moreover, the diagnostic efficiency did not show bias toward age, gender, smoking, and the presence of other (nonlung) cancers. The performance of the panel in the validation cohort confirmed the diagnostic value. These findings clearly showed that this panel of DNA methylation biomarkers was effective in detecting lung cancer noninvasively and may provide clinical utility in stand-alone or in combination with current imaging techniques to improve the diagnosis of lung cancer.

scholarly journals Exhaled breath condensate biomarkers for the early diagnosis of lung cancer using proteomics

2017 ◽  
Vol 313 (4) ◽  
pp. L664-L676 ◽  
Author(s):  
Laura M. López-Sánchez ◽  
Bernabé Jurado-Gámez ◽  
Nuria Feu-Collado ◽  
Araceli Valverde ◽  
Amanda Cañas ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Proteomic Analysis ◽  
Exhaled Breath Condensate ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Diagnostic Value ◽  
Chronic Obstructive ◽  
Exhaled Breath ◽  
Diagnosis Of Lung Cancer ◽  
Breath Condensate

We explored whether the proteomic analysis of exhaled breath condensate (EBC) may provide biomarkers for noninvasive screening for the early detection of lung cancer (LC). EBC was collected from 192 individuals [49 control (C), 49 risk factor-smoking (S), 46 chronic obstructive pulmonary disease (COPD) and 48 LC]. With the use of liquid chromatography and tandem mass spectrometry, 348 different proteins with a different pattern among the four groups were identified in EBC samples. Significantly more proteins were identified in the EBC from LC compared with other groups (C: 12.4 ± 1.3; S: 15.3 ± 1; COPD: 14 ± 1.6; LC: 24.2 ± 3.6; P = 0.0001). Furthermore, the average number of proteins identified per sample was significantly higher in LC patients, and receiver operating characteristic curve (ROC) analysis showed an area under the curve of 0.8, indicating diagnostic value. Proteins frequently detected in EBC, such as dermcidin and hornerin, along with others much less frequently detected, such as hemoglobin and histones, were identified. Cytokeratins (KRTs) were the most abundant proteins in EBC samples, and levels of KRT6A, KRT6B, and KRT6C isoforms were significantly higher in samples from LC patients ( P = 0.0031, 0.0011, and 0.0009, respectively). Moreover, the amount of most KRTs in EBC samples from LC patients showed a significant positive correlation with tumor size. Finally, we used a random forest algorithm to generate a robust model using EBC protein data for the diagnosis of patients with LC where the area under the ROC curve obtained indicated a good classification (82%). Thus this study demonstrates that the proteomic analysis of EBC samples is an appropriated approach to develop biomarkers for the diagnosis of lung cancer.

scholarly journals Evaluation Of The Diagnostic Value Of Electromagnetic Navigational Guided Targeted-Bronchoalveolar Lavage In The Diagnosis Of Lung Cancer

2020 ◽  
Vol 6 (4) ◽  
pp. 1-6
Author(s):  
Ismael Matus ◽  
Keyword(s):  
Lung Cancer ◽  
Diagnostic Yield ◽  
Pulmonary Nodules ◽  
Diagnostic Value ◽  
Electromagnetic Navigation ◽  
Diagnostic Bronchoscopy ◽  
Tissue Acquisition ◽  
Diagnosis Of Lung Cancer ◽  
The Ideal

Electromagnetic Navigation Bronchoscopy (ENB) is recommended for the evaluation of Peripheral Pulmonary Nodules (PPNs). Current diagnostic bronchoscopy and pulmonary nodule evaluation guidelines do not establish recommendations regarding the role of individual tissue acquisition techniques, the ideal combination or sequence of executing them to optimize diagnostic yield.

scholarly journals 0588 Ring Pulse Oximeter for Screening of Moderate to Severe OSA: A Pilot Study

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A225-A225
Author(s):  
J Xue ◽  
R Zhao ◽  
J Li ◽  
L Zhao ◽  
B Zhou ◽  
...  
Keyword(s):  
Pilot Study ◽  
Pulse Oximeter ◽  
Characteristic Curve ◽  
Diagnostic Value ◽  
Receiver Operator Characteristic Curve ◽  
Oxygen Desaturation ◽  
Significant Difference ◽  
Acceptance Range ◽  
The Difference ◽  
Two Parameters

