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.
Abnormal thyroid hormone secretion is the most important feature of hypothyroidism and plays an important role in lipid metabolism. However, their connection has not been clearly established. This study aimed to identify the serum biomarkers and metabolic pathways associated with hyperthyroidism and hypothyroidism. The study enrolled discovery and validation sets of 175 and 300 participants, respectively, to identify and validate the serum biomarkers of hyperthyroidism and hypothyroidism via ultrahigh performance liquid chromatography−quadrupole time-of-flight mass spectrometry lipidomics through univariate and multivariate analyses. Eight and six biomarkers were identified for hyperthyroidism and hypothyroidism, respectively. Spearman correlation analysis was used to assess the correlation between the biomarkers and thyroid dysfunction indicators; subsequently, metabolic pathway and network analyses were performed for these biomarkers. Most biomarkers exhibited significant correlation with thyroid dysfunction indicators, mainly being enriched in the glycerophospholipid (GPL) metabolism. The diagnostic accuracies of the biomarkers and biomarker panels were assessed via receiver operating characteristic curve analysis. All the biomarkers demonstrated good diagnostic performance, and the hyperthyroidism and hypothyroidism biomarker panels reached an area under the curve value of 1.000. The results were validated using the validation set. Therefore, our findings revealed that thyroid dysfunction primarily affects the human metabolism via the GPL metabolism, thus providing a theoretical basis for the clinical prevention and control of thyroid dysfunction.
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