BMC Cancer (Mar 2023)

Integrated transcriptional analysis reveals macrophage heterogeneity and macrophage-tumor cell interactions in the progression of pancreatic ductal adenocarcinoma

  • Kaidi Yang,
  • Tongxin Yang,
  • Jian Yu,
  • Fang Li,
  • Xiang Zhao

DOI
https://doi.org/10.1186/s12885-023-10675-y
Journal volume & issue
Vol. 23, no. 1
pp. 1 – 14

Abstract

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Abstract Background Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal disease harboring significant microenvironment heterogeneity, especially for the macrophages. Tumor-associated macrophages (TAMs) orchestrate PDAC malignancy, but their dynamics during disease progression remains poorly understood. There is a pressing need to identify the molecular mechanism underlying tumor-macrophage interactions and thus design novel therapeutic strategies. Methods Herein, we developed an insilico computational method incorporating bulk and single-cell transcriptome profiling to characterize macrophage heterogeneity. CellPhoneDB algorithm was applied to infer macrophage-tumor interaction networks, whereas pseudotime trajectory for dissecting cell evolution and dynamics. Results We demonstrated myeloid compartment was an interactive hub of tumor microenvironment (TME) essential for PDAC progression. Dimensionality reduction classified seven clusters within the myeloid cells wherein five subsets of macrophages were characterized by diverse cell states and functionality. Remarkably, tissue-resident macrophages and inflammatory monocyte were identified as potential sources of TAMs. Further, we uncovered several ligand-receptor pairs lining tumor cells and macrophages. Among them, HBEGF-CD44, HBEGF-EGFR, LGALS9-CD44, LGALS9-MET, and GRN-EGFR were correlated with worse overall survival. Notably, as in vitro experiments indicated, TAM-derived HBEGF promoted proliferation and invasion of the pancreatic cancer cell line. Conclusion Together, our work deciphered a comprehensive single-cell atlas of the macrophage compartment of PDAC and provided novel macrophage-tumor interaction features with potential value in developing targeted immunotherapies and molecular diagnostics for predicting patient outcome.

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