Abstract Introduction To evaluate the utility of the ring pulse oximeter for screening of OSA in adults. Methods 87 adults were monitored by a ring pulse oximeter and PSG simultaneously during a nocturnal in-lab sleep testing. 3% oxygen desaturation index (ODI3); Mean oxygen saturation(MSpO2), Saturation impair time below 90% (SIT90) derived from an automated algorithm of the ring pulse oximeter. Meanwhile, the parameters of PSG were scored manually according to the AASM Manual. Correlation and receiver operator characteristic curve analysis were used to measure the accuracy of ring pulse oximeter and its diagnostic value for moderate to severe OSA (AHI≥15). Results Among the 87 participants, 18 cases were AHI&lt;5, 17 cases were diagnosed with mild OSA (AHI:5-14.9), 25 cases were diagnosed with moderate OSA (AHI:15-29.9) and 27 cases were diagnosed with severe OSA (AHI≥30). There was no significant difference between PSG and ring pulse oximeter in regard to ODI3 (23.4±23.5 vs 24.7 ± 21.7), and SIT90 (1.54%, range 0.14%-8.99% vs. 3.20%, range 0.60%, 12.30%) (P&gt;0.05], Further analysis indicated that two parameters from the oximeter correlated well with that derived from PSG (r=0.889, 0.567, respectively, both p&lt;0.05). Although MSpO2 correlated significantly (r=0.448, P&lt;0.05), the difference was remarkable [95.9%, range 94.0% to 97.0% vs. 94.5%, range 93.3% to 95.7%, p&lt;0.05]. Bland-Altman plots showed that the agreement of these three parameters was within the clinical acceptance range. The ROC curve showed that the sensitivity and specificity of the ring pulse oximeter when the oximeter derived ODI3 ≥12.5 in the diagnosis of moderate to severe OSA were 82.7% and 74.3%, respectively. Conclusion The pilot study indicated that ring pulse oximeter can detect oxygen desaturation events accurately, therefore to be used as a screening tool for moderate to severe OSA. Support The study was supported by the National Natural Science Foundation of China (No. 81420108002 and NO. 81570083).

Systemic immune inflammation index: a novel predictor for coronary collateral circulation

Perfusion ◽  
2021 ◽  
pp. 026765912110148
Author(s):  
Saban Kelesoglu ◽  
Yucel Yilmaz ◽  
Deniz Elcık ◽  
Nihat Kalay
Keyword(s):  
Collateral Circulation ◽  
Stable Coronary Artery Disease ◽  
Characteristic Curve ◽  
Multivariate Logistic Regression Analysis ◽  
Receiver Operator Characteristic Curve ◽  
Coronary Collateral Circulation ◽  
Neutrophil Lymphocyte Ratio ◽  
Lymphocyte Ratio ◽  
Coronary Collateral ◽  
Immune Inflammation

Aim: Recently, a new inflammatory and prognostic marker has emerged called as Systemic Immune Inflammation Index (SII). In the current study, we searched the relation between SII and Coronary Collateral Circulation (CCC) formation in stable Coronary Artery Disease (CAD). Materials & methods: 449 patients with stable CAD who underwent coronary angiography and documented coronary stenosis of 95% or more in at least one major coronary vessel were included in the study. The study patients were divided into two groups according to the Rentrop score as well CCC (Rentrop 2–3) and bad CCC (Rentrop 0–1). Blood samples for SII and other laboratory parameters were gathered from all the patients on admission. The SII score was formulized as platelet × neutrophil/lymphocyte counts. Results: Patients, who had developed bad CCC had a higher C-reactive protein (CRP), neutrophil/lymphocyte ratio (NLR), platelets/lymphocyte ratio (PLR) and SII levels compared to those who had developed well CCC (p < 0.001, for all). Multivariate logistic regression analysis showed that high levels of SII was an independent predictor of bad CCC (OR: 1.005, 95% confidence interval (CI): 1.003–1.006, p < 0.001) together with dyslipidemia, high levels of CRP and NLR. In Receiver Operator Characteristic curve (ROC) analysis, the optimal cutoff value of SII to predict poor CCC was found to be 729.8, with 78.4% sensitivity and 74.6% specificity (area under ROC curve = 0.833 (95% CI: 0.777–0.889, p < 0.001). Conclusion: We have demonstrated that SII, a novel cardiovascular risk marker, might be used as one of the independent predictors of CCC development.

scholarly journals Diagnosis of pulmonary nodules by DNA methylation analysis in bronchoalveolar lavage fluids

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Lei Li ◽  
Zhujia Ye ◽  
Sai Yang ◽  
Hao Yang ◽  
Jing Jin ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Dna Methylation ◽  
Bronchoalveolar Lavage ◽  
Pulmonary Nodules ◽  
Squamous Carcinoma ◽  
Lavage Fluid ◽  
Smoking History ◽  
Lung Cancer Diagnosis ◽  
Auc Value ◽  
Validation Set

Abstract Background Lung cancer is the leading cause of cancer-related mortality. The alteration of DNA methylation plays a major role in the development of lung cancer. Methylation biomarkers become a possible method for lung cancer diagnosis. Results We identified eleven lung cancer-specific methylation markers (CDO1, GSHR, HOXA11, HOXB4-1, HOXB4-2, HOXB4-3, HOXB4-4, LHX9, MIR196A1, PTGER4-1, and PTGER4-2), which could differentiate benign and malignant pulmonary nodules. The methylation levels of these markers are significantly higher in malignant tissues. In bronchoalveolar lavage fluid (BALF) samples, the methylation signals maintain the same differential trend as in tissues. An optimal 5-marker model for pulmonary nodule diagnosis (malignant vs. benign) was developed from all possible combinations of the eleven markers. In the test set (57 tissue and 71 BALF samples), the area under curve (AUC) value achieves 0.93, and the overall sensitivity is 82% at the specificity of 91%. In an independent validation set (111 BALF samples), the AUC is 0.82 with a specificity of 82% and a sensitivity of 70%. Conclusions This model can differentiate pulmonary adenocarcinoma and squamous carcinoma from benign diseases, especially for infection, inflammation, and tuberculosis. The model’s performance is not affected by gender, age, smoking history, or the solid components of nodules.

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