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Xie Y, Pan X, Wang Z, Ma H, Xu W, Huang H, Zhang J, Wang X, Lian C. Multi-omics identification of GPCR gene features in lung adenocarcinoma based on multiple machine learning combinations. J Cancer 2024; 15:776-795. [PMID: 38213730 PMCID: PMC10777041 DOI: 10.7150/jca.90990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 11/28/2023] [Indexed: 01/13/2024] Open
Abstract
Background: Lung adenocarcinoma is a common malignant tumor that ranks second in the world and has a high mortality rate. G protein-coupled receptors (GPCRs) have been reported to play an important role in cancer; however, G protein-coupled receptor-associated features have not been adequately investigated. Methods: In this study, GPCR-related genes were screened at single-cell and bulk transcriptome levels based on AUcell, single-sample gene set enrichment analysis (ssGSEA) and weighted gene co-expression network (WGCNA) analysis. And a new machine learning framework containing 10 machine learning algorithms and their multiple combinations was used to construct a consensus G protein-coupled receptor-related signature (GPCRRS). GPCRRS was validated in the training set and external validation set. We constructed GPCRRS-integrated nomogram clinical prognosis prediction tools. Multi-omics analyses included genomics, single-cell transcriptomics, and bulk transcriptomics to gain a more comprehensive understanding of prognostic features. We assessed the response of risk subgroups to immunotherapy and screened for personalized drugs targeting specific risk subgroups. Finally, the expression of key GPCRRS genes was verified by RT-qPCR. Results: In this study, we identified 10 GPCR-associated genes that were significantly associated with the prognosis of lung adenocarcinoma by single-cell transcriptome and bulk transcriptome. Univariate and multivariate showed that the survival rate was higher in low risk than in high risk, which also suggested that the model was an independent prognostic factor for LUAD. In addition, we observed significant differences in biological function, mutational landscape, and immune cell infiltration in the tumor microenvironment between high and low risk groups. Notably, immunotherapy was also relevant in the high and low risk groups. In addition, potential drugs targeting specific risk subgroups were identified. Conclusion: In this study, we constructed and validated a lung adenocarcinoma G protein-coupled receptor-related signature, which has an important role in predicting the prognosis of lung adenocarcinoma and the effect of immunotherapy. It is hypothesized that LDHA, GPX3 and DOCK4 are new potential targets for lung adenocarcinoma, which can achieve breakthroughs in prognosis prediction, targeted prevention and treatment of lung adenocarcinoma and provide important guidance for anti-tumor.
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Affiliation(s)
- Yiluo Xie
- Department of Clinical Medicine, Bengbu Medical College, Bengbu 233030, China
- Anhui Province Key Laboratory of Clinical and Preclinical Research in Respiratory Disease, Molecular Diagnosis Center pulmonary critical care medicine, First Affiliated Hospital of Bengbu Medical College, Bengbu, 233000, China
| | - Xinyu Pan
- Department of Medical Imaging, Bengbu Medical College, Bengbu 233030, China
| | - Ziqiang Wang
- Research Center of Clinical Laboratory Science, Bengbu Medical College, Bengbu 233030, China
| | - Hongyu Ma
- Department of Clinical Medicine, Bengbu Medical College, Bengbu 233030, China
| | - Wanjie Xu
- Department of Clinical Medicine, Bengbu Medical College, Bengbu 233030, China
| | - Hua Huang
- Research Center of Clinical Laboratory Science, Bengbu Medical College, Bengbu 233030, China
| | - Jing Zhang
- Department of Genetics, School of Life Sciences, Bengbu Medical College, Bengbu 233000, China
| | - Xiaojing Wang
- Anhui Province Key Laboratory of Clinical and Preclinical Research in Respiratory Disease, Molecular Diagnosis Center pulmonary critical care medicine, First Affiliated Hospital of Bengbu Medical College, Bengbu, 233000, China
| | - Chaoqun Lian
- Research Center of Clinical Laboratory Science, Bengbu Medical College, Bengbu 233030, China
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Ciccolini J, Milano G. Immune check points in cancer treatment: current challenges and perspectives. Br J Cancer 2023; 129:1365-1366. [PMID: 37898723 PMCID: PMC10628071 DOI: 10.1038/s41416-023-02478-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/30/2023] Open
Affiliation(s)
- Joseph Ciccolini
- SMARTc COMPO, Inria Inserm U1068 Centre de Recherche en Cancérologie de Marseille, Marseille, France.
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