Dong Hai-Long, Sui Yan-Fang
Department of Pathology, Fourth Military Medical University, Xi'an 710032, Shaanxi Province, China.
World J Gastroenterol. 2005 Jan 14;11(2):208-11. doi: 10.3748/wjg.v11.i2.208.
To predict the HLA-A2-restricted CTL epitopes of tumor antigens associated with hepatocellular carcinoma (HCC).
MAGE-1, MAGE-3, MAGE-8, P53 and AFP were selected as objective antigens in this study for the close association with HCC. The HLA-A*0201 restricted CTL epitopes of objective tumor antigens were predicted by SYFPEITHI prediction method combined with the polynomial quantitative motifs method. The threshold of polynomial scores was set to -24.
The SYFPEITHI prediction values of all possible nonamers of a given protein sequence were added together and the ten high-scoring peptides of each protein were chosen for further analysis in primary prediction. Thirty-five candidates of CTL epitopes (nonamers) derived from the primary prediction results were selected by analyzing with the polynomial method and compared with reported CTL epitopes.
The combination of SYFPEITHI prediction method and polynomial method can improve the prediction efficiency and accuracy. These nonamers may be useful in the design of therapeutic peptide vaccine for HCC and as immunotherapeutic strategies against HCC after identified by immunology experiment.
预测与肝细胞癌（HCC）相关的肿瘤抗原的HLA - A2限制性CTL表位。
本研究选择MAGE - 1、MAGE - 3、MAGE - 8、P53和AFP作为与HCC密切相关的目标抗原。采用SYFPEITHI预测方法结合多项式定量基序方法预测目标肿瘤抗原的HLA - A*0201限制性CTL表位。多项式得分阈值设定为 - 24。
将给定蛋白质序列的所有可能九聚体的SYFPEITHI预测值相加，在初步预测中选择每种蛋白质得分最高的十个肽段进行进一步分析。通过多项式方法分析从初步预测结果中筛选出35个CTL表位（九聚体）候选序列，并与已报道的CTL表位进行比较。
SYFPEITHI预测方法与多项式方法相结合可提高预测效率和准确性。这些九聚体在经免疫学实验鉴定后，可能有助于设计HCC治疗性肽疫苗以及作为针对HCC的免疫治疗策略。
