Single-cell RNA sequencing and large-scale bulk combination with machine learning reveal gastric cancer-related macrophage heterogeneity

Background: The tumor microenvironment (TME) significantly impacts cancer progression and overall patient survival. However, the complexity of tumor cell-TME interactions in gastric cancer (GC) and their underlying molecular basis remain to be systematically elucidated. Methods: We performed single-cell RNA sequencing (scRNA-seq) on paired tumor and adjacent tissues from 3 treatment-naive GC patients. We conducted an in-depth characterization of the cellular composition and molecular features of the GC TME, with a particular focus on tumor-associated macrophages (TAMs) subsets and their mediated intercellular communication. Investigating the effect of C1q on the polarisation of THP-1 induced macrophages through migration and invasion experiments. Results: Transcriptomic analysis of 56,151 single cells identified a key TAM subset - C1QA⁺ TAMs. This subset is characterized by high expression of genes including C1QA, C1QB, C1QC and FN1, exhibiting a distinct M2-like macrophage phenotype. We developed a LASSO-based predictive model, C1Q-LASSO, to accurately stratify patients based on survival outcomes and chemotherapy responses, independently of established prognostic parameters. In vitro functional experiments further confirmed that C1q directly promotes malignant phenotypes in GC cells. Additionally, C1q significantly enhances the malignant progression of gastric cancer induced by M2 macrophages. Conclusions: Utilizing scRNA-seq technology, this study systematically delineated the heterogeneous landscape of the GC immune microenvironment and its complex cellular interaction network at single-cell resolution. We first identified and functionally validated the C1QA⁺ TAM subset, demonstrating its association with poor prognosis and pro-tumorigenic functions.