TY - JOUR
T1 - A miRNA-based diagnostic model predicts resectable lung cancer in humans with high accuracy
AU - Asakura, Keisuke
AU - Kadota, Tsukasa
AU - Matsuzaki, Juntaro
AU - Yoshida, Yukihiro
AU - Yamamoto, Yusuke
AU - Nakagawa, Kazuo
AU - Takizawa, Satoko
AU - Aoki, Yoshiaki
AU - Nakamura, Eiji
AU - Miura, Junichiro
AU - Sakamoto, Hiromi
AU - Kato, Ken
AU - Watanabe, Shun ichi
AU - Ochiya, Takahiro
N1 - Funding Information:
The authors thank Tomomi Fukuda, Takumi Sonoda, Hiroko Tadokoro, Megumi Miyagi, Tatsuya Suzuki, Junpei Kawauchi, Makiko Ichikawa, and Kamakura Techno-Science Inc. for performing the microarray assays, Satoshi Kondo for technical support, Noriko Abe for the management of serum samples, Michiko Ohori for the management of personal information, Hitoshi Fujimiya for developing in-house analytic tools, and Kazuki Sudo for independent confirmation of participant eligibility. The National Cancer Center Biobank is supported by the National Cancer Center Research and Development Fund (29-A-1). This study was financially supported through a “Development of Diagnostic Technology for Detection of miRNA in Body Fluids” grant from the Japan Agency for Medical Research and Development (to TO).
Publisher Copyright:
© 2020, The Author(s).
PY - 2020/12/1
Y1 - 2020/12/1
N2 - 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.
AB - 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.
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U2 - 10.1038/s42003-020-0863-y
DO - 10.1038/s42003-020-0863-y
M3 - Article
C2 - 32193503
AN - SCOPUS:85083261055
SN - 2399-3642
VL - 3
JO - Communications Biology
JF - Communications Biology
IS - 1
M1 - 134
ER -
