Network Analysis Integrating microRNA Expression Profiling with MRI Biomarkers and Clinical Data for Prostate Cancer Early Detection: A Proof of Concept Study.

Valeria Panebianco,Carlo Catalano,Zein Besharat,Elisabetta Ferretti,Giorgia Carnicelli,Federica Conte,Alessandro Sciarra,Martina Pecoraro,Paola Paci,Lorenzo Farina,Elena Splendiani,Giuseppina Catanzaro

doi:10.3390/biomedicines9101470

Abstract

The MRI of the prostate is the gold standard for the detection of clinically significant prostate cancer (csPCa). Nonetheless, MRI still misses around 11% of clinically significant disease. The aim was to comprehensively integrate tissue and circulating microRNA profiling, MRI biomarkers and clinical data to implement PCa early detection. In this prospective cohort study, 76 biopsy naïve patients underwent MRI and MRI directed biopsy. A sentinel sample of 15 patients was selected for a pilot molecular analysis. Weighted gene coexpression network analysis was applied to identify the microRNAs drivers of csPCa. MicroRNA–target gene interaction maps were constructed, and enrichment analysis performed. The ANOVA on ranks test and ROC analysis were performed for statistics. Disease status was associated with the underexpression of the miRNA profiled; a correlation was found with ADC (r = −0.51, p = 0.02) and normalized ADC values (r = −0.64, p = 0.002). The overexpression of miRNAs from plasma was associated with csPCa (r = 0.72; p = 0.02), and with PI-RADS assessment score (r = 0.73; p = 0.02); a linear correlation was found with biomarkers of diffusion and perfusion. Among the 800 profiled microRNA, eleven were identified as correlating with PCa, among which miR-548a-3p, miR-138-5p and miR-520d-3p were confirmed using the RT-qPCR approach on an additional cohort of ten subjects. ROC analysis showed an accuracy of >90%. Provided an additional validation set of the identified miRNAs on a larger cohort, we propose a diagnostic paradigm shift that sees molecular data and MRI biomarkers as the prebiopsy triage of patients at risk for PCa. This approach will allow for accurate patient allocation to biopsy, and for stratification into risk group categories, reducing overdiagnosis and overtreatment.

Highlights

Prostate cancer (PCa) is the fourth most common malignancy worldwide and the second cause of cancer related death in men [1]
We found that the brown module had the strongest overall statistical correlation with the Gleason grade (r = 0.72; p = 0.02), and that miRNAs overexpression in this module was predictive of clinically significant prostate cancer (csPCa) (Figure 4a)
The Magnetic resonance imaging (MRI) biomarkers that most significantly correlated with differential miRNA expression were the apparent diffusion coefficient (ADC) and the normalized ADC (nADC) values

Summary

Introduction

Prostate cancer (PCa) is the fourth most common malignancy worldwide and the second cause of cancer related death in men [1]. Biomedicines 2021, 9, 1470 ferent prostate cancers in the past two decades yielded the identification of 97 significant gene mutations, in addition to seven hallmark signatures [2,3]. Significant PCa (csPCa) is often aggressive and potentially metastatic, requiring early detection and possibly multimodal therapy. Insignificant PCa (ciPCa) often never progress and can be safely treated with active surveillance strategies. Magnetic resonance imaging (MRI) is recommended by the European Association of Urology (EAU) guidelines as a triage test to identify men with csPCa, as it has shown excellent results in terms of negative predictive value (NPV) and accuracy [6,7,8,9,10,11].

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Discussion

Conclusion