Depiction of neuroendocrine features associated with immunotherapy response using a novel one-class predictor in lung adenocarcinoma.

ABSTRACT:

Background

Tumours with no evidence of neuroendocrine transformation histologically but harbouring neuroendocrine features are collectively referred to as non-small cell lung cancer (NSCLC) with neuroendocrine differentiation (NED). Investigating the mechanisms underlying NED is conducive to designing appropriate treatment options for NSCLC patients.

Methods

In the present study, we integrated multiple lung cancer datasets to identify neuroendocrine features using a one-class logistic regression (OCLR) machine learning algorithm trained on small cell lung cancer (SCLC) cells, a pulmonary neuroendocrine cell type, based on the transcriptome of NSCLC and named the NED index (NEDI). Single-sample gene set enrichment analysis, pathway enrichment analysis, ESTIMATE algorithm anal

SUBMITTER: Liu H 

PROVIDER: S-EPMC10195954 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature