Sviluppo di un modello diagnostico basato su MicroRNA sierici per la diagnosi di endometriosi

Cosetta Bergamaschi, Cássia T. Bergamaschi, Antonella Ravaggi, C. Galbiati, Laura Zanotti, Jacopo Conforti, Matteo Epis, Aline S.C. Fabricio, María Gión, Massimo Gion, Antonette E. Leon, Elia Cappelletto, Massimo Gennarelli, Cesare Romagnolo, Giuseppe Ciravolo, Stefano Calza, Eliana Bignotti, Franco Odicino
In: Biochimica Clinica · 2025 · vol. 49(3) · doi:10.23736/s0393-0564.25.00034-2 · W4410317512
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A diagnostic model using serum microRNAs was developed with Random Forest analysis, achieving an AUC of 0.863 to discriminate between endometriosis and control groups.

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Abstract

INTRODUCTION: 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.METHODS: 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.”RESULTS: 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.DISCUSSION: 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.
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Introduction

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.

Methods

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”.

Results

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.

Discussion

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. KEY WORDS: Endometriosi; MicroRNA; Algoritmi diagnostici

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endometriosis

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