GSE113255
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. Overall design: RNA-seq data of 140 fresh-frozen tissues, including diffuse-type gastric cancer tissues (n = 107), intestinal-type gastric cancer tissues (n = 23), and normal gastric tissues (n = 10), were generated. Total RNA was extracted using the RNeasy Mini Kit (Qiagen, CA, USA) following the manufacturer's instructions. RNA quality and integrity were confirmed by agarose-gel electrophoresis with ethidium-bromide staining and visual inspection under ultraviolet light. Sequencing libraries were prepared using the TruSeq Stranded Total RNA Sample Prep Kit (Illumina, CA, USA) according to the manufacturer's protocol. Briefly, ribosomal RNA (rRNA) was depleted from total RNA, and the remaining RNA fragments were randomly primed and converted into strand-specific cDNA libraries. After end repair and adapter ligation, the libraries were amplified by PCR. Sequencing was performed in paired-end mode (2 × 75 bp) on the Illumina NextSeq 500 platform.