Machine learning-derived prognostic signature integrating programmed cell death and mitochondrial function in renal clear cell carcinoma: identification of PIF1 as a novel target.

ABSTRACT:

Background

The pathogenesis and progression of renal cell carcinoma (RCC) involve complex programmed cell death (PCD) processes. As the powerhouse of the cell, mitochondria can influence cell death mechanisms. However, the prognostic significance of the interplay between mitochondrial function (MF) and PCD remains unclear.

Methods

We collected sets of genes related to PCD and MF. Using a powerful machine learning algorithm framework, we investigated the relationship between MF and PCD in different cohorts of patients and developed a machine learning-derived prognostic signature (mpMLDPS) related to MF and PCD. Finally, the most appropriate prognostic markers for RCC were screened by survival analysis and clinical correlation analysis, and the effects on renal cancer cells we

SUBMITTER: Cheng G 

PROVIDER: S-EPMC11861773 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature