Analyzing hub genes related to tumorigenesis based on biological big data has recently become a hotspot in biomedicine. Nanoprobes, nanobodies and theranostic molecules targeting hub genes delivered by nanocarriers have been widely applied in tumor theranostics. Hepatocellular carcinoma (HCC) is one of the most common cancers, with a poor prognosis and high mortality. Identifying hub genes according to the gene expression levels and constructing prognostic signatures related to the onset and outcome of HCC will be of great significance. In this study, the expression profiles of HCC and normal tissue were obtained from the GEO database and analyzed by GEO₂R to identify DEGs. GO terms and KEGG pathways were enriched in DAVID software. The STRING database was consulted to find protein-protein interactions between proteins encoded by the DEGs, which were visualized by Cytoscape. Then, overall survival associated with the hub genes was calculated by the Kaplan-Meier plotter online tool, and verification of the results was carried out on TCGA samples and their corresponding clinical information. A total of 603 DEGs were obtained, of which 479 were upregulated and 124 were downregulated. PPI networks including 603 DEGs and 18 clusters were constructed, of which 7 clusters with MCODE score ≥3 and nodes ≥5 were selected. The 5 genes with the highest degrees of connectivity were identified as hub genes, and a prognostic model was constructed. The expression and prognostic potential of this model was validated on TCGA clinical data. In conclusion, a five-gene signature (TOP2A, PCNA, AURKA, CDC20, CCNB2) overexpressed inHCC was identified, and a prognostic model was constructed. This gene signature may act as a prognostic model for HCC and provide potential targets of nanotechnology.

中文翻译：

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分子纳米探针在原发性肝细胞癌差异表达基因分析和预后模型中的应用。

基于生物大数据分析与肿瘤发生相关的枢纽基因已成为生物医学领域的热点。通过纳米载体传递的靶向中枢基因的纳米探针、纳米抗体和治疗诊断分子已广泛应用于肿瘤治疗诊断。肝细胞癌（HCC）是最常见的癌症之一，预后差，死亡率高。根据基因表达水平识别枢纽基因并构建与 HCC 发病和结果相关的预后特征将具有重要意义。在本研究中，HCC 和正常组织的表达谱从 GEO 数据库中获得，并通过 GEO2R 分析以识别 DEG。DAVID 软件中丰富了 GO 术语和 KEGG 通路。查阅 STRING 数据库以发现 DEG 编码的蛋白质之间的蛋白质-蛋白质相互作用，由 Cytoscape 可视化。然后，通过Kaplan-Meier绘图仪在线工具计算与hub基因相关的总生存期，并对TCGA样本及其相应的临床信息进行结果验证。共获得 603 个 DEG，其中 479 个上调，124 个下调。构建了包括603个DEGs和18个集群的PPI网络，其中选择了7个MCODE评分≥3和节点≥5的集群。将连接度最高的5个基因确定为枢纽基因，并构建预后模型。该模型的表达和预后潜力在 TCGA 临床数据上得到验证。总之，鉴定了在HCC中过表达的五基因特征（TOP2A、PCNA、AURKA、CDC20、CCNB2），并构建了预后模型。