A miRNA-based diagnostic model predicts resectable lung cancer in humans with high accuracy

Keisuke Asakura, Tsukasa Kadota, Juntaro Matsuzaki, Yukihiro Yoshida, Yusuke Yamamoto, Kazuo Nakagawa, Satoko Takizawa, Yoshiaki Aoki, Eiji Nakamura, Junichiro Miura, Hiromi Sakamoto, Ken Kato, Shun ichi Watanabe, Takahiro Ochiya

Research output: Contribution to journalArticlepeer-review

66 Citations (Scopus)


Lung cancer, the leading cause of cancer death worldwide, is most frequently detected through imaging tests. In this study, we investigated serum microRNAs (miRNAs) as a possible early screening tool for resectable lung cancer. First, we used serum samples from participants with and without lung cancer to comprehensively create 2588 miRNAs profiles; next, we established a diagnostic model based on the combined expression levels of two miRNAs (miR-1268b and miR-6075) in the discovery set (208 lung cancer patients and 208 non-cancer participants). The model displayed a sensitivity of 99% and specificity of 99% in the validation set (1358 patients and 1970 non-cancer participants) and exhibited high sensitivity regardless of histological type and pathological TNM stage of the cancer. Moreover, the diagnostic index markedly decreased after lung cancer resection. Thus, the model we developed has the potential to markedly improve screening for resectable lung cancer.

Original languageEnglish
Article number134
JournalCommunications biology
Issue number1
Publication statusPublished - 2020 Dec 1

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • General Biochemistry,Genetics and Molecular Biology
  • General Agricultural and Biological Sciences


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