Dataset Information

The Urine proteomics of pulmonary tuberculosis

ABSTRACT: In the present study, we aimed to identify biomarkers for diagnosis of pulmonary tuberculosis (PTB) using urinary metabolomic and proteomic analysis. Methods: 40 urine samples were collected from PTB, lung cancer (LCA), community-acquired pneumonia (CAP) patients and healthy controls (HC), respectively. Biomarker panels were selected based on random forest (RF) analysis. Results: A total of 3,868 proteins and 47,528 metabolic features detected using pairwise comparisons. Using AUC≥0.8000 as a cutoff value, we picked up five protein biomarkers for PTB diagnosis. The five-protein panel yielded an AUC of PTB/HC, PTB/CAP and PTB/LCA were 0.9840, 0.9680 and 0.9310, respectively. Additionally, five metabolism biomarkers were selected for differential diagnosis purpose. By employment of the five-metabolism panel, we could differentiate PTB/HC at an AUC of 0.9940, PTB/CAP of 0.8920, and PTB/LCA of 0.8570, respectively. When the five protein and five metabolism biomarkers were combined, yielded an AUC of PTB/HC, PTB/CAP and PTB/LCA were 1.0000, 0.9220 and 0.9500, respectively. Conclusion: Our data demonstrate that the combination of metabolomic and proteomic analyses can identify a novel urine biomarker panel to diagnose PTB with high sensitivity and specificity.

INSTRUMENT(S): Q Exactive HF

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Urine

DISEASE(S): Pulmonary Tuberculosis

SUBMITTER: kaixin zhong

LAB HEAD: Yu Pang

PROVIDER: PXD048717 | Pride | 2025-05-06

REPOSITORIES: Pride

ACCESS DATA

Dataset's files

Source:

Items per page:

1 - 5 of 62

Publications

Combined urine proteomics and metabolomics analysis for the diagnosis of pulmonary tuberculosis.

Yu Jiajia J Yuan Jinfeng J Liu Zhidong Z Ye Huan H Lin Minggui M Ma Liping L Liu Rongmei R Ding Weimin W Li Li L Ma Tianyu T Tang Shenjie S Pang Yu Y

Clinical proteomics 20241218 1

<h4>Background</h4>Tuberculosis (TB) diagnostic monitoring is paramount to clinical decision-making and the host biomarkers appears to play a significant role. The currently available diagnostic technology for TB detection is inadequate. In the present study, we aimed to identify biomarkers for diagnosis of pulmonary tuberculosis (PTB) using urinary metabolomic and proteomic analysis.<h4>Methods</h4>In the study, urine from 40 PTB, 40 lung cancer (LCA), 40 community-acquired pneumonia (CAP) pati ...[more]

PMID: 39695396

Similar Datasets

Project description:Differentially methylated oral squamous cell carcinoma (OSCC) biomarkers, identified in-vitro and validated in well-characterized surgical specimens, have shown poor clinical correlation in cohorts with different risk profiles. To overcome this lack of relevance we used the HumanMethylation27 BeadChip, publicly available methylation and expression array data, and Quantitative Methylation Specific PCR to uncover differential methylation in OSCC clinical samples with heterogeneous risk profiles. A two stage-design consisting of Discovery and Prevalence screens was used to identify differential promoter methylation and deregulated pathways in patients diagnosed with OSCC and head and neck squamous cell carcinoma. Promoter methylation of KIF1A (k=0.64), HOXA9 (k=0.60), NID2 (k=0.60), and EDNRB (k=0.60) had a moderate to substantial agreement with clinical diagnosis in the Discovery screen. HOXA9 had 68% sensitivity, 100% specificity and a 0.81 AUC. NID2 had 71% sensitivity, 100% specificity and a 0.79 AUC. In the Prevalence screen HOXA9 (k=0.82) and NID2 (k=0.80) had an almost perfect agreement with histologic diagnosis. HOXA9 had 85% sensitivity, 97% specificity and a 0.95 AUC. NID2 had 87% sensitivity, 95% specificity and a 0.91 AUC. A HOXA9 and NID2 gene panel had 94% sensitivity, 97% specificity and a 0.97 AUC. In saliva from OSCC cases and controls HOXA9 had 75% sensitivity, 53% specificity and a 0.75 AUC. NID2 had 87% sensitivity, 21% specificity and a 0.73 AUC. This Phase I Biomarker Development Trial identified a panel of differentially methylated genes in normal and OSCC clinical samples from patients with heterogeneous risk profiles. This panel may be useful for early detection and cancer prevention studies. The 12 samples analyzed comprise equal numbers of three tissue-types: Primary oral squamous cell carcinoma, Oral leukoplakia and Normal oral mucosa. Gender, age and smoking-status were approximately equally represented in these goups. Normal oral mucosa served as a control.

			Action	DRS
	Ctrl_1.raw	Raw
	Ctrl_10.raw	Raw
	Ctrl_11.raw	Raw
	Ctrl_12.raw	Raw
	Ctrl_13.raw	Raw