Dataset Information

Serum organic acid metabolites can be used as potential biomarkers to identify prostatitis, benign prostatic hyperplasia, and prostate cancer (Untargeted assay)

ABSTRACT:

BACKGROUND: Noninvasive methods for the early identify diagnosis of prostatitis, benign prostatic hyperplasia (BPH), and prostate cancer (PCa) are current clinical challenges.

METHODS: The serum metabolites of 20 healthy individuals and patients with prostatitis, BPH, or PCa were identified using untargeted liquid chromatography-mass spectrometry (LC-MS).

RESULTS: Organic acid metabolites had good sensitivity and specificity in differentiating prostatitis, BPH, and PCa. In addition, targeted LC-MS was used to verify the organic acid metabolites in the serum of a validation cohort. Three diagnostic models identified patients with PROSTATITIS: phenyllactic acid (area under the curve [AUC]=0.773), pyroglutamic acid (AUC=0.725), and pantothenic acid (AUC=0.721). Three diagnostic models identified BPH: citric acid (AUC=0.859), malic acid (AUC=0.820), and D-glucuronic acid (AUC=0.810). Four diagnostic models identified PCa: 3-hydroxy-3-methylglutaric acid (AUC=0.804), citric acid (AUC=0.918), malic acid (AUC=0.862), and phenyllactic acid (AUC=0.713). Two diagnostic models distinguished BPH from PCa: phenyllactic acid (AUC=0.769) and pyroglutamic acid (AUC=0.761). Three diagnostic models distinguished benign BPH from PROSTATITIS: citric acid (AUC=0.842), ethylmalonic acid (AUC=0.814), and hippuric acid (AUC=0.733). Six diagnostic models distinguished BPH from prostatitis: citric acid (AUC=0.926), pyroglutamic acid (AUC=0.864), phenyllactic acid (AUC=0.850), ethylmalonic acid (AUC=0.843), 3-hydroxy-3-methylglutaric acid (AUC=0.817), and hippuric acid (AUC=0.791). Three diagnostic models distinguished PCa patients with PROSTATITISA < 4.0 ng/mL from those with PSA > 4.0 ng/mL: 5-hydromethyl-2-furoic acid (AUC=0.749), ethylmalonic acid (A UC=0.750), and pyroglutamic acid (AUC=0.929).

CONCLUSIONS: These results suggest that serum organic acid metabolites can be used as biomarkers to differentiate prostatitis, BPH, and PCa.

Untargeted assay is reported in the current study MTBLS6039.

Targeted organic acid assay is reported in MTBLS6038.

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

SUBMITTER: Zeping Han

PROVIDER: MTBLS6039 | MetaboLights | 2023-04-17

REPOSITORIES: MetaboLights

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	MTBLS6039	Other
	FILES	Other
	a_MTBLS6039_LC-MS_negative_reverse-phase_metabolite_profiling.txt	Txt
	a_MTBLS6039_LC-MS_positive_reverse-phase_metabolite_profiling.txt	Txt
	files-all.json	Other

Items per page:

1 - 5 of 9

Publications

Serum organic acid metabolites can be used as potential biomarkers to identify prostatitis, benign prostatic hyperplasia, and prostate cancer.

He Jinhua J Han Zeping Z Luo Wenfeng W Shen Jian J Xie Fangmei F Liao Liyin L Zou Ge G Luo Xin X Guo Zhonghui Z Li Yuguang Y Li Jianhao J Chen Hanwei H

Frontiers in immunology 20230104

<h4>Background</h4>Noninvasive methods for the early identify diagnosis of prostatitis, benign prostatic hyperplasia (BPH), and prostate cancer (PCa) are current clinical challenges.<h4>Methods</h4>The serum metabolites of 20 healthy individuals and patients with prostatitis, BPH, or PCa were identified using untargeted liquid chromatography-mass spectrometry (LC-MS). In addition, targeted LC-MS was used to verify the organic acid metabolites in the serum of a validation cohort.<h4>Results</h4>O ...[more]

