Machine learning-based identification of exosome-related biomarkers and drugs prediction in nasopharyngeal carcinoma
Abstract Purpose Exosomes are recognized as essential mediators in the intercellular communication between tumor cells, serving a pivotal function in tumor development. Nevertheless, the patterns of expression and medical relevance of exosome-related genes (ERGs) in nasopharyngeal carcinoma (NPC) re...
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| Format: | Article |
| Language: | English |
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Springer
2025-06-01
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| Series: | Discover Oncology |
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| Online Access: | https://doi.org/10.1007/s12672-025-02962-w |
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| author | Zhengyu Wei Guoli Wang Yanghao Hu Chongchang Zhou Yuna Zhang Yi Shen Yaowen Wang |
| author_facet | Zhengyu Wei Guoli Wang Yanghao Hu Chongchang Zhou Yuna Zhang Yi Shen Yaowen Wang |
| author_sort | Zhengyu Wei |
| collection | DOAJ |
| description | Abstract Purpose Exosomes are recognized as essential mediators in the intercellular communication between tumor cells, serving a pivotal function in tumor development. Nevertheless, the patterns of expression and medical relevance of exosome-related genes (ERGs) in nasopharyngeal carcinoma (NPC) remain insufficiently characterized. Methods Datasets retrieved from the Gene Expression Omnibus database were consolidated into a comprehensive gene dataset, which was then employed to ascertain differentially expressed genes (DEGs) by comparing NPC samples with controls. ERGs were intersected with the DEGs, yielding the detection of exosome-related DEGs. These identified genes underwent functional annotation and pathway enrichment evaluation. The least absolute shrinkage and selection operator regression, support vector machine, and random forest approaches were utilized to develop NPC diagnostic model. Key genes were determined through intersection analysis and subsequently confirmed in an independent cohort. Furthermore, drug screening, molecular docking, and molecular dynamics simulation were executed to generate meaningful insights for developing therapeutic compounds. Results Through the application of three machine learning algorithms, five key genes (LTF, IDH1, ITGAV, CCL2, and LGALS3BP) were identified for the construction of a diagnostic model. Validation results demonstrated the strong discriminative and calibration abilities of the model. Furthermore, molecular docking analysis revealed that the interaction between IDH1 and nelfinavir exhibited the lowest Vina score, suggesting a stable binding affinity. Conclusion This study identifies five exosome-related key genes, utilizing machine learning approaches to develop a diagnostic model and uncover potential drug targets for NPC. These findings offer novel perspectives for both the diagnosis and therapeutic development of NPC. |
| format | Article |
| id | doaj-art-9d0418546a4c4c1dba656c091990f1cd |
| institution | Kabale University |
| issn | 2730-6011 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Oncology |
| spelling | doaj-art-9d0418546a4c4c1dba656c091990f1cd2025-08-20T03:47:13ZengSpringerDiscover Oncology2730-60112025-06-0116112310.1007/s12672-025-02962-wMachine learning-based identification of exosome-related biomarkers and drugs prediction in nasopharyngeal carcinomaZhengyu Wei0Guoli Wang1Yanghao Hu2Chongchang Zhou3Yuna Zhang4Yi Shen5Yaowen Wang6Department of Otorhinolaryngology Head and Neck Surgery, The First Affiliated Hospital of Ningbo UniversityDepartment of Otorhinolaryngology Head and Neck Surgery, The Affiliated Lihuili Hospital, Ningbo UniversityHealth Science Center, Ningbo UniversityDepartment of Otorhinolaryngology Head and Neck Surgery, The Affiliated Lihuili Hospital, Ningbo UniversityDepartment of Operating Room, The Affiliated Lihuili Hospital, Ningbo UniversityDepartment of Otorhinolaryngology Head and Neck Surgery, The First Affiliated Hospital of Ningbo UniversityDepartment of Otorhinolaryngology Head and Neck Surgery, The First Affiliated Hospital of Ningbo UniversityAbstract Purpose Exosomes are recognized as essential mediators in the intercellular communication between tumor cells, serving a pivotal function in tumor development. Nevertheless, the patterns of expression and medical relevance of exosome-related genes (ERGs) in nasopharyngeal carcinoma (NPC) remain insufficiently characterized. Methods Datasets retrieved from the Gene Expression Omnibus database were consolidated into a comprehensive gene dataset, which was then employed to ascertain differentially expressed genes (DEGs) by comparing NPC samples with controls. ERGs were intersected with the DEGs, yielding the detection of exosome-related DEGs. These identified genes underwent functional annotation and pathway enrichment evaluation. The least absolute shrinkage and selection operator regression, support vector machine, and random forest approaches were utilized to develop NPC diagnostic model. Key genes were determined through intersection analysis and subsequently confirmed in an independent cohort. Furthermore, drug screening, molecular docking, and molecular dynamics simulation were executed to generate meaningful insights for developing therapeutic compounds. Results Through the application of three machine learning algorithms, five key genes (LTF, IDH1, ITGAV, CCL2, and LGALS3BP) were identified for the construction of a diagnostic model. Validation results demonstrated the strong discriminative and calibration abilities of the model. Furthermore, molecular docking analysis revealed that the interaction between IDH1 and nelfinavir exhibited the lowest Vina score, suggesting a stable binding affinity. Conclusion This study identifies five exosome-related key genes, utilizing machine learning approaches to develop a diagnostic model and uncover potential drug targets for NPC. These findings offer novel perspectives for both the diagnosis and therapeutic development of NPC.https://doi.org/10.1007/s12672-025-02962-wDiagnostic markerDrug predictionExosomeNasopharyngeal carcinoma |
| spellingShingle | Zhengyu Wei Guoli Wang Yanghao Hu Chongchang Zhou Yuna Zhang Yi Shen Yaowen Wang Machine learning-based identification of exosome-related biomarkers and drugs prediction in nasopharyngeal carcinoma Discover Oncology Diagnostic marker Drug prediction Exosome Nasopharyngeal carcinoma |
| title | Machine learning-based identification of exosome-related biomarkers and drugs prediction in nasopharyngeal carcinoma |
| title_full | Machine learning-based identification of exosome-related biomarkers and drugs prediction in nasopharyngeal carcinoma |
| title_fullStr | Machine learning-based identification of exosome-related biomarkers and drugs prediction in nasopharyngeal carcinoma |
| title_full_unstemmed | Machine learning-based identification of exosome-related biomarkers and drugs prediction in nasopharyngeal carcinoma |
| title_short | Machine learning-based identification of exosome-related biomarkers and drugs prediction in nasopharyngeal carcinoma |
| title_sort | machine learning based identification of exosome related biomarkers and drugs prediction in nasopharyngeal carcinoma |
| topic | Diagnostic marker Drug prediction Exosome Nasopharyngeal carcinoma |
| url | https://doi.org/10.1007/s12672-025-02962-w |
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