Dataset Information

Serum metabolomic profiles for distinguishing lung cancer from pulmonary tuberculosis: identification of rapid and noninvasive biomarker

ABSTRACT:

BACKGROUND: Pulmonary tuberculosis (TB) and lung cancer (LC) have similar clinical symptoms and atypical imaging findings which are easily misdiagnosed. There is an urgent need for a noninvasive, rapid and accurate biomarker for distinguishing LC from TB.

METHODS: A total of 694 subjects were enrolled and divided into discovery set (n=122), validation set (n=214) and another validation set (n=358). Metabolites were identified by multivariate and univariate analyses. The random forest (RF) algorithm and receiver operating characteristic curve analysis were used to evaluate the diagnostic efficacy of the biomarkers.

RESULTS: Seven metabolites were identified and validated. The mean decrease accuracy of phenylalanylphenylalanine ranked first in RF analysis. The area under the curve of phenylalanylphenylalanine was 0.8885 (95% CI 0.8444-0.9326), the sensitivity was 71.43%, and the specificity was 92.13%. Compared with that in NC subjects, the level of phenylalanylphenylalanine was elevated in LC patients (fold change = 2.92, p value < 0.01) and reduced in TB patients (fold change = 0.80, p value < 0.05).

CONCLUSIONS: The metabolomic profile of LC and TB was described and a key biomarker, phenylalanylphenylalanine, was identified. We produced a rapid and noninvasive method to supplement existing clinical diagnostic examinations for distinguishing LC from TB.

INSTRUMENT(S): Liquid Chromatography MS - positive - reverse phase

SUBMITTER: siyu chen

PROVIDER: MTBLS6990 | MetaboLights | 2024-01-16

REPOSITORIES: MetaboLights

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	MTBLS6990	Other
	FILES	Other
	a_MTBLS6990_LC-MS_targeted_positive_reverse-phase_metabolite_profiling.txt	Txt
	a_MTBLS6990_LC-MS_untargeted_positive_reverse-phase_metabolite_profiling.txt	Txt
	i_Investigation.txt	Txt

Items per page:

1 - 5 of 8

Publications

Serum Metabolomic Profiles for Distinguishing Lung Cancer From Pulmonary Tuberculosis: Identification of Rapid and Noninvasive Biomarker.

Chen Siyu S Li Chunyan C Qin Zhonghua Z Song Lili L Zhang Shiyuan S Sun Chongxiang C Zhuang Pengwei P Wang Yuming Y Yang Bin B Ning Li L Li Yubo Y

The Journal of infectious diseases 20231101 9

<h4>Background</h4>Pulmonary tuberculosis (PTB) and lung cancer (LC) have similar clinical symptoms and atypical imaging findings, which are easily misdiagnosed. There is an urgent need for a noninvasive and accurate biomarker to distinguish LC from PTB.<h4>Methods</h4>A total of 694 subjects were enrolled and divided into discovery set (n = 122), identification set (n = 214), and validation set (n = 358). Metabolites were identified by multivariate and univariate analyses. Receiver operating ch ...[more]

PMID: 37246562

Similar Datasets

Project description:In cancer management, early and accurate diagnosis of hepatocellular carcinoma (HCC) is important for enhancing survival rate of patients. Currently, serum alpha-fetoprotein (AFP) is the only one biomarker for detection of HCC. However, serum AFP is not satisfactory for diagnosis of HCC due to its low accuracy (about 60-70%). In this study, we collected 109 serum samples (discovery set) from healthy control (HC) and patients with chronic hepatitis B (CHB), liver cirrhosis (LC) and HCC, and analyzed them with custom lncRNA microarray. Profiling analysis shows 181 differentially expressed lncRNAs between HCs and patients with CHB, LC and HCC. Then a 48-lncRNA diagnostic signature was identified with 100% predictive accuracy for all subjects in the discovery set. This diagnostic signature was verified with a cross-validation analysis in the discovery set. To further corroborate the signature, we gathered another 66 serum samples (validation set) and also analyzed them with microarray. The result indicates that the same signature has similar diagnostic accuracy for HC (100%), CHB (73%), LC (88%) and HCC (95%), implying a reproducible diagnostic biomarker for HCC. Receiver operating characteristic (ROC) analysis exhibits that this signature has significantly higher diagnostic accuracy for HCC and non-cancerous subjects (area under curve [AUC]: 0.994) than AFP (AUC: 0.773) in the discovery set and this was also verified in the validation set (0.964 vs 0.792). More importantly, the signature detected small HCC (<3cm) with 100% (13/13) accuracy while AFP with only 61.5% (8/13). Altogether, this study demonstrates that the serum 48-lncRNA signature is not only a powerful and sensitive biomarker for diagnosis of HCC but also a potential biomarker for LC. ***************************************************************** Submitter declares these data are subject to patent number ZL 2016 1 0397094. *****************************************************************

