Zhang Tianyue, Song Xiaoxiao, Qiao Jie, Zhu Ruiliang, Ren Yuezhong, Shan Peng-Fei
Department of Endocrinology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.
Front Med (Lausanne). 2022 May 16;9:856606. doi: 10.3389/fmed.2022.856606. eCollection 2022.
The impact of hypoxia on ferroptosis is important in cancer proliferation, but no predictive model combining hypoxia and ferroptosis for adrenocortical carcinoma (ACC) has been reported. The purpose of this study was to construct a predictive model based on hypoxia- and ferroptosis-related gene expression in ACC.
We assessed hypoxia- and ferroptosis-related gene expression using data from 79 patients with ACC in The Cancer Genome Atlas (TCGA). Then, a predictive model was constructed to stratify patient survival using least absolute contraction and selection operation regression. Gene expression profiles of patients with ACC in the Gene Expression Omnibus (GEO) database were used to verify the predictive model.
Based on hypoxia-related gene expression, 79 patients with ACC in the TCGA database were divided into three molecular subtypes (C1, C2, and C3) with different clinical outcomes. Patients with the C3 subtype had the shortest survival. Ferroptosis-related genes exhibited distinct expression patterns in the three subtypes. A predictive model combining hypoxia- and ferroptosis-related gene expression was constructed. A nomogram was constructed using age, sex, tumor stage, and the predictive gene model. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses revealed that the gene signature was mainly related to the cell cycle and organelle fission.
This hypoxia-and ferroptosis-related gene signature displayed excellent predictive performance for ACC and could serve as an emerging source of novel therapeutic targets in ACC.
缺氧对铁死亡的影响在癌症增殖中很重要，但尚未有关于肾上腺皮质癌（ACC）的结合缺氧和铁死亡的预测模型的报道。本研究的目的是基于ACC中缺氧和铁死亡相关基因表达构建一个预测模型。
我们使用来自癌症基因组图谱（TCGA）的79例ACC患者的数据评估缺氧和铁死亡相关基因的表达。然后，使用最小绝对收缩和选择算子回归构建一个预测模型来对患者生存进行分层。基因表达综合数据库（GEO）中ACC患者的基因表达谱用于验证该预测模型。
基于缺氧相关基因表达，TCGA数据库中的79例ACC患者被分为具有不同临床结局的三种分子亚型（C1、C2和C3）。C3亚型患者的生存期最短。铁死亡相关基因在三种亚型中表现出不同的表达模式。构建了一个结合缺氧和铁死亡相关基因表达的预测模型。使用年龄、性别、肿瘤分期和预测基因模型构建了一个列线图。基因本体论和京都基因与基因组百科全书分析表明，基因特征主要与细胞周期和细胞器分裂有关。
这种与缺氧和铁死亡相关的基因特征对ACC显示出优异的预测性能，并且可以作为ACC中新的治疗靶点的一个新来源。
