ISSN: 1837-9664Journal of Cancer
Global reach, higher impact
J Cancer 2024; 15(3):776-795. doi:10.7150/jca.90990 This issue Cite
Research Paper
Multi-omics identification of GPCR gene features in lung adenocarcinoma based on multiple machine learning combinations
1. Department of Clinical Medicine, Bengbu Medical College, Bengbu 233030, China.
2. Department of Medical Imaging, Bengbu Medical College, Bengbu 233030, China.
3. Research Center of Clinical Laboratory Science, Bengbu Medical College, Bengbu 233030, China.
4. Department of Genetics, School of Life Sciences, Bengbu Medical College, Bengbu 233000, China.
5. 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.
*Contributed equally.
#Contributed equally.
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.
Keywords: Lung adenocarcinoma, G-protein-coupled receptors, Multi-omics, Single-cell RNA-seq, Prognosis, Immunotherapy efficacy, Machine learning
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