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<p>Colorectal cancer (CRC) progression is closely associated with chronic inflammation and immune suppression mediated by M2 macrophages. This study systematically analyzed immune-related and macrophage polarization–related genes in CRC by integrating The Cancer Genome Atlas CRC dataset to identify differentially expressed genes. Prognostic genes were identified using regression and machine learning methods, followed by the construction of a risk model. Comprehensive analyses included gene set enrichment, immune microenvironment assessment, somatic mutation profiling, and drug sensitivity evaluation. Single-cell RNA sequencing identified key cell populations, whereas cell–cell communication and pseudotime trajectory analyses further characterized cellular dynamics. RT-qPCR validation preliminarily confirmed the findings. A three-gene signature comprising <i>FABP4</i>, <i>PPARGC1A</i>, and <i>LEP</i> demonstrated strong prognostic value. Risk stratification based on this signature was associated with distinct immune cell profiles, pathway activities, mutational patterns, and drug sensitivity. Single-cell analysis identified macrophages as central interacting cells, with stable expression of the three genes during macrophage differentiation. Experimental validation further confirmed significant downregulation of these genes in CRC tissues. Overall, the <i>FABP4</i>/<i>PPARGC1A</i>/<i>LEP</i> signature and its associated risk model demonstrate strong potential for predicting outcomes in patients with CRC.</p>

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Prognostic signatures associated with immune and macrophage phenotypes in colorectal cancer: integrated single-cell and bulk transcriptomic analyses

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月1日

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  • Yuan Yuan,
  • Bingxi Zhou,
  • Jin Liu,
  • Binbin Yan,
  • Jianmin Zhang,
  • Xiaofang Li,
  • Xiuling Li

摘要

Colorectal cancer (CRC) progression is closely associated with chronic inflammation and immune suppression mediated by M2 macrophages. This study systematically analyzed immune-related and macrophage polarization–related genes in CRC by integrating The Cancer Genome Atlas CRC dataset to identify differentially expressed genes. Prognostic genes were identified using regression and machine learning methods, followed by the construction of a risk model. Comprehensive analyses included gene set enrichment, immune microenvironment assessment, somatic mutation profiling, and drug sensitivity evaluation. Single-cell RNA sequencing identified key cell populations, whereas cell–cell communication and pseudotime trajectory analyses further characterized cellular dynamics. RT-qPCR validation preliminarily confirmed the findings. A three-gene signature comprising FABP4, PPARGC1A, and LEP demonstrated strong prognostic value. Risk stratification based on this signature was associated with distinct immune cell profiles, pathway activities, mutational patterns, and drug sensitivity. Single-cell analysis identified macrophages as central interacting cells, with stable expression of the three genes during macrophage differentiation. Experimental validation further confirmed significant downregulation of these genes in CRC tissues. Overall, the FABP4/PPARGC1A/LEP signature and its associated risk model demonstrate strong potential for predicting outcomes in patients with CRC.