A 23-gene multi-omics signature predicts prognosis and treatment response in non-small cell lung cancer

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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Bibliographic Details
Main Authors: Yinxu Zhang, Siwang Wang, Xiaoyang Chen, Guangyu Zhang, Yuxi Wang, Xiaomei Liu
Format: Article
Language:English
Published: Springer 2025-07-01
Series:Discover Oncology
Subjects:
Non-small cell lung cancer
Programmed cell death
Organelle function
Single-cell analysis
Machine learning
Prognosis
Online Access:https://doi.org/10.1007/s12672-025-03243-2
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Summary: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.
ISSN:2730-6011

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