PMID: 36685547

Similar Datasets

Project description:BACKGROUND: Noninvasive methods for the early identify diagnosis of prostatitis, benign prostatic hyperplasia (BPH) and prostate cancer (PCa) are current clinical challenges.METHODS: The serum metabolites of 20 healthy individuals and patients with prostatitis, BPH, or PCa were identified using untargeted liquid chromatography-mass spectrometry (LC-MS).RESULTS: Organic acid metabolites had good sensitivity and specificity in differentiating prostatitis, BPH, and PCa. In addition, targeted LC-MS was used to verify the organic acid metabolites in the serum of a validation cohort. Three diagnostic models identified patients with PROSTATITIS: phenyllactic acid (area under the curve [AUC]=0.773), pyroglutamic acid (AUC=0.725), and pantothenic acid (AUC=0.721). Three diagnostic models identified BPH: citric acid (AUC=0.859), malic acid (AUC=0.820), and D[1]glucuronic acid (AUC=0.810). Four diagnostic models identified PCa: 3-hydroxy-3-methylglutaric acid (AUC=0.804), citric acid (AUC=0.918), malic acid (AUC=0.862), and phenyllactic acid (AUC=0.713). Two diagnostic models distinguished BPH from PCa: phenyllactic acid (AUC=0.769) and pyroglutamic acid (AUC=0.761). Three diagnostic models distinguished benign BPH from PROSTATITIS: citric acid (AUC=0.842), ethylmalonic acid (AUC=0.814), and hippuric acid (AUC=0.733). Six diagnostic models distinguished BPH from prostatitis: citric acid (AUC=0.926), pyroglutamic acid (AUC=0.864), phenyllactic acid (AUC=0.850), ethylmalonic acid (AUC=0.843), 3-hydroxy- 3-methylglutaric acid (AUC=0.817), and hippuric acid (AUC=0.791). Three diagnostic models distinguished PCa patients with PROSTATITISA < 4.0 ng/mL from those with PSA > 4.0 ng/mL: 5-hydromethyl-2-furoic acid (AUC=0.749), ethylmalonic acid (A UC=0.750), and pyroglutamic acid (AUC=0.929).CONCLUSIONS: These results suggest that serum organic acid metabolites can be used as biomarkers to differentiate prostatitis, BPH, and PCa. Targeted organic acid assay is reported in the current study MTBLS6038.Untargeted assay is reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS6039' rel='noopener noreferrer' target='_blank'>MTBLS6039</a>.

Project description:Staphylococcus aureus causes a spectrum of human infection. Diagnostic delays and uncertainty lead to treatment delays and inappropriate antibiotic use. A growing literature suggests the hostM-bM-^@M-^Ys inflammatory response to the pathogen represents a potential tool to improve upon current diagnostics. The hypothesis of this study is that the host responds differently to S. aureus than to E. coli infection in a quantifiable way, providing a new diagnostic avenue. This study uses Bayesian sparse factor modeling and penalized binary regression to define peripheral blood gene-expression classifiers of murine and human S. aureus infection. The murine-derived classifier distinguished S. aureus infection from healthy controls and Escherichia coli-infected mice across a range of conditions (mouse and bacterial strain, time post infection) and was validated in outbred mice (AUC>0.97). A S. aureus classifier derived from a cohort of 95 human subjects distinguished S. aureus blood stream infection (BSI) from healthy subjects (AUC 0.99) and E. coli BSI (AUC 0.82). Murine and human responses to S. aureus infection share common biological pathways, allowing the murine model to classify S. aureus BSI in humans (AUC 0.84). Both murine and human S. aureus classifiers were validated in an independent human cohort (AUC 0.95 and 0.94, respectively). The approach described here lends insight into the conserved and disparate pathways utilized by mice and humans in response to these infections. Furthermore, this study advances our understanding of S. aureus infection; the host response to it; and identifies new diagnostic and therapeutic avenues. To create a host gene expression-based classifier for S. aureus infection, mice from a variety of experimental conditions were utilized. Seven different strains of inbred mice (n=187 total) were challenged with four different S. aureus strains via intraperitoneal inoculation and sacrificed at various time points as described in Methods. The comparator group for model derivation included 50 A/J or C57BL/6J mice inoculated with E. coli (O18:K1:H7) as well as 54 uninfected mice. Next, the murine S. aureus classifier was externally validated in outbred CD-1 mice with S. aureus infection (Sanger 476 or USA300), E. coli infection (O18:K1:H7), or uninfected controls (10 animals per condition). Method: All experiments were performed on mice 6-8 weeks old. For the murine S. aureus predictor, seven inbred mouse strains (3 mice/strain: 129S1/SvImJ, A/J, AKR/J, BALB/cByJ, C57BL/6J, C3H/HeJ, and NOD/LtJ) were IP inoculated with 107 CFU/g of S. aureus Sanger476, euthanized at 2h after injection, and bled. This was repeated using four different S. aureus strains (USA100, USA300, MW2, and Sanger476) in A/J mice (n=3 per S. aureus strain). For time series experiments, both A/J and C57BL/6J mouse strains were IP inoculated with S. aureus Sanger476 as above, and sacrificed at 2, 4, 6, and 12h after injection (n=5 per time point).