Identification of a novel autophagy signature for predicting survival in patients with lung adenocarcinoma
Background Lung adenocarcinoma (LUAD) is the most commonhistological lung cancer subtype, with an overall five-year survivalrate of only 17%. In this study, we aimed to identify autophagy-related genes (ARGs) and develop an LUAD prognostic signature. Methods In this study, we obtained ARGs from thre...
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PeerJ Inc.
2021-04-01
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| author | Jin Duan Youming Lei Guoli Lv Yinqiang Liu Wei Zhao Qingmei Yang Xiaona Su Zhijian Song Leilei Lu Yunfei Shi |
| author_facet | Jin Duan Youming Lei Guoli Lv Yinqiang Liu Wei Zhao Qingmei Yang Xiaona Su Zhijian Song Leilei Lu Yunfei Shi |
| author_sort | Jin Duan |
| collection | DOAJ |
| description | Background Lung adenocarcinoma (LUAD) is the most commonhistological lung cancer subtype, with an overall five-year survivalrate of only 17%. In this study, we aimed to identify autophagy-related genes (ARGs) and develop an LUAD prognostic signature. Methods In this study, we obtained ARGs from three databases and downloaded gene expression profiles from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. We used TCGA-LUAD (n = 490) for a training and testing dataset, and GSE50081 (n = 127) as the external validation dataset.The least absolute shrinkage and selection operator (LASSO) Cox and multivariate Cox regression models were used to generate an autophagy-related signature. We performed gene set enrichment analysis (GSEA) and immune cell analysis between the high- and low-risk groups. A nomogram was built to guide the individual treatment for LUAD patients. Results We identified a total of 83 differentially expressed ARGs (DEARGs) from the TCGA-LUAD dataset, including 33 upregulated DEARGs and 50 downregulated DEARGs, both with thresholds of adjusted P < 0.05 and |Fold change| > 1.5. Using LASSO and multivariate Cox regression analyses, we identified 10 ARGs that we used to build a prognostic signature with areas under the curve (AUCs) of 0.705, 0.715, and 0.778 at 1, 3, and 5 years, respectively. Using the risk score formula, the LUAD patients were divided into low- or high-risk groups. Our GSEA results suggested that the low-risk group were enriched in metabolism and immune-related pathways, while the high-risk group was involved in tumorigenesis and tumor progression pathways. Immune cell analysis revealed that, when compared to the high-risk group, the low-risk group had a lower cell fraction of M0- and M1- macrophages, and higher CD4 and PD-L1 expression levels. Conclusion Our identified robust signature may provide novel insight into underlying autophagy mechanisms as well as therapeutic strategies for LUAD treatment. |
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| spelling | doaj.art-df3fedf7ff304de1b16723f458122a6e2023-12-03T10:52:30ZengPeerJ Inc.PeerJ2167-83592021-04-019e1107410.7717/peerj.11074Identification of a novel autophagy signature for predicting survival in patients with lung adenocarcinomaJin Duan0Youming Lei1Guoli Lv2Yinqiang Liu3Wei Zhao4Qingmei Yang5Xiaona Su6Zhijian Song7Leilei Lu8Yunfei Shi9Department of Geriatric Thoracic Surgery, The First Hospital of Kunming Medical University, Kunming City, Yunnan Province, P.R. ChinaDepartment of Geriatric Thoracic Surgery, The First Hospital of Kunming Medical University, Kunming City, Yunnan Province, P.R. ChinaDepartment of Geriatric Thoracic Surgery, The First Hospital of Kunming Medical University, Kunming City, Yunnan Province, P.R. ChinaDepartment of Geriatric Thoracic Surgery, The First Hospital of Kunming Medical University, Kunming City, Yunnan Province, P.R. ChinaDepartment of Geriatric Thoracic Surgery, The First Hospital of Kunming Medical University, Kunming City, Yunnan Province, P.R. ChinaDepartment of Geriatric Thoracic Surgery, The First Hospital of Kunming Medical University, Kunming City, Yunnan Province, P.R. ChinaDepartment of Cancer Center, Daping Hospital, Army Medical University, Chongqing, ChinaOrigimed Co. Ltd., Shanghai, P.R. ChinaOrigimed Co. Ltd., Shanghai, P.R. ChinaDepartment of Geriatric Thoracic Surgery, The First Hospital of Kunming Medical University, Kunming City, Yunnan Province, P.R. ChinaBackground Lung adenocarcinoma (LUAD) is the most commonhistological lung cancer subtype, with an overall five-year survivalrate of only 17%. In this study, we aimed to identify autophagy-related genes (ARGs) and develop an LUAD prognostic signature. Methods In this study, we obtained ARGs from three databases and downloaded gene expression profiles from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. We used TCGA-LUAD (n = 490) for a training and testing dataset, and GSE50081 (n = 127) as the external validation dataset.The least absolute shrinkage and selection operator (LASSO) Cox and multivariate Cox regression models were used to generate an autophagy-related signature. We performed gene set enrichment analysis (GSEA) and immune cell analysis between the high- and low-risk groups. A nomogram was built to guide the individual treatment for LUAD patients. Results We identified a total of 83 differentially expressed ARGs (DEARGs) from the TCGA-LUAD dataset, including 33 upregulated DEARGs and 50 downregulated DEARGs, both with thresholds of adjusted P < 0.05 and |Fold change| > 1.5. Using LASSO and multivariate Cox regression analyses, we identified 10 ARGs that we used to build a prognostic signature with areas under the curve (AUCs) of 0.705, 0.715, and 0.778 at 1, 3, and 5 years, respectively. Using the risk score formula, the LUAD patients were divided into low- or high-risk groups. Our GSEA results suggested that the low-risk group were enriched in metabolism and immune-related pathways, while the high-risk group was involved in tumorigenesis and tumor progression pathways. Immune cell analysis revealed that, when compared to the high-risk group, the low-risk group had a lower cell fraction of M0- and M1- macrophages, and higher CD4 and PD-L1 expression levels. Conclusion Our identified robust signature may provide novel insight into underlying autophagy mechanisms as well as therapeutic strategies for LUAD treatment.https://peerj.com/articles/11074.pdfLung adenocarcinomaLASSO Cox regressionThe Cancer Genome AtlasGene set enrichment analysisImmune cell analysisAutophagy |
| spellingShingle | Jin Duan Youming Lei Guoli Lv Yinqiang Liu Wei Zhao Qingmei Yang Xiaona Su Zhijian Song Leilei Lu Yunfei Shi Identification of a novel autophagy signature for predicting survival in patients with lung adenocarcinoma PeerJ Lung adenocarcinoma LASSO Cox regression The Cancer Genome Atlas Gene set enrichment analysis Immune cell analysis Autophagy |
| title | Identification of a novel autophagy signature for predicting survival in patients with lung adenocarcinoma |
| title_full | Identification of a novel autophagy signature for predicting survival in patients with lung adenocarcinoma |
| title_fullStr | Identification of a novel autophagy signature for predicting survival in patients with lung adenocarcinoma |
| title_full_unstemmed | Identification of a novel autophagy signature for predicting survival in patients with lung adenocarcinoma |
| title_short | Identification of a novel autophagy signature for predicting survival in patients with lung adenocarcinoma |
| title_sort | identification of a novel autophagy signature for predicting survival in patients with lung adenocarcinoma |
| topic | Lung adenocarcinoma LASSO Cox regression The Cancer Genome Atlas Gene set enrichment analysis Immune cell analysis Autophagy |
| url | https://peerj.com/articles/11074.pdf |
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