ArticleTranslational lung cancer research2025
Tumor-educated platelet RNA as a diagnostic biomarker for ground-glass opacity-related lung adenocarcinoma.
Article in Translational lung cancer research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Non-small cell lung cancer and tumor-educated platelets: screening of biomarkers and construction of a prognostic model.Translational cancer research · 2026Article
- Liquid biopsy, multi-cancer early detection, and artificial intelligence: new frontiers in cancer screening from a technological and immunological perspective.Frontiers in immunology · 2026Review
- Comprehensive circRNA profiling of platelets and exosomes identifies hsa_circ_0061274 as a novel biomarker for lung adenocarcinoma.Frontiers in immunology · 2026Article
- Early Detection of Lung Cancer: A Review of Innovative Milestones and Techniques.Journal of clinical medicine · 2025Review
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Authors and funding
8 authors.
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Abstract
Background: Ground-glass opacities (GGOs) are a common computed tomography (CT) imaging feature in various pulmonary diseases, including early-stage lung adenocarcinoma (LUAD). The investigation of tumor-specific biomarkers for the diagnosis of early LUAD holds significant clinical importance. The aim of this study was to integrate highly specific tumor-educated platelet (TEP)-derived biomarkers to optimize preoperative evaluation. Methods: After identifying differentially expressed genes (DEGs), six machine learning methods were applied to the sequencing results of cohort 1, generating a DEG-TEP data set. The DEGs in cohort 2 were considered to be the DEG-TISSUES data set. The intersection of these two gene sets was designated as the intersection-17 data set. Validation of the genes in intersection-17 was conducted in cohort 3 (tissues) and cohort 4 (platelets) via real-time reverse-transcription quantitative polymerase chain reaction (RT-qPCR). si- Results: A total of 7,753 DEGs were identified in cohort 1. After machine learning was applied, the DEG-TEP data set, consisting of 108 DEGs, was established, while the DEG-TISSUES data set contained 1,909 genes. The RT-qPCR results in cohort 3 indicated that the messenger RNA expression of Conclusions: We found that
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