GC‐MS metabolomics identifies novel biomarkers to distinguish tuberculosis pleural effusion from malignant pleural effusion

Liu, Yongxia; Mei, Bin; Chen, Deying; Cai, Long

doi:10.1002/jcla.23706

Cited by 10 publications

(7 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Also, other tumor markers have been reported to be useful in lung cancer (such as CYFRA 21-1, NSE, or CR), which were not evaluated in this study. In addition, novel biomarkers, such as serum interleukin-27 (IL-27), gas chromatography-mass spectrometry (GC-MS) metabolomics, and serum reactive oxygen species modulator 1 (ROMO1), have been proposed as potential indexes for diagnosing lung cancer-associated MPE, but only a few were routinely detected in clinical practice (18,(37)(38)(39).…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of a Scoring System for Early Diagnosis of Malignant Pleural Effusion Based on a Nomogram

Liang

Yuan

et al. 2021

Front. Oncol.

View full text Add to dashboard Cite

BackgroundThe diagnostic value of clinical and laboratory features to differentiate between malignant pleural effusion (MPE) and benign pleural effusion (BPE) has not yet been established.ObjectivesThe present study aimed to develop and validate the diagnostic accuracy of a scoring system based on a nomogram to distinguish MPE from BPE.MethodsA total of 1,239 eligible patients with PE were recruited in this study and randomly divided into a training set and an internal validation set at a ratio of 7:3. Logistic regression analysis was performed in the training set, and a nomogram was developed using selected predictors. The diagnostic accuracy of an innovative scoring system based on the nomogram was established and validated in the training, internal validation, and external validation sets (n = 217). The discriminatory power and the calibration and clinical values of the prediction model were evaluated.ResultsSeven variables [effusion carcinoembryonic antigen (CEA), effusion adenosine deaminase (ADA), erythrocyte sedimentation rate (ESR), PE/serum CEA ratio (CEA ratio), effusion carbohydrate antigen 19-9 (CA19-9), effusion cytokeratin 19 fragment (CYFRA 21-1), and serum lactate dehydrogenase (LDH)/effusion ADA ratio (cancer ratio, CR)] were validated and used to develop a nomogram. The prediction model showed both good discrimination and calibration capabilities for all sets. A scoring system was established based on the nomogram scores to distinguish MPE from BPE. The scoring system showed favorable diagnostic performance in the training set [area under the curve (AUC) = 0.955, 95% confidence interval (CI) = 0.942–0.968], the internal validation set (AUC = 0.952, 95% CI = 0.932–0.973), and the external validation set (AUC = 0.973, 95% CI = 0.956–0.990). In addition, the scoring system achieved satisfactory discriminative abilities at separating lung cancer-associated MPE from tuberculous pleurisy effusion (TPE) in the combined training and validation sets.ConclusionsThe present study developed and validated a scoring system based on seven parameters. The scoring system exhibited a reliable diagnostic performance in distinguishing MPE from BPE and might guide clinical decision-making.

show abstract

Section: Discussionmentioning

confidence: 99%

Development and Validation of a Scoring System for Early Diagnosis of Malignant Pleural Effusion Based on a Nomogram

Liang

Yuan

et al. 2021

Front. Oncol.

View full text Add to dashboard Cite

show abstract

“…The analysis was performed using a well-established previously published statistical method [ 59 , 60 ]. Following the acquisition of the raw data, these data are aligned with the aid of Compound Discover using the 3.0 system from Thermo based on their m/z ratio and the retention times of ion signals.…”

Section: Methodsmentioning

confidence: 99%

Untargeted Metabolomics Based Prediction of Therapeutic Potential for Apigenin and Chrysin

Cochran

Martin

Rafferty

et al. 2023

IJMS

View full text Add to dashboard Cite

The prominent flavonoids apigenin and chrysin have been demonstrated to have systemic benefits. Our previous work was first to establish the impact of apigenin and chrysin on cellular transcriptome. In the current study, we have revealed the ability of apigenin and chrysin to alter the cellular metabolome based on our untargeted metabolomics. Based on our metabolomics data, both these structurally related flavonoids demonstrate diverging and converging properties. Apigenin demonstrated the potential to possess anti-inflammatory and vasorelaxant properties through the upregulation of intermediate metabolites of alpha-linolenic acid and linoleic acid pathways. Chrysin, on the other hand, exhibited abilities to inhibit protein and pyrimidine synthesis along with downregulation of gluconeogenesis pathways based on the altered metabolites detected. Chrysin-mediated metabolite changes are mostly due to its ability to modulate L-alanine metabolism and the urea cycle. On the other hand, both the flavonoids also demonstrated converging properties. Apigenin and chrysin were able to downregulate metabolites involved in cholesterol biosynthesis and uric acid synthesis, namely 7-dehydrocholesterol and xanthosine, respectively. This work will provide understanding regarding the diverse therapeutic potential of these naturally occurring flavonoids and help us in curbing an array of metabolic complications.

show abstract

“…The result indicated more activation of the downstream kynurenine metabolism. A recent non-targeted metabolomics study ( Liu et al, 2021 ) with relatively small cohorts of TPE (n = 17) and MPE (n = 17) patients found that stearic acid, L-cystine, and citric acid may be potential biomarkers for distinguishing between TBPE and MPE, with decent sensitivity and specificity. The OPLS-DA resulted in a fairly low Q 2 = 0.30, possibly due to the small sample size.…”

Section: Biomarkers Based On Host Responsesmentioning

confidence: 99%

Biomarker discovery for tuberculosis using metabolomics

Jiang

2023

Front. Mol. Biosci.

View full text Add to dashboard Cite

Tuberculosis (TB) is the leading cause of death among infectious diseases, and the ratio of cases in which its pathogen Mycobacterium tuberculosis (Mtb) is drug resistant has been increasing worldwide, whereas latent tuberculosis infection (LTBI) may develop into active TB. Thus it is important to understand the mechanism of drug resistance, find new drugs, and find biomarkers for TB diagnosis. The rapid progress of metabolomics has enabled quantitative metabolite profiling of both the host and the pathogen. In this context, we provide recent progress in the application of metabolomics toward biomarker discovery for tuberculosis. In particular, we first focus on biomarkers based on blood or other body fluids for diagnosing active TB, identifying LTBI and predicting the risk of developing active TB, as well as monitoring the effectiveness of anti-TB drugs. Then we discuss the pathogen-based biomarker research for identifying drug resistant TB. While there have been many reports of potential candidate biomarkers, validations and clinical testing as well as improved bioinformatics analysis are needed to further substantiate and select key biomarkers before they can be made clinically applicable.

show abstract

GC‐MS metabolomics identifies novel biomarkers to distinguish tuberculosis pleural effusion from malignant pleural effusion

Cited by 10 publications

References 38 publications

Development and Validation of a Scoring System for Early Diagnosis of Malignant Pleural Effusion Based on a Nomogram

Development and Validation of a Scoring System for Early Diagnosis of Malignant Pleural Effusion Based on a Nomogram

Untargeted Metabolomics Based Prediction of Therapeutic Potential for Apigenin and Chrysin

Biomarker discovery for tuberculosis using metabolomics

Contact Info

Product

Resources

About