Integrated Analysis of Single-Cell and Bulk RNA-Sequencing Based on EcoTyper Machine Learning Framework Identifies Cell-State-Specific M2 Macrophage Markers Associated with Gastric Cancer Prognosis.

Background: Tumor is a complex and dynamic ecosystem formed by the interaction of numerous diverse cells types and the microenvironments they inhabit. Determining how cellular states change and develop distinct cellular communities in response to the tumor microenvironment is critical to understanding cancer progression. Tumour-associated macrophages (TAMs) are an important component of the tumour microenvironment and play a crucial role in cancer progression. This study was designed to identify cell-state-specific M2 macrophage markers associated with gastric cancer (GC) prognosis through integrative analysis of single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data using a machine learning framework named EcoTyper. Results: The results showed that TAMs were classified into M1 macrophages, M2 macrophages, monocytes, undefined macrophages and dendritic cells, with M2 macrophages predominating. EcoTyper assigned macrophages to different cell states and ecotypes. A total of 168 cell-state-specific M2 macrophage markers were obtained by integrative analysis of scRNA-seq and bulk RNA-seq data. These markers could categorize GC patients into two clusters (clusters A and B) with different survival and M2 macrophages infiltration abundance. Cell adhesion molecules, cytokine-cytokine receptor interaction, JAK/STAT pathway, MAPK pathway were significantly enriched in cluster A, which had worse survival and higher M2 macrophages infiltration. Conclusion: In conclusion, this study profiles a single-cell atlas of intratumor heterogeneity and defines the cell states and ecotypes of TAMs in GC. Furthermore, we have identified prognostically relevant cell-state-specific M2 macrophage markers. These findings provide novel insights into the tumor ecosystem and cancer progression.