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通过整合生物信息学分析和实验验证鉴定乳腺癌的关键枢纽基因和潜在分子机制。

Identification of crucial hub genes and potential molecular mechanisms in breast cancer by integrated bioinformatics analysis and experimental validation.

机构信息

Department of Life science, School of Sciences, Gujarat University, Ahmedabad 380009, Gujarat, India.

Department of Biochemistry & Forensic Science, University School of Sciences, Gujarat University, Ahmedabad, 380009, Gujarat, India.

出版信息

Comput Biol Med. 2022 Oct;149:106036. doi: 10.1016/j.compbiomed.2022.106036. Epub 2022 Aug 25.

Abstract

Breast cancer (BC) is a malignancy that affects a large number of women around the world. The purpose of the current study was to use bioinformatics analysis to uncover gene signatures during BC and their potential mechanisms. The gene expression profiles (GSE29431, GSE10810, and GSE42568) were retrieved from the Gene Expression Omnibus database, and the differential expressed genes (DEGs) were identified in normal tissues and tumour tissue samples from BC patients. In total, 296 DEGs were identified in BC, including 46 upregulated genes and 250 downregulated genes. GO and KEGG pathway analysis were performed. A PPI network of the DEGs was also constructed. GO analysis results showed that upregulated DEGs were significantly enriched in biological processes (BP), including cell division, mitotic cell cycle, chromosome separation, and cell division. MF analysis showed that upregulated DEGs controlled the microtubule cytoskeleton, the microtubule organising center, the cytoskeleton, and the chromosome-centric region. KEGG analysis revealed the upregulated DEGs mainly regulated p53 signaling, while the downregulated DEGs were enriched in the AMPK signalling pathway and PPAR signalling pathway. Moreover, five hub genes with a high degree of stability were identified, including NUSAP1, MELK, CENPF, TOP2A, and PPARG. Experimental validation showed that all five hub genes had the same expression trend as predicted. The overall survival and expression levels of hub genes were detected by Kaplan-Meier-plotter and the UALCAN database and were further validated using the Human Protein Atlas database. Taken together, the identified key genes enhance our understanding of the molecular pathways that underpin BC pathogenesis. As a result, our novel findings could be used as molecular targets and diagnostic biomarkers in the treatment of BC. This study is based on empirical evidence, making it an appealing read for the global scientific community.

摘要

乳腺癌(BC)是一种影响全球大量女性的恶性肿瘤。本研究旨在使用生物信息学分析揭示 BC 过程中的基因特征及其潜在机制。从基因表达综合数据库(GEO)中检索到基因表达谱(GSE29431、GSE10830 和 GSE42568),并在 BC 患者的正常组织和肿瘤组织样本中鉴定差异表达基因(DEGs)。共鉴定出 296 个 DEGs,包括 46 个上调基因和 250 个下调基因。进行了 GO 和 KEGG 通路分析。还构建了 DEGs 的 PPI 网络。GO 分析结果表明,上调的 DEGs 在生物过程(BP)中显著富集,包括细胞分裂、有丝分裂细胞周期、染色体分离和细胞分裂。MF 分析表明,上调的 DEGs 控制微管细胞骨架、微管组织中心、细胞骨架和染色体中心区域。KEGG 分析表明,上调的 DEGs 主要调控 p53 信号通路,而下调的 DEGs 富集在 AMPK 信号通路和 PPAR 信号通路中。此外,还鉴定出五个具有高稳定性的枢纽基因,包括 NUSAP1、MELK、CENPF、TOP2A 和 PPARG。实验验证表明,这五个枢纽基因的表达趋势与预测结果相同。通过 Kaplan-Meier-plotter 和 UALCAN 数据库检测了枢纽基因的总生存和表达水平,并使用人类蛋白质图谱数据库进一步验证。综上所述,鉴定出的关键基因增强了我们对乳腺癌发病机制的分子途径的理解。因此,我们的新发现可以作为治疗乳腺癌的分子靶点和诊断生物标志物。本研究基于经验证据,对全球科学界具有吸引力。

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