Department of Molecular Diagnostic Pathology, School of Medicine, Iwate Medical University, Shiwagun'yahabachou, Japan.
Diagnostic Pathology Center, Southern Tohoku General Hospital, Kooriyama, Japan.
J Pathol Clin Res. 2024 Mar;10(2):e12368. doi: 10.1002/2056-4538.12368.
We performed comprehensive analyses of somatic copy number alterations (SCNAs) and gene expression profiles of gastric intramucosal neoplasia (IMN) using array-based methods in 97 intestinal-type IMNs, including 39 low-grade dysplasias (LGDs), 37 high-grade dysplasias (HGDs), and 26 intramucosal carcinomas (IMCs) with stromal invasion of the lamina propria to identify the molecular mechanism of IMN. In addition, we examined gene mutations using gene panel analyses. We used cluster analyses for exclusion of arbitrariness to identify SCNA patterns and expression profiles. IMNs were classified into two distinct subgroups (subgroups 1 and 2) based on SCNA patterns. Subgroup 1 showed a genomic stable pattern due to the low frequency of SCNAs, whereas subgroup 2 exhibited a chromosomal instability pattern due to the high frequencies of SCNAs and TP53 mutations. Interestingly, although the frequencies of LGD and HGD were significantly higher in subgroup 1 than in subgroup 2, IMC was commonly found in both types. Although the expression profiles of specific mRNAs could be used to categorise subgroups 1 and 2, no clinicopathological findings correlated with either subgroup. We examined signalling pathways specific to subgroups 1 and 2 to identify the association of each subgroup with signalling pathways based on gene ontology tree visualisation: subgroups 1 and 2 were associated with haem metabolism and chromosomal instability, respectively. These findings reveal a comprehensive genomic landscape that highlights the molecular complexity of IMNs and provide a road map to facilitate our understanding of gastric IMNs.
我们使用基于阵列的方法对 97 例肠型黏膜内肿瘤(IMN)进行了体细胞拷贝数改变(SCNAs)和基因表达谱的综合分析,包括 39 例低级别发育不良(LGD)、37 例高级别发育不良(HGD)和 26 例固有层间质浸润的黏膜内癌,以确定 IMN 的分子机制。此外,我们还使用基因面板分析检查了基因突变。我们使用聚类分析排除任意性来识别 SCNAs 模式和表达谱。根据 SCNAs 模式,将 IMNs 分为两个不同的亚组(亚组 1 和 2)。亚组 1 由于 SCNAs 的频率较低,表现出基因组稳定的模式,而亚组 2 由于 SCNAs 和 TP53 突变的高频,表现出染色体不稳定的模式。有趣的是,尽管亚组 1 的 LGD 和 HGD 频率明显高于亚组 2,但两种类型都常见 IMC。尽管特定 mRNA 的表达谱可用于分类亚组 1 和 2,但没有与任何亚组相关的临床病理发现。我们检查了亚组 1 和 2 特异性的信号通路,以根据基因本体树可视化来确定每个亚组与信号通路的关联:亚组 1 和 2 分别与血红素代谢和染色体不稳定性相关。这些发现揭示了一个全面的基因组景观,突出了 IMN 的分子复杂性,并为我们理解胃 IMN 提供了路线图。