A 23-gene multi-omics signature predicts prognosis and treatment response in non-small cell lung cancer
Abstract Background We developed the first multi-omics prognostic signature integrating 19 programmed cell death (PCD) pathways and organelle functions (mitochondria, lysosomes, Golgi apparatus) to predict prognosis and immunotherapy response in non-small cell lung cancer (NSCLC). (2) Methods: By co...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2025-07-01
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| Series: | Discover Oncology |
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| Online Access: | https://doi.org/10.1007/s12672-025-03243-2 |
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| _version_ | 1849343454688051200 |
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| author | Yinxu Zhang Siwang Wang Xiaoyang Chen Guangyu Zhang Yuxi Wang Xiaomei Liu |
| author_facet | Yinxu Zhang Siwang Wang Xiaoyang Chen Guangyu Zhang Yuxi Wang Xiaomei Liu |
| author_sort | Yinxu Zhang |
| collection | DOAJ |
| description | Abstract Background We developed the first multi-omics prognostic signature integrating 19 programmed cell death (PCD) pathways and organelle functions (mitochondria, lysosomes, Golgi apparatus) to predict prognosis and immunotherapy response in non-small cell lung cancer (NSCLC). (2) Methods: By combining single-cell RNA-seq, bulk transcriptomics, and deep neural networks (DNN), we identified a 23-gene signature validated across four cohorts (AUC 0.696–0.812). Conducted MR analysis to explore causal links between signature genes and NSCLC incidence, providing biological insights. (3) Results: A prognostic signature was developed, including 23 prognostic genes related to 19 PCD patterns and three organelle functions. The signature demonstrated powerful performance in predicting NSCLC prognosis, immune in-filtration, and therapeutic response. Established DNN models showed high value in predicting risk score groupings of NSCLC. MR analysis for combined SNP information of the 23 prognostic genes suggested a link to the high incidence of NSCLC. Individual MR analysis showed that HIF1A and SQLE expression had a causal effect on NSCLC incidence. (4) Conclusion: This signature stratifies high-risk patients with immunosuppressive microenvironments and predicts enhanced sensitivity to gemcitabine and PD-1 inhibitors, offering a roadmap for personalized NSCLC management. |
| format | Article |
| id | doaj-art-7a8c554d583b4f70adf66f6cf6da4e10 |
| institution | Kabale University |
| issn | 2730-6011 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Oncology |
| spelling | doaj-art-7a8c554d583b4f70adf66f6cf6da4e102025-08-20T03:42:57ZengSpringerDiscover Oncology2730-60112025-07-0116112410.1007/s12672-025-03243-2A 23-gene multi-omics signature predicts prognosis and treatment response in non-small cell lung cancerYinxu Zhang0Siwang Wang1Xiaoyang Chen2Guangyu Zhang3Yuxi Wang4Xiaomei Liu5Department of Surgery, The First Affiliated Hospital of Jinzhou Medical UniversityDepartment of Surgery, The First Affiliated Hospital of Jinzhou Medical UniversityDepartment of Oncology, The First Affiliated Hospital of Jinzhou Medical UniversityDepartment of Oncology, The First Affiliated Hospital of Jinzhou Medical UniversityDepartment of Oncology, The First Affiliated Hospital of Jinzhou Medical UniversityDepartment of Oncology, The First Affiliated Hospital of Jinzhou Medical UniversityAbstract Background We developed the first multi-omics prognostic signature integrating 19 programmed cell death (PCD) pathways and organelle functions (mitochondria, lysosomes, Golgi apparatus) to predict prognosis and immunotherapy response in non-small cell lung cancer (NSCLC). (2) Methods: By combining single-cell RNA-seq, bulk transcriptomics, and deep neural networks (DNN), we identified a 23-gene signature validated across four cohorts (AUC 0.696–0.812). Conducted MR analysis to explore causal links between signature genes and NSCLC incidence, providing biological insights. (3) Results: A prognostic signature was developed, including 23 prognostic genes related to 19 PCD patterns and three organelle functions. The signature demonstrated powerful performance in predicting NSCLC prognosis, immune in-filtration, and therapeutic response. Established DNN models showed high value in predicting risk score groupings of NSCLC. MR analysis for combined SNP information of the 23 prognostic genes suggested a link to the high incidence of NSCLC. Individual MR analysis showed that HIF1A and SQLE expression had a causal effect on NSCLC incidence. (4) Conclusion: This signature stratifies high-risk patients with immunosuppressive microenvironments and predicts enhanced sensitivity to gemcitabine and PD-1 inhibitors, offering a roadmap for personalized NSCLC management.https://doi.org/10.1007/s12672-025-03243-2Non-small cell lung cancerProgrammed cell deathOrganelle functionSingle-cell analysisMachine learningPrognosis |
| spellingShingle | Yinxu Zhang Siwang Wang Xiaoyang Chen Guangyu Zhang Yuxi Wang Xiaomei Liu A 23-gene multi-omics signature predicts prognosis and treatment response in non-small cell lung cancer Discover Oncology Non-small cell lung cancer Programmed cell death Organelle function Single-cell analysis Machine learning Prognosis |
| title | A 23-gene multi-omics signature predicts prognosis and treatment response in non-small cell lung cancer |
| title_full | A 23-gene multi-omics signature predicts prognosis and treatment response in non-small cell lung cancer |
| title_fullStr | A 23-gene multi-omics signature predicts prognosis and treatment response in non-small cell lung cancer |
| title_full_unstemmed | A 23-gene multi-omics signature predicts prognosis and treatment response in non-small cell lung cancer |
| title_short | A 23-gene multi-omics signature predicts prognosis and treatment response in non-small cell lung cancer |
| title_sort | 23 gene multi omics signature predicts prognosis and treatment response in non small cell lung cancer |
| topic | Non-small cell lung cancer Programmed cell death Organelle function Single-cell analysis Machine learning Prognosis |
| url | https://doi.org/10.1007/s12672-025-03243-2 |
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