ArticleFrontiers in oncology2022
Investigating the Clinico-Molecular and Immunological Evolution of Lung Adenocarcinoma Using Pseudotime Analysis.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- CT based quantification of intratumoral and peritumoral heterogeneity for diagnosing lymphovascular invasion for early stage non-small cell lung cancer.BMC medical imaging · 2025Article
- Development and validation of a combined ferroptosis and immune prognostic signature for lung adenocarcinoma.Translational cancer research · 2022Article
- Construction and evaluation of a prognostic risk model of tumor metastasis-related genes in patients with non-small cell lung cancer.BMC medical genomics · 2022Article
- Article
- Genomics Analysis and Nomogram Risk Prediction of Occult Lymph Node Metastasis in Non-Predominant Micropapillary Component of Lung Adenocarcinoma Measuring ≤ 3 cm.Frontiers in oncology · 2022Article
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2 authors.
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Abstract
Introduction: As the molecular features of lung adenocarcinoma (LUAD) have been evaluated as a cross-sectional study, the course of tumor characteristics has not been modeled. The temporal evolution of the tumor immune microenvironment (TIME), as well as the clinico-molecular features of LUAD, could provide a precise strategy for immunotherapy and surrogate biomarkers for the course of LUAD. Methods: A pseudotime trajectory was constructed in patients with LUAD from the Cancer Genome Atlas and non-small cell lung cancer radiogenomics datasets. Correlation analyses were performed between clinical features and pseudotime. Genes associated with pseudotime were selected, and gene ontology analysis was performed. F-18 fluorodeoxyglucose positron emission tomography images of subjects were collected, and imaging parameters, including standardized uptake value (SUV), were obtained. Correlation analyses were performed between imaging parameters and pseudotime. Correlation analyses were performed between the enrichment scores of various immune cell types and pseudotime. In addition, correlation analyses were performed between the expression of PD-L1, tumor mutation burden, and pseudotime. Results: Pseudotime trajectories of LUAD corresponded to clinical stages. Molecular profiles related to cell division and natural killer cell activity were changed along the pseudotime. The maximal SUV of LUAD tumors showed a positive correlation with pseudotime. Type 1 helper T (Th1) cells showed a positive correlation, whereas M2 macrophages showed a negative correlation with pseudotime. PD-L1 expression showed a negative correlation, whereas tumor mutation burden showed a positive correlation with pseudotime. Conclusion: The estimated pseudotime associated with the stage suggested that it could reflect the clinico-molecular evolution of LUAD. Specific immune cell types in the TIME as well as cell division and glucose metabolism were dynamically changed according to the progression of the pseudotime. As a molecular progression of LUAD, different cellular targets should be considered for immunotherapy.
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