Comparative immunological landscape between pre- and early-stage LUAD manifested as ground-glass nodules revealed by scRNA and scTCR integrated analysis
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Updated: Jul 11, 2025

Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Ziqi Wang1, Li Yang1, Wenqiang Wang1
1Department of Respiratory and Critical Care Medicine, Zhengzhou University People's Hospital, Henan Provincial People's Hospital, Weiwu Road No.7, Zhengzhou, 450003, Henan, China.
Background:
Mechanism underlying the malignant progression of precancer to early-stage lung adenocarcinoma (LUAD) as well as their indolence nature remains elusive.
Methods:
Single-cell RNA sequencing (scRNA) with simultaneous T cell receptor (TCR) sequencing on 5 normal lung tissues, 3 precancerous and 4 early-stage LUAD manifested as pulmonary ground-glass nodules (GGNs) were performed.
Results:
Through this integrated analysis, we have delineated five key modules that drive the malignant progression of early-stage LUAD in a disease stage-dependent manner. These modules are related to cell proliferation and metabolism, immune response, mitochondria, cilia, and cell adhesion. We also find that the tumor micro-environment (TME) of early-stage LUAD manifested as GGN are featured with regulatory T (Tregs) cells accumulation with three possible origins, and loss-functional state (decreased clonal expansion and cytotoxicity) of CD8 + T cells. Instead of exhaustion, the CD8 + T cells are featured with a shift to memory phenotype, which is significantly different from the late stage LUAD. Furthermore, we have identified monocyte-derived macrophages that undergo a lipid-phenotype transition and may contribute to the suppressive TME. Intense interaction between stromal cells, myeloid cells including lipid associated macrophages and LAMP3 + DCs, and lymphocytes were also characterized.
Conclusions:
Our work provides new insight into the molecular and cellular mechanism underlying malignant progression of LUAD manifested as GGN, and pave way for novel immunotherapies for GGN. Video Abstract.
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