This study addresses the limitations of the traditional M1/M2 binary classification for tumor-associated macrophages (TAMs) in non-small cell lung cancer (NSCLC) by introducing a NSCLC-specific functional framework based on the CXCL9/SPP1 (CS) expression ratio. Through the integration of single-cell and bulk transcriptomic data, the research identified four distinct TAM subpopulations. Among these, the CXCL9 + SPP1 − subpopulation exhibited macrophages with anti-tumor features, whereas the CXCL9 − SPP1 + subpopulation showed macrophages with pro-tumor features. A robust CS-polarity-associated tumor microenvironment (TME) six-gene signature was constructed and validated using extensive machine-learning optimization. This model effectively stratified NSCLC patients into high-risk and low-risk groups, with high-risk patients displaying an immunosuppressive TME enriched in M0/M2 macrophages. The study further demonstrated the dynamic plasticity of TAM polarity through pseudotime trajectory analysis and validated key gene expression. For the first time, this study introduces the CXCL9/SPP1 polarity axis into the field of non-small cell lung cancer (NSCLC). By integrating single-cell trajectory analysis, we reveal the dynamic differentiation patterns of TAM polarity in NSCLC. Furthermore, utilizing a combination of 101 machine learning algorithms, we constructed the first six-gene prognostic model based on this polarity axis, achieving precise risk stratification for NSCLC patients and enabling correlative analysis of the immune status within the tumor microenvironment.
Li et al. (Fri,) studied this question.