Development of a novel prognostic signature derived from enhancer RNA-regulated genes in head neck squamous cell carcinoma

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

OBJECTIVES

Intricate associations between programmed cell death (PCD) and cancer development and treatment outcomes have been increasingly appreciated. Here, we integrated 12 PCD patterns to construct a novel biomarker, cell death index (CDI), for oral squamous cell carcinoma (OSCC) prognostication and therapeutic prediction.

MATERIALS AND METHODS

Univariate Cox regression, Kaplan-Meier survival, and LASSO analyses were performed to construct the CDI. A nomogram combining CDI and selected clinicopathological parameters was established by multivariate Cox regression. The associations between CDI and immune landscape and therapeutic sensitivity were estimated. Single-cell RNA-seq data of OSCC was used to infer CDI genes in selected cell types and determine their expression along cell differentiation trajectory.

RESULTS

Ten selected PCD genes derived a novel prognostic signature for OSCC. The predictive prognostic performance of CDI and nomogram was robust and superior across multiple independent patient cohorts. CDI was negatively associated with tumor-infiltrating immune cell abundance and immunotherapeutic outcomes. Moreover, scRNA-seq data reanalysis revealed that GSDMB, IL-1A, PRKAA2, and SFRP1 from this signature were primarily expressed in cancer cells and involved in cell differentiation.

CONCLUSIONS

Our findings established CDI as a novel powerful predictor for prognosis and therapeutic response for OSCC and suggested its potential involvement in cancer cell differentiation.

Development of a novel senescence-related gene signature to predict clinical outcomes, immune landscape, and chemotherapeutic sensitivity in oral squamous cell carcinoma

BACKGROUND

Cellular senescence significantly associates with tumor initiation, progression, and therapeutic response across multiple cancers. Here, we sought to develop a novel senescence-related genes (SRGs)-derived signature for oral squamous cell carcinoma (OSCC) prognostication and therapeutic response prediction.

METHODS

OSCC-specific SRG prognostic signature was established with univariate Cox regression, Kaplan-Meier survival, and LASSO-penalized multivariate Cox regression analyses. A SRG nomogram integrating this signature and selected clinicopathological parameters were constructed by multivariate Cox regression. SiRNA-mediated gene knockdown was exploited to validate its function in vitro. The utilities of SRG signature in predicting immune status and chemotherapeutic sensitivities were analyzed.

RESULTS

The prognostic performance of SRG signature/nomogram was satisfactory in multiple independent cohorts. CDK1 knockdown induced senescence phenotype in vitro. Moreover, SRG signature scores negatively correlated with tumor-infiltrating immune cells and associated with multiple chemotherapeutic drug sensitivities.

CONCLUSIONS

Our results established SRG-derived signature/nomogram as powerful predictors for prognosis and chemotherapeutic response for OSCC.

Prognostic value of CDKN2A in head and neck squamous cell carcinoma via pathomics and machine learning

This study aims to enhance the prognosis prediction of Head and Neck Squamous Cell Carcinoma (HNSCC) by employing artificial intelligence (AI) to analyse CDKN2A gene expression from pathology images, directly correlating with patient outcomes. Our approach introduces a novel AI-driven pathomics framework, delineating a more precise relationship between CDKN2A expression and survival rates compared to previous studies. Utilizing 475 HNSCC cases from the TCGA database, we stratified patients into high-risk and low-risk groups based on CDKN2A expression thresholds. Through pathomics analysis of 271 cases with available slides, we extracted 465 distinctive features to construct a Gradient Boosting Machine (GBM) model. This model was then employed to compute Pathomics scores (PS), predicting CDKN2A expression levels with validation for accuracy and pathway association analysis. Our study demonstrates a significant correlation between higher CDKN2A expression and improved median overall survival (66.73 months for high expression vs. 42.97 months for low expression, p = 0.013), establishing CDKN2A's prognostic value. The pathomic model exhibited exceptional predictive accuracy (training AUC: 0.806; validation AUC: 0.710) and identified a strong link between higher Pathomics scores and cell cycle activation pathways. Validation through tissue microarray corroborated the predictive capacity of our model. Confirming CDKN2A as a crucial prognostic marker in HNSCC, this study advances the existing literature by implementing an AI-driven pathomics analysis for gene expression evaluation. This innovative methodology offers a cost-efficient and non-invasive alternative to traditional diagnostic procedures, potentially revolutionizing personalized medicine in oncology.