综合生物信息学分析揭示自噬相关基因特征,可预测肝细胞癌的预后、免疫状态和药物敏感性

Peng Liu, Yan Zhou, Shun Zeng, Xiangjuan Chen
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引用次数: 0

摘要

背景:肝细胞癌(HCC)是世界上最常见的恶性肿瘤之一:肝细胞癌(HCC)是世界上最常见的恶性肿瘤之一,但分子的复杂性和肿瘤的异质性使得HCC预后预测模型失效。最近的许多研究表明,自噬在肿瘤进展中起着重要作用。我们试图利用自噬相关基因(ARGs)创建一种新的特征,用于预测HCC患者的个体预后:方法:我们使用 TCGA 数据集筛选了 HCC 和正常样本中差异表达的 ARGs(DE-ARGs)。进行了单变量Cox和多变量Cox回归分析,以确定与HCC患者生存相关的ARGs。利用LASSO构建了基于自噬的特征,并探讨了其与HCC患者预后和免疫浸润的相关性:本研究通过分析TCGA数据集筛选出了32个与生存相关的DE-ARGs。功能富集表明,这32个DE-ARGs可能在细胞自噬、多种信号通路调控以及癌症和神经系统疾病中发挥重要的功能和调控作用。基于 PPI 网络,我们发现了几个枢纽基因。LASSO Cox回归分析选择了五个基因(CASP8、FOXO1、PRKCD、SPHK1和SQSTM1)作为新的预后模型。TCGA全集的AUC分别为0.752、0.686和0.665,表明该模型能准确预测1年、3年和5年的总生存率。Cox 回归分析表明,五基因特征是预测 HCC 患者的一个独立且稳健的指标。高危组有更高水平的免疫细胞浸润,并与免疫微环境和肿瘤干细胞密切相关。此外,我们还进一步发现 PRKCD 和 SQSTM1 是参与 HCC 进展的关键调控因子。PRKCD和SQSTM1基因的表达水平在化疗药物敏感性和耐药性中起着至关重要的作用:我们在此介绍一种基于 ARG 的新型 HCC 患者预测特征。结论:我们在此介绍了一种基于 ARG 的新型 HCC 患者预测特征,有效利用该特征将有助于确定患者的预后,并为临床决策和治疗提供新方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comprehensive Bioinformatic Analysis Reveals an Autophagy-related Gene Signature for Predicting Outcome, Immune Status, and Drug Sensitivity in Hepatocellular Carcinoma.

Background: Hepatocellular carcinoma (HCC) is one of the most common malignancies in the world, but molecular complexity and tumor heterogeneity make predictive models for HCC prognosis ineffective. Many recent studies have suggested that autophagy is important in tumor progression. Using autophagy-related genes (ARGs), we attempted to create a novel signature for individual prognosis prediction in patients with HCC.

Methods: Differentially expressed ARGs (DE-ARGs) in HCC and normal samples were screened using TCGA datasets. Univariate Cox and multivariate Cox regression analyses were performed to determine ARGs related to survival in HCC. An autophagy-based signature was constructed using LASSO, and its correlation with the prognosis and the immune infiltration of HCC patients was explored.

Results: In this study, we screened 32 survival-related DE-ARGs by analyzing TCGA datasets. Functional enrichment indicated that the 32 DE-ARGs may play important functional and regulatory roles in cellular autophagy, the regulation of multiple signaling pathways, as well as in the context of cancer and neurological diseases. Based on PPI Network, we identified several hub genes. LASSO Cox regression analysis selected five genes (CASP8, FOXO1, PRKCD, SPHK1, and SQSTM1) for a novel prognostic model. AUCs of 0.752, 0.686, and 0.665 in the TCGA whole set indicated that the model accurately predicted 1-, 3-, and 5-year overall survival, respectively. Cox regression analysis showed that the five-gene signature is an independent and robust predictor in patients with HCC. The high-risk group demonstrated higher levels of immune cell infiltration and exhibited a strong correlation with the immune microenvironment and tumor stem cells. In addition, we further identified PRKCD and SQSTM1 as critical regulators involved in HCC progression. The expression levels of PRKCD and SQSTM1 genes play a crucial role in chemotherapy drug sensitivity and resistance.

Conclusion: We introduce here a novel ARG-based predictive feature for HCC patients. Effective use of this signature will aid in determining a patient's prognosis and may lead to novel approaches to clinical decision-making and therapy.

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