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サカモト チヨミ
SAKAMOTO Chiyomi
坂本 智代美 所属 熊本保健科学大学 生物毒素・抗毒素共同研究講座 職位 特命助教 |
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| 言語種別 | 日本語 |
| 発行・発表の年月 | 2020/02/03 |
| 形態種別 | 研究論文 |
| 標題 | Computational analysis of morphological and molecular features in gastric cancer tissues |
| 執筆形態 | 共著 |
| 掲載区分 | 国外 |
| 担当範囲 | Quantitation of morphological differences |
| 著者・共著者 | Yoko Yasuda, Kazuaki Tokunaga Tomoaki Koga, Chiyomi Sakamoto, Ilya G. Goldberg, Noriko Saitoh, Mitsuyoshi Nakao |
| 概要 | The importance of quantitative assessment of morphological information has been highly recognized in clinical diagnosis and therapeutic strategies.In this study, we used a supervised machine learning algorithm wndchrm to classify hematoxylin and eosin (H&E)-stained images of human gastric cancer tissues. This analysis distinguished between noncancer and cancer tissues with different histological grades. We then classified the H&E-stained images by expression levels of cancer-associated nuclear ATF7IP/MCAF1 and membranous PD-L1 proteins using immunohistochemistry of serial sections. Interestingly, classes with low and high expressions of each protein exhibited significant morphological dissimilarity in H&E images. These results indicated that morphological features in cancer tissues are correlated with expression of specific cancer-associated proteins, suggesting the usefulness of biomolecular-based morphological classification. |