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Single-cell RNA sequencing identifies stem-like subpopulations and their gene signature in lung adenocarcinoma progression

Lijuan Shao, Jiali Guo, Yujia Chen, Hua Wu, Yanming Zhong, Jiayi Dong, Wenxin Huang, Yanfeng Sun, Junzheng Yang, Size Chen, Qian He

Frontiers in Pharmacology · 2026-11-04 · 17

DOI: 10.3389/fphar.2026.1935165 ↗
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Resumo

Lung adenocarcinoma (LUAD) demonstrates significant intra-tumor heterogeneity (ITH), which plays a critical role in disease progression and complicates therapeutic interventions. In the present study, we employed single-cell RNA sequencing (scRNA-seq) to analyze cellular populations derived from stage I and stage III LUAD tumors, alongside matched adjacent normal tissues, with the objective of delineating malignant cell states and identifying tumor-propagating subpopulations. Unsupervised clustering analysis identified eight principal cell lineages, with epithelial cells predominating within tumor samples. Notably, advanced-stage tumors demonstrated a shift toward an immunosuppressive microenvironment, characterized by elevated T cell exhaustion and an increased presence of pro-angiogenic macrophages. Further stratification of malignant cells revealed eleven distinct subclusters, with stage III tumors enriched for invasive and immunosuppressive phenotypes. Pseudotime trajectory analysis identified evolutionary transitions from alveolar-like states toward squamous and stress-responsive lineages. Employing CytoTRACE to evaluate stemness independently of predefined markers, we identified subpopulations of cancer stem cells (sub-CSCs) predominantly in stage III tumors. These sub-CSCs exhibited heightened transcriptional heterogeneity and a core gene signature associated with cytoskeletal motility, immune regulation, metabolic reprogramming, and protein homeostasis. A four-gene panel derived from this signature demonstrated robust prognostic significance in independent LUAD cohorts. Collectively, these findings enhance the current understanding of stemness-driven tumor progression in LUAD and suggest potential therapeutic targets to address tumor heterogeneity.

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