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Serum lipidomics study looking for specific endometrial polyps biomarkers distinguishing from endometrial cancer or hyperplasia

ABSTRACT:

Endometrial diseases are common gynecological diseases that disorder women of childbearing age and perimenopausal women, including endometrial polyps (EP), endometrial cancer (EC), and endometrial hyperplasia (EH). Clinically, biopsy or imaging methods are usually used to screen and diagnose endometrial diseases, but due to their invasiveness and heterogeneity, a noninvasive, convenient, objective and accurate biomarker is needed for the differential diagnosis of EP and EC or EH. In the present study, serum samples from 396 patients with endometrial disease and 225 healthy volunteers were analyzed by UPLC-Q-TOF/MS non-targeted lipidomics. A combination of multivariate (Orthogonal partial least-squares discriminant analysis) and univariate (Student t-test) analyses were used to identify and qualify 6, 8, and 7 potential biomarkers in serum from patients with EP, EC, and EH, respectively. With the aid of logistic regression algorithm and receiver operating characteristic (ROC) curve analysis, A biomarker panel including four specific EP biomarkers, 6-Keto-PGF1α, PA(37:4), LysoPC(20:1) and PS (36:0), had good classification and diagnostic ability in distinguishing EP from EC or EH. The biomarker panel can be used as a rapid diagnostic method to assist imaging examination to effectively differentially diagnose endometrial diseases, and provide a reference for clinicians in the identification and diagnosis of endometrial diseases.

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

SUBMITTER: Yubo Li

PROVIDER: MTBLS3444 | MetaboLights | 2022-01-14

REPOSITORIES: MetaboLights

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Project description:More humans have died of tuberculosis (TB) than any other infectious disease and millions still die each year. Experts advocate for blood-based, protein biomarkers to help diagnosis TB, which is critical in high-burden countries. However, the protein biomarker pipeline is small. Here, we used the Diversity Outbred (DO) mouse population to address this gap, identifying five protein biomarker candidates. One biomarker, serum CXCL1, met the World Health Organization’s Targeted Product Profile for a triage test to diagnose active TB from latent M.tb infection (LTBI), non-TB lung disease, and normal sera in HIV-negative, adults from South Africa and Vietnam. To find the biomarker candidates, we quantified seven immune mediators and four proteins corresponding to a subset of highly expressed genes unique to progressor DO mice. Next, we applied statistical and machine learning methods to the data, i.e., 11 proteins in lungs from 453 infected and 29 non-infected mice. After searching all combinations of five algorithms and 239 protein subsets, validating, and testing the findings on independent data, two combinations accurately diagnosed progressor DO mice: Logistic Regression using MMP8; and Gradient Tree Boosting using a panel of 4: CXCL1, CXCL2, TNF, IL-10. Of those five protein biomarker candidates, two (MMP8 and CXCL1) were crucial for classifying DO mice; were above the limit of detection in most human serum samples; and had not been widely assessed for diagnostic performance in humans before. In patient sera, CXCL1 exceeded the triage diagnostic test criteria (>90% sensitivity; >70% specificity), while MMP8 did not. Using Area Under the Curve analyses, CXCL1 averaged 94.5% sensitivity and 88.8% specificity for active pulmonary TB (ATB) vs LTBI; 90.9% sensitivity and 71.4% specificity for ATB vs non-TB; and 100.0% sensitivity and 98.4% specificity for ATB vs normal sera. Our findings overall show that the DO mouse population can discover diagnostic-quality, serum protein biomarkers of human TB.

			Action	DRS
	MTBLS3444	Other
	FILES	Other
	MTBLS-fixed-isa.json	Other
	a_MTBLS3444_LC-MS_positive_reverse-phase_metabolite_profiling.txt	Txt
	files-all.json	Other

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Serum lipidomics study looking for specific endometrial polyps biomarkers distinguishing from endometrial cancer or hyperplasia

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