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Integrated Analysis of Single-Cell and Bulk RNA-Sequencing Based on EcoTyper Machine Learning Framework Identifies
A-Kao Zhu1, Guang-Yao Li2, Fang-Ci Chen3
1Department of Colorectal Surgery and Oncology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, People's Republic of China.
Immunotargets and Therapy
|December 16, 2024
Summary
This study identified specific M2 macrophage markers in gastric cancer (GC) to predict patient prognosis. These markers reveal insights into the tumor microenvironment and cancer progression, aiding in understanding tumor ecosystems.
Area of Science:
- Oncology
- Immunology
- Bioinformatics
Background:
- Tumors are complex ecosystems where cellular states interact with the microenvironment, influencing cancer progression.
- Tumor-associated macrophages (TAMs) are key players in the tumor microenvironment and cancer progression.
- Understanding cellular dynamics within the tumor microenvironment is crucial for cancer research.
Purpose of the Study:
- To identify cell-state-specific M2 macrophage markers for gastric cancer (GC) prognosis.
- To analyze the role of TAMs in GC progression using advanced computational methods.
- To define TAM ecotypes and cell states within the GC tumor microenvironment.
Main Methods:
- Integrative analysis of single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data.
- Utilized the machine learning framework EcoTyper to classify macrophage states and ecotypes.
- Identified 168 cell-state-specific M2 macrophage markers.
Main Results:
- M2 macrophages predominated among TAMs in GC.
- Identified markers classified GC patients into two prognostic clusters (A and B).
- Cluster A, with worse survival, showed enrichment in cell adhesion molecules and signaling pathways like JAK/STAT and MAPK.
Conclusions:
- Generated a single-cell atlas of intratumor heterogeneity in GC.
- Defined TAM cell states and ecotypes, identifying prognostically relevant M2 macrophage markers.
- Provided novel insights into the tumor ecosystem and cancer progression dynamics.
