ArticleJournal of cellular and molecular medicine2024
Integrating machine learning and single-cell analysis to uncover lung adenocarcinoma progression and prognostic biomarkers.
Article in Journal of cellular and molecular medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- A Mitochondrial-Related Gene Signature for Diagnosis and Immune Microenvironment Modulation in Lung Cancer and Venous Thromboembolism.World journal of oncology · 2026Article
- Single-cell and spatial transcriptomics identify CAPG as a key driver of cisplatin resistance in bladder cancer.Functional & integrative genomics · 2026Article
- Decoding chromatin regulator-LAIR1Clinical and experimental medicine · 2026Article
- Basal cell subsets, as biomarker to predict the therapeutic effect of neoadjuvant therapy for esophageal carcinoma.Journal of cancer research and clinical oncology · 2026Article
- MiR-4788 promotes NSCLC progression by targeting DLG5 to enhance mitochondrial function.Human genomics · 2026Article
- Pan-cancer analysis reveals HDAC1 as a key regulator of immune infiltration and T cell exhaustion.Discover oncology · 2026Article
- Pan-cancer analysis reveals the oncogenic and immunomodulatory roles of PTGFRN across human cancers.Scientific reports · 2026Article
- Single-cell analysis identifies a stemness-associated tumor cell subpopulation and develops a prognostic scoring model in esophageal squamous cell carcinoma.Translational oncology · 2026Article
- Single‑cell mapping of neutrophil extracellular trap signatures in lung adenocarcinoma reveals immune landscapes, prognostic potential, and therapeutic targets.Translational oncology · 2026Article
- Machine learning-derived identification of an obesity and lipid metabolism-related genes signature for the diagnosis and molecular typing of acute myocardial infarction.Frontiers in cardiovascular medicine · 2026Article
- Driver Mutation Subtypes Differentially Shape Immune Evasion Landscapes in Melanoma: An AI-Driven Inflammatory Pathway Model Implicating CCNE1.Human mutation · 2026Article
- Article
- A prognostic risk prediction model for gastric cancer based on the EFNA4 and ETS1 regulatory axis in tumor cells.Scientific reports · 2025Article
- Single-cell RNA sequencing technology was employed to construct a risk prediction model for genes associated with pyroptosis and ferroptosis in lung adenocarcinoma.Respiratory research · 2025Article
- Identification of a deubiquitinating gene-related signature in ovarian cancer using integrated transcriptomic analysis and machine learning framework.Discover oncology · 2025Article
- Prognostic and immunological implications of protein kinases in gastric cancer: a focus on hub gene ABL2 and its impact on the polarization of M2 macrophages.Biology direct · 2025Article
- Machine Learning-Based Glycolipid Metabolism Gene Signature Predicts Prognosis and Immune Landscape in Oesophageal Squamous Cell Carcinoma.Journal of cellular and molecular medicine · 2025Article
- Fatty acid metabolism-derived prognostic model for lung adenocarcinoma: unraveling the link to survival and immune response.Frontiers in immunology · 2025Article
- Investigation of sphingolipid-related genes in lung adenocarcinoma.Frontiers in molecular biosciences · 2025Article
- Peripheral blood markers predict prognosis and irAEs of stage IV driver gene-negative lung adenocarcinoma treated with ICIs.Frontiers in immunology · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
The progression of lung adenocarcinoma (LUAD) from atypical adenomatous hyperplasia (AAH) to invasive adenocarcinoma (IAC) involves a complex evolution of tumour cell clusters, the mechanisms of which remain largely unknown. By integrating single-cell datasets and using inferCNV, we identified and analysed tumour cell clusters to explore their heterogeneity and changes in abundance throughout LUAD progression. We applied gene set variation analysis (GSVA), pseudotime analysis, scMetabolism, and Cytotrace scores to study biological functions, metabolic profiles and stemness traits. A predictive model for prognosis, based on key cluster marker genes, was developed using CoxBoost and plsRcox (CPM), and validated across multiple cohorts for its prognostic prediction capabilities, tumour microenvironment characterization, mutation landscape and immunotherapy response. We identified nine distinct tumour cell clusters, with Cluster 6 indicating an early developmental stage, high stemness and proliferative potential. The abundance of Clusters 0 and 6 increased from AAH to IAC, correlating with prognosis. The CPM model effectively distinguished prognosis in immunotherapy cohorts and predicted genomic alterations, chemotherapy drug sensitivity, and immunotherapy responsiveness. Key gene S100A16 in the CPM model was validated as an oncogene, enhancing LUAD cell proliferation, invasion and migration. The CPM model emerges as a novel biomarker for predicting prognosis and immunotherapy response in LUAD patients, with S100A16 identified as a potential therapeutic target.
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