Multi-omics identification of GPCR gene features in lung adenocarcinoma based on multiple machine learning combinations
Yiluo Xie, Xinyu Pan, Ziqiang Wang, Hongyu Ma, Wanjie Xu, Hua Huang, Jing Zhang, Xiaojing Wang, Chaoqun Lian
Abstract
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.
Topics & Concepts
TranscriptomeNomogramComputational biologyUnivariateAdenocarcinomaBiologyBioinformaticsGeneMultivariate statisticsOncologyMedicineComputer scienceGene expressionCancerMachine learningGeneticsFerroptosis and cancer prognosisCancer Immunotherapy and BiomarkersCancer Genomics and Diagnostics