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基于 90 个基因表达模式分析 Barrett 食管患者的异型增生。

Analysis of dysplasia in patients with Barrett's esophagus based on expression pattern of 90 genes.

机构信息

MRC Cancer Unit, University of Cambridge, Hutchison/MRC Research Centre, Cambridge, United Kingdom.

MRC Biostatistics Unit, Cambridge, United Kingdom.

出版信息

Gastroenterology. 2015 Nov;149(6):1511-1518.e5. doi: 10.1053/j.gastro.2015.07.053. Epub 2015 Aug 3.

Abstract

BACKGROUND & AIMS: Diagnoses of dysplasia, based on histologic analyses, dictate management decisions for patients with Barrett's esophagus (BE). However, there is much intra- and inter-observer variation in identification of dysplasia-particularly low-grade dysplasia. We aimed to identify a biomarker that could be used to assign patients with low-grade dysplasia to a low- or high-risk group.

METHODS

We performed a stringent histologic assessment of 150 frozen esophageal tissues samples collected from 4 centers in the United Kingdom (from 2000 through 2006). The following samples with homogeneous diagnoses were selected for gene expression profiling: 28 from patients with nondysplastic BE, 10 with low-grade dysplasia, 13 with high-grade dysplasia (HGD), and 8 from patients with esophageal adenocarcinoma. A leave-one-out cross-validation analysis was used identify a gene expression signature associated with HGD vs nondysplastic BE. Functional pathways associated with gene signature sets were identified using the MetaCore analysis. Gene expression signature sets were validated using gene expression data on BE and esophageal adenocarcinoma accessed through National Center for Biotechnology Information Gene Expression Omnibus, as well as a separate set of samples (n = 169) collected from patients who underwent endoscopy in the United Kingdom or the Netherlands and analyzed histologically.

RESULTS

We identified an expression pattern of 90 genes that could separate nondysplastic BE tissues from those with HGD (P < .0001). Genes in a pathway regulated by retinoic acid-regulated nuclear protein made the largest contribution to this gene set (P < .0001); the transcription factor MYC regulated at least 30% of genes within the signature (P < .0001). In the National Center for Biotechnology Information Gene Expression Omnibus validation set, the signature separated nondysplastic BE samples from esophageal adenocarcinoma samples (P = .0012). In the UK and Netherlands validation cohort, the signature identified dysplastic tissues with an area under the curve value of 0.87 (95% confidence interval: 0.82-0.93). Of samples with low-grade dysplasia (LGD), 64% were considered high risk according to the 90-gene signature; these patients had a higher rate of disease progression than those with a signature categorized as low risk (P = .047).

CONCLUSIONS

We identified an expression pattern of 90 genes in esophageal tissues of patients with BE that was associated with low- or high-risk for disease progression. This pattern might be used in combination with histologic analysis of biopsy samples to stratify patients for treatment. It would be most beneficial for analysis of patients without definitive evidence of HGD but for whom early endoscopic intervention is warranted.

摘要

背景与目的

基于组织学分析的异型增生诊断决定了 Barrett 食管(BE)患者的治疗决策。然而,在识别异型增生方面,尤其是低级别异型增生方面,存在着很大的观察者内和观察者间的差异。我们的目的是确定一种生物标志物,以便将低级别异型增生患者分为低风险或高风险组。

方法

我们对来自英国 4 个中心的 150 个冷冻食管组织样本进行了严格的组织学评估(2000 年至 2006 年)。选择以下具有同质诊断的样本进行基因表达谱分析:28 例无异型增生 BE 患者,10 例低级别异型增生,13 例高级别异型增生(HGD)和 8 例食管腺癌患者。采用留一法交叉验证分析来识别与 HGD 相比无异型增生 BE 的基因表达特征。使用 MetaCore 分析鉴定与基因特征集相关的功能途径。使用通过国家生物技术信息中心基因表达综合数据库(National Center for Biotechnology Information Gene Expression Omnibus)获取的 BE 和食管腺癌基因表达数据以及从英国和荷兰接受内镜检查并进行组织学分析的单独样本集(n=169)对基因表达特征集进行验证。

结果

我们发现了一组 90 个基因的表达模式,可以将无异型增生 BE 组织与 HGD 组织区分开来(P<0.0001)。受维甲酸调节核蛋白调节的通路中的基因对该基因集的贡献最大(P<0.0001);转录因子 MYC 至少调控了特征中的 30%的基因(P<0.0001)。在国家生物技术信息中心基因表达综合数据库验证集中,该特征将无异型增生 BE 样本与食管腺癌样本区分开来(P=0.0012)。在英国和荷兰验证队列中,该特征识别出异型增生组织的曲线下面积值为 0.87(95%置信区间:0.82-0.93)。在低级别异型增生(LGD)样本中,根据 90 个基因特征,有 64%被认为是高风险;与特征分类为低风险的患者相比,这些患者的疾病进展率更高(P=0.047)。

结论

我们在 BE 患者的食管组织中发现了 90 个与疾病进展的低风险或高风险相关的基因表达模式。这种模式可以与活检样本的组织学分析结合使用,对患者进行治疗分层。对于没有明确的 HGD 证据但需要早期内镜干预的患者,该分析将最有益。

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