Dataset Information

Discovery and validation of potential bacterial biomarkers for lung cancer.

ABSTRACT: Microbes are residents in a number of body sites, including the oral and nasal cavities, which are connected to the lung via the pharynx. The associations between oral diseases and increased risk of lung cancer have been reported in previous prospective studies. In this study, we measured variations of salivary microbiota and evaluated their potential association with lung cancer, including squamous cell carcinoma (SCC) and adenocarcinoma (AC). A three-phase study was performed: First, we investigated the salivary microbiota from 20 lung cancer patients (10 SCC and 10 AC) and control subjects (n=10) using a deep sequencing analysis. Salivary Capnocytophaga, Selenomonas, Veillonella and Neisseria were found to be significantly altered in patients with SCC and AC when compared to that in control subjects. Second, we confirmed the significant changes of Capnocytophaga, Veillonella and Neisseria in the same lung cancer patients using quantitative PCR (qPCR). Finally, these bacterial species were further validated on new patient/control cohorts (n=56) with qPCR. The combination of two bacterial biomarkers, Capnocytophaga and Veillonella, yielded a receiver operating characteristic (ROC) value of 0.86 with an 84.6% sensitivity and 86.7% specificity in distinguishing patients with SCC from control subjects and a ROC value of 0.80 with a 78.6% sensitivity and 80.0% specificity in distinguishing patients with AC from control subjects. In conclusion, we have for the first time demonstrated the association of saliva microbiota with lung cancer. Particularly, the combination of the 16S sequencing discovery with qPCR validation studies revealed that the levels of Capnocytophaga and Veillonella were significantly higher in the saliva from lung cancer patients, which may serve as potential biomarkers for the disease detection/classification.

SUBMITTER: Yan X

PROVIDER: S-EPMC4656734 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Discovery and validation of potential bacterial biomarkers for lung cancer.

Yan Xinmin X Yang Mingxia M Liu Juan J Gao Ruichen R Hu Jihong J Li Jiong J Zhang Lijun L Shi Yujia Y Guo Hongrong H Cheng Jinluo J Razi Miriam M Pang Shen S Yu Xiaowei X Hu Shen S

American journal of cancer research 20150915 10

Microbes are residents in a number of body sites, including the oral and nasal cavities, which are connected to the lung via the pharynx. The associations between oral diseases and increased risk of lung cancer have been reported in previous prospective studies. In this study, we measured variations of salivary microbiota and evaluated their potential association with lung cancer, including squamous cell carcinoma (SCC) and adenocarcinoma (AC). A three-phase study was performed: First, we invest ...[more]

PMID: 26693063

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Project description:BACKGROUND:Breath volatile organic compounds (VOCs) have been reported as biomarkers of lung cancer, but it is not known if biomarkers identified in one group can identify disease in a separate independent cohort. Also, it is not known if combining breath biomarkers with chest CT has the potential to improve the sensitivity and specificity of lung cancer screening. METHODS:Model-building phase (unblinded): Breath VOCs were analyzed with gas chromatography mass spectrometry in 82 asymptomatic smokers having screening chest CT, 84 symptomatic high-risk subjects with a tissue diagnosis, 100 without a tissue diagnosis, and 35 healthy subjects. Multiple Monte Carlo simulations identified breath VOC mass ions with greater than random diagnostic accuracy for lung cancer, and these were combined in a multivariate predictive algorithm. Model-testing phase (blinded validation): We analyzed breath VOCs in an independent cohort of similar subjects (n = 70, 51, 75 and 19 respectively). The algorithm predicted discriminant function (DF) values in blinded replicate breath VOC samples analyzed independently at two laboratories (A and B). Outcome modeling: We modeled the expected effects of combining breath biomarkers with chest CT on the sensitivity and specificity of lung cancer screening. RESULTS:Unblinded model-building phase. The algorithm identified lung cancer with sensitivity 74.0%, specificity 70.7% and C-statistic 0.78. Blinded model-testing phase: The algorithm identified lung cancer at Laboratory A with sensitivity 68.0%, specificity 68.4%, C-statistic 0.71; and at Laboratory B with sensitivity 70.1%, specificity 68.0%, C-statistic 0.70, with linear correlation between replicates (r = 0.88). In a projected outcome model, breath biomarkers increased the sensitivity, specificity, and positive and negative predictive values of chest CT for lung cancer when the tests were combined in series or parallel. CONCLUSIONS:Breath VOC mass ion biomarkers identified lung cancer in a separate independent cohort, in a blinded replicated study. Combining breath biomarkers with chest CT could potentially improve the sensitivity and specificity of lung cancer screening. TRIAL REGISTRATION:ClinicalTrials.gov NCT00639067.

Project description:Finding robust biomarkers for Parkinson disease (PD) is currently hampered by inherent technical limitations associated with imaging or antibody-based protein assays. To circumvent the challenges, we adapted a staged pipeline, starting from our previous proteomic profiling followed by high-throughput targeted mass spectrometry (MS), to identify peptides in human cerebrospinal fluid (CSF) for PD diagnosis and disease severity correlation. In this multicenter study consisting of training and validation sets, a total of 178 subjects were randomly selected from a retrospective cohort, matching age and sex between PD patients, healthy controls, and neurological controls with Alzheimer disease (AD). From ∼14,000 unique peptides displaying differences between PD and healthy control in proteomic investigations, 126 peptides were selected based on relevance and observability in CSF using bioinformatic analysis and MS screening, and then quantified by highly accurate and sensitive selected reaction monitoring (SRM) in the CSF of 30 PD patients versus 30 healthy controls (training set), followed by diagnostic (receiver operating characteristics) and disease severity correlation analyses. The most promising candidates were further tested in an independent cohort of 40 PD patients, 38 AD patients, and 40 healthy controls (validation set). A panel of five peptides (derived from SPP1, LRP1, CSF1R, EPHA4, and TIMP1) was identified to provide an area under curve (AUC) of 0.873 (sensitivity = 76.7%, specificity = 80.0%) for PD versus healthy controls in the training set. The performance was essentially confirmed in the validation set (AUC = 0.853, sensitivity = 82.5%, specificity = 82.5%). Additionally, this panel could also differentiate the PD and AD groups (AUC = 0.990, sensitivity = 95.0%, specificity = 97.4%). Furthermore, a combination of two peptides belonging to proteins TIMP1 and APLP1 significantly correlated with disease severity as determined by the Unified Parkinson's Disease Rating Scale motor scores in both the training (r = 0.381, p = 0.038)j and the validation (r = 0.339, p = 0.032) sets. The novel panel of CSF peptides, if validated in independent cohorts, could be used to assist in clinical diagnosis of PD and has the potential to help monitoring or predicting disease progression.

Dataset Information

Discovery and validation of potential bacterial biomarkers for lung cancer.

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Discovery and validation of potential bacterial biomarkers for lung cancer.

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