Serum microRNA profile enables preoperative diagnosis of uterine leiomyosarcoma

Akira Yokoi, Juntaro Matsuzaki, Yusuke Yamamoto, Keisei Tate, Yutaka Yoneoka, Hanako Shimizu, Takashi Uehara, Mitsuya Ishikawa, Satoko Takizawa, Yoshiaki Aoki, Ken Kato, Tomoyasu Kato, Takahiro Ochiya

研究成果: Article査読

22 被引用数 (Scopus)


Uterine leiomyosarcoma (ULMS) is the major subtype of uterine sarcoma (US) and contributes to uterine cancer deaths. Although preoperative diagnosis of US remains challenging, frequent application of laparoscopic surgery for benign uterine leiomyomas (ULM) requires precise exclusion of US. MicroRNAs are stably present in the bloodstream, and the application of circulating miRNAs as disease biomarkers has been recognized. In the present study, we aimed to identify diagnostic biomarkers for distinguishing US from ULM by focusing on circulating miRNAs. All serum samples were collected preoperatively between 2009 and 2017, and all cases were histopathologically diagnosed. Whole miRNA profiles were obtained using a miRNA microarray. By analyzing expression levels of the miRNAs, candidate miRNAs were selected based on diagnostic performance in discriminating US from ULM, and a diagnostic model was then constructed. A total of 90 serum samples were analyzed, and clustering analyses revealed that the profiles of ULMS were distinct from those of controls. Based on leave-one-out cross-validation, seven miRNAs were selected as biomarker candidates. Based on model construction, the optimal model consisted of two miRNAs (miR-1246 and miR-191-5p), with an area under the receiver operating characteristic curve (AUC) for identifying ULMS of 0.97 (95% confidence interval [CI], 0.91-1.00). In contrast, serum lactate dehydrogenase had an AUC of only 0.64 (95% CI, 0.34-0.94). Seven serum miRNAs with high diagnostic performance for preoperative US screening were detected, and a promising diagnostic model for ULMS was generated.

ジャーナルCancer science
出版ステータスPublished - 2019 12月 1

ASJC Scopus subject areas

  • 腫瘍学
  • 癌研究


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