Identification of a novel ADCC-related gene signature for predicting the prognosis and therapy response in lung adenocarcinoma
© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG..
BACKGROUND: Previous studies have largely neglected the role of ADCC in LUAD, and no study has systematically compiled ADCC-associated genes to create prognostic signatures.
METHODS: In this study, 1564 LUAD patients, 2057 NSCLC patients, and more than 5000 patients with various cancer types from diverse cohorts were included. R package ConsensusClusterPlus was utilized to classify patients into different subtypes. A number of machine-learning algorithms were used to construct the ADCCRS. GSVA and ClusterProfiler were used for enrichment analyses, and IOBR was used to quantify immune cell infiltration level. GISTIC2.0 and maftools were used to analyze the CNV and SNV data. The Oncopredict package was used to predict drug information based on the GDSC1. Three immunotherapy cohorts were used to evaluate patient response to immunotherapy. The Seurat package was used to process single-cell data, the AUCell package was used to calculate cells' geneset activity scores, and the Scissor algorithm was used to identify ADCCRS-associated cells.
RESULTS: Through unsupervised clustering, two distinct subtypes of LUAD were identified, each exhibiting distinct clinical characteristics. The ADCCRS, consisted of 16 genes, was constructed by integrated machine-learning methods. The prognostic power of ADCCRS was validated in 28 independent datasets. Further, ADCCRS shows better predictive abilities than 102 previously published signatures in predicting LUAD patients' survival. A nomogram incorporating ADCCRS and clinical features was constructed, demonstrating high predictive performance. ADCCRS positively correlates with patients' gene mutation, and integrated analysis of bulk and single-cell transcriptome data revealed the association of ADCCRS with TME modulators. Cells representing high-ADCCRS phenotype exhibited more malignant features. LUAD patients with high ADCCRS levels exhibited sensitivity to chemotherapy and targeted therapy, while displaying resistance to immunotherapy. In pan-cancer analysis, ADCCRS still exhibited significant prognostic value and was found to be a risk factor for most cancer patients.
CONCLUSIONS: ADCCRS offers a critical prognostic insight for patients with LUAD, shedding light on the tumor microenvironment and forecasting treatment responsiveness.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2024 |
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| Enthalten in: |
Inflammation research : official journal of the European Histamine Research Society ... [et al. - (2024) vom: 20. März |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Zhang, Liangyu [VerfasserIn] |
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Date Revised 20.03.2024 published: Print-Electronic Citation Status Publisher |
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| doi: |
10.1007/s00011-024-01871-y |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM369966694 |
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| 100 | 1 | |a Zhang, Liangyu |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Identification of a novel ADCC-related gene signature for predicting the prognosis and therapy response in lung adenocarcinoma |
| 264 | 1 | |c 2024 | |
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| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status Publisher | ||
| 520 | |a © 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG. | ||
| 520 | |a BACKGROUND: Previous studies have largely neglected the role of ADCC in LUAD, and no study has systematically compiled ADCC-associated genes to create prognostic signatures | ||
| 520 | |a METHODS: In this study, 1564 LUAD patients, 2057 NSCLC patients, and more than 5000 patients with various cancer types from diverse cohorts were included. R package ConsensusClusterPlus was utilized to classify patients into different subtypes. A number of machine-learning algorithms were used to construct the ADCCRS. GSVA and ClusterProfiler were used for enrichment analyses, and IOBR was used to quantify immune cell infiltration level. GISTIC2.0 and maftools were used to analyze the CNV and SNV data. The Oncopredict package was used to predict drug information based on the GDSC1. Three immunotherapy cohorts were used to evaluate patient response to immunotherapy. The Seurat package was used to process single-cell data, the AUCell package was used to calculate cells' geneset activity scores, and the Scissor algorithm was used to identify ADCCRS-associated cells | ||
| 520 | |a RESULTS: Through unsupervised clustering, two distinct subtypes of LUAD were identified, each exhibiting distinct clinical characteristics. The ADCCRS, consisted of 16 genes, was constructed by integrated machine-learning methods. The prognostic power of ADCCRS was validated in 28 independent datasets. Further, ADCCRS shows better predictive abilities than 102 previously published signatures in predicting LUAD patients' survival. A nomogram incorporating ADCCRS and clinical features was constructed, demonstrating high predictive performance. ADCCRS positively correlates with patients' gene mutation, and integrated analysis of bulk and single-cell transcriptome data revealed the association of ADCCRS with TME modulators. Cells representing high-ADCCRS phenotype exhibited more malignant features. LUAD patients with high ADCCRS levels exhibited sensitivity to chemotherapy and targeted therapy, while displaying resistance to immunotherapy. In pan-cancer analysis, ADCCRS still exhibited significant prognostic value and was found to be a risk factor for most cancer patients | ||
| 520 | |a CONCLUSIONS: ADCCRS offers a critical prognostic insight for patients with LUAD, shedding light on the tumor microenvironment and forecasting treatment responsiveness | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a ADCC | |
| 650 | 4 | |a Immune | |
| 650 | 4 | |a LUAD | |
| 650 | 4 | |a Machine-learning | |
| 650 | 4 | |a Prognosis | |
| 700 | 1 | |a Zhang, Xun |e verfasserin |4 aut | |
| 700 | 1 | |a Guan, Maohao |e verfasserin |4 aut | |
| 700 | 1 | |a Zeng, Jianshen |e verfasserin |4 aut | |
| 700 | 1 | |a Yu, Fengqiang |e verfasserin |4 aut | |
| 700 | 1 | |a Lai, Fancai |e verfasserin |4 aut | |
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