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建立和验证一个用于乳腺癌患者的新型自噬相关基因特征。

Establishment and validation of a novel autophagy-related gene signature for patients with breast cancer.

机构信息

Department of General Surgery, Zhongshan Hospital, Fudan University, Shanghai 200032, China.

Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China; State Key Laboratory of Genetic Engineering, Fudan University, Shanghai 200032, China.

出版信息

Gene. 2020 Dec 15;762:144974. doi: 10.1016/j.gene.2020.144974. Epub 2020 Jul 22.

Abstract

BACKGROUND

There exists considerable evidence conforming that autophagy may play an important role in the biological process of breast cancer. This study aimed to construct and evaluate a novel autophagy-related gene signature as a potential prognostic factor and therapeutic target in breast cancer patients based on high-throughput sequencing datasets.

MATERIALS & METHODS: Autophagy-related genes obtained from the Human Autophagy Database and high-sequencing data obtained from The Cancer Genome Atlas (TCGA) were analyzed to identify differential expressed genes (DEGs) between tumor and normal tissues. Then GO and KEGG analysis were performed to explore potential biological and pathological functions of DEGs. Autophagy-related prognostic genes were identified by univariate COX regression analysis. Subsequently stepwise model selection using the Alkaike information criterion (AIC) and multivariate COX regression model was performed to construct autophagy-related gene signature. Then patients were divided into high- and low-risk groups based on the risk score identified by the autophagy-related gene signature. Multivariate COX regression model and stratification analysis were used to specify the prognostic value of this gene signature in whole cohort and various subgroups. T-test and ANOVA analysis were used to compare the expression differences of continuous variables (5 prognostic genes and risk score) in binary and multiple category groups respectively. Kaplan-Meier analysis, log-rank tests and the area under receiver operating characteristic (ROC) curve (AUC) were conducted to validate the accuracy and precise of the autophagy-related gene signature based on GSE20685 and GSE21653 datasets.

RESULTS

We profiled autophagy-related DEGs in normal and breast tumor tissues. GO and KEGG analysis indicated that autophagy-related DEGs might participate in breast cancer occurrence, development and drug resistance. Then we identified five autophagy-related genes (EIF4EBP1, ATG4A, BAG1, MAP1LC3A and SERPINA1) that had significantly prognostic values for breast cancer. Autophagy-related gene signature was constructed and patients were divided into high- and low- risk groups based on their risk score. Patients in the high-risk group tended to have shorter overall survival (OS) and relapse-free survival (RFS) times than those in the low-risk group (OS: HR = 1.620, 95%CIs: 1.345-1.950; P < 0.001; RFS: HR = 1.487, 95%CIs: 1.248-1.771, P < 0.001). Autophagy-related gene signature had significant prognostic value in stratified subgroups especially in advanced breast cancer subgroups (T3-4; N2-3; stage III-IV). Its prognostic value was further confirmed in two GEO validation datasets (GSE20685: P = 6.795e-03; GSE21653: P = 1.383e-03). Finally, association analysis between clinicopathological factors and gene signature showed the risk score was higher in patients with ER/PR negative, higher clinical stage or T stage (P < 0.01).

CONCLUSION

We established and confirmed a novel autophagy-related gene signature for patients with breast cancer that had independent survival prognostic value especially in advanced breast cancer subgroups. Our research might promote the molecular mechanism study of autophagy-related genes in breast cancer.

摘要

背景

有大量证据表明自噬可能在乳腺癌的生物学过程中发挥重要作用。本研究旨在基于高通量测序数据集构建和评估一种新的自噬相关基因特征作为乳腺癌患者的潜在预后因素和治疗靶点。

材料与方法

从人类自噬数据库和高通量测序数据(来自癌症基因组图谱(TCGA))中获取自噬相关基因,以鉴定肿瘤组织与正常组织之间的差异表达基因(DEGs)。然后进行 GO 和 KEGG 分析,以探讨 DEGs 的潜在生物学和病理功能。通过单因素 COX 回归分析鉴定自噬相关预后基因。随后,使用 Akaike 信息准则(AIC)进行逐步模型选择和多因素 COX 回归模型,以构建自噬相关基因特征。然后根据自噬相关基因特征确定的风险评分将患者分为高风险组和低风险组。使用多因素 COX 回归模型和分层分析,确定该基因特征在整个队列和各种亚组中的预后价值。使用 T 检验和 ANOVA 分析分别比较连续变量(5 个预后基因和风险评分)在二分类和多分类组中的表达差异。使用 GSE20685 和 GSE21653 数据集进行 Kaplan-Meier 分析、对数秩检验和接受者操作特征(ROC)曲线下面积(AUC),以验证自噬相关基因特征的准确性和精确性。

结果

我们分析了正常和乳腺癌组织中的自噬相关 DEGs。GO 和 KEGG 分析表明,自噬相关 DEGs 可能参与乳腺癌的发生、发展和耐药性。然后,我们确定了五个具有显著乳腺癌预后价值的自噬相关基因(EIF4EBP1、ATG4A、BAG1、MAP1LC3A 和 SERPINA1)。构建了自噬相关基因特征,并根据其风险评分将患者分为高风险组和低风险组。高风险组的患者倾向于具有更短的总生存期(OS)和无复发生存期(RFS)(OS:HR=1.620,95%CI:1.345-1.950;P<0.001;RFS:HR=1.487,95%CI:1.248-1.771,P<0.001)。自噬相关基因特征在分层亚组中具有显著的预后价值,尤其是在晚期乳腺癌亚组中(T3-4;N2-3;III-IV 期)。在两个 GEO 验证数据集(GSE20685:P=6.795e-03;GSE21653:P=1.383e-03)中进一步证实了其预后价值。最后,临床病理因素与基因特征的关联分析表明,风险评分在 ER/PR 阴性、较高临床分期或 T 分期的患者中更高(P<0.01)。

结论

我们建立并验证了一种新的乳腺癌患者自噬相关基因特征,该特征具有独立的生存预后价值,尤其是在晚期乳腺癌亚组中。我们的研究可能会促进自噬相关基因在乳腺癌中的分子机制研究。

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