线粒体自噬相关基因在胃腺癌中的差异表达与核心基因挖掘

Differential expression and core genes mining of mitophagy-related genes in stomach adenocarcinoma

  • 摘要:
    目的 探讨线粒体自噬相关基因(MRGs)在胃腺癌(STAD)中的差异表达并挖掘核心基因。
    方法 从基因表达综合数据库(GEO)和癌症基因组图谱(TCGA)数据库获得STAD患者的临床样本信息, 基于基因集富集分析(GSEA)网站和基因卡片(GeneCards)数据库获取有统计学意义的MRGs共26个; 基于最小绝对值收缩与选择算子(LASSO)回归构建MRGs在STAD中的预后模型,筛选出线粒体自噬预后相关基因(MPRGs)。通过基因本体论(GO)和京都基因与基因组百科全书(KEGG)富集分析,获得关键基因参与的生物学过程和通路; 利用STRING数据库构建MPRGs的蛋白质-蛋白质相互作用(PPI)网络,并应用Cytoscape软件进行可视化。
    结果 基于LASSO回归构建的预后模型中共筛选出15个MPRGs, 即ATG12CSNK2A2CSNK2BFUNDC1MAP1LC3AMAP1LC3BPGAM5PINK1SQSTM1TOMM20TOMM22TOMM40TOMM5UBA52UBC, 均为STAD的危险因素; 15个MPRGs中, UBCUBA52基因对STAD的进展和预后影响更大, PGAM5表达与STAD的发生显著相关, ATG12基因与其他基因的功能相似性得分最高; PPI网络分析结果显示, PINK1蛋白与其他蛋白的相互作用最多。
    结论 15个MPRGs在STAD的发生与发展中起重要作用,或可作为STAD基因检测、治疗的靶点和STAD预后的独立预测工具。

     

    Abstract:
    Objective To explore differential expression of mitophagy-related genes (MRGs) in stomach adenocarcinoma(STAD) and to investigate related core genes mining.
    Methods Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases to obtain clinical sample information of patients with STAD. A total of 26 MRGs with statistical significance were obtained based on the Gene Set Enrichment Analysis (GSEA) website and GeneCards database. A prognostic model of MRGs in STAD was constructed based on Least Absolute Shrinkage and Selection Operator (LASSO) regression, and mitochondrial autophagy prognostic genes (MPRGs) were screened out. Through gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, key genes involved in biological processes and pathways were obtained. Protein-protein interaction(PPI) network was established by means of STRING database, and the Cytoscape software was utilized for visualization.
    Results Fifteen MRGs were selected in the LASSO regression model: ATG12, CSNK2A2, CSNK2B, FUNDC1, MAP1LC3A, MAP1LC3B, PGAM5, PINK1, SQSTM1, TOMM20, TOMM22, TOMM40, TOMM5, UBA52 and UBC, which were risk factors for STAD. Among 15 MPRGs, UBC and UBA52 genes had more impact on progression and prognosis of STAD, and the expression of PGAM5 was significantly correlated with the occurrence of STAD. ATG12 gene had the highest functional similarity score with other genes. PPI network analysis showed that PINK1 protein had the most interactions with other proteins.
    Conclusion A total of 15 MPRGs play important roles in the occurrence and development of STAD, and may be used as targets for STAD gene detection, treatment and independent prognostic tools for STAD.

     

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