MtPCDI: a machine learning-based prognostic model for prostate cancer recurrence.

ABSTRACT:

Background

This research seeks to formulate a prognostic model for forecasting prostate cancer recurrence by examining the interaction between mitochondrial function and programmed cell death (PCD).

Methods

The research involved analyzing four gene expression datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) using univariate Cox regression. These analyses identified genes linked with mitochondrial function and PCD that correlate with recurrence prognosis. Various machine learning algorithms were then employed to construct an optimal predictive model.

Results

A key outcome was the creation of a mitochondrial-related programmed cell death index (mtPCDI), which effectively predicts the prognosis of prostate cancer patients. It was observed th

SUBMITTER: Cheng G 

PROVIDER: S-EPMC11408181 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature