Identification of key genes in pancreatic ductal adenocarcinoma with biologically informed deep neural network.

ABSTRACT:

Background

Pancreatic ductal adenocarcinoma (PDAC) is a prevalent and lethal form of cancer. According to clinical trials results, immunotherapy has not been successful in PDAC so far, which may be due to the high heterogeneity of the tumour immune microenvironment (TIME) of PDAC patients. This study aimed to identify significant genes associated with the prognosis and immune microenvironment of PDAC using interpretive deep learning.

Methods

The importance of genes was appraised by P-NET, a potent interpretive deep learning model based on a biologically informed neural network. In addition to differential expression and survival analyses, tumour immune dysfunction and exclusion (TIDE) and CellPhoneDB analyses were carried out to identify genes that exert an influence on the

SUBMITTER: Gao R 

PROVIDER: S-EPMC12598363 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature