A Machine-Learning Approach to Developing a Predictive Signature Based on Transcriptome Profiling of Ground-Glass Opacities for Accurate Classification and Exploring the Immune Microenvironment of Early-Stage LUAD
Screening for early-stage lung cancer with low-dose computed tomography is recommended for high-risk populations; consequently, the incidence of pure ground-glass opacity (pGGO) is increasing. Ground-glass opacity (GGO) is considered the appearance of early lung cancer, and there remains an unmet cl...
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| Format: | Article |
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Frontiers Media S.A.
2022-05-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2022.872387/full |
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| author | Zhenyu Zhao Zhenyu Zhao Wei Yin Wei Yin Xiong Peng Xiong Peng Qidong Cai Qidong Cai Boxue He Boxue He Shuai Shi Shuai Shi Weilin Peng Weilin Peng Guangxu Tu Guangxu Tu Yunping Li Dateng Li Yongguang Tao Yongguang Tao Yongguang Tao Yongguang Tao Muyun Peng Muyun Peng Xiang Wang Xiang Wang Fenglei Yu Fenglei Yu |
| author_facet | Zhenyu Zhao Zhenyu Zhao Wei Yin Wei Yin Xiong Peng Xiong Peng Qidong Cai Qidong Cai Boxue He Boxue He Shuai Shi Shuai Shi Weilin Peng Weilin Peng Guangxu Tu Guangxu Tu Yunping Li Dateng Li Yongguang Tao Yongguang Tao Yongguang Tao Yongguang Tao Muyun Peng Muyun Peng Xiang Wang Xiang Wang Fenglei Yu Fenglei Yu |
| author_sort | Zhenyu Zhao |
| collection | DOAJ |
| description | Screening for early-stage lung cancer with low-dose computed tomography is recommended for high-risk populations; consequently, the incidence of pure ground-glass opacity (pGGO) is increasing. Ground-glass opacity (GGO) is considered the appearance of early lung cancer, and there remains an unmet clinical need to understand the pathology of small GGO (<1 cm in diameter). The objective of this study was to use the transcriptome profiling of pGGO specimens <1 cm in diameter to construct a pGGO-related gene risk signature to predict the prognosis of early-stage lung adenocarcinoma (LUAD) and explore the immune microenvironment of GGO. pGGO-related differentially expressed genes (DEGs) were screened to identify prognostic marker genes with two machine learning algorithms. A 15-gene risk signature was constructed from the DEGs that were shared between the algorithms. Risk scores were calculated using the regression coefficients for the pGGO-related DEGs. Patients with Stage I/II LUAD or Stage IA LUAD and high-risk scores had a worse prognosis than patients with low-risk scores. The prognosis of high-risk patients with Stage IA LUAD was almost identical to that of patients with Stage II LUAD, suggesting that treatment strategies for patients with Stage II LUAD may be beneficial in high-risk patients with Stage IA LUAD. pGGO-related DEGs were mainly enriched in immune-related pathways. Patients with high-risk scores and high tumor mutation burden had a worse prognosis and may benefit from immunotherapy. A nomogram was constructed to facilitate the clinical application of the 15-gene risk signature. Receiver operating characteristic curves and decision curve analysis validated the predictive ability of the nomogram in patients with Stage I LUAD in the TCGA-LUAD cohort and GEO datasets. |
| first_indexed | 2024-04-12T17:23:14Z |
| format | Article |
| id | doaj.art-2ccd6d10ec35421dac075298e3b6e39f |
| institution | Directory Open Access Journal |
| issn | 1664-3224 |
| language | English |
| last_indexed | 2024-04-12T17:23:14Z |
| publishDate | 2022-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj.art-2ccd6d10ec35421dac075298e3b6e39f2022-12-22T03:23:23ZengFrontiers Media S.A.Frontiers in Immunology1664-32242022-05-011310.3389/fimmu.2022.872387872387A Machine-Learning Approach to Developing a Predictive Signature Based on Transcriptome Profiling of Ground-Glass Opacities for Accurate Classification and Exploring the Immune Microenvironment of Early-Stage LUADZhenyu Zhao0Zhenyu Zhao1Wei Yin2Wei Yin3Xiong Peng4Xiong Peng5Qidong Cai6Qidong Cai7Boxue He8Boxue He9Shuai Shi10Shuai Shi11Weilin Peng12Weilin Peng13Guangxu Tu14Guangxu Tu15Yunping Li16Dateng Li17Yongguang Tao18Yongguang Tao19Yongguang Tao20Yongguang Tao21Muyun Peng22Muyun Peng23Xiang Wang24Xiang Wang25Fenglei Yu26Fenglei Yu27Department of Thoracic Surgery, The Second Xiangya Hospital of Central South University, Changsha, ChinaHunan Key Laboratory of Early Diagnosis and Precise Treatment of Lung Cancer, The Second Xiangya Hospital of Central South University, Changsha, ChinaDepartment of Thoracic Surgery, The Second Xiangya Hospital of Central South University, Changsha, ChinaHunan Key Laboratory of Early Diagnosis and Precise Treatment of Lung Cancer, The Second Xiangya Hospital of Central South University, Changsha, ChinaDepartment of Thoracic Surgery, The Second Xiangya Hospital of Central South University, Changsha, ChinaHunan Key Laboratory of Early Diagnosis and Precise Treatment of Lung Cancer, The Second Xiangya Hospital of Central South University, Changsha, ChinaDepartment of Thoracic Surgery, The Second Xiangya Hospital of Central South University, Changsha, ChinaHunan Key Laboratory of Early Diagnosis and Precise Treatment of Lung Cancer, The Second Xiangya Hospital of Central South University, Changsha, ChinaDepartment of Thoracic Surgery, The Second Xiangya Hospital of Central South University, Changsha, ChinaHunan Key Laboratory of Early Diagnosis and Precise Treatment of Lung Cancer, The Second Xiangya Hospital of Central South University, Changsha, ChinaDepartment of Thoracic Surgery, The Second Xiangya Hospital of Central South University, Changsha, ChinaHunan Key Laboratory of Early Diagnosis and Precise Treatment of Lung Cancer, The Second Xiangya Hospital of Central South University, Changsha, ChinaDepartment of Thoracic Surgery, The Second Xiangya Hospital of Central South University, Changsha, ChinaHunan Key Laboratory of Early Diagnosis and Precise Treatment of Lung Cancer, The Second Xiangya Hospital of Central South University, Changsha, ChinaDepartment of Thoracic Surgery, The Second Xiangya Hospital of Central South University, Changsha, ChinaHunan Key Laboratory of Early Diagnosis and Precise Treatment of Lung Cancer, The Second Xiangya Hospital of Central South University, Changsha, ChinaDepartment of Ophthalmology, The Second Xiangya Hospital of Central South University, Changsha, ChinaWhite Plains, NY, United StatesDepartment of Thoracic Surgery, The Second Xiangya Hospital of Central South University, Changsha, ChinaHunan Key Laboratory of Early Diagnosis and Precise Treatment of Lung Cancer, The Second Xiangya Hospital of Central South University, Changsha, ChinaKey Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Department of Pathology, Xiangya Hospital, Central South University, Changsha, ChinaNational Health Council (NHC) Key Laboratory of Carcinogenesis (Central South University), Cancer Research Institute and School of Basic Medicine, Central South University, Changsha, ChinaDepartment of Thoracic Surgery, The Second Xiangya Hospital of Central South University, Changsha, ChinaHunan Key Laboratory of Early Diagnosis and Precise Treatment of Lung Cancer, The Second Xiangya Hospital of Central South University, Changsha, ChinaDepartment of Thoracic Surgery, The Second Xiangya Hospital of Central South University, Changsha, ChinaHunan Key Laboratory of Early Diagnosis and Precise Treatment of Lung Cancer, The Second Xiangya Hospital of Central South University, Changsha, ChinaDepartment of Thoracic Surgery, The Second Xiangya Hospital of Central South University, Changsha, ChinaHunan Key Laboratory of Early Diagnosis and Precise Treatment of Lung Cancer, The Second Xiangya Hospital of Central South University, Changsha, ChinaScreening for early-stage lung cancer with low-dose computed tomography is recommended for high-risk populations; consequently, the incidence of pure ground-glass opacity (pGGO) is increasing. Ground-glass opacity (GGO) is considered the appearance of early lung cancer, and there remains an unmet clinical need to understand the pathology of small GGO (<1 cm in diameter). The objective of this study was to use the transcriptome profiling of pGGO specimens <1 cm in diameter to construct a pGGO-related gene risk signature to predict the prognosis of early-stage lung adenocarcinoma (LUAD) and explore the immune microenvironment of GGO. pGGO-related differentially expressed genes (DEGs) were screened to identify prognostic marker genes with two machine learning algorithms. A 15-gene risk signature was constructed from the DEGs that were shared between the algorithms. Risk scores were calculated using the regression coefficients for the pGGO-related DEGs. Patients with Stage I/II LUAD or Stage IA LUAD and high-risk scores had a worse prognosis than patients with low-risk scores. The prognosis of high-risk patients with Stage IA LUAD was almost identical to that of patients with Stage II LUAD, suggesting that treatment strategies for patients with Stage II LUAD may be beneficial in high-risk patients with Stage IA LUAD. pGGO-related DEGs were mainly enriched in immune-related pathways. Patients with high-risk scores and high tumor mutation burden had a worse prognosis and may benefit from immunotherapy. A nomogram was constructed to facilitate the clinical application of the 15-gene risk signature. Receiver operating characteristic curves and decision curve analysis validated the predictive ability of the nomogram in patients with Stage I LUAD in the TCGA-LUAD cohort and GEO datasets.https://www.frontiersin.org/articles/10.3389/fimmu.2022.872387/fullGGO (ground-glass opacity)LUADTCGAGEOprognosis |
| spellingShingle | Zhenyu Zhao Zhenyu Zhao Wei Yin Wei Yin Xiong Peng Xiong Peng Qidong Cai Qidong Cai Boxue He Boxue He Shuai Shi Shuai Shi Weilin Peng Weilin Peng Guangxu Tu Guangxu Tu Yunping Li Dateng Li Yongguang Tao Yongguang Tao Yongguang Tao Yongguang Tao Muyun Peng Muyun Peng Xiang Wang Xiang Wang Fenglei Yu Fenglei Yu A Machine-Learning Approach to Developing a Predictive Signature Based on Transcriptome Profiling of Ground-Glass Opacities for Accurate Classification and Exploring the Immune Microenvironment of Early-Stage LUAD Frontiers in Immunology GGO (ground-glass opacity) LUAD TCGA GEO prognosis |
| title | A Machine-Learning Approach to Developing a Predictive Signature Based on Transcriptome Profiling of Ground-Glass Opacities for Accurate Classification and Exploring the Immune Microenvironment of Early-Stage LUAD |
| title_full | A Machine-Learning Approach to Developing a Predictive Signature Based on Transcriptome Profiling of Ground-Glass Opacities for Accurate Classification and Exploring the Immune Microenvironment of Early-Stage LUAD |
| title_fullStr | A Machine-Learning Approach to Developing a Predictive Signature Based on Transcriptome Profiling of Ground-Glass Opacities for Accurate Classification and Exploring the Immune Microenvironment of Early-Stage LUAD |
| title_full_unstemmed | A Machine-Learning Approach to Developing a Predictive Signature Based on Transcriptome Profiling of Ground-Glass Opacities for Accurate Classification and Exploring the Immune Microenvironment of Early-Stage LUAD |
| title_short | A Machine-Learning Approach to Developing a Predictive Signature Based on Transcriptome Profiling of Ground-Glass Opacities for Accurate Classification and Exploring the Immune Microenvironment of Early-Stage LUAD |
| title_sort | machine learning approach to developing a predictive signature based on transcriptome profiling of ground glass opacities for accurate classification and exploring the immune microenvironment of early stage luad |
| topic | GGO (ground-glass opacity) LUAD TCGA GEO prognosis |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2022.872387/full |
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