A serum lipidomics study for the identification of specific biomarkers for endometrial polyps to distinguish them from endometrial cancer or hyperplasia

Abstract

Endometrial diseases, including endometrial polyps (EP), endometrial cancer (EC) and endometrial hyperplasia (EH), are common gynecological diseases that affect women of childbearing and perimenopausal age. Clinically, biopsy or imaging methods are usually used to screen and diagnose these diseases; however, due to the invasiveness and heterogeneity of these tests, a noninvasive, convenient, objective and accurate biomarker is needed for the differential diagnosis of EP, EC or EH. In the present study, serum samples from 326 patients with endometrial diseases and 225 healthy volunteers were analyzed using nontargeted lipidomics. A combination of multivariate and univariate analyses was used to identify and qualify six, eight and seven potential biomarkers in the sera from patients with EP, EC and EH, respectively. Using a 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), showed good classification and diagnostic ability in distinguishing EP from EC or EH. The biomarker panel for distinguishing EP from EC yielded an area under the curve (AUC) of 0.915, sensitivity of 100% and specificity of 72.41%, while that for distinguishing EP from EH yielded an AUC of 1.000, sensitivity of 100% and specificity of 100%. The two diagnostic models also showed good diagnostic abilities in the validation set. Therefore, this biomarker panel can be used as a rapid diagnostic method to assist in imaging examinations and provide a reference for clinicians in the identification and diagnosis of endometrial diseases.

Serum Lipidomic Analysis Reveals Biomarkers and Metabolic Pathways of Thyroid Dysfunction

“…To ensure data quality in the lipidomic analysis, we analyzed the samples randomly and prepared mixed QC samples by mixing 10 μL of supernatant from each sample. The QC samples were inserted uniformly into each set of analytical run sequences for the methodology validation of the large-scale metabolomics analysis method . We investigated the precision of the instrument by performing six consecutive injections of the same QC sample.…”

“…The QC samples were inserted uniformly into each set of analytical run sequences for the methodology validation of the large-scale metabolomics analysis method. 41 We investigated the precision of the instrument by performing six consecutive injections of the same QC sample. To determine the method repeatability, seven QC samples were parallelly prepared and analyzed consecutively.…”

Endometrial cancer diagnostic and prognostic algorithms based on proteomics, metabolomics, and clinical data: a systematic review

“…In the context of proteomics, four studies only validated their data using independent sample cohorts (104,107,113,119), and one study (115), validated their proteomic-based model in a subsequent publication using TMAs (126). Three metabolomic studies performed similar validations in independent cohorts (138,142,144), and one study in particular successfully validated the metabolic profiles identified in serum samples (136) also using dried blood and reported excellent diagnostic characteristics (144). For validation, the authors adopted a prospective design on a very large study cohort (over 1,000 subjects), therefore, candidate biomarkers identified in this study bear great potential for translation into clinical practice.…”

“…For validation, the authors adopted a prospective design on a very large study cohort (over 1,000 subjects), therefore, candidate biomarkers identified in this study bear great potential for translation into clinical practice. Of interest, these biomarkers include steroids, which were also selected as candidate molecules in other studies (135,157) A caveat in a number of the included studies on proteomics (102, 104, 108) or metabolomics (136,138,142,143) is the use of healthy (not age/comorbidity matched) women, likely resulting in an overestimated diagnostic accuracy. Also, if pre-menopausal controls are included, this may induce biases as EC is predominantly a postmenopausal disease (114-116, 122, 147, 149, 154).…”

Small molecule metabolites: discovery of biomarkers and therapeutic targets

“… 551 Using a ROC curve and logistic regression analysis, a biomarker panel of PA(37:4), 6-keto-PGF1α, PS(36:0), and LysoPC(20:1) demonstrated good classification and diagnostic ability in distinguishing endometrial polyps from endometrial hyperplasia or endometrial cancer in the validation set. 552 Notably, targeted metabolomics analyses identified gamma-aminobutyric acid markedly reduced in COVID-19 patients and its change levels with high sensitivity that allowed for COVID-19 stratification. 553 …”