Computational analysis of morphological and molecular features in gastric cancer tissues.
Yoko YasudaKazuaki TokunagaTomoaki KogaChiyomi SakamotoIlya G GoldbergNoriko SaitohMitsuyoshi NakaoPublished in: Cancer medicine (2020)
Biological morphologies of cells and tissues represent their physiological and pathological conditions. 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.
Keyphrases
- machine learning
- deep learning
- gene expression
- poor prognosis
- convolutional neural network
- papillary thyroid
- artificial intelligence
- endothelial cells
- induced apoptosis
- binding protein
- squamous cell
- transcription factor
- healthcare
- endoplasmic reticulum stress
- big data
- squamous cell carcinoma
- health information
- oxidative stress
- long non coding rna
- young adults
- lymph node metastasis
- cell death
- amino acid
- social media
- childhood cancer
- neural network
- pluripotent stem cells