Dual-Function RNA Biomarkers: Integrating Relapse Prediction and Immune Profiling in Triple-Negative Breast Cancer

Area of Science:

• Oncology
• Genomics
• Bioinformatics

Background:

• Triple-negative breast cancer (TNBC) presents aggressive behavior and high recurrence rates.
• Understanding TNBC relapse mechanisms is crucial for improving patient outcomes.
• Current prognostic tools for TNBC require enhancement for better accuracy.

Purpose of the Study:

• To identify long non-coding RNA (lncRNA) pairs associated with recurrence and immune response in TNBC.
• To develop and validate a robust risk prediction model for TNBC prognosis.
• To explore the therapeutic potential of identified lncRNAs, specifically LINC01605.

Main Methods:

• Transcriptomic data analysis from The Cancer Genome Atlas (TCGA).
• Integration of LASSO regression, Cox analysis, and random forest for model construction.
• External cohort validation and backpropagation neural network (BPNN) assessment.

Main Results:

• A novel risk prediction model strongly correlates with immune cell infiltration, immunotherapy response, tumor mutational burden (TMB), and chemotherapy sensitivity.
• A nomogram integrating risk scores with clinical parameters showed superior predictive accuracy.
• Silencing LINC01605 significantly inhibited TNBC cell proliferation, migration, and invasion in experimental validation.

Conclusions:

• The developed risk model offers a promising approach for predicting TNBC recurrence and prognosis.
• LINC01605 emerges as a potential therapeutic target for personalized TNBC treatment strategies.
• This research provides new insights into the molecular mechanisms driving TNBC relapse and immune evasion.