Abstract

Simple SummaryWe investigated the tumor microenvironment of gastric cancer (GC) by combining single cell and bulk transcriptomic profiles. We built a novel signature matrix to dissect epithelium and stroma signals from tissue samples using a scRNA-seq data set for GC and then applied cell mixture deconvolution to estimate diverse epithelial, stromal, and immune cell proportions from bulk transcriptome data in four independent GC cohorts. Using a robust computational pipeline, we identified an early malignant epithelial cell (EMEC) population whose proportions were significantly higher in patients with stage I cancer than other stages, and it was predominantly present in tumor samples but not typically found in normal samples. By using univariate and multivariate analyses in the training cohort, we identified that the ratio of EMECs to stromal cells and the ratio of adaptive T cells to monocytes were the most significant prognostic factors within the non-immune and immune factors, respectively. The STEM score, which unifies these two prognostic factors, was an independent prognostic factor of overall survival for GC.Gastric cancer (GC) is the third leading cause of cancer-related deaths worldwide. Tumor heterogeneity continues to confound researchers’ understanding of tumor growth and the development of an effective therapy. Digital cytometry allows interpretation of heterogeneous bulk tissue transcriptomes at the cellular level. We built a novel signature matrix to dissect epithelium and stroma signals using a scRNA-seq data set (GSE134520) for GC and then applied cell mixture deconvolution to estimate diverse epithelial, stromal, and immune cell proportions from bulk transcriptome data in four independent GC cohorts (GSE62254, GSE15459, GSE84437, and TCGA-STAD) from the GEO and TCGA databases. Robust computational methods were applied to identify strong prognostic factors for GC. We identified an EMEC population whose proportions were significantly higher in patients with stage I cancer than other stages, and it was predominantly present in tumor samples but not typically found in normal samples. We found that the ratio of EMECs to stromal cells and the ratio of adaptive T cells to monocytes were the most significant prognostic factors within the non-immune and immune factors, respectively. The STEM score, which unifies these two prognostic factors, was an independent prognostic factor of overall survival (HR = 0.92, CI = 0.89–0.94, ). The entire GC cohort was stratified into three risk groups (high-, moderate-, and low-risk), which yielded incremental survival times (). For stage III disease, patients in the moderate- and low-risk groups experienced better survival benefits from radiation therapy ((HR = 0.16, 95% CI = 0.06–0.4, ), whereas those in the high-risk group did not (HR = 0.49, 95% CI = 0.14–1.72, ). We concluded that the STEM score is a promising prognostic factor for gastric cancer.

Highlights

  • Gastric cancer (GC) is a complex and heterogeneous disease from morphological, molecular, and cellular standpoints [1]

  • Hierarchical clustering of the cell types in the cascade stages from non-atrophic gastritis (NAG), chronic atrophic gastritis (CAG), intestinal metaplasia (IM), and early gastric cancer (EGC) in the single cell reference revealed that 24,874 non-immune cells fell into three large groups: epithelial cell types, stromal cell types, and endothelial cell types (Figure 1)

  • We examined several large cohorts of GC patients at the cellular level and found a new and strong independent prognostic factor (STEM score) for GC patients

Read more

Summary

Introduction

Gastric cancer (GC) is a complex and heterogeneous disease from morphological, molecular, and cellular standpoints [1]. The Asian Cancer Research Group (ACRG) classified GC into four molecular subtypes that, based on gene expression data, were associated with distinct molecular alterations, disease progression, and survival outcomes [4] These subtypes are epithelial-to-mesenchymal transition (EMT), MSS/TP53-, MSS/TP53+, and microsatellite instability (MSI). The Cancer Genome Atlas (TCGA) research network characterized GC into four genomic subtypes by integrating data from six molecular platforms: array-based somatic copy number analysis, wholeexome sequencing, array-based DNA methylation profiling, messenger RNA sequencing, microRNA (miRNA) sequencing, and reverse-phase protein array, as well as Microsatellite instability (MSI) testing [5].

Methods
Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call