Gene expression profiling study to identify distinct subtypes in diffuse type gastric cancer

Although recent advances in high-throughput technology have provided many insights into gastric cancer (GC), few reliable biomarkers for handling diffuse type GC are identified. Here, we aim to identify a signature classifying high risk diffuse type GC. To identify molecular subtypes of diffuse type GC, we generated RNA-seq based transcriptome data, which were generated using normal mucosa and tumor cells from 140 fresh frozen tissues including diffuse type GCs (n = 107). Unsupervised hierarchical cluster analysis of the RNA-seq data revealed three distinct subgroups of GC. Based on these subtypes, we generated a signature reflecting the best characteristics of aggressive diffuse type GC. When estimating prognostic value, the signature showed a strong prediction ability and an independent clinical utility in diffuse type GC patients. Our signature represents a promising diagnostic tool for the identification of diffuse type GC patients that have a high risk of poor clinical behavior. RNA-seq data of 140 fresh frozen tissues including diffuse type gastric cancer tissues (n = 107), intestinal type gastric cancer tissue (n = 23) and normal gastric tissues (n = 10) were generated. Total RNA was isolated by RNeasy Mini Kit (Qiagen, CA, USA), according to the manufacturer's protocol. The quality and integrity of the RNA were confirmed by agarose gel electrophoresis and ethidium bromide staining, followed by visual examination under ultraviolet light. Sequencing library was prepared using TruSeq RNA Sample Preparation kit v2 (Illumina, CA, USA) according to the manufacturer’s protocols. Briefly, mRNA was purified from total RNA using poly-T oligo-attached magnetic beads, fragmented, and converted into cDNAs. Then, adapters were ligated and the fragments were amplified on a PCR. Sequencing was performed in paired end reads (2x75 bp) using NextSeq 500 platform (Illumina).

尽管高通量技术的最新进展已为胃癌（gastric cancer, GC）研究提供了诸多洞见，但目前可用于弥漫型胃癌的可靠生物标志物仍较为匮乏。本研究旨在构建可分类高风险弥漫型胃癌的基因签名（signature）。为鉴定弥漫型胃癌的分子亚型，我们生成了基于RNA测序（RNA-seq）的转录组数据（transcriptome data），样本来自140份新鲜冰冻组织的正常黏膜与肿瘤细胞，其中包含107份弥漫型胃癌组织样本。对RNA-seq数据进行无监督层次聚类分析（unsupervised hierarchical cluster analysis）后，鉴定出3个具有显著差异的胃癌亚型。基于这些亚型，我们构建了可反映侵袭性弥漫型胃癌核心特征的基因签名。在评估预后价值（prognostic value）时，该基因签名在弥漫型胃癌患者中展现出强劲的预测能力与独立的临床应用价值（clinical utility）。本研究构建的基因签名有望成为鉴定具有不良临床行为高风险的弥漫型胃癌患者的有效诊断工具（diagnostic tool）。本研究生成了140份新鲜冰冻组织的RNA-seq数据，其中包含107份弥漫型胃癌组织、23份肠型胃癌组织以及10份正常胃组织。总RNA提取采用RNeasy迷你试剂盒（Qiagen，美国加利福尼亚州凯杰公司），严格遵循制造商的操作流程。RNA的质量与完整性通过琼脂糖凝胶电泳结合溴化乙锭染色进行验证，并在紫外灯下进行目视检查。测序文库构建采用TruSeq RNA样本制备试剂盒v2（Illumina，美国加利福尼亚州因美纳公司），同样遵循制造商的操作流程。简要而言，首先利用poly-T寡核苷酸偶联磁珠从总RNA中纯化mRNA，随后将其片段化并逆转录为cDNA；之后连接测序接头，通过聚合酶链式反应（PCR）扩增得到测序片段。测序采用NextSeq 500测序平台（Illumina），以双端读长（2×75 bp）模式完成。