医学
生物标志物
诊断生物标志物
肿瘤科
生物标志物发现
接收机工作特性
疾病
内科学
神经内分泌肿瘤
诊断准确性
生物信息学
生物
基因
蛋白质组学
生物化学
作者
Yudai Higuchi,Katsutoshi Shoda,Hirotaka Konishi,Takashi Nakayama,Rie Shibata,Ryo Saito,Suguru Maruyama,Kensuke Shiraishi,Shinji Furuya,Yoshihiko Kawaguchi,Hidetake Amemiya,Atsushi Shiozaki,Daisuke Ichikawa
标识
DOI:10.1097/sla.0000000000006816
摘要
Objective: This study aimed to identify and validate miR-7-5p as a non-invasive biomarker for diagnosing and monitoring neuroendocrine tumors (NETs). Summary Background Data: The incidence of NETs has been increasing globally in recent years, yet standardized non-invasive biomarkers for early detection and monitoring of these malignancies are lacking. Although diagnostic imaging is valuable, its resource-intensive nature limits its practicality for routine screening. Thus, reliable, non-invasive biomarkers are urgently needed. Methods: In the multicenter SKY-NET study, we performed comprehensive genome-wide transcriptome profiling to identify candidate microRNAs (miRNAs) in the tissues of patients with NET. Promising candidates were validated in plasma samples from two independent cohorts using quantitative polymerase chain reaction. Results: We identified miR-7-5p as a diagnostic candidate for NET. In the multicenter SKY-NET study, miR-7-5p exhibited robust diagnostic performance in both tissue and plasma samples, with high sensitivity and specificity. The biomarker differentiated NET tissues from normal tissues, with an area under the receiver operating characteristic curve (AUC) of 0.97 and 0.88 for tissue and plasma samples, respectively. In an independent clinical cohort, plasma miR-7-5p maintained strong diagnostic performance (AUC=0.85). Notably, plasma miR-7-5p levels correlated with tumor dynamics, reflecting treatment response and disease recurrence. Conclusion: MiR-7-5p demonstrates substantial potential as a non-invasive biomarker for diagnosing and monitoring NET. Our findings support its potential as a diagnostic tool and for assessing disease progression, offering a new avenue for early detection and treatment evaluation in patients with NET.
科研通智能强力驱动
Strongly Powered by AbleSci AI