Integrating transcriptomic data and digital pathology for NRG-based prediction of prognosis and therapy response in gastric cancer.

ABSTRACT:

Background

Cancer is characterized by its ability to resist cell death, and emerging evidence suggests a potential correlation between non-apoptotic regulated cell death (RCD), tumor progression, and therapy response. However, the prognostic significance of non-apoptotic RCD-related genes (NRGs) and their relationships with immune response in gastric cancer (GC) remain unclear.

Methods

In this study, RNA-seq gene expression and clinical information of GC patients were acquired from The Cancer Genome Atlas and the Gene Expression Omnibus databases. Cox and LASSO regression analyses were used to construct the NRG signature. Moreover, we developed a deep learning model based on ResNet50 to predict the NRG signature from digital pathology slides. The expression of signature hub

SUBMITTER: Sun Q 

PROVIDER: S-EPMC11556273 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature