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Machine learning framework develops neutrophil extracellular traps model for clinical outcome and immunotherapy response in lung adenocarcinoma

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Abstract

Neutrophil extracellular traps (NETs) are novel inflammatory cell death in neutrophils. Emerging studies demonstrated NETs contributed to cancer progression and metastases in multiple ways. This study intends to provide a prognostic NETs signature and therapeutic target for lung adenocarcinoma (LUAD) patients. Consensus cluster analysis performed by 38 reported NET-related genes in TCGA-LUAD cohorts. Then, WGCNA network was conducted to investigate characteristics genes in clusters. Seven machine learning algorithms were assessed for training of the model, the optimal model was picked by C-index and 1-, 3-, 5-year ROC value. Then, we constructed a NETs signature to predict the overall survival of LUAD patients. Moreover, multi-omics validation was performed based on NETs signature. Finally, we constructed stable knockdown critical gene LUAD cell lines to verify biological functions of Phospholipid Scramblase 1 (PLSCR1) in vitro and in vivo. Two NETs-related clusters were identified in LUAD patients. Among them, C2 cluster was provided as “hot” tumor phenotype and exhibited a better prognosis. Then, WGCNA network identified 643 characteristic genes in C2 cluster. Then, Coxboost algorithm proved its optimal performance and provided a prognostic NETs signature. Multi-omics revealed that NETs signature was involved in an immunosuppressive microenvironment and predicted immunotherapy efficacy. In vitro and in vivo experiments demonstrated that knockdown of PLSCR1 inhibited tumor growth and EMT ability. Besides, cocultural assay indicated that the knockdown of PLSCR1 impaired the ability of neutrophils to generate NETs. Finally, tissue microarray (TMA) for LUAD patients verified the prognostic value of PLSCR1 expression. In this study, we focus on emerging hot topic NETs in LUAD. We provide a prognostic NETs signature and identify PLSCR1 with multiple roles in LUAD. This work can contribute to risk stratification and screen novel therapeutic targets for LUAD patients.

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Data availability

Public datasets were analyzed in this study. TCGA-LUAD cohort was downloaded from TCGA database (http://cancergenome.nih.gov/). GSE50081, GSE72094, GSE31210, GSE81089, GSE91061, and GSE100797 were downloaded from the GEO database(http://www.ncbi.nlm.nih.gov/geo/). PRJNA482620, IMvigor210, PRJEB23709, Nathanson’s cohort, phs000452, PRJEB23709, and PRJEB25780 were obtained from Tumor Immunotherapy Gene Expression Resource databases (http://tiger.canceromics.org/).

Material availability

The original data and material were available when required.

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Acknowledgements

We thank all the healthy donors who took part in this study. We thank nurses in the Department of Oncology at PLA General Hospital for their assistance in sample collection.

Funding

This research fund by the National Natural Science Foundation of China under Grant No. 81902912, the Military Health Special Research Project under grant 20BJZ37, and the key project of the National Health Commission under Grant No. GWJJ2021100304.

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Author notes

  1. A. Xuan Han, B. Yaping Long and C. Yao Li have equally contributed to this work.

Authors and Affiliations

  1. Department of General Surgery, Aerospace Central Hospital, 15 Yuquan Road, Haidian District, Beijing, China

    A. Xuan Han

  2. Department of Medical Oncology, Senior Department of Oncology, Fengtai District, The Fifth Medical Center of PLA General Hospital, No. 100, West Fourth Ring Middle Road, Beijing, 100039, China

    B. Yaping Long, C. Yao Li, D. Di Huang, E. Qi Xiong, Qiaowei Liu, G. Bo Yang & H. Yi Hu

  3. School of Medicine, Nankai University, Nankai District, 94 Weijin Road, Tianjin, 300071, China

    B. Yaping Long & H. Yi Hu

  4. Medical School of Chinese People’s Liberation Army (PLA), Haidian District, 28 Fuxing Road, Beijing, 100853, People’s Republic of China

    C. Yao Li & H. Yi Hu

  5. Institute of Oncology, The First Medical Center of Chinese, PLA General Hospital, Beijing, 100853, China

    F. Jinfeng Li, G. Liangliang Wu & H. Yi Hu

  6. Department of Emergency, Senior Department of Oncology, The Fifth Medical Center of PLA General Hospital, 8 Dongdajie Road, Fengtai District, Beijing, 100071, China

    Qiaowei Liu

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  1. A. Xuan Han
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Contributions

Xuan Han: data curation, writing–original draft. Yaping Long: methodology, editing, validation. Yao Li: visualization, software. Qi Xiong: editing. Jinfeng Li: IHC assistant. Liangliang Wu: cell cultural guidance. Qiaowei Liu: conceptualization. Bo Yang: conceptualization, writing review and editing. Yi Hu: check and approve.

Corresponding authors

Correspondence to Qiaowei Liu, G. Bo Yang or H. Yi Hu.

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The authors declare that they have no known competing financial interests in this paper.

Ethics approval and consent to participate

This study was approved by the ethics committee of the Chinese PLA General Hospital [No. IACUC-DWZX-2023–524]. This study’s healthy donors signed informed consent before blood sample collection. The blood sample used in this study followed the principles of the Helsinki Declaration.

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Han, A.X., Long, B.Y., Li, C.Y. et al. Machine learning framework develops neutrophil extracellular traps model for clinical outcome and immunotherapy response in lung adenocarcinoma. Apoptosis (2024). https://doi.org/10.1007/s10495-024-01947-4

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