To systematically dissect the molecular mechanisms underlying the transcriptional regulatory network governing malignant proliferation of lung adenocarcinoma cells through integrated analysis of multiple public human lung cancer single-cell transcriptomic datasets, and to identify core genes and transcription factors driving tumor proliferation, thereby providing a theoretical basis for early diagnosis and targeted therapy of lung cancer.

Four public lung cancer scRNA-seq datasets (GSE131907, GSE149655, GSE148071, and E-MTAB-6149) were integrated and processed. After quality control, normalization, and batch correction using Harmony, a total of 199 489 high-quality cells were retained. Cell clustering was performed using the Louvain algorithm, and cell types were annotated using canonical marker genes. InferCNV combined with Gaussian mixture modeling (GMM) was applied to accurately distinguish tumor epithelial cells from normal epithelial cells based on copy number variation (CNV) profiles. Differentially expressed genes (DEGs) were identified using the Wilcoxon rank-sum test. Weighted gene co-expression network analysis (WGCNA) was performed to identify key functional modules, and the GENIE3 algorithm was used to construct transcription factor regulatory networks. Finally, HMGB2 was knocked down in A549 cells via siRNA transfection, and the expression changes of core transcription factors and their downstream target genes were validated by qRT-PCR.

After batch correction, cells from different datasets were uniformly mixed. A total of 20 cell clusters were identified and classified into four major lineages: immune cells (65.18%), epithelial cells (27.43%), endothelial cells (4.60%), and fibroblasts (2.79%). Using CNV-based GMM analysis, 23 505 tumor cells were successfully identified (44.90% of all epithelial cells), among which 80.59% were classified as high-confidence malignant cells. WGCNA identified the blue module as the key module highly associated with the cell cycle, whose activity was markedly elevated in S and G2/M phases, with all hub genes being canonical cell cycle drivers. Transcriptional regulatory network analysis uncovered a tightly interacting regulatory circuit consisting of seven core transcription factors, including FOXM1, E2F7, and HMGB2. Functional cell experiments confirmed that knockdown of HMGB2 significantly downregulated the expression of key genes including FOXM1, E2F7, and TOP2A.

This study successfully integrated multiple lung cancer single-cell transcriptomic datasets and established a comprehensive single-cell atlas of lung cancer epithelial cell heterogeneity. We elucidated the molecular mechanism by which the core transcriptional regulatory axis centered on HMGB2-FOXM1/E2F7/HMGA1-cell cycle genes drives malignant proliferation of lung cancer epithelial cells. These findings provide a novel theoretical basis and potential molecular targets for the identification of early diagnostic biomarkers and the development of targeted therapeutic strategies for lung cancer.