Expression of cell cycle markers and human papillomavirus infection in oral squamous cell carcinoma:Use of fuzzy neural networks

Our aim was to evaluate in oral squamous cell carcinoma (OSCC) the relationship between some cell cycle markers and HPV infection, conditionally to age, gender and certain habits of patients, and to assess the ability of fuzzy neural networks (FNNs) in building up an adequate predictive model based on logic inference rules. Eighteen cases of OSCC were examined by immunohistochemistry for MIB-1, PCNA and survivin expression; presence of HPV DNA was investigated in exfoliated oral mucosa cells by nested PCR (nPCR, MY09-MY11/GP5-GP6), and HPV genotype was determined by direct DNA sequencing. Data were analyzed by traditional statistics (TS) and FNNs. HPV DNA was found in 9/18 OSCCs (50.0 %) without any significant higher risk of HPV infection with respect to the sociodemographic variables considered (p > 0.2), apart from tobacco smoking, reported in 44.4% of OSCC HPV-positive vs. 100% HPV-negative subjects (p = 0.029). Regarding cell cycle markers, TS and FNN revealed that survivin was expressed significantly more in HPV-negative than in HPV-positive OSCC [root mean-square error (RMSE) = 5.89 x 10(-6), % predicted 100.0]; furthermore, smoking played a protective role for survivin expression in HPV-positive cases (OR = 0.019, 95%CI 0.001-0.723, RMSE = 0.20, % of prevision 94.4). FNN, although on a small sample size, allowed us to confirm data by TS and to hypothesize a different cell cycle pattern for HPV-positive vs. HPV-negative OSCC. In the latter cases, the relevance of apoptotic vs. proliferative markers suggested that they may be related to the different supposed outcome of HPV-negative OSCC and that HPV may have a protective role in the expression level of survivin, especially in tobacco smokers.