Combination of p53AIP1 and survivin expression is a powerful prognostic marker in non-small cell lung cancer

<p>Abstract</p> <p>Background</p> <p>p53AIP1 is a potential mediator of apoptosis depending on p53, which is mutated in many kinds of carcinoma. High survivin expression in non-small cell lung cancer is related with poor prognosis. To investigate the role of these genes in non-small cell lung cancer, we compared the relationship between p53AIP1 or survivin gene expression and the clinicopathological status of lung cancer.</p> <p>Materials and methods</p> <p>Forty-seven samples from non-small cell lung cancer patients were obtained between 1997 and 2003. For quantitative evaluation of RNA expression by PCR, we used Taqman PCR methods.</p> <p>Results</p> <p>Although no correlation between p53AIP1 or survivin gene expression and clinicopathological factors was found, the relationship between survivin gene expression and nodal status was significant (p = 0.03). Overall survival in the p53AIP1-negative group was significantly worse than in the positive group (p = 0.04); however, although survivin expression was not a prognostic factor, the combination of p53AIP1 and survivin was a significant prognostic predictor (p = 0.04). In the multivariate cox proportional hazard model, the combination was an independent predictor of overall survival (p53AIP1 (+) survivin (+), HR 0.21, 95%CI = [0.01–1.66]; p53AIP1 (+) survivin (-), HR 0.01, 95%CI = [0.002–0.28]; p53AIP1 (-) survivin (-), HR 0.01, 95%CI = [0.002–3.1], against p53AIP1 (-) survivin (+), p = 0.03).</p> <p>Conclusion</p> <p>These data suggest that the combination of p53AIP1 and survivin gene expression may be a powerful tool to stratify subgroups with better or worse prognosis from the variable non-small cell lung cancer population.</p

Understanding the role of text length, sample size and vocabulary size in determining text coverage

Although the use of "text coverage" to measure the intelligibility of reading materials is increasing in the field of vocabulary teaching and learning, to date there have been few studies which address the methodological variables that can affect reliable text coverage calculations. The objective of this paper is to investigate how differing vocabulary size, text length, and sample size might affect the stability of text coverage, and to define relevant parameters. In this study, 23 varying vocabulary sizes taken from the high frequency words of the British National Corpus and 26 different text lengths taken from the Time Almanac corpus were analyzed using 10 different sample sizes in 1,000 iterations to calculate text coverage, and the results were analyzed using the distribution of the mean score and standard deviation. The results of the study empirically demonstrate that text coverage is more stable when the vocabulary size is larger, the text length is longer, and more samples are used. It was also found that the stability of text coverage is greater from a larger number of shorter samples than from a fewer number of longer samples. As a practical guideline for educators, a table showing minimum parameters is included for reference in computing text coverage calculations