The choice of surgical treatment for hepatocellular carcinoma (HCC) depends on several prognostic variables, among which histological features, like microvascular invasion and tumor grade, are well established. This study aims to identify the tissue miRNAs predictive of recurrence after liver resection in "histologically advanced" HCC. We selected 54 patients: 15 retrospective resected patients without recurrence (group A), 19 retrospective resected patients with HCC recurrence (group B), and 20 prospective patients (group C), with 4 recurrence cases. All selected HCC were "histologically advanced" (high Edmondson grade and/or presence of microvascular invasion). A wide spectrum of miRNAs was studied with TaqMan Human microRNA Arrays; qRT-PCR assays were used to validate results on selected miRNAs; immunohistochemistry for IGF2 was applied to study the mechanism of miR-483-3p. As a result, a significant differential expression between group A and B was found for 255 miRNAs. Among them we selected miR-483-3p and miR-548e (P < 0.001). As a single variable (group C), HCC with miR-483-3p downregulation (mean fold increase 0.21) had 44.4% of recurrence cases; HCC with miR-483-3p upregulation (mean fold increase 5.94) showed no recurrence cases (P=0.011). At immunohistochemistry (group C), the HCC with loss of cytoplasmic IGF2 expression showed a down-regulation of miR-483-3p (fold increase 0.57). In conclusion, in patients with "histologically advanced" HCC, the analysis of specific tissue miRNAs (particularly miR-483-3p) could help identify the recurrence risk and choose which treatment algorithm to implement (follow-up, resection or transplantation). This could have an important impact on patient survival and transplantation outcome, improving organ allocation.

Tissue miRNA 483-3p expression predicts tumor recurrence after surgical resection in histologically advanced hepatocellular carcinomas

Vasuri, Francesco;Fittipaldi, Silvia;DE PACE, VANESSA;Gramantieri, Laura;Bertuzzo, Valentina;Cescon, Matteo;Pinna, Antonio D.;Fiorentino, Michelangelo;D'Errico, Antonia;Ravaioli, Matteo
2018

Abstract

The choice of surgical treatment for hepatocellular carcinoma (HCC) depends on several prognostic variables, among which histological features, like microvascular invasion and tumor grade, are well established. This study aims to identify the tissue miRNAs predictive of recurrence after liver resection in "histologically advanced" HCC. We selected 54 patients: 15 retrospective resected patients without recurrence (group A), 19 retrospective resected patients with HCC recurrence (group B), and 20 prospective patients (group C), with 4 recurrence cases. All selected HCC were "histologically advanced" (high Edmondson grade and/or presence of microvascular invasion). A wide spectrum of miRNAs was studied with TaqMan Human microRNA Arrays; qRT-PCR assays were used to validate results on selected miRNAs; immunohistochemistry for IGF2 was applied to study the mechanism of miR-483-3p. As a result, a significant differential expression between group A and B was found for 255 miRNAs. Among them we selected miR-483-3p and miR-548e (P < 0.001). As a single variable (group C), HCC with miR-483-3p downregulation (mean fold increase 0.21) had 44.4% of recurrence cases; HCC with miR-483-3p upregulation (mean fold increase 5.94) showed no recurrence cases (P=0.011). At immunohistochemistry (group C), the HCC with loss of cytoplasmic IGF2 expression showed a down-regulation of miR-483-3p (fold increase 0.57). In conclusion, in patients with "histologically advanced" HCC, the analysis of specific tissue miRNAs (particularly miR-483-3p) could help identify the recurrence risk and choose which treatment algorithm to implement (follow-up, resection or transplantation). This could have an important impact on patient survival and transplantation outcome, improving organ allocation.
2018
Vasuri, Francesco; Fittipaldi, Silvia; De Pace, Vanessa; Gramantieri, Laura; Bertuzzo, Valentina; Cescon, Matteo; Pinna, Antonio D.; Fiorentino, Michelangelo; D'Errico, Antonia*; Ravaioli, Matteo
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/655617
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