ArticleNPJ precision oncology2025
Integrative multi-omics and machine learning reveal critical functions of proliferating cells in prognosis and personalized treatment of lung adenocarcinoma.
Article in NPJ precision oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- The cell with many faces: lung macrophage plasticity and function in response to environmental and pathogenic insults.Physiological reviews · 2026Review
- Machine learning-driven QSAR modeling combined with single cell transcriptomics identifies novel drug targets for lung cancer.Journal of translational medicine · 2026Article
- A weakly supervised deep learning-based recurrence prediction and risk stratification of lung adenocarcinoma from pathology whole-slide images.BMC cancer · 2026Article
- A clinically relevant SLC2A1-associated malignant epithelial cell state predicts prognosis and immunotherapy response in lung adenocarcinoma.Functional & integrative genomics · 2026Article
- A new era of precision diagnosis and treatment for lung cancer: artificial intelligence-driven multimodal data integration and clinical applications.Cell death & disease · 2026Review
- Review
- Cell type-specific pharmacological modulation of the neuro-immune axis: the role of ADRB2 signaling in reshaping the tumor ecosystem.Frontiers in pharmacology · 2026Review
- Multi-omics comprehensive analysis identified KIF22 and KRAS as highly synthetic lethal pairs for triple-negative breast cancer.Frontiers in oncology · 2026Article
- Multi-omics profiling of sodium-overload (NECSO) programs identifies NEK8 as a central driver of colorectal cancer progression through single-cell and spatial transcriptomics.Frontiers in immunology · 2026Article
- Plying potency assays for immunotherapy of solid tumors.Frontiers in immunology · 2026Review
- A scissor-guided single-cell framework defines a macrophage-derived risk score for prognostic and immunotherapy stratification in lung adenocarcinoma.Frontiers in immunology · 2026Article
- Integrative Multimodal Profiling of TAp73 and DNp73 Reveals Isoform-Specific Transcriptomic Coregulator Landscapes in Cancer Programs.Biomolecules · 2025Article
- Emerging hallmarks and the rise of complexities and heterogeneity of tumor.Biochemistry and biophysics reports · 2025Review
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Authors and funding
5 authors.
Funding
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Abstract
Lung adenocarcinoma (LUAD) is a major cause of cancer-related mortality globally. Proliferating cells, crucial components of the tumor immune microenvironment (TIME), play a significant role in cancer progression and immunotherapy response. Herein, we utilized multi-omics data and employed a multifaceted approach to delineate the proliferating cell landscape in LUAD. The Scissor algorithm was applied to identify Scissor+ proliferating cell genes associated with prognosis. An integrative machine learning program, comprising 111 algorithms, was developed to construct a Scissor+ proliferating cell risk score (SPRS). The SPRS model demonstrated superior performance in predicting prognosis and clinical outcomes compared to 30 previously published models. The role of SPRS and five pivotal genes in immunotherapy response was evaluated, and their expression was experimentally verified. Multifactorial analysis confirmed SPRS as an independent prognostic factor affecting LUAD patient survival. High- and low-SPRS groups exhibited different biological functions and immune cell infiltration in the TIME. High SPRS patients showed resistance to immunotherapy but increased sensitivity to chemotherapeutic and targeted therapeutic agents. Our study elucidates the dynamics of proliferating cells in LUAD, enhancing prognostic accuracy and highlighting the potential of SPRS and its constituent genes for personalized therapeutic interventions.
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