Clinical genitourinary cancer · 2025
Macrophage Cell and Diagnostic Biomarkers in BLCA: Integrating Machine Learning With Single-Cell Analysis.
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Che Guangliang, Zhao Xuejun, Luo Rongtuan, Yu Quanfeng, Guo Xiaofeng, Gao Ke, Chen Kangyu, Zhang Erfeng
Аннотация
INTRODUCTION: Bladder cancer (BLCA) has a poor prognosis and continues to pose a significant challenge for clinicians. Prior studies have demonstrated a close relationship of BLCA with macrophages. However, the key subpopulations and molecular functions of macrophages in BLCA have not been uncovered. It becomes possible to use single-cell sequencing technology to explore macrophage heterogeneity and identify new biomarkers.
PATIENTS AND METHODS: Single-cell analysis, pseudo-time analysis, and cell-to-cell communication analysis were performed using single-cell sequencing data of PBMC, BLCA, and urothelial-derived cells (UDCs) obtained from the Gene Expression Omnibus (GEO). Scale-free network analysis was performed using hdWGCNA to select feature genes. LASSO regression and random forest analysis were performed based on TCGA_BLCA data. A prognostic model was established, which was then validated using the Gene Expression Omnibus (GEO) dataset. In addition, immune infiltration analysis was performed between high-risk and low-risk groups using the CIBERSORT, ESTIMATE algorithms, and the TIDE database.
RESULTS: Nine macrophage subtypes were identified through single-cell analysis. 571 macrophage-associated genes were identified via hdWGCNA. Seven key genes, including MYO5A, KCNK6, DNAJB4, DEDD2, NFKBID, PSMB10, and ITPRID2, were selected by integrating LASSO regression and RF analysis. A prognostic model was constructed based on these genes. The high-risk cohort displayed markedly poorer overall survival (OS). The model's prediction accuracy was verified in an independent group. Immune analysis revealed enhanced immune evasion in the high-risk cohort, potentially facilitating tumor progression and increasing the metastatic capacity of BLCA cells.
CONCLUSIONS: The prognostic model built on 7 macrophage-linked genes exhibited robust prediction performance in BLCA. The M2 macrophage phenotype was notably enriched in the high-risk cohort and appeared to cause elevated tumor invasiveness. Immune infiltration analysis suggested that the high-risk population showed a weaker response to immune checkpoint inhibitor (ICI) treatment. Among all identified genes, DEDD2 may serve as a promising prognostic biomarker for BLCA.
PATIENTS AND METHODS: Single-cell analysis, pseudo-time analysis, and cell-to-cell communication analysis were performed using single-cell sequencing data of PBMC, BLCA, and urothelial-derived cells (UDCs) obtained from the Gene Expression Omnibus (GEO). Scale-free network analysis was performed using hdWGCNA to select feature genes. LASSO regression and random forest analysis were performed based on TCGA_BLCA data. A prognostic model was established, which was then validated using the Gene Expression Omnibus (GEO) dataset. In addition, immune infiltration analysis was performed between high-risk and low-risk groups using the CIBERSORT, ESTIMATE algorithms, and the TIDE database.
RESULTS: Nine macrophage subtypes were identified through single-cell analysis. 571 macrophage-associated genes were identified via hdWGCNA. Seven key genes, including MYO5A, KCNK6, DNAJB4, DEDD2, NFKBID, PSMB10, and ITPRID2, were selected by integrating LASSO regression and RF analysis. A prognostic model was constructed based on these genes. The high-risk cohort displayed markedly poorer overall survival (OS). The model's prediction accuracy was verified in an independent group. Immune analysis revealed enhanced immune evasion in the high-risk cohort, potentially facilitating tumor progression and increasing the metastatic capacity of BLCA cells.
CONCLUSIONS: The prognostic model built on 7 macrophage-linked genes exhibited robust prediction performance in BLCA. The M2 macrophage phenotype was notably enriched in the high-risk cohort and appeared to cause elevated tumor invasiveness. Immune infiltration analysis suggested that the high-risk population showed a weaker response to immune checkpoint inhibitor (ICI) treatment. Among all identified genes, DEDD2 may serve as a promising prognostic biomarker for BLCA.