Yun Ding, Han Zhang, Hui Wen, Shutong Zhao, Jiuzhen Li, Xin Liu, Kai Wang, Yangyun Huang, Meilin Xu, Daqiang Sun
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Single-Cell and Spatial Transcriptomics-Based Research Reveals Association Between M2a Macrophages and Tumor Spread through Air Spaces in Lung Adenocarcinoma.
Tumor spread through air spaces (STAS) is a pathologic feature of lung cancer with prognostic significance, and it is also a cutting-edge hotspot in the clinical field of lung cancer. However, the current mechanisms underlying the occurrence and colonization of STAS in lung adenocarcinoma (LADC) remain unclear. Single-cell RNA sequencing combined with Digital Spatial Profiling spatial transcriptomics sequencing, which aligns well with the spatial characteristics of STAS and the tumor microenvironment, was innovatively used in this research to explore the mechanisms by which specific types of tumor-associated macrophages influence the occurrence and development of STAS in LADC. This study suggests that M2a macrophages are associated with STAS in LADC and patient prognosis. M2a macrophages may enhance STAS in LADC by promoting β-catenin signaling through the chemokine (C-C motif) ligand 17/CCR4 axis. Therefore, targeting tumor-associated macrophages could be a beneficial precision treatment strategy for patients with STAS-positive LADC. In addition, the tumor microenvironment exhibits significant spatial heterogeneity, and its spatial characteristics should be fully considered when studying tumors.
期刊介绍:
The American Journal of Pathology, official journal of the American Society for Investigative Pathology, published by Elsevier, Inc., seeks high-quality original research reports, reviews, and commentaries related to the molecular and cellular basis of disease. The editors will consider basic, translational, and clinical investigations that directly address mechanisms of pathogenesis or provide a foundation for future mechanistic inquiries. Examples of such foundational investigations include data mining, identification of biomarkers, molecular pathology, and discovery research. Foundational studies that incorporate deep learning and artificial intelligence are also welcome. High priority is given to studies of human disease and relevant experimental models using molecular, cellular, and organismal approaches.