Department of Experimental Pharmacology and Toxicology, School of Pharmaceutical Sciences, Jilin University, 1266 Fujin Road, 130021, Changchun, Jilin, China.
Department of Pharmacology, School of Pharmaceutical Sciences, Jilin University, 1266 Fujin Road, 130021, Changchun, Jilin, China.
Cancer Gene Ther. 2022 Nov;29(11):1578-1589. doi: 10.1038/s41417-022-00473-2. Epub 2022 Apr 26.
Triple-negative breast cancer (TNBC) has a high degree of malignancy, lack of effective diagnosis and treatment, and poor prognosis. Bioinformatics methods are used to screen the hub genes and signal pathways involved in the progress of TNBC to provide reliable biomarkers for the diagnosis and treatment of TNBC. Download the raw data of four TNBC-related datasets from the Gene Expression Omnibus (GEO) database and use them for bioinformatics analysis. GEO2R tool was used to analyze and identify differentially expressed (DE) mRNAs. DAVID database was used to carry out gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genome Pathways (KEGG) signal pathway enrichment analysis for DE mRNAs. STRING database and Cytoscape were used to build DE mRNAs protein-protein interaction (PPI) network diagram and visualize PPI network, respectively. Through cytoHubba, cBioPortal database, Kaplan-Meier mapper database, Gene Expression Profiling Interactive Analysis (GEPIA) Database, UALCAN Database, The Cancer Genome Atlas (TCGA) database, Tumor Immunity Estimation Resource identify hub genes. Perform qRT-PCR, Human Protein Atlas analysis, mutation analysis, survival analysis, clinical-pathological characteristics, and infiltrating immune cell analysis. 22 DE mRNAs were identified from the four datasets, including 16 upregulated DE mRNAs and six downregulated DE mRNAs. Enrichment analysis of the KEGG showed that DE mRNAs were principally enriched in pathways in cancer, mismatch repair, cell cycle, platinum drug resistance, breast cancer. Six hub genes were screened based on the PPI network diagram of DE mRNAs. Survival analysis found that TOP2A, CCNA2, PCNA, MSH2, CDK6 are related to the prognosis of TNBC. In addition, mutations, clinical indicators, and immune infiltration analysis show that these five hub genes play an important role in the progress of TNBC and immune monitoring. Compared with MCF-10A, MCF-7, and SKBR-3 cells, TOP2A, PCNA, MSH2, and CDK6 were significantly upregulated in MDA-MB-321 cells. Compared with normal, luminal, and Her-2 positive tissues, CCNA2, MSH2, and CDK6 were significantly upregulated in TNBC. Through comparative analysis of GEO datasets related to colorectal cancer and lung adenocarcinoma, it was determined that these five hub genes were unique differentially expressed genes of TNBC. At last, the hub genes related to the progression, prognosis, and immunity of TNBC have been successfully screened. They are indeed specific to TNBC as prognostic features. They can be used as potential markers for the prognosis of TNBC and provide potential therapeutic targets.
三阴性乳腺癌(TNBC)具有高度恶性、缺乏有效诊断和治疗以及预后不良的特点。本研究采用生物信息学方法筛选 TNBC 进展相关的枢纽基因和信号通路,为 TNBC 的诊断和治疗提供可靠的生物标志物。从基因表达综合数据库(GEO)中下载四个与 TNBC 相关的数据集的原始数据,并进行生物信息学分析。使用 GEO2R 工具分析和识别差异表达(DE)mRNA。使用 DAVID 数据库对 DE mRNAs 进行基因本体论(GO)分析和京都基因与基因组百科全书(KEGG)信号通路富集分析。使用 STRING 数据库和 Cytoscape 分别构建 DE mRNAs 蛋白-蛋白相互作用(PPI)网络图谱和可视化 PPI 网络。通过 cytoHubba、cBioPortal 数据库、Kaplan-Meier mapper 数据库、基因表达谱交互式分析(GEPIA)数据库、UALCAN 数据库、癌症基因组图谱(TCGA)数据库和 Tumor Immunity Estimation Resource 识别枢纽基因。进行 qRT-PCR、人类蛋白质图谱分析、突变分析、生存分析、临床病理特征和浸润免疫细胞分析。从四个数据集中共鉴定出 22 个 DE mRNAs,包括 16 个上调 DE mRNAs 和 6 个下调 DE mRNAs。KEGG 富集分析显示,DE mRNAs 主要富集在癌症、错配修复、细胞周期、铂类药物耐药、乳腺癌等信号通路中。基于 DE mRNAs 的 PPI 网络图筛选出 6 个枢纽基因。生存分析发现 TOP2A、CCNA2、PCNA、MSH2、CDK6 与 TNBC 的预后相关。此外,突变、临床指标和免疫浸润分析表明,这 5 个枢纽基因在 TNBC 的进展和免疫监测中发挥着重要作用。与 MCF-10A、MCF-7 和 SKBR-3 细胞相比,MDA-MB-321 细胞中 TOP2A、PCNA、MSH2 和 CDK6 的表达明显上调。与正常、腔面和 Her-2 阳性组织相比,TNBC 中 CCNA2、MSH2 和 CDK6 的表达明显上调。通过比较与结直肠癌和肺腺癌相关的 GEO 数据集,确定这 5 个枢纽基因是 TNBC 特有的差异表达基因。最后,成功筛选出与 TNBC 进展、预后和免疫相关的枢纽基因。它们确实是 TNBC 预后特征的特异性标志物。它们可作为 TNBC 预后的潜在标志物,并为其提供潜在的治疗靶点。