Project description:Background: Lung cancer (LC) is the leading cause of cancer deaths worldwide with more than 1.7 million deaths each year. Early detection of LC may be crucial to achieve efficient treatment and to increase survival. However, there are limitations to current methods for early detection of LC as they are too invasive and represent potential health hazards, necessitating improved tools to detect LC at an early stage. Blood-based gene expression profiling is an alternative, or additional tool for diagnostic purposes as blood samples are easily available, essentially non-invasive, and can be collected at a low cost. However, as the blood collection method can affect measured gene expression, the aim of this study was to identify mRNAs that are robust biomarkers for LC in blood. How: We sampled blood from 123 LC patients at the St. Olavs university hospital, and 180 controls from two different biobanks sampled on two different blood tubes (PAXgene and Tempus) and used Illumina (HT-12 v4) microarrays to measure whole blood gene expression. We did three analyses: (i) finding relevant gene expression differences between cases and controls, (ii) finding a robust set of genes to be used as a panel to identify presence of LC in blood, and (iii) identifying differences between patients with different pathological traits such as stage and histology. We collected phenotype data from questionnaires and hospital medical records, and evaluated the potential effects of CRP, tumour size, gender, and smoking habits. Results: By comparing cases and controls sampled on the same RNA sampling system (training set), we identified 355 significant genes (Bonferroni adjusted p < 0.05) with biological relevance. When evaluating these on our test set comprising of the same cases as used in the training set but analysed against controls from a different biobank sampled on a different RNA sampling system (test set), we found 50 genes that were robust as they were unaffected by either technical issues in sampling systems, biobank differences, gender, or pathological traits such as LC subtype or stadium. These findings were confirmed in three validation sets and the robust list’s diagnostic potential was validated on RNA sequencing data from a new cohort. By analysing the cases separately, we found seven genes distinguishing squamous cell carcinoma (SCC) from other LC subtypes, and six genes distinguishing early from late-stage LC patients. Pathway analyses and a literature survey indicated that the identified genes show biological relevance for cancer development and lung related diseases.

Project description:Bronchoscopy is a frequently used initial diagnostic procedure for patients with suspected lung cancer (LC). Cytological examinations of bronchial washing (BW) samples obtained during bronchoscopy often yield inconclusive results regarding LC diagnosis. The present study aimed to identify molecular biomarkers as a non-invasive method for LC diagnosis. Aberrant DNA methylation is used as a useful biomarker for LC. Therefore, microarray-based methylation profiling analyses on 13 patient-matched tumor tissues at stages I-III vs. non-tumor tissues were performed, and a group of highly differentially methylated genes was identified. A subsequent analysis using bisulfite-pyrosequencing with additional tissues and cell lines revealed six methylated genes [ADAM metallopeptidase with thrombospondin type 1 motif 20, forkhead box C2 (mesenchyme forkhead 1), NK2 transcription factor related, locus 5 (Drosophila), oligodendrocyte transcription factor 3, protocadherin γ subfamily A 12 (PCDHGA12) and paired related homeobox 1 (PRRX1)] associated with LC. Next, a highly sensitive and accurate detection method, linear target enrichment-quantitative methylation-specific PCR in a single closed tube, was applied for clinical validation using BW samples from patients with LC (n=68) and individuals with benign diseases (n=33). PCDHGA12 and PRRX1 methylation were identified as the best-performing biomarkers to detect LC. The two-marker combination showed a sensitivity of 82.4% and a specificity of 87.9%, with an area under the curve of 0.891. Notably, the sensitivity for small cell LC was 100%. The two-marker combination had a positive predictive value of 93.3% and a negative predictive value of 70.7%. The sensitivity was higher than that of cytology, which only had a sensitivity of 50%. The methylation status of the two-marker combination showed no association with sex, age or stage, but was associated with tumor location and histology. In conclusion, the present study showed that the regulatory regions of PCDHGA12 and PRRX1 are highly methylated in LC and can be used to detect LC in BW specimens as a diagnostic adjunct to cytology in clinical practice.