{"paper_id":"0f67f750-d74b-4411-8832-6850b4a2a8bd","body_text":"JOURNAL TOOLS |\n| Journal policies |\n| Publishing options |\n| eTOC |\n| To subscribe |\n| Submit an article |\n| Recommend to your librarian |\nARTICLE TOOLS |\n| Publication history |\n| Reprints |\n| Permissions |\n| Cite this article as |\n| Share |\nYOUR ACCOUNT\nYOUR ORDERS\nSHOPPING BASKET\nItems: 0\nTotal amount: € 0,00\nHOW TO ORDER\nYOUR SUBSCRIPTIONS\nYOUR ARTICLES\nYOUR EBOOKS\nCOUPON\nACCESSIBILITY\nARTICOLO ORIGINALE\nBiochimica Clinica 2025 Settembre;49(3):245-51\nDOI: 10.23736/S0393-0564.25.00034-2\nCopyright © 2025 Società Italiana di Biochimica Clinica e Biologia Molecolare Clinica - Medicina di Laboratorio\nlanguage: Italian\nDevelopment of a diagnostic model based on serum microRNAs for the diagnosis of endometriosis\nCosetta BERGAMASCHI 1, 2, 3 ✉, Antonella RAVAGGI 2, 3, 4, Chiara GALBIATI 5, Laura ZANOTTI 2, 3, Jacopo CONFORTI 2, 4, Matteo EPIS 2, 4, Aline S. FABRICIO 6, Massimo GION 7, Antonette E. LEON 7, Elia CAPPELLETTO 6, Massimo GENNARELLI 8, Cesare ROMAGNOLO 9, Giuseppe CIRAVOLO 2, Stefano CALZA 10, Eliana BIGNOTTI 2, 3, Franco E. ODICINO 2, 4\n1 Scuola di Specializzazione in Patologia Clinica e Biochimica Clinica, Dipartimento di Medicina Molecolare e Traslazionale, Università degli Studi di Brescia, Brescia, Italia; 2 Struttura di Ostetricia e Ginecologia, ASST Spedali Civili di Brescia, Brescia, Italia; 3 Istituto di Medicina Molecolare Angelo Nocivelli, ASST Spedali Civili di Brescia, Brescia, Italia; 4 Dipartimento di Scienze Cliniche e Sperimentali, Università di Brescia, Brescia, Italia; 5 Dipartimento di Scienze Teoriche e Applicate, Università eCampus, Novedrate, Como, Italia; 6 Oncologia di Base Sperimentale e Traslazionale, Istituto Oncologico Veneto IOV - IRCCS, Padova, Italia; 7 Dipartimento di Patologia Clinica, Centro Regionale dei Biomarcatori, AULSS3 Serenissima, Venezia, Italia; 8 Divisione di Biotecnologie, Dipartimento di Medicina Molecolare e Traslazionale, Università degli Studi di Brescia, Brescia, Italia; 9 Unità di Ostetricia e Ginecologia, Ospedale dell’Angelo, Mestre, Venezia, Italia; 10 Unità di Biostatistica e Bioinformatica, Dipartimento di Medicina Molecolare e Traslazionale, Università degli Studi di Brescia, Brescia, Italia\nINTRODUCTION: Endometriosis (END) is a debilitating gynecological disorder. Clinical examination, imaging, and laparoscopy can provide a definitive diagnosis of END. The discovery of non-invasive biomarkers is necessary to overcome the disadvantages of surgical practice. MicroRNAs (miRNAs) are a family of small non-coding RNAs that, thanks to their high stability in biologic fluids, can represent excellent biomarkers for END. The purpose of this study was to develop a diagnostic model based on serum miRNA to identify patients affected by END.\nMETHODS: Serum samples were collected before surgery and total RNA was extracted from 400 uL of serum of 67 patients with END and 60 patients with benign gynecological non endometriotic pathology, used as controls (CNT). miRNA expression profiling was performed via TaqMan OpenArray technology. For the development of the diagnostic algorithm to discriminate between END and CNT, the ‘Random Forest’ method was used, along with “Recursive Forward Elimination”.\nRESULTS: To explore the discrimination ability between END and CNT, a Random Forest algorithm based on 18 miRNAs was developed. The diagnostic performance of this model was characterized by Area Under the Curve (AUC)=0.863, False Positve Rate (FPR)=0.227, False Negative Rate (FNR)=0.196, Specificity=0.773 and Sensitivity=0.804.\nDISCUSSION: Our study identified a diagnostic algorithm that shows good performance in discriminating between END and CNT, supporting the potential role of circulating miRNA as non-invasive biomarkers of END.\nKEY WORDS: Endometriosi; MicroRNA; Algoritmi diagnostici","source_license":"CC0","license_restricted":false}