ReviewCancer reports (Hoboken, N.J.)2026
Clinical Significance of Biomarkers in Oropharyngeal Squamous Cell Carcinoma: Recurrence Prediction and Treatment Response.
Review in Cancer reports (Hoboken, N.J.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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8 authors.
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Abstract
backgroundOropharyngeal 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.
methodsThis 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.
resultsAmong 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.
conclusionsWhile 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.
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