Suppr超能文献

通过基因表达谱鉴定出的两种与小细胞和大细胞神经内分泌癌无关的具有预后意义的高级别肺神经内分泌肿瘤亚型。

Two prognostically significant subtypes of high-grade lung neuroendocrine tumours independent of small-cell and large-cell neuroendocrine carcinomas identified by gene expression profiles.

作者信息

Jones Michael H, Virtanen Carl, Honjoh Daisuke, Miyoshi Tatsu, Satoh Yukitoshi, Okumura Sakae, Nakagawa Ken, Nomura Hitoshi, Ishikawa Yuichi

机构信息

Chugai Pharmaceuticals c/o Proliferation Signal Division, University of Tokyo, Tokyo, Japan.

出版信息

Lancet. 2004 Mar 6;363(9411):775-81. doi: 10.1016/S0140-6736(04)15693-6.

Abstract

BACKGROUND

Classification of high-grade neuroendocrine tumours (HGNT) of the lung currently recognises large-cell neuroendocrine carcinoma (LCNEC) and small-cell lung carcinoma (SCLC) as distinct groups. However, a similarity in histology for these two carcinomas and uncertain clinical course have led to suggestions that a single HGNT classification would be more appropriate. Gene expression profiling, which can reproduce histopathological classification, and often defines new subclasses with prognostic significance, can be used to resolve HGNT classification.

METHODS

We used cDNA microarrays with 40?386 elements to analyse the gene expression profiles of 38 surgically resected samples of lung neuroendocrine tumours and 11 SCLC cell lines. Samples of large-cell carcinoma, adenocarcinoma, and normal lung were also included to give a total of 105 samples analysed. The data were subjected to filtering to yield informative genes before unsupervised hierarchical clustering that identified relatedness of tumour samples.

FINDINGS

Distinct groups for carcinoids, large-cell carcinoma, adenocarcinoma, and normal lung were readily identified. However, we were unable to distinguish LCNEC from SCLC by gene expression profiling. Three independent rounds of unsupervised hierarchical clustering consistently divided SCLC samples into two main groups with LCNEC samples largely integrated with these groups. Furthermore, patients in one of the groups identified by clustering had a significantly better clinical outcome than the other (83% vs 12% survived for 5 years; p=0.0094. None of the highly proliferative SCLC cell lines subsequently analysed clustered with this good-prognosis group.

INTERPRETATION

Our findings show that HGNT of the lung can be classified into two groups independent of SCLC and LCNEC. To this end, we have identified many genes, some of which encode well-characterised markers of cancer that distinguish the HGNT groups. These results have implications for the diagnosis, classification, and treatment of lung neuroendocrine tumours, and provide important insights into their underlying biology.

摘要

背景

目前,肺高级别神经内分泌肿瘤(HGNT)的分类将大细胞神经内分泌癌(LCNEC)和小细胞肺癌(SCLC)视为不同的类别。然而,这两种癌在组织学上的相似性以及不确定的临床病程引发了一种观点,即单一的HGNT分类可能更为合适。基因表达谱分析能够重现组织病理学分类,并且常常能定义具有预后意义的新亚类,可用于解决HGNT的分类问题。

方法

我们使用含有40386个元件的cDNA微阵列,分析了38例手术切除的肺神经内分泌肿瘤样本和11个SCLC细胞系的基因表达谱。还纳入了大细胞癌、腺癌和正常肺组织样本,共计105个样本进行分析。在进行无监督层次聚类以确定肿瘤样本的相关性之前,对数据进行筛选以获得信息丰富的基因。

研究结果

类癌、大细胞癌、腺癌和正常肺组织的不同类别很容易被识别出来。然而,通过基因表达谱分析,我们无法区分LCNEC和SCLC。三轮独立的无监督层次聚类一致地将SCLC样本分为两个主要组,LCNEC样本大多与这些组整合在一起。此外,聚类识别出的其中一组患者的临床结局明显优于另一组(5年生存率分别为83%和12%;p = 0.0094)。随后分析的高增殖性SCLC细胞系中,没有一个与这个预后良好的组聚类。

解读

我们的研究结果表明,肺HGNT可分为独立于SCLC和LCNEC的两组。为此,我们鉴定了许多基因,其中一些基因编码已充分表征的癌症标志物,可区分HGNT组。这些结果对肺神经内分泌肿瘤的诊断、分类和治疗具有重要意义,并为其潜在生物学特性提供了重要见解。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验