Takahashi Hiro, Nemoto Takeshi, Yoshida Teruhiko, Honda Hiroyuki, Hasegawa Tadashi
Department of Biotechnology, School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan.
BMC Bioinformatics. 2006 Sep 4;7:399. doi: 10.1186/1471-2105-7-399.
Recent advances in genome technologies have provided an excellent opportunity to determine the complete biological characteristics of neoplastic tissues, resulting in improved diagnosis and selection of treatment. To accomplish this objective, it is important to establish a sophisticated algorithm that can deal with large quantities of data such as gene expression profiles obtained by DNA microarray analysis.
Previously, we developed the projective adaptive resonance theory (PART) filtering method as a gene filtering method. This is one of the clustering methods that can select specific genes for each subtype. In this study, we applied the PART filtering method to analyze microarray data that were obtained from soft tissue sarcoma (STS) patients for the extraction of subtype-specific genes. The performance of the filtering method was evaluated by comparison with other widely used methods, such as signal-to-noise, significance analysis of microarrays, and nearest shrunken centroids. In addition, various combinations of filtering and modeling methods were used to extract essential subtype-specific genes. The combination of the PART filtering method and boosting--the PART-BFCS method--showed the highest accuracy. Seven genes among the 15 genes that are frequently selected by this method--MIF, CYFIP2, HSPCB, TIMP3, LDHA, ABR, and RGS3--are known prognostic marker genes for other tumors. These genes are candidate marker genes for the diagnosis of STS. Correlation analysis was performed to extract marker genes that were not selected by PART-BFCS. Sixteen genes among those extracted are also known prognostic marker genes for other tumors, and they could be candidate marker genes for the diagnosis of STS.
The procedure that consisted of two steps, such as the PART-BFCS and the correlation analysis, was proposed. The results suggest that novel diagnostic and therapeutic targets for STS can be extracted by a procedure that includes the PART filtering method.
基因组技术的最新进展为确定肿瘤组织的完整生物学特征提供了绝佳机会，从而改善诊断和治疗选择。为实现这一目标，建立一种能够处理大量数据（如通过DNA微阵列分析获得的基因表达谱）的复杂算法非常重要。
此前，我们开发了投影自适应共振理论（PART）滤波方法作为一种基因滤波方法。这是一种聚类方法，可针对每个亚型选择特定基因。在本研究中，我们应用PART滤波方法分析从软组织肉瘤（STS）患者获得的微阵列数据，以提取亚型特异性基因。通过与其他广泛使用的方法（如信噪比、微阵列显著性分析和最近收缩质心）进行比较，评估了滤波方法的性能。此外，还使用了滤波和建模方法的各种组合来提取基本的亚型特异性基因。PART滤波方法与增强算法的组合——PART-BFCS方法——显示出最高的准确性。该方法经常选择的15个基因中的7个基因——MIF、CYFIP2、HSPCB、TIMP3、LDHA、ABR和RGS3——是其他肿瘤已知的预后标志物基因。这些基因是STS诊断的候选标志物基因。进行相关性分析以提取未被PART-BFCS选择的标志物基因。其中提取的16个基因也是其他肿瘤已知的预后标志物基因，它们可能是STS诊断的候选标志物基因。
提出了由PART-BFCS和相关性分析两个步骤组成的程序。结果表明，通过包含PART滤波方法的程序可以提取STS的新型诊断和治疗靶点。
