ABSTRACT
BACKGROUND: Tumour relapse is recognized to be the prime fatal burden in patients affected by head and neck squamous cell carcinoma (HNSCC), but no discrete molecular trait has yet been identified to make reliable early predictions of tumour recurrence. Expression of cell surface proteoglycans (PGs) is frequently altered in carcinomas and several of them are gradually emerging as key prognostic factors. METHODS: A PG expression analysis at both mRNA and protein level, was pursued on primary lesions derived from 173 HNSCC patients from whom full clinical history and 2 years post-surgical follow-up was accessible. Gene and protein expression data were correlated with clinical traits and previously proposed tumour relapse markers to stratify high-risk patient subgroups. RESULTS: HNSCC lesions were indeed found to exhibit a widely aberrant PG expression pattern characterized by a variable expression of all PGs and a characteristic de novo transcription/translation of GPC2, GPC5 and NG2/CSPG4 respectively in 36%, 72% and 71% on 119 cases. Importantly, expression of NG2/CSPG4, on neoplastic cells and in the intralesional stroma (Hazard Ratio [HR], 6.76, p = 0.017) was strongly associated with loco-regional relapse, whereas stromal enrichment of SDC2 (HR, 7.652, p = 0.007) was independently tied to lymphnodal infiltration and disease-related death. Conversely, down-regulated SDC1 transcript (HR, 0.232, p = 0.013) uniquely correlated with formation of distant metastases. Altered expression of PGs significantly correlated with the above disease outcomes when either considered alone or in association with well-established predictors of poor prognosis (i.e. T classification, previous occurrence of precancerous lesions and lymphnodal metastasis). Combined alteration of all three PGs was found to be a reliable predictor of shorter survival. CONCLUSIONS: An unprecedented PG-based prognostic portrait is unveiled that incisively diversifies disease course in HNSCC patients beyond the currently known clinical and molecular biomarkers.
