基于TCGA数据库分析乳腺癌中TERT相关分子网络机制

Analysis of the network mechanism of TERT related molecular in breast cancer based on TCGA database

目的通过TCGA数据库中的数据分析TERT基因在乳腺恶性肿瘤中的表达及生物学作用。方法从癌症基因组图谱(TCGA)数据库下载乳腺癌的基因表达数据与临床资料。利用R软件包进行TERT表达差异分析及其与患者预后相关性;通过共表达网络及LinkedOmics在线数据库分析共表达基因及潜在调控mRNA分子。使用Spearman和CIBERSORT算法,分析TERT与肿瘤微环境的相关性。使用GDSC在线数据库分析TERT基因与常见化疗药物的敏感性。通过GSEA富集分析TERT可能参与的信号通路。结果 TERT在乳腺癌样本中的表达水平显著上调,差异有统计学意义(P<0.05),其表达量与患者的stage分期呈显著正相关(P<0.05)。通过单、多因素Cox分析表明TERT基因可作为乳腺癌的独立预后因子[P<0.05,HR=1.474 (1.138~1.909)]。通过共表达及LinkedOmics数据库分析结果表明TERT表达与ALKAL1、CNPY1、FSD1、GF1B、HMGB1P1等5个基因正相关及与DERA、MCM5、AC004858.1、SLCTA14、CACNA1G-AS1等5个基因呈负相关,与mir-1245、mir-337、mir-10b、mir-199a-1、mir-377等5个miRNA呈正相关。CIBERSORT算法和Spearman相关系数分析表明TERT与T cells CD4 memory activated、Macrophages M0、Macrophages M1显著正相关,与Mast cells resting、T cells CD4 memory resting显著负相关。通过GDSC数据库分析表明TERT的表达与达沙替尼、吉西他滨、埃罗替尼以及伊马替尼、吉非替尼、顺铂等药物的敏感性相关(P<0.05)。TERT可富集在核糖核酸聚合酶、错配修复、原发性免疫缺陷信号通路。结论 TERT基因在乳腺癌样本中高表达及其与患者预后较差有关,表明TERT基因是乳腺癌的发病机制、诊断及治疗的靶点。

Objective To explore the expression and biological effects of TERT gene in malignant breast tumor through the TCGA database. Methods The gene expression data and clinical data of breast cancer were downloaded from the Cancer Genome Atlas(TCGA) database. R software package was used to analyze the difference of TERT expression and its correlation with patient’s prognosis. Co-expression genes and potentially regulated mRNA molecules were analyzed through the co-expression network and LinkedOmics online database. Spearman and CIBERSORT algorithm were used to analyze the correlation between TERT and tumor microenvironment. GDSC online database was used to analyze the sensitivity of TERT gene and common chemotherapy drugs. The possible signaling pathways of TERT were analyzed through GSEA enrichment. Results The expression level of TERT in breast cancer samples was significantly increased with statistically significant difference(P<0.05);its expression was significantly positively correlated with the stage staging of patients(P<0.05). Single and multivariate Cox analysis showed that TERT gene could be used as an independent prognostic factor for breast cancer(P<0.05, HR=1.474 [1.138-1.909]). Co-expression and LinkedOmics database analysis showed that TERT expression was positively correlated with 5 genes, including ALKAL1,CNPY1, FSD1, GF1 B, and HMGB1 P1;negatively correlated with 5 genes, including DERA, MCM5, AC004858.1,SLCTA14, and CACNA1 G-AS1;and positively correlated with 5 miRNAs, including mir-1245, mir-337, miR-10 b,mir-199 a-1, and mir-377. CIBERSORT algorithm and Spearman correlation coefficient analysis showed that TERT was significantly positively correlated with T cells CD4 memory activated, Macrophases M0, Macrophases M1, and significantly negatively correlated with Mast cells resting and T cells CD4 memory resting. GDSC database analysis showed that TERT expression was associated with the sensitivity of dasatinib, gemitabine, erotinib, imatinib, giefetinib, and cisplatin(P<0.05). TERT could be enriched in ribonuclease polymerase, mismatch repair, and primary immunodeficiency signaling pathways. Conclusion The high expression of TERT gene in breast cancer samples is associated with the poor prognosis of patients, indicating that TERT gene is the target of the pathogenesis, diagnosis and treatment of breast cancer.