Dataset Information

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

ABSTRACT: 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.

ORGANISM(S): Homo sapiens

PROVIDER: GSE113255 | GEO | 2020/03/18

REPOSITORIES: GEO

ACCESS DATA

Similar Datasets

Project description:Gastric cancer (GC) heterogeneity between and within tumors represents a barrier to disease management. We generated a comprehensive single-cell atlas of GC comprising 31 primary gastric tumor samples representing clinical stages I-IV and histological subtypes. We identified 34 distinct cell-lineage states, related by developmental trajectories and previously unreported rare cell populations. Many lineage states exhibited a distinctive tumor-associated expression profile, each contributing individually to a combined tumor-wide molecular collage. We observed increased plasma cell proportions as a feature of diffuse-type tumors associated with epithelial-resident KLF2, and a stagewise accrual of novel cancer-associated fibroblast sub-populations marked by high INHBA and FAP co-expression. Our results provide a high-resolution molecular resource for GC translational studies, identifying intra- and inter-patient lineage-states across distinct GC subtypes. #### GEO title mapping with the ID in PMID:34642171 #### Manuscript_ID Primary_Tumor Primary_Normal Peritoneal_Tumor Peritoneal_Normal NGCII521 sample7.csv sample6.csv - - NGCII513 sample22.csv sample21.csv - - NGCII512 sample26.csv sample25.csv - - NGCII520 sample5.csv sample4.csv - - NGCII514 sample24.csv sample23.csv - - NGCII518 sample2.csv sample1.csv - - NGCII525 sample10.csv sample9.csv - - NGCII527 sample12.csv sample11.csv - - NGCII538 sample36.csv sample35.csv sample38.csv sample37.csv NGCII539 sample32.csv - - - NGCII529 sample13.csv - - - NGCII511 sample15.csv - - - NGCII498 sample18.csv - - - NGCII522 sample29.csv - - - NGCII499 sample16.csv - - - NGCII502 sample14.csv - - - NGCII509 sample17.csv - - - NGCII540 sample33.csv - - - NGCII541 sample34.csv - - - NGCII543 sample39.csv - - - NGCII510 sample27.csv - - - NGCII519 sample3.csv - - - NGCII531 sample28.csv - - - NGCII545 sample40.csv - - - NGCII533 sample30.csv - - - NGCII524 sample8.csv - - - NGCII536 - sample31.csv - - NGCII011 - - sample19.csv - NGCII015 - - sample20.csv -

Dataset Information

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure