A Six-gene Prognostic Model Based on Neutrophil Extracellular Traps (NETs)-related Gene Signature for Lung Adenocarcinoma

Guiyan Mo; Xuan Long; Limin Cao; Yuling Tang; Yusheng Yan; Ting Guo

doi:10.2174/0113862073282003240119064337

A Six-gene Prognostic Model Based on Neutrophil Extracellular Traps (NETs)-related Gene Signature for Lung Adenocarcinoma

Comb Chem High Throughput Screen. 2024 Feb 13. doi: 10.2174/0113862073282003240119064337. Online ahead of print.

Authors

Guiyan Mo¹, Xuan Long², Limin Cao³, Yuling Tang¹, Yusheng Yan¹, Ting Guo¹

Affiliations

¹ Department of Respiratory and Critical Care Medicine, The First Hospital of Changsha, Changsha 410005, Hunan, China.
² Department of Respiratory and Critical Care Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai 200072, China.
³ Department of Respiratory Medicine, Lianyungang Second People's Hospital, Lianyungang 222000, Jiangsu, China.

PMID: 38357943
DOI: 10.2174/0113862073282003240119064337

Abstract

Background: Lung adenocarcinoma (LUAD) is one of the most common malignant cancers. Neutrophil extracellular traps (NETs) have been discovered to play a crucial role in the pathogenesis of LUAD. We aimed to establish an innovative prognostic model for LUAD based on the distinct expression patterns of NETs-related genes.

Methods: The TCGA LUAD dataset was utilized as the training set, while GSE31210, GSE37745, and GSE50081 were undertaken as the verification sets. The patients were grouped into clusters based on the expression signature of NETs-related genes. Differentially expressed genes between clusters were identified through the utilization of the random forest and LASSO algorithms. The NETs score model for LUAD prognosis was developed by multiplying the expression levels of specific genes with their corresponding LASSO coefficients and then summing them. The validity of the model was confirmed by analysis of the survival curves and ROC curves. Additionally, immune infiltration, GSEA, mutation analysis, and drug analysis were conducted. Silencing ABCC2 in A549 cells was achieved to investigate its effect.

Results: We identified six novel NETs-related genes, namely UPK1B, SFTA3, GGTLC1, SCGB3A1, ABCC2, and NTS, and developed a NETs score signature, which exhibited a significant correlation with the clinicopathological and immune traits of the LUAD patients. High-risk patients showed inhibition of immune-related processes. Mutation patterns exhibited variability among the different groups. AZD3759, lapatinib, and dasatinib have been identified as potential candidates for LUAD treatment. Moreover, the downregulation of ABCC2 resulted in the induction of apoptosis and suppression of migration and invasion in A549 cells.

Conclusion: Altogether, this study has identified a novel NET-score signature based on six novel NET-related genes to predict the prognosis of LUAD and ABCC2 and has also explored a new method for personalized chemo-/immuno-therapy of LUAD.

Keywords: ABCC2.; Lung adenocarcinoma; drug prediction; neutrophil extracellular traps; prognostic model; random forest.