Kuno Suzuki, Hideyoshi Igata, Motoki Abe, Yusuke Yamamoto, Terunao Iwanaga, Hiroaki Kanzaki, Naoya Kato, Nobuko Tanaka, Kenji Kawasaki, Kazuyuki Matsushita, Ryu Samashima, Keiji Tsukino, Akira Yokomizo, Yosuke Miyashita, Issei Sumiyoshi, Kazuhisa Takahashi, Nobuko Serizawa, Ko Tomishima, Akihito Nagahara, Yumiko Ishizuka, Yoshiya Horimoto, Masayoshi Nagata, Keisuke Ishikawa, Shigeo Horie, Shuichiro Shiina, Motomi Nasu, Takashi Hashimoto, Shinji Mine, Shingo Kawano, Kiichi Sugimoto, Kazuhiro Sakamoto, Hiroyuki Takemura, Mitsuru Wakita, Yoko Tabe, Shunsuke Kato, Yohei Miyagi, Hiroyuki Adachi, Tetsuya Isaka, Hiroyuki Ito, Takashi Yamanaka, Tatsuya Yoshida, Toshinari Yamashita, Takashi Ogata, Takanobu Yamada, Takashi Oshima, Naoto Yamamoto, Masaaki Murakawa, Soichiro Morinaga, Satoshi Kobayashi, Shun Tezuka, Makoto Ueno, Mitsuyuki Koizumi, Kimito Osaka, Takeshi Kishida, Sumito Sato, Yo Mikayama, Manabu Shiozawa, Yasuhiro Inokuchi, Mitsuhiro Furuta, Nozomu Machida, Shinya Sato, Yoshihiko Yano, Atsushi Miwa, Kazuto Ito, Isao Kurosawa, Osamu Kikuchi, Hiromitsu Tazawa, Manabu Muto, Takashi Honda, Hiroki Kawashima, Masatoshi Ishigami, Yutaka Saito, Hiroyuki Daiko, Takaki Yoshikawa, Yukihide Kanemitsu, Ken Kato, Minoru Esaki, Takuji Okusaka, Hiromi Sakamoto, Teruhiko Yoshida, Takahiro Ochiya, Mitsuhito Sasaki, Masafumi Ikeda, Masashi Kudo, Naoto Gotohda, Shuichi Mitsunaga, Takeshi Kuwata, Takashi Kojima, Tatsuro Murano, Tomonori Yano, Taiki Yamaji, Takahisa Matsuda, Shoichiro Tsugane, Kazuki Hashimoto, Kazuhiko Yamada, Nobuyuki Takemura, Kyoji Ito, Fuminori Mihara, Akihiko Shimomura, Kunitoshi Shigeyasu, Kazuhiro Noma, Toshiyoshi Fujiwara, Hideki Yamamoto, Mizuki Morita, Shinichi Toyooka, Akihiro Tamori, Tasuku Nakabori, Kenji Ikezawa, Kazuyoshi Ohkawa, Kei Kunimasa, Kazumi Nishino, Toru Kumagai, Toshihiro Kudo, Naotoshi Sugimoto, Masayoshi Yasui, Takeshi Omori, Hiroshi Miyata, Toru Kimura, Tomohiro Maniwa, Jiro Okami, Hiroki Kusama, Nobuyoshi Kittaka, Takahiro Nakayama, Masashi Nakayama, Yasutomo Nakai, Kazuo Nishimura, Shoji Yotsui, Takashi Yamamoto, Tomoyuki Yamasaki, Emi Yamashita, Kazune Saito, Keiichi Yoshida, Masayuki Ohue, Masakazu Koda, Tatsuya Yamaguchi, Masami Tanaka, Takashi Nishizawa, Tetsuhiko Taira, Junko Kawano, Yasuaki Sagara, Yosuke Horita, Yoshiaki Mihara, Tetsuya Hamaguchi, Okihide Suzuki, Yoichi Kumagai, Hideyuki Ishida, Motoki Yamagishi, Hideaki Shimoyama, Haruaki Sasaki, Takehiko Nakasato, Takeshi Shichijo, Takashi Fukagai, Kota Nishimura, Kidai Hirayama, Masashi Morita, Yujin Kudo, Susumu Takeuchi, Norihiko Ikeda, Naohiro Kamoda, Kazunori Namiki, Yoshio Ohno, Tomohiro Umezu, Yoshiki Murakami, Masahiko Kuroda
Cancer Science 113(6) 2144-2166 2022年6月
Liquid biopsy is expected to be a promising cancer screening method because of its low invasiveness and the possibility of detecting multiple types in a single test. In the last decade, many studies on cancer detection using small RNAs in blood have been reported. To put small RNA tests into practical use as a multiple cancer type screening test, it is necessary to develop a method that can be applied to multiple facilities. We collected samples of eight cancer types and healthy controls from 20 facilities to evaluate the performance of cancer type classification. A total of 2,475 cancer samples and 496 healthy control samples were collected using a standardized protocol. After obtaining a small RNA expression profile, we constructed a classification model and evaluated its performance. First, we investigated the classification performance using samples from five single facilities. Each model showed areas under the receiver curve (AUC) ranging from 0.67 to 0.89. Second, we performed principal component analysis (PCA) to examine the characteristics of the facilities. The degree of hemolysis and the data acquisition period affected the expression profiles. Finally, we constructed the classification model by reducing the influence of these factors, and its performance had an AUC of 0.76. The results reveal that small RNA can be used for the classification of cancer types in samples from a single facility. However, interfacility biases will affect the classification of samples from multiple facilities. These findings will provide important insights to improve the performance of multiple cancer type classifications using small RNA expression profiles acquired from multiple facilities.