Leveraging programmed cell death patterns to predict prognosis and therapeutic sensitivity in OSCC

Area of Science:

• Oncology
• Molecular Biology
• Biomarker Discovery

Background:

• Programmed cell death (PCD) plays a critical role in cancer development and treatment response.
• Understanding these intricate associations is crucial for improving patient outcomes in oral squamous cell carcinoma (OSCC).

Purpose of the Study:

• To develop a novel prognostic and predictive biomarker for OSCC by integrating multiple programmed cell death patterns.
• To establish a Cell Death Index (CDI) for assessing patient prognosis and predicting therapeutic response in OSCC.

Main Methods:

• Constructed the CDI using univariate Cox regression, Kaplan-Meier survival, and LASSO analyses on 12 PCD patterns.
• Developed a nomogram combining CDI with clinicopathological parameters via multivariate Cox regression.
• Analyzed associations between CDI, immune landscape, and therapeutic sensitivity using scRNA-seq data.

Main Results:

• A prognostic signature comprising 10 selected PCD genes was derived for OSCC.
• The CDI and nomogram demonstrated robust and superior prognostic performance across independent patient cohorts.
• CDI showed a negative association with tumor-infiltrating immune cells and immunotherapy outcomes, with key genes involved in cell differentiation.

Conclusions:

• The Cell Death Index (CDI) is established as a powerful predictor for OSCC prognosis and therapeutic response.
• The study suggests a potential role for CDI in cancer cell differentiation processes.
• CDI offers a novel tool for personalized medicine approaches in OSCC treatment.