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

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 hypoxia and lactate metabolism. Its accuracy and reliability are evaluated through constructing prognostic nomograms, drawing ROC curves, and validating with clinical datasets. Additionally, risk subgroups are evaluated based on functional enrichment, tumor mutational burden (TMB), immune cell infiltration degree, and immune checkpoint expression level. Finally, we evaluate the responsiveness of risk subgroups to immunotherapy and determine personalized drugs for specific risk subgroups.

Results: 85 valuable prognostic genes were screened out. Functional enrichment analysis shows that the group with high-risk hypoxia and lactate metabolism-related genes scores (HLMRGS) is mainly involved in the activation of immune-related activities, while the low risk HLMRGS group is more active in metabolic and tumor-related pathways. At the same time, differences in the cellular functional states in the tumor microenvironment between the high risk HLMRGS group and the low risk HLMRGS group were observed. Finally, potential drugs for specific risk subgroups were determined.

Conclusion: We have developed a novel prognostic signature that integrates hypoxia and lactate metabolism. It is expected to become an effective tool for prognosis prediction, immunotherapy and personalized medicine of ccRCC.