Integrative multi-omics analysis of gastric cancer evolution from precancerous lesions to metastasis identifies a deep learning-based prognostic model

TL;DR

This study uses multi-omics data to map how the tumor microenvironment changes during gastric cancer progression and develops a deep learning model to predict patient outcomes.

Contribution

The study introduces a deep learning-based prognostic model derived from integrative single-cell and spatial omics data in gastric cancer.

Findings

Dynamic tumor microenvironment remodeling involves dysfunctional CD8+ T cells and pro-tumorigenic fibroblasts during gastric cancer progression.

A 657-gene module was identified that correlates with immune and stromal alterations and tumor stage.

A deep learning model based on the gene module accurately stratified patient survival in TCGA and validation cohorts.

Abstract

Gastric cancer progression involves complex interactions among tumor cells, immune components, and stromal elements within the tumor microenvironment. However, a comprehensive understanding of cellular heterogeneity, spatial organization, and cell-cell communication in gastric cancer remains incomplete.

Single-cell RNA sequencing was performed on 252, 399 cells from six tissue types, spanning gastritis, intestinal metaplasia, primary tumors, adjacent normal tissue, and metastatic lesions. Integration with spatial transcriptomics enabled spatial mapping of cellular interactions. Pseudotime, cell-cell communication, and transcriptional heterogeneity analyses were conducted. Tumor stage-associated gene modules were identified using Weighted Gene Co-expression Network Analysis (WGCNA) of The Cancer Genome Atlas (TCGA) data. Finally, a deep learning-based prognostic model was developed and externally validated.

Our analysis revealed dynamic remodeling of the tumor microenvironment during gastric cancer progression, characterized by the expansion of dysfunctional CD8+ T cells, pro-tumorigenic fibroblasts (e.g., ITGBL1+, PI16+, and ITLN1+), and altered myeloid populations. Stromal-immune crosstalk, particularly fibroblast-driven immunosuppressive signaling, was prominent. Spatial transcriptomics revealed the colocalization of immune and stromal cells, supporting spatially organized cellular interactions. WGCNA identified a gene module (657 genes) associated with T cell, myeloid, and stromal alterations, as well as tumor stage. A deep learning model based on this gene set accurately stratified patients according to survival in both TCGA and independent validation cohorts. Risk scores were correlated with clinical features, including tumor stage and therapeutic response.

Our integrative single-cell, spatial, and computational analysis provides a high-resolution map of gastric cancer microenvironment remodeling. We identified key stromal and immune subpopulations, extensive cellular communication networks, and spatial structures that collectively drive tumor progression and metastasis. The derived gene signature and prognostic model have the potential for clinical risk stratification and therapeutic targeting in gastric cancer.

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