Identification of an Amino Acid Metabolism-Related Gene Signature for Predicting Prognosis in Lung Adenocarcinoma
Wuguang Chang, Hongmu Li, Chun Wu, Leqi Zhong, Tengfei Zhu, Zeng-Hao Chang, Wei Ou, Siyu Wang
Abstract
Dysregulation of amino acid metabolism (AAM) is an important factor in cancer progression. This study intended to study the prognostic value of AAM-related genes in lung adenocarcinoma (LUAD). Methods: The mRNA expression profiles of LUAD datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were applied as the training and validation sets. After identifying the differentially expressed AAM-related genes, an AAM-related gene signature (AAMRGS) was constructed and validated. Additionally, we systematically analyzed the differences in immune cell infiltration, biological pathways, immunotherapy response, and drug sensitivity between the two AAMRGS subgroups. Results: The prognosis-related signature was constructed on the grounds of key AAM-related genes. LUAD patients were divided into AAMRGS-high and -low groups. Patients in the two subgroups differed in prognosis, tumor microenvironment (TME), biological pathways, and sensitivity to chemotherapy and immunotherapy. The area under the receiver operating characteristics (ROC) and calibration curves showed good predictive ability for the nomogram. Analysis of immune cell infiltration revealed that the TME of the AAMRGS-low group was in a state of immune activation. Conclusion: We constructed an AAMRGS that could effectively predict prognosis and guide treatment strategies for patients with LUAD.