M1 macrophage-related prognostic model by combining bulk and single-cell transcriptomic data in NSCLC - Zhe Liu; Fang Liu; Olutomilayo Olayemi Petinrin; Muhammad Toseef; Nanjun Chen; Zhongxu Zhu; Ka-Chun Wong (Open Exploration Publishing Inc., 2024)
Aim: Lung cancer is the leading cause of cancer-related deaths worldwide. Non-small cell lung cancer (NSCLC) is the most common subtype. Despite recent advancements in diagnostics and therapies, only a small percentage of patients benefit from immunotherapies. This underscores the urgent need to identify prognostic biomarkers for accurately assessing outcomes and providing treatment recommendations for NSCLC patients. Single-cell RNA sequencing (scRNA-seq) has revealed the heterogeneity of tumor-associated macrophages. Macrophages consist of M0, M1, and M2 subsets. M1 macrophages are often associated with improved clinical outcomes in various malignancies. However, there are no systematic studies on risk biomarkers for prognosticating NSCLC. Methods: CIBERSORT was used to calculate the macrophage subset infiltration percentage in bulk RNA-seq data from TCGA and GEO. The M1-related module was identified using the WGCNA algorithm. Potential M1 macrophage prognosis-associated genes were defined as the overlapping genes between marker genes in the M1 subpopulation from scRNA-seq data and prognosis-associated genes in M1 infiltrating cells. Results: Four risk genes (ADAM19, ICAM3, WIPF1, and LAP3) were identified through LASSO and multivariate Cox regression analysis. Forest plots demonstrated that the scoring model was an independent risk factor. A nomogram was provided to predict the prognosis of NSCLC patients. Finally, we validated the four risk genes at the protein expression levels and for copy number variations. Conclusions: In summary, our studies identified four risk genes related to M1 macrophages and presented a risk-scoring system to predict the outcomes of patients with NSCLC by integrating bulk and single-cell data.