Department of Molecular Diagnostic Pathology, School of Medicine, Iwate Medical University, 19-1, Uchimaru, Morioka, 020-8505, Japan.
Department of Pharmacodynamics and Molecular Genetics, School of Pharmacy, Iwate Medical University, 19-1, Uchimaru, Morioka, 020-8505, Japan.
Gastric Cancer. 2018 Sep;21(5):765-775. doi: 10.1007/s10120-018-0810-5. Epub 2018 Feb 21.
We attempted to identify the molecular profiles of gastric intramucosal neoplasia (IMN; low-grade dysplasia, LGD; high-grade dysplasia, HGD; intramucosal cancer, IMC) by assessing somatic copy number alterations (SCNAs) stratified by microsatellite status (microsatellite stable, MSS; microsatellite instable, MSI). Thus, microsatellite status was determined in 84 tumors with MSS status and 16 tumors with MSI status.
One hundred differentiated type IMNs were examined using SCNAs. In addition, genetic mutations (KRAS, BRAF, PIK3CA, and TP53) and DNA methylation status (low, intermediate and high) were also analyzed. Finally, we attempted to identify molecular profiles using a hierarchical clustering analysis.
Three patterns could be categorized according to SCNAs in IMNs with the MSS phenotype: subgroups 1 and 2 showing a high frequency of SCNAs, and subgroup 3 displaying a low frequency of SCNAs (subgroup 1 > 2 > 3 for SCNA). Subgroup 1 could be distinguished from subgroup 2 by the numbers of total SCNAs (gains and losses) and SCN gains (subgroup 1 > 2). The SCNA pattern of LGD was different from that of HGD and IMC. Moreover, IMNs with the MSI phenotype could be categorized into two subtypes: high frequency of SCNAs and low frequency of SCNAs. Genetic mutations and DNA methylation status did not differ among subgroups in IMNs.
Molecular profiles stratified by SCNAs based on microsatellite status may be useful for elucidation of the mechanisms of early gastric carcinogenesis.
我们试图通过评估微卫星状态(微卫星稳定,MSS;微卫星不稳定,MSI)分层的体细胞拷贝数改变(SCNAs)来确定胃黏膜内肿瘤(IMN;低级别上皮内瘤变,LGD;高级别上皮内瘤变,HGD;黏膜内癌,IMC)的分子特征。因此,我们在 84 例 MSS 状态肿瘤和 16 例 MSI 状态肿瘤中确定了微卫星状态。
我们使用 SCNAs 检查了 100 例分化型 IMN。此外,还分析了遗传突变(KRAS、BRAF、PIK3CA 和 TP53)和 DNA 甲基化状态(低、中和高)。最后,我们尝试使用层次聚类分析来识别分子特征。
在 MSS 表型的 IMNs 中,根据 SCNAs 可以分为三种模式:亚组 1 和 2 显示出高频 SCNAs,亚组 3 显示出低频 SCNAs(亚组 1>2>3 用于 SCNAs)。亚组 1 可以通过总 SCNAs(增益和缺失)和 SCN 增益的数量与亚组 2 区分开(亚组 1>2)。LGD 的 SCN 模式与 HGD 和 IMC 不同。此外,MSI 表型的 IMNs 可以分为两种亚型:高频 SCNAs 和低频 SCNAs。在 IMNs 中,遗传突变和 DNA 甲基化状态在亚组之间没有差异。
基于微卫星状态的 SCNAs 分层的分子特征可能有助于阐明早期胃癌发生的机制。