Makondi Precious Takondwa, Lee Chia-Hwa, Huang Chien-Yu, Chu Chi-Ming, Chang Yu-Jia, Wei Po-Li
International PhD Program in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
Graduate Institute of Clinical Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
PLoS One. 2018 Jan 17;13(1):e0189582. doi: 10.1371/journal.pone.0189582. eCollection 2018.
Bevacizumab combined with cytotoxic chemotherapy is the backbone of metastatic colorectal cancer (mCRC) therapy; however, its treatment efficacy is hampered by therapeutic resistance. Therefore, understanding the mechanisms underlying bevacizumab resistance is crucial to increasing the therapeutic efficacy of bevacizumab. The Gene Expression Omnibus (GEO) database (dataset, GSE86525) was used to identify the key genes and pathways involved in bevacizumab-resistant mCRC. The GEO2R web tool was used to identify differentially expressed genes (DEGs). Functional and pathway enrichment analyses of the DEGs were performed using the Database for Annotation, Visualization, and Integrated Discovery(DAVID). Protein-protein interaction (PPI) networks were established using the Search Tool for the Retrieval of Interacting Genes/Proteins database(STRING) and visualized using Cytoscape software. A total of 124 DEGs were obtained, 57 of which upregulated and 67 were downregulated. PPI network analysis showed that seven upregulated genes and nine downregulated genes exhibited high PPI degrees. In the functional enrichment, the DEGs were mainly enriched in negative regulation of phosphate metabolic process and positive regulation of cell cycle process gene ontologies (GOs); the enriched pathways were the phosphoinositide 3-kinase-serine/threonine kinase signaling pathway, bladder cancer, and microRNAs in cancer. Cyclin-dependent kinase inhibitor 1A(CDKN1A), toll-like receptor 4 (TLR4), CD19 molecule (CD19), breast cancer 1, early onset (BRCA1), platelet-derived growth factor subunit A (PDGFA), and matrix metallopeptidase 1 (MMP1) were the DEGs involved in the pathways and the PPIs. The clinical validation of the DEGs in mCRC (TNM clinical stages 3 and 4) revealed that high PDGFA expression levels were associated with poor overall survival, whereas high BRCA1 and MMP1 expression levels were associated with favorable progress free survival(PFS). The identified genes and pathways can be potential targets and predictors of therapeutic resistance and prognosis in bevacizumab-treated patients with mCRC.
贝伐单抗联合细胞毒性化疗是转移性结直肠癌(mCRC)治疗的基础;然而,其治疗效果受到治疗耐药性的阻碍。因此,了解贝伐单抗耐药的潜在机制对于提高贝伐单抗的治疗效果至关重要。利用基因表达综合数据库(GEO)(数据集GSE86525)来识别参与贝伐单抗耐药性mCRC的关键基因和通路。使用GEO2R网络工具来识别差异表达基因(DEG)。利用注释、可视化和综合发现数据库(DAVID)对DEG进行功能和通路富集分析。使用检索相互作用基因/蛋白质数据库(STRING)建立蛋白质-蛋白质相互作用(PPI)网络,并使用Cytoscape软件进行可视化。共获得124个DEG,其中57个上调,67个下调。PPI网络分析表明,7个上调基因和9个下调基因具有较高的PPI度数。在功能富集方面,DEG主要富集于磷酸代谢过程的负调控和细胞周期过程基因本体(GO)的正调控;富集的通路为磷酸肌醇3激酶-丝氨酸/苏氨酸激酶信号通路、膀胱癌和癌症中的微小RNA。细胞周期蛋白依赖性激酶抑制剂1A(CDKN1A)、Toll样受体4(TLR4)、CD19分子(CD19)、乳腺癌1、早发性(BRCA1)、血小板衍生生长因子亚基A(PDGFA)和基质金属肽酶1(MMP1)是参与通路和PPI的DEG。对mCRC(TNM临床分期3和4)中DEG的临床验证表明,高PDGFA表达水平与总体生存率差相关,而高BRCA1和MMP1表达水平与良好的无进展生存期(PFS)相关。所识别的基因和通路可能是贝伐单抗治疗的mCRC患者治疗耐药性和预后的潜在靶点和预测指标。