Developing hypoxia and lactate metabolism-related molecular subtypes and prognostic signature for clear cell renal cell carcinoma through integrating machine learning.

ABSTRACT:

Background

The microenvironment of clear cell renal cell carcinoma (ccRCC) is characterized by hypoxia and increased lactate production. However, the impact of hypoxia and lactate metabolism on ccRCC remains incompletely understood. In this study, a new molecular subtype is developed based on hypoxia-related genes (HRGs) and lactate metabolism-related genes (LMRGs), aiming to create a tool that can predict the survival rate, immune microenvironment status, and responsiveness to treatment of ccRCC patients.

Method

We obtained RNA-seq data and clinical information of patients with ccRCC from TCGA and GEO. HRGs and LMRGs are sourced from the Molecular Signatures Database. Integrating 10 machine learning algorithms and 101 frameworks, we constructed a prognostic model related to

SUBMITTER: Liu J 

PROVIDER: S-EPMC11561225 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature