Harnessing machine learning-driven multiomics integration: deciphering programmed cell death networks for prognostication and immunotherapy prediction in lung adenocarcinoma.

ABSTRACT:

Background

Programmed cell death (PCD) patterns play important roles in lung adenocarcinoma (LUAD) development as well as treatment resistance, and in-depth study of PCD is beneficial for improving the therapeutic paradigm for LUAD.

Methods

Fourteen PCD-related patterns were integrated and multiple datasets from TCGA and GEO were collected to develop a PCD signature using 101 machine learning algorithm combinations. Prognosis, immune cell infiltration, and sensitivity to chemotherapy and immunotherapy were compared between different risk groups and validated by multiple bulk RNA-seq and scRNA-seq datasets of patients receiving immunotherapy. CellChat was used to analyze the cellular interactions between patients with different PCD groups. Immune cell infiltration in the tumo

SUBMITTER: Chang W 

PROVIDER: S-EPMC12823722 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature