Identification of Serum Metabolites to improve diagnostic efficacy in pulmonary embolism
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ABSTRACT:
ORGANISM(S): Homo Sapiens
SUBMITTER: 
Wen Gu
PROVIDER: PXD057168 | iProX | Thu Oct 24 00:00:00 BST 2024
REPOSITORIES: iProX
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2022-06-01 | GSE197514 | GEO
Project description:Objectives: Central nervous system (CNS) infections are common causes of morbidity and mortality worldwide. We aimed to discover protein biomarkers that could rapidly and accurately identify the likely cause of the infections, essential for clinical management and improving outcome. Methods: We applied liquid chromatography tandem mass-spectrometry on 45 cerebrospinal fluid (CSF) samples from a cohort of adults with/without CNS infections to discover potential diagnostic biomarkers. We then validated the diagnostic performance of a selected biomarker candidate in an independent cohort of 364 consecutively treated adults with CNS infections admitted to a referral hospital in Vietnam. Results: In the discovery cohort, we identified lipocalin 2 (LCN2) as a potential biomarker of bacterial meningitis (BM) other than tuberculous meningitis. The analysis of the validation cohort showed that LCN2 could discriminate BM from other CNS infections (including tuberculous meningitis, cryptococcal meningitis and viral/antibody-mediated encephalitis), with the sensitivity: 0.88 (95% confident interval (CI): 0.77-0.94), the specificity: 0.91 (95%CI: 0.88-0.94) and the diagnostic odd ratio: 73.8 (95%CI: 31.8-171.4)). LCN2 outperformed other CSF markers (leukocytes, glucose, protein and lactate) commonly used in routine care worldwide. The combination of LCN2, CSF leukocytes, glucose, protein and lactate resulted in the highest diagnostic performance for BM (area under receiver-operating-characteristic-curve 0.96; 95%CI: 0.93-0.99). Conclusions: Our results suggest that LCN2 is a sensitive and specific biomarker for discriminating BM from a broad spectrum of other CNS infections. A prospective study is needed to assess the diagnostic utility of LCN2 in the diagnosis and management of CNS infections..
2020-08-20 | PXD020510 | Pride
Project description:Background & Aims: MicroRNAs have been shown to offer great potential in the diagnosis of cancer. We aimed to identify microRNAs in peripheral blood mononuclear cells (PBMCs) for diagnosing pancreatic cancer (PC). Methods: PBMCs microRNA expression was investigated in three independent cohorts including 352 participants (healthy, benign pancreatic/peripancreatic diseases (BPD), and PC). First, we used sequencing technology to identify differentially expressed microRNAs in 60 PBMCs samples for diagnosing PC. Quantitative reverse-transcriptase polymerase chain reaction assay was then applied to evaluate the expression of selected microRNAs. A logistic regression model was constructed using an independent cohort. Area under the receiver operating characteristic curve (AUC) was used to evaluate diagnostic accuracy. Results: We found that PBMCs miR-27a-3p could efficiently discriminate PC from BPD (AUC=0.840; 95% CI, 0.787 to 0.885; sensitivity=82.2%, specificity=76.7%). A panel composed of PBMCs miR-27a-3p and serum CA19-9 provided a high diagnostic accuracy in differentiating PC from BPD in the clinical setting (AUC=0.886; 95% CI, 0.837 to 0.923; sensitivity=85.3%, specificity=81.6%). The satisfactory diagnostic performance of the panel persisted regardless of disease status (AUCs for tumour-node-metastasis stages?,?, and ? were 0.881, 0.884, and 0.893, respectively). Conclusion: PBMCs miR-27a-3p could be a potential marker for PC screening. A panel composed of PBMCs miR-27a-3p and serum CA19-9 has considerable clinical value in diagnosing early-stage PC. Therefore, patients who would have otherwise missed the curative treatment window can benefit from optimal therapy. Examination of different MicroRNA profiles in 3 types of PBMCs samples
2012-05-03 | E-GEOD-37710 | biostudies-arrayexpress
Project description:BACKGROUND: In patients with suspicious pulmonary lesions, bronchoscopy is frequently non-diagnostic. This often results in additional invasive testing, including surgical biopsy, although many patients have benign disease. We sought to validate an airway gene-expression classifier for lung cancer in patients undergoing diagnostic bronchoscopy. METHODS: Two multicenter prospective studies (AEGIS 1 and 2) enrolled 1357 current or former smokers undergoing bronchoscopy for suspected lung cancer. Bronchial epithelial cells were collected from normal appearing mucosa in the mainstem bronchus during bronchoscopy. Patients without a definitive diagnosis from bronchoscopy were followed for 12 months. A gene-expression classifier was used to assess the risk of lung cancer, and its performance was evaluated. RESULTS: A total of 298 patients from AEGIS 1 and 341 from AEGIS 2 met criteria for analysis. Bronchoscopy was non-diagnostic for cancer in 272 of 639 patients (43%; 95%CI, 39-46%). The gene expression classifier correctly identified 431 of 487 patients with cancer (89% sensitivity; 95%CI, 85-91%), and 72 of 152 patients without cancer (47% specificity; 95%CI, 40-55%). The combination of the classifier and bronchoscopy had a sensitivity of 97% (95%CI, 95-98%), which was independent of size, location, stage, and histological subtype of lung cancer. In patients with an intermediate pre-test risk (10-60%) of lung cancer, the NPV of the classifier was 91% (95%CI 75-98%). CONCLUSIONS: In patients with an intermediate risk of lung cancer and a non-diagnostic bronchoscopy, a gene-expression classification of “low-risk” warrants consideration of a more conservative diagnostic approach that could reduce unnecessary invasive testing in patients with benign disease. 680 CEL files from 639 BEC specimens, 152 benign and 487 malignant samples
2015-05-17 | E-GEOD-66499 | biostudies-arrayexpress
Project description:BACKGROUND: In patients with suspicious pulmonary lesions, bronchoscopy is frequently non-diagnostic. This often results in additional invasive testing, including surgical biopsy, although many patients have benign disease. We sought to validate an airway gene-expression classifier for lung cancer in patients undergoing diagnostic bronchoscopy. METHODS: Two multicenter prospective studies (AEGIS 1 and 2) enrolled 1357 current or former smokers undergoing bronchoscopy for suspected lung cancer. Bronchial epithelial cells were collected from normal appearing mucosa in the mainstem bronchus during bronchoscopy. Patients without a definitive diagnosis from bronchoscopy were followed for 12 months. A gene-expression classifier was used to assess the risk of lung cancer, and its performance was evaluated. RESULTS: A total of 298 patients from AEGIS 1 and 341 from AEGIS 2 met criteria for analysis. Bronchoscopy was non-diagnostic for cancer in 272 of 639 patients (43%; 95%CI, 39-46%). The gene expression classifier correctly identified 431 of 487 patients with cancer (89% sensitivity; 95%CI, 85-91%), and 72 of 152 patients without cancer (47% specificity; 95%CI, 40-55%). The combination of the classifier and bronchoscopy had a sensitivity of 97% (95%CI, 95-98%), which was independent of size, location, stage, and histological subtype of lung cancer. In patients with an intermediate pre-test risk (10-60%) of lung cancer, the NPV of the classifier was 91% (95%CI 75-98%). CONCLUSIONS: In patients with an intermediate risk of lung cancer and a non-diagnostic bronchoscopy, a gene-expression classification of “low-risk” warrants consideration of a more conservative diagnostic approach that could reduce unnecessary invasive testing in patients with benign disease.
2015-05-17 | GSE66499 | GEO
Project description:Recent progress in unbiased metagenomic next-generation sequencing (mNGS) allows simultaneous examination of microbial and host genetic material in a single test. Leveraging affordable bronchoalveolar lavage fluid (BALF) mNGS data, we employed machine learning to create a diagnostic approach distinguishing lung cancer from pulmonary infections, conditions prone to misdiagnosis in clinical settings. This prospective study analyzed BALF-mNGS data from lung cancer and pulmonary infection patients, delineating differences in DNA/RNA microbial composition, bacteriophage abundances, and host responses, including gene expression, transposable element levels, immune cell composition, and tumor fraction derived from copy number variation (CNV). Integrating these metrics into a host/microbe metagenomics-driven machine learning model (Model VI) demonstrated robustness, achieving an AUC of 0.87 (95% CI = 0.857-0.883), sensitivity = 73.8%, and specificity = 84.5% in the training cohort, and an AUC of 0.831 (95% CI = 0.819-0.843), sensitivity = 67.1%, and specificity = 94.4% in the validation cohort for distinguishing lung cancer from pulmonary infections. The application of a rule-in and rule-out strategy-based composite predictive model significantly enhances accuracy (ACC) in distinguishing between lung cancer and tuberculosis (ACC=0.913), fungal infection (ACC=0.955), and bacterial infection (ACC=0.836). These findings highlight the potential of cost-effective mNGS-based analysis as a valuable tool for early differentiation between lung cancer and pulmonary infections, offering significant benefits through a single comprehensive testing.
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Project description:Background: Palindromic rheumatism (PR) is a unique disease characterized by the intermittent inflammation of different joints that may progress to a variety of immune-related diseases. Unclear diagnostic criteria have limited the research on its pathogenesis and treatment options. Recently, microRNAs (miRNAs) have been used in the diagnosis of various diseases; however, the role of miRNAs in PR diagnosis remains unexplored. Using next-generation high-throughput sequencing (NGS), this study aimed to screen miRNAs specifically expressed in the serum of patients with PR to construct a diagnostic signature and verify its diagnostic efficacy. Methods: Patients with PR (N=4), patients with rheumatoid arthritis (RA; N=3), and healthy controls (N=3) were included in an exploration cohort. Differentially expressed miRNAs were screened using NGS to construct diagnostic signatures and bioinformatics tools were used to perform target gene enrichment analysis of the top 25 differentially expressed miRNAs, both upregulated and downregulated. RT-qPCR was used to verify the differential expression of the diagnostic signatures in the three validation cohorts of patients with PR (N=27) and RA (N=30), and healthy controls (N=31), and the diagnostic efficiency of the diagnostic signatures was evaluated using receiver operator characteristic (ROC) curves. Results: A total of 130 differentially expressed miRNAs–35 upregulated and 95 downregulated miRNAs–were found in the PR exploration cohort, which differed between both RA and healthy controls. miRNA-186-3p showed the largest upregulated difference, and miRNA-382-3p showed the largest downregulated difference, and consequently, were selected to construct the diagnostic signature. In the ROC analysis of the validation cohort, the diagnostic signature produced an area under the ROC curve (AUC) of 1 (95% CI 1.000-1.000) compared with healthy controls. For patients with RA, the diagnostic signature produced an AUC of 0.915 (95% CI 0.842-0.988). However, miRNA-186-3p and miRNA-382-3p levels were not associated with disease activity in patients with PR. Conclusion: A diagnostic signature comprising miRNA-186-3p and miRNA-382-3p can effectively diagnose and differentiate PR from RA. This study provides a basis for the creation of a clinical miRNA signature for the diagnosis of PR.
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Project description:Background & Aims: MicroRNAs have been shown to offer great potential in the diagnosis of cancer. We aimed to identify microRNAs in peripheral blood mononuclear cells (PBMCs) for diagnosing pancreatic cancer (PC). Methods: PBMCs microRNA expression was investigated in three independent cohorts including 352 participants (healthy, benign pancreatic/peripancreatic diseases (BPD), and PC). First, we used sequencing technology to identify differentially expressed microRNAs in 60 PBMCs samples for diagnosing PC. Quantitative reverse-transcriptase polymerase chain reaction assay was then applied to evaluate the expression of selected microRNAs. A logistic regression model was constructed using an independent cohort. Area under the receiver operating characteristic curve (AUC) was used to evaluate diagnostic accuracy. Results: We found that PBMCs miR-27a-3p could efficiently discriminate PC from BPD (AUC=0.840; 95% CI, 0.787 to 0.885; sensitivity=82.2%, specificity=76.7%). A panel composed of PBMCs miR-27a-3p and serum CA19-9 provided a high diagnostic accuracy in differentiating PC from BPD in the clinical setting (AUC=0.886; 95% CI, 0.837 to 0.923; sensitivity=85.3%, specificity=81.6%). The satisfactory diagnostic performance of the panel persisted regardless of disease status (AUCs for tumour-node-metastasis stagesⅠ,Ⅱ, and Ⅲ were 0.881, 0.884, and 0.893, respectively). Conclusion: PBMCs miR-27a-3p could be a potential marker for PC screening. A panel composed of PBMCs miR-27a-3p and serum CA19-9 has considerable clinical value in diagnosing early-stage PC. Therefore, patients who would have otherwise missed the curative treatment window can benefit from optimal therapy.
2012-05-04 | GSE37710 | GEO
Project description:Importance: Severe alcoholic hepatitis (AH) is a highly lethal disease with a 3-month mortality up to 50%. Corticosteroids are the current standard of care although nearly 40% of the patients do not respond and accurate pre-treatment predictors of survival are lacking. Objective: To develop a prognostic score based on molecular and clinical variables before initiation of corticosteroids. Design, Setting, and Participants: Gene expression profiling of fixed liver biopsy obtained for diagnosis of severe AH assessed in 3 independent cohorts (1 prospective cohort for prognostic gene signature and integrative score derivation, and 2 retrospective cohorts for validation). Samples were obtained from 4 European and 1 US medical centers between July 2006 and February 2015. Exposures: Biopsy proven severe AH patients treated with corticosteroids. Main Outcome Measures: Identification of a gene signature combined with a validated clinical index to develop an integrative prognostic score of survival without death or liver transplantation in a derivation cohort (n=71). Prognostic performance of the score was validated in an independent multi-center validation cohort 1 (n=48). Finally, the score was implemented in an FDA-approved clinical diagnostic platform (NanoString) and tested in another independent validation cohort 2 (n=20). Results: A prognostic score, integrating a 123-gene signature and the model for end-stage liver disease (gs-MELD score), was defined in the derivation cohort, in which poor- and good-prognosis patients were discriminated with a cut-off value of 2.66 at 3 months (event-free survival rates of 32% vs. 76%, respectively, p<.001) and 6 months (26% vs. 65%, respectively, p<.001). In the validation cohort 1, the score similarly discriminated poor- and good-prognosis patients at 3 months (43%, [95% CI, 26%-70%] vs. 96% [95% CI, 89%-100%], respectively, p<.001, c-index of 0.83 [95% CI, 0.66-0.99]) and 6 months (34% [95% CI, 18%-61%] vs 84% [95% CI, 72%-100%], respectively, p<.001, c-index of 0.80 [95% CI, 0.66-0.94]), and outperformed other existing clinical indices. The NanoString-based gs-MELD score remained predictive of 3- (p=.03) and 6-month (p=.009) even-free survival in the validation cohort 2. Conclusion and Relevance: The gs-MELD score, incorporating clinically applicable gene signature test and the MELD score, enables pre-treatment survival prediction in severe AH patients.
2018-02-18 | GSE95246 | GEO
Project description:Platelets are key mediators of atherothrombosis; yet, limited tools exist to identify individuals with a hyperreactive platelet phenotype. Here we derive and validate a Platelet Reactivity ExpreSsion Score (PRESS) integrating platelet aggregation responses and RNA sequencing in multiple cohorts. PRESS performs well in identifying a hyperreactive phenotype in patients with cardiovascular disease (AUC [cross-validation] 0.81, 95% CI 0.68 to 0.94), with similar discrimination in an independent cohort of healthy participants (AUC [validation] 0.77, 95% CI 0.75 to 0.79); accuracy 80%, sensitivity 71%, and specificity 86%. Next, we validate PRESS in multiple cohorts of patients with platelet RNASeq available. Following multivariable adjustment, PRESS was significantly higher in MI (β=1.8, p=0.02), and individuals with a score above the median more frequently develop a future cardiovascular event (adjusted HR 1.90, CI 1.07 to 3.36), p=0.027). Altogether, we provide strong evidence that PRESS is a robust prognostic marker, which could have an essential role in facilitating a personalized approach to cardiovascular risk management.
2024-06-30 | GSE232027 | GEO