A Six-gene Prognostic Model Based on Neutrophil Extracellular Traps (NETs)-related Gene Signature for Lung Adenocarcinoma
| Dublin Core | PKP Metadata Items | Metadata for this Document | |
| 1. | Title | Title of document | A Six-gene Prognostic Model Based on Neutrophil Extracellular Traps (NETs)-related Gene Signature for Lung Adenocarcinoma |
| 2. | Creator | Author's name, affiliation, country | Guiyan Mo; Department of Respiratory and Critical Care Medicine, The Affiliated Changsha Hospital of Xiangya School of Medicine, Central South University |
| 2. | Creator | Author's name, affiliation, country | Xuan Long; Department of Respiratory and Critical Care Medicine, Shanghai Tenth Peoples Hospital, Tongji University School of Medicine |
| 2. | Creator | Author's name, affiliation, country | Limin Cao; Department of Respiratory Medicine, Lianyungang Second People's Hospital |
| 2. | Creator | Author's name, affiliation, country | Yuling Tang; Department of Respiratory and Critical Care Medicine, The Affiliated Changsha Hospital of Xiangya School of Medicine, Central South University |
| 2. | Creator | Author's name, affiliation, country | Yusheng Yan; Department of Respiratory and Critical Care Medicine, The Affiliated Changsha Hospital of Xiangya School of Medicine, Central South University |
| 2. | Creator | Author's name, affiliation, country | Ting Guo; Department of Respiratory and Critical Care Medicine, The Affiliated Changsha Hospital of Xiangya School of Medicine, Central South University |
| 3. | Subject | Discipline(s) | |
| 3. | Subject | Keyword(s) | Lung adenocarcinoma; neutrophil extracellular traps; prognostic model; drug prediction; random forest; ABCC2. |
| 4. | Description | 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. |
| 5. | Publisher | Organizing agency, location | Bentham Science |
| 6. | Contributor | Sponsor(s) | |
| 7. | Date | (DD-MM-YYYY) | 01.01.2024 |
| 8. | Type | Status & genre | Peer-reviewed Article |
| 8. | Type | Type | Research Article |
| 9. | Format | File format | |
| 10. | Identifier | Uniform Resource Identifier | https://rjraap.com/1386-2073/article/view/645264 |
| 10. | Identifier | Digital Object Identifier (DOI) | 10.2174/0113862073282003240119064337 |
| 11. | Source | Title; vol., no. (year) | Combinatorial Chemistry & High Throughput Screening; Vol 27, No 13 (2024) |
| 12. | Language | English=en | |
| 13. | Relation | Supp. Files | |
| 14. | Coverage | Geo-spatial location, chronological period, research sample (gender, age, etc.) | |
| 15. | Rights | Copyright and permissions |
Copyright (c) 2024 Bentham Science Publishers |