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Prognostic, oncogenic roles, and pharmacogenomic features of AMD1 in hepatocellular carcinoma

肝细胞癌 列线图 肿瘤科 医学 药物基因组学 比例危险模型 内科学 癌症研究 药理学
作者
Youliang Zhou,Yi Zhou,Jing Hu,Yao Xiao,Yan Zhou,Liping Yu
出处
期刊:Cancer Cell International [Springer Nature]
卷期号:24 (1)
标识
DOI:10.1186/s12935-024-03593-x
摘要

AMD1 is the gene encoding S-adenosylmethionine decarboxylase 1. How AMD1 affects the prognosis of hepatocellular carcinoma (HCC) patients is unclear. Using the Cancer Genome Atlas (TCGA) liver hepatocellular carcinoma datasets, gene enrichment and immunological traits were compared between groups with high and low AMD1 expression. After altering AMD1 expression in HCC cells, cell viability, the clonal formation rate, and migration and invasion ability were detected. Univariate Cox regression analysis and Pearson correlation were used to screen for AMD1-related genes (ARGs). Multidimensional bioinformatic algorithms were utilized to establish a risk score model for ARGs. AMD1 expression was notably increased in the majority of cancer types. High AMD1 expression was associated with adverse outcomes and poorer immunotherapy response in HCC patients. AMD1 exhibited higher expression levels in HCC cell lines. The efficient inhibition of HCC cell proliferation, migration, and invasion in vitro can be achieved through the downregulation of AMD1. The AMD1-related risk score was constructed with the expression of 9 ARGs, and demonstrated high predictive efficacy in multiple validation cohorts. Patients with high risk scores exhibited greater resistance to classical chemotherapy drugs. The nomogram, which consists of age, stage, and the AMD1-related risk score, was used to calculate the probability of survival for each individual. The present study indicates that AMD1 functions as a potential role in HCC progression and may serve as a therapeutic target in HCC. This study constructed a novel AMD1-related scoring system for predicting the prognosis and treatment responsiveness of patients with HCC, enabling the prediction of prognosis and identification of potential treatment targets.
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