New ProteomeXchange dataset PXD048518

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Dear ProteomeXchange subscriber, a new ProteomeXchange dataset is being announced. To see more information, click here:

https://proteomecentral.proteomexchange.org/dataset/PXD048518

Summary of dataset

Status: new
Identifier: PXD048518
HostingRepository: MassIVE
Species: Homo sapiens
Title: Proteomic analysis of gastric cancero patient-derived tissue ECM
Submitter: Yeji Yang
LabHead: Jin Young Kim
Description: Gastric cancer (GC) is a highly heterogeneous disease regarding histologic features, genotypes, and molecular phenotypes. Here, we investigated extracellular matrix (ECM)-centric analysis, examining its association with histologic subtypes and patient prognosis in human gastric cancer. We performed quantitative proteomic analysis of decellularized GC tissues that uncovered tumorous ECM, highlighting intertumoral heterogeneity in ECM composition. We identified 20 tumor-enriched proteins including four glycoproteins, serpin family H Member 1 (SERPINH1), Annexin family (ANXA3/4/5/13), S100A family (S100A6/8/9), MMP14, and other matrisome-associated proteins. In addition to these overall tumor-specific ECM proteins, histopathological characteristics of GC revealed variations in ECM composition, with the poorly cohesive carcinoma not otherwise specified (PCC-NOS) subtype being distinctly demarcated from other histologic subtypes. Integrating ECM proteomics with single-cell RN
A sequencing, we identified crucial molecular markers in the PCC-NOS-specific stroma. PCC-NOS-enriched matrisome proteins (PEMs) and gene expression signatures of adipogenic cancer-associated fibroblasts (CAFadi) were closely linked, both associated with adverse outcomes in GC. Using tumor microarray analysis, we confirmed the CAFadi surface marker, ATP binding cassette subfamily A member 8 (ABCA8), predominantly present in PCC-NOS tumors. Our ECM-focused approach in identifying stromal characteristics paves the way for studies to determine their utility as biomarkers for patient stratification, offering valuable insights for linking molecular and histologic features in GC.

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