# Diagnostic, Prognostic, and Predictive Molecular Biomarkers in Head and Neck Squamous Cell Carcinoma: A Comprehensive Review

## TL;DR

This review explores molecular biomarkers for head and neck cancer, highlighting new markers and the shift toward multi-omic approaches for better diagnosis and treatment.

## Contribution

The paper introduces a comprehensive review of recent molecular biomarkers and emphasizes the need for multi-omic and AI-driven strategies in HNSCC.

## Key findings

- High-risk lncRNA signatures and immune checkpoints like TIGIT show strong prognostic value.
- Epigenetic silencing of p16 affects only 25% of patients, suggesting limited utility as a universal marker.
- Multi-omic approaches integrating DNA, RNA, proteins, and metabolic indicators are recommended for future clinical use.

## Abstract

Background: Head and neck squamous cell carcinoma (HNSCC) remains the seventh most common cancer worldwide, characterized by late-stage diagnosis and poor 5-year survival rates. Oral squamous cell carcinoma (OSCC) is the most prevalent subtype. The identification of robust diagnostic, prognostic, and predictive markers is essential for personalized treatment monitoring. Methods: Following PRISMA and PICO standards, we conducted a comprehensive review of studies published over the past 10 years across PubMed/MEDLINE, Scopus, and Web of Science. The selection process was facilitated by AI-powered tools (Rayyan QCRI), and study quality was assessed using NOS or QUIPS. Results: 34 articles (including meta-analyses and original trials) were identified. Established clinical markers, such as p16-positivity (HR ≈ 0.55) and PD-L1 (CPS), remain significant. However, the molecular landscape is expanding to include high-risk lncRNA signatures (HR ≈ 2.50), immune checkpoints such as TIGIT (HR ≈ 1.85), and genomic alterations, including IL-10 promoter polymorphisms. We highlight that epigenetic silencing of p16 affects only about 25% of patients, while metabolic regulators (e.g., GLUT-1) and protein markers (e.g., MASPIN) offer critical predictive value for therapy response. Conclusions: The diagnostic and predictive paradigm is shifting toward a multi-omic approach that integrates DNA, RNA, proteins, and metabolic indicators. Future clinical use will rely on AI-driven multimarker panels and non-invasive liquid biopsies to enable real-time monitoring and de-escalation of treatment strategies.