Machine Learning Developed a Programmed Cell Death Signature for Predicting Prognosis, Ecosystem, and Drug Sensitivity in Ovarian Cancer.

ABSTRACT:

Background

Ovarian cancer (OC) is the leading cause of gynecological cancer death and the fifth most common cause of cancer-related death in women in America. Programmed cell death played a vital role in tumor progression and immunotherapy response in cancer.

Methods

The prognostic cell death signature (CDS) was constructed with an integrative machine learning procedure, including 10 methods, using TCGA, GSE14764, GSE26193, GSE26712, GSE63885, and GSE140082 datasets. Several methods and single-cell analysis were used to explore the correlation between CDS and the ecosystem and therapy response of OC patients.

Results

The prognostic CDS constructed by the combination of StepCox (n = both) + Enet (alpha = 0.2) acted as an independent risk factor for the overall s

SUBMITTER: Wang L 

PROVIDER: S-EPMC10586435 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature