An outcome model for human bladder cancer: A comprehensive study based on weighted gene co-expression network...

Area of Science:

• Oncology
• Bioinformatics
• Genetics

Background:

• Accurate prognosis is vital for bladder cancer (BCa) treatment decisions.
• Developing effective prognostic models for BCa has significant clinical implications.

Purpose of the Study:

• To establish an effective prognostic model for bladder cancer (BCa) using integrative bioinformatics analysis.
• To identify progression-related genes and construct a predictive model for patient outcomes.

Main Methods:

• Weighted Gene Co-expression Network Analysis (WGCNA) and Differentially Expressed Gene (DEG) screening.
• LASSO Cox regression analysis to construct the prognostic model.
• Internal/external validation, pan-cancer validation, time-dependent ROC, and nomogram construction.

Main Results:

• Eight progression-related differentially expressed genes were identified in BCa.
• A 3-gene prognostic model was built using LASSO Cox regression, demonstrating good performance in predicting progression-free survival (PFS) and overall survival (OS).
• The model showed good predictive accuracy and clinical utility across multiple datasets and in pan-cancer analysis.

Conclusions:

• The developed prognostic model is the first of its kind for human bladder cancer progression prediction via integrative bioinformatics.
• This model shows promise in aiding clinical decision-making for BCa patients.
• The model exhibits robust performance and clinical utility in predicting patient outcomes.