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Decoding early lung adenocarcinoma progression by single-cell and spatial transcriptomics reveals a CMA-related
Junkang Wang1, Wenxuan Wang2, Shengnan Li3
1Department of Neurosurgery, The First Affiliated Hospital of China Medical University, Shenyang, China.
Background:
Lung adenocarcinoma (LUAD) progression from adenocarcinoma in situ (AIS) to minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC) is accompanied by molecular heterogeneity and tumor microenvironment remodeling. Chaperone-mediated autophagy (CMA) regulates tumor cell homeostasis, metabolic adaptation, and stress responses, but its dynamic alterations and prognostic significance during the AIS/MIA-to-IAC progression of LUAD remain unclear.
Methods:
We integrated the single-cell transcriptomic dataset GSE189357 and the spatial transcriptomic dataset GSE189487 with bulk transcriptomic data from TCGA-LUAD, GTEx, and the GEO validation cohorts GSE31210 and GSE50081 to characterize CMA-related features during the AIS/MIA-to-IAC progression of LUAD. CMA activity and myeloid remodeling were analyzed at the single-cell and spatial levels. Candidate genes were identified by combining tumor-normal differential expression analysis in TCGA-LUAD with weighted gene co-expression network analysis. Multiple machine learning algorithms were compared to construct and externally validate a prognostic model. Biological and clinical relevance was further assessed through clinicopathological, pathway, immune, cell-cell communication, drug sensitivity, and in vitro analyses.
Results:
CMA-related activity showed marked cell-type specificity and spatial heterogeneity during the AIS/MIA-to-IAC progression of LUAD, with the most prominent changes in the myeloid compartment. Myeloid re-clustering revealed enrichment of cDC2 and APOE+ lipid-associated TAMs in IAC, whereas FABP4+ metabolic TAMs and immature neutrophils decreased. By integrating tumor-normal differential expression analysis with weighted gene co-expression network analysis, 122 candidate genes were identified, and a 15-gene CMA-related prognostic signature was established using a random survival forest model. This signature showed robust prognostic stratification in TCGA-LUAD, GSE31210, and GSE50081. The high-risk group had poorer survival, more advanced stage, and enrichment of malignant pathways including GLYCOLYSIS, G2M CHECKPOINT, MTORC1 SIGNALING, E2F TARGETS, and MYC TARGETS. The low-risk group showed higher stromal and immune scores and stronger immune activity. THBS1 signaling was restricted to high-risk epithelial communication, with fibroblasts as the major signal senders. In vitro experiments showed that MGP overexpression inhibited lung cancer cell proliferation, colony formation, migration, and invasion.
Conclusions:
This study characterized CMA-related heterogeneity during LUAD progression from AIS to IAC and established a robust 15-gene prognostic signature. Fibroblast-derived THBS1 signaling and MGP may contribute to the high-risk phenotype and provide insight into early LUAD evolution and risk stratification.
