SMR-guided molecular subtyping and machine learning model reveals novel prognostic biomarkers and therapeutic targets in non-small cell lung adenocarcinoma
Abstract Non-small cell lung adenocarcinoma (LUAD) is a markedly heterogeneous disease, with its underlying molecular mechanisms and prognosis prediction presenting ongoing challenges. In this study, we integrated data from multiple public datasets, including TCGA, GSE31210, and GSE13213, encompassi...
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Nature Portfolio
2025-01-01
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| Online Access: | https://doi.org/10.1038/s41598-025-85471-8 |
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| author | Baozhen Wang Yichen Yin Anqi Wang Weidi Liu Jing Chen Tao Li |
| author_facet | Baozhen Wang Yichen Yin Anqi Wang Weidi Liu Jing Chen Tao Li |
| author_sort | Baozhen Wang |
| collection | DOAJ |
| description | Abstract Non-small cell lung adenocarcinoma (LUAD) is a markedly heterogeneous disease, with its underlying molecular mechanisms and prognosis prediction presenting ongoing challenges. In this study, we integrated data from multiple public datasets, including TCGA, GSE31210, and GSE13213, encompassing a total of 867 tumor samples. By employing Mendelian randomization (MR) analysis, machine learning techniques, and comprehensive bioinformatics approaches, we conducted an in-depth investigation into the molecular characteristics, prognostic markers, and potential therapeutic targets of LUAD. Our analysis identified 321 genes significantly associated with LUAD, with CENP-A, MCM7, and DLGAP5 emerging as highly connected nodes in network analyses. By performing correlation analysis and Cox regression analysis, we identified 26 prognostic genes and classified LUAD samples into two molecular subtypes with significantly distinct survival outcomes. The Random Survival Forest (RSF) model exhibited robust prognostic predictive capabilities across multiple independent cohorts (AUC > 0.75). Beyond merely predicting patient outcomes, this model also captures key features of the tumor immune microenvironment and potential therapeutic responses. Functional enrichment analysis revealed the complex interplay of cell cycle regulation, DNA repair, immune response, and metabolic reprogramming in the progression of LUAD. Furthermore, we observed a strong correlation between risk scores and the expression of specific cytokines, such as CCL17, CCR2, and CCL20, suggesting novel avenues for developing cytokine network-based therapeutic strategies. This study offers fresh insights into the molecular subtyping, prognostic prediction, and personalized therapeutic decision-making in LUAD, laying a critical foundation for future clinical applications and targeted therapy research. |
| format | Article |
| id | doaj-art-317a7ea9b7c243efaf4fdee016ff56f2 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-317a7ea9b7c243efaf4fdee016ff56f22025-01-12T12:16:01ZengNature PortfolioScientific Reports2045-23222025-01-0115111810.1038/s41598-025-85471-8SMR-guided molecular subtyping and machine learning model reveals novel prognostic biomarkers and therapeutic targets in non-small cell lung adenocarcinomaBaozhen Wang0Yichen Yin1Anqi Wang2Weidi Liu3Jing Chen4Tao Li5School of Clinical Medicine, Ningxia Medical UniversitySchool of Clinical Medicine, Ningxia Medical UniversityKey Laboratory of Fertility Preservation and Maintenance (Ningxia Medical University), Ministry of EducationSchool of Clinical Medicine, Ningxia Medical UniversityKey Laboratory of Fertility Preservation and Maintenance (Ningxia Medical University), Ministry of EducationDepartment of Surgical Oncology II, The General Hospital of Ningxia Medical UniversityAbstract Non-small cell lung adenocarcinoma (LUAD) is a markedly heterogeneous disease, with its underlying molecular mechanisms and prognosis prediction presenting ongoing challenges. In this study, we integrated data from multiple public datasets, including TCGA, GSE31210, and GSE13213, encompassing a total of 867 tumor samples. By employing Mendelian randomization (MR) analysis, machine learning techniques, and comprehensive bioinformatics approaches, we conducted an in-depth investigation into the molecular characteristics, prognostic markers, and potential therapeutic targets of LUAD. Our analysis identified 321 genes significantly associated with LUAD, with CENP-A, MCM7, and DLGAP5 emerging as highly connected nodes in network analyses. By performing correlation analysis and Cox regression analysis, we identified 26 prognostic genes and classified LUAD samples into two molecular subtypes with significantly distinct survival outcomes. The Random Survival Forest (RSF) model exhibited robust prognostic predictive capabilities across multiple independent cohorts (AUC > 0.75). Beyond merely predicting patient outcomes, this model also captures key features of the tumor immune microenvironment and potential therapeutic responses. Functional enrichment analysis revealed the complex interplay of cell cycle regulation, DNA repair, immune response, and metabolic reprogramming in the progression of LUAD. Furthermore, we observed a strong correlation between risk scores and the expression of specific cytokines, such as CCL17, CCR2, and CCL20, suggesting novel avenues for developing cytokine network-based therapeutic strategies. This study offers fresh insights into the molecular subtyping, prognostic prediction, and personalized therapeutic decision-making in LUAD, laying a critical foundation for future clinical applications and targeted therapy research.https://doi.org/10.1038/s41598-025-85471-8Non-small cell lung adenocarcinoma, LUADMendelian randomizationMolecular subtypesMachine learning prognostic modelMulti-omics integrative analysis |
| spellingShingle | Baozhen Wang Yichen Yin Anqi Wang Weidi Liu Jing Chen Tao Li SMR-guided molecular subtyping and machine learning model reveals novel prognostic biomarkers and therapeutic targets in non-small cell lung adenocarcinoma Scientific Reports Non-small cell lung adenocarcinoma, LUAD Mendelian randomization Molecular subtypes Machine learning prognostic model Multi-omics integrative analysis |
| title | SMR-guided molecular subtyping and machine learning model reveals novel prognostic biomarkers and therapeutic targets in non-small cell lung adenocarcinoma |
| title_full | SMR-guided molecular subtyping and machine learning model reveals novel prognostic biomarkers and therapeutic targets in non-small cell lung adenocarcinoma |
| title_fullStr | SMR-guided molecular subtyping and machine learning model reveals novel prognostic biomarkers and therapeutic targets in non-small cell lung adenocarcinoma |
| title_full_unstemmed | SMR-guided molecular subtyping and machine learning model reveals novel prognostic biomarkers and therapeutic targets in non-small cell lung adenocarcinoma |
| title_short | SMR-guided molecular subtyping and machine learning model reveals novel prognostic biomarkers and therapeutic targets in non-small cell lung adenocarcinoma |
| title_sort | smr guided molecular subtyping and machine learning model reveals novel prognostic biomarkers and therapeutic targets in non small cell lung adenocarcinoma |
| topic | Non-small cell lung adenocarcinoma, LUAD Mendelian randomization Molecular subtypes Machine learning prognostic model Multi-omics integrative analysis |
| url | https://doi.org/10.1038/s41598-025-85471-8 |
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