Systemic inflammatory indices primarily reflect tumor burden rather than independently predict prognosis in
Tomotaka Kakubari1, Hideyuki Takahashi1, Tomohiro Shimizu1
1Department of Otolaryngology-Head and Neck Surgery, Gunma University Graduate School of Medicine, Maebashi, Japan.
Objectives:
Approximately 30% of patients with human papillomavirus (HPV)-associated oropharyngeal squamous cell carcinoma (OPSCC) have poor outcomes despite standardized treatment. Reliable biomarkers for identifying high-risk patients remain insufficiently established.
Abstract:
To investigate the clinical significance of systemic inflammatory indices for identifying high-risk patients and to explore their associations with clinicopathological characteristics.
Methods:
A total of 99 patients with HPV-associated (p16-positive) OPSCC were included. Systemic inflammatory indices, including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), systemic immune-inflammation index (SII), prognostic nutritional index (PNI), C-reactive protein-to-albumin ratio (CAR), systemic inflammation response index (SIRI), and aggregate index of systemic inflammation (AISI), were evaluated for their associations with clinical outcomes and clinicopathological characteristics. Their prognostic significance was assessed using Cox proportional hazards regression and Kaplan-Meier survival analyses.
Results:
Most systemic inflammatory indices were associated with overall survival (OS) in univariate analysis, whereas only NLR was associated with progression-free survival (PFS); furthermore, none remained independently significant in multivariate analyses. Notably, all indices were significantly associated with T classification, whereas associations with N and M classifications were limited.
Conclusions:
Systemic inflammatory indices primarily reflected tumor burden rather than serving as independent prognostic determinants in HPV-associated OPSCC. Biomarkers derived from the local tumor immune microenvironment may provide more informative tools for risk stratification and treatment optimization.