Deep Learning Identifies HAT1 as a Morphological Regulator in Esophageal Squamous Carcinoma Cells through Controlling Cell Senescence

Abstract

Histopathology is a critical approach for diagnostic tasks and precision treatment. However, histopathological deep learning tools for auto-identification remain poorly developed. Meanwhile, the interpretation of the computer vision attention into a cellular process is less efficient in a systematic way. Herein, it is identified that histone acetyltransferase 1 (HAT1) is an aging-associated gene in the esophagus epithelium by machine learning. An interpretable deep learning model is developed to distinguish morphological changes with varied HAT1 expressions in esophageal squamous carcinoma cells (ESCC). The gradient-weighted class activation mapping and prediction score analysis reveal that the computer's vision focuses on the nuclear sizes of ESCC. The hypothesized phenotype is verified in HAT1-knockdown ESCCs. Finally, HAT1 regulating cell senescence by affecting the H3K27 acetylation and E2F transcription factor 7 (E2F7) expression is shown. Herein, the feasibility and benefits of applying histopathological deep learning assistance systems in routine practice scenarios and connecting phenotype and genotype for further genetic research are suggested.