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Intramucosal Inoculation of Squamous Cell Carcinoma Cells in Mice for Tumor Immune Profiling and Treatment Response Assessment
Published on: April 22, 2019
Clinical Significance of Biomarkers in Oropharyngeal Squamous Cell Carcinoma: Recurrence Prediction and Treatment
Yunxia Chen1, Wenyu Zhang2, Xiang Gao1
1Second Ward, Department of Radiotherapy, The First Affiliated Hospital of Hainan Medical University, Haikou, China.
Background:
Oropharyngeal squamous cell carcinoma (OPSCC) accounts for a substantial proportion of head and neck cancers, with a rising incidence largely driven by human papillomavirus (HPV) infection. Despite advances in multimodal treatment, disease recurrence remains common and limits long-term survival, highlighting the need for reliable biomarkers to guide prognosis and treatment.
Methods:
This review summarizes recent advances in biomarker development in OPSCC across multiple biological domains. We examined molecular biomarkers, including genomic alterations, DNA methylation, and non-coding RNAs; protein biomarkers associated with oncogenic signaling, cell-cycle regulation, apoptosis, inflammation, and angiogenesis; as well as circulating biomarkers such as circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), and exosome-derived RNA.
Results:
Among currently available biomarkers, p16, PD-L1, and circulating HPV DNA demonstrate the strongest clinical applicability, particularly for risk stratification and post-treatment surveillance. Emerging evidence also supports the use of combined biomarker panels to improve prediction of treatment response to radiotherapy, chemotherapy, and immunotherapy. However, many candidate biomarkers show inconsistent performance due to methodological variability, limited sensitivity and specificity, and insufficient prospective validation.
Conclusions:
While several biomarkers show promise in OPSCC, further standardization of detection methods and large-scale prospective studies are required. Integration of multi-omics data with computational approaches, including artificial intelligence, may facilitate the development of robust and clinically actionable predictive models, ultimately enabling more personalized management and earlier detection of recurrence.
