BLCA prognostic model creation and validation based on immune gene-metabolic gene combination

Area of Science:

• Oncology
• Immunology
• Metabolomics
• Bioinformatics

Background:

• Bladder cancer (BLCA) is a common malignancy of the urinary system.
• Understanding the interaction between immune and metabolic genes is vital for BLCA patient prognosis and treatment strategies.

Purpose of the Study:

• To identify prognostic genes related to immune and metabolic pathways in bladder cancer.
• To develop a predictive signature for bladder cancer prognosis and immunotherapy response.

Main Methods:

• Extraction and expression profile analysis of immune/metabolism genes.
• Non-negative Matrix Factorization (NMF) clustering for prognostic gene identification.
• Development of a prognostic risk signature using Cox and LASSO regression.
• Analysis of immunocyte infiltration, tumor microenvironment, and immunotherapy response.

Main Results:

• A 7-gene signature (POLE2, AHNAK, SHMT2, NR2F1, TFRC, OAS1, CHKB) was developed, demonstrating superior predictive performance in 614 BLCA samples.
• The immune and metabolism-related gene (IMRG) signature showed independence from clinical indicators and correlated with immunotherapy response and immune cell infiltration.
• Functional enrichment analysis revealed distinct biological pathways between low- and high-risk groups, with the signature outperforming others in prediction accuracy.

Conclusions:

• The developed model serves as a novel tool for assessing immunotherapy effectiveness and predicting BLCA patient prognosis.
• This signature offers potential as an immunological biomarker and guides targeted treatment strategies for bladder cancer.