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p53是人类恶性肿瘤中最常见的突变基因,与人类肿瘤的发生相关性最高,也是基因研究中热度最高的基因之一。为深入研究人类p53基因突变机理,选取了6条人类p53基因mRNA序列。首先,利用基于RSCU方法下的多重变量分析软件CodonW对影响密码子使用的各项参数进行计算和统计分析,分析可得,密码子适应指数(CAI)与最优密码子使用频率(FOP)、密码子偏爱指数(CBI)均呈极显著正相关(p<0.01);有效密码子数(ENC)与密码子偏爱指数(CBI)、GC含量、GC3s、最优密码子使用频率(FOP)、第三位碱基G3s、密码子适应指数(CAI)均呈极显著负相关(p<0.01)。然后,再利用QRSCU编码方法,对p53密码子偏好性间距进行了分析设计,得出了基于拟氨基酸编码的方法能充分体现p53密码子对同义密码子的一致偏好性的结论,且p53基因更偏好使用以c或g结尾的同义密码子。以上各项参数也充分验证了拟氨基酸编码方法与p53密码子偏好性研究结果的紧密关联性。最后,结合前人对抑癌基因p53突变致癌的研究,对该6条基因序列作了病变预测,从而为人类恶性肿瘤的预防和预测提供重要的理论依据。
p53 is the most common mutation in human malignancies and has the highest correlation with human carcinogenesis. It is also one of the hottest genes in gene research. In order to further study the mechanism of human p53 gene mutation, six human p53 gene mRNA sequences were selected. Firstly, CodonW, a multiple variable analysis software based on the RSCU method, was used to calculate and statistically analyze the parameters affecting the usage of codons. The codon usage index (CAI) and optimal codon usage frequency (FOP) (P <0.01); ENC and CBI, GC content, GC3s, optimal codon usage frequency (FOP), codon usage preference index The third base G3s, codon adaptation index (CAI) showed a significant negative correlation (p <0.01). Then QRSCU encoding method was used to analyze the design of p53 codon preference spacing. The results showed that the method based on quasi-amino acid encoding could fully reflect the consensus preference of p53 codon for synonymous codons. And p53 gene Preference is given to using synonymous codons ending in c or g. The above parameters also fully validate the close correlation between the proposed method of quasi-amino acid coding and the results of p53 codon bias study. Finally, combined with the previous studies on the carcinogenesis of the p53 gene mutation, we made the prediction of the six gene sequences and provided an important theoretical basis for the prevention and prediction of human malignant tumors.