首都医科大学学报 ›› 2023, Vol. 44 ›› Issue (2): 248-257.doi: 10.3969/j.issn.1006-7795.2023.02.011

• 基础研究 • 上一篇    下一篇

凋亡诱导因子2在乳腺癌中的表达及其相关作用

杨一凡1,王雅梅2*   

  1. 1.首都医科大学基础医学院(2020级五年制临床医学专业),北京 100069; 2. 首都医科大学基础医学院生化与分子生物学系,北京 100069
  • 收稿日期:2022-07-12 出版日期:2023-04-21 发布日期:2023-04-18
  • 通讯作者: 王雅梅 E-mail:ymwang@ccmu.edu.cn
  • 基金资助:
    北京市教育委员会科技计划一般项目(KM201710025005),首都医科大学第二课堂项目(D2KT2022016)

Expression and mechanism of apoptosis-inducing factor 2 gene in breast cancer

Yang Yifan1, Wang Yamei2*   

  1. 1.2020 the Five-year Clinical Medicine, School of Basic Medical Sciences, Capital Medical University, Beijing 100069, China; 2.Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Capital Medical University, Beijing 100069, China
  • Received:2022-07-12 Online:2023-04-21 Published:2023-04-18
  • Supported by:
    This study was supported by Scientific Research Program of Beijing Municipal Commission of Education (KM201710025005), the Second Classroom Project of Capital Medical University (D2KT2022016)

摘要: 目的  利用生物信息学方法分析凋亡诱导因子2(apoptosis-inducing factor 2,AIFM2)在乳腺癌患者肿瘤组织中的表达及作用机制。方法  检索GEPIA、Kaplan-Meier Plotter、UALCAN数据库中AIFM2的研究信息,分析该基因在乳腺癌中的表达。利用Kaplan-Meier方法分析AIFM2的表达与乳腺癌患者生存及临床病理因素间的关系。同时应用LinkedOmic数据库筛选与AIFM2共同表达的基因,并对靶标基因进行基因本体(Gene Ontology,GO)和京都基因与基因组百科全书(Kyoto Encyclopedia of Genes and Genomes,KEGG)富集分析,并通过肿瘤免疫评估资源(tumor immune estimate resource,TIMER)数据库探究AIFM2表达与肿瘤组织中免疫细胞浸润的相关性。结果  GEPIA数据库分析发现AIFM2基因在乳腺癌组织中的表达显著低于癌旁组织(P<0.05);Kaplan-Meier Plotter数据库生存分析显示AIFM2基因高表达患者组的无复发生存期、总生存期、远处无转移生存期均显著高于低表达患者组(P<0.05);UALCAN数据库分析显示乳腺癌中AIFM2基因在不同人种、分子分型、组织类型表达水平不同(P<0.05);在不同年龄、性别、分期、TP53突变、围闭经期状态和淋巴结转移中表达水平无差异 (P>0.05);LinkedOmic数据库对AIFM2的共表达基因进行GO和KEGG富集分析发现AIFM2可能参与乳腺癌免疫反应的调节;TIMER数据库分析显示乳腺癌中AIFM2的表达水平与CD4T细胞、中性粒细胞、树突状细胞的浸润正相关(P<0.05);在乳腺癌中低水平的浸润性 CD4+T细胞、 CD8+T细胞、中性粒细胞和树突状细胞与较差的存活率显著相关(P<0.05)。结论  在乳腺癌中AIFM2的表达与肿瘤组织中免疫细胞浸润有关。AIFM2基因可能成为潜在的乳腺癌免疫治疗靶点。

关键词: 乳腺癌, 铁死亡, 凋亡诱导因子2, 免疫浸润, 生物信息学

Abstract: Objective To analyze the expression and mechanism of apoptosis-inducing factor 2 (AIFM2) in breast cancer tissue using bioinformatics methods. Methods The research information of AIFM2 in GEPIA, Kaplan-Meier Plotter, and UALCAN databases were searched, and the expression of this gene in breast cancer was analyzed. The Kaplan-Meier method was used to analyze the relationship between AIFM2 expression and breast cancer patients' survival and clinicopathological factors. Additionally, the LinkedOmic database was used to screen genes co-expressed with AIFM2, and the target genes were subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and the tumor immune estimate resource (TIMER) database was used to explore the correlation between AIFM2 expression and infiltration of immune cells into tumor tissues. Results GEPIA database analysis showed that the expression of AIFM2 in breast cancer was significantly lower than that in normal adjacent tissue (P<0.05). Metastasis-free survival was substantially higher in all patients than in the low-expression group (P<0.05); UALCAN database analysis showed that the expression levels of AIFM2 in breast cancer were different in different races, molecular types, and tissue types (P<0.05). There was no difference in expression levels among age, sex, stage, TP53 mutation, menopausal status and lymph node metastasis (P>0.05). GO and KEGG enrichment analysis of AIFM2 co-expressed genes in the LinkedOmic database revealed that the expression level of AIFM2 in breast cancer was positively correlated with the infiltration of CD4T cells, neutrophils, and dendritic cells (P<0.05). Low levels of infiltrating CD4T cells, CD8T cells, neutrophils, and dendritic cells in breast cancer were significantly associated with poorer survival (P<0.05). ConclusionThe expression of AIFM2 in breast cancer is related to the infiltration of immune cells into tumor tissues. Hence, AIFM2 can be considered a biomarker of the immune cell infiltration, metastasis, and prognosis in breast cancer.

Key words: breast cancer, ferroptosis, apoptosis-inducing factor 2 (AIFM2), immune infiltration, bioinformatics

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