肝细胞癌患者外周血单个核细胞诊断候选基因的筛选及其调控网络分析

Identification of differentially expressed genes in peripheral blood mononuclear cells of patients with hepatocellular carcinoma and its regulatory network analysis

目的:通过筛选肝细胞癌(HCC)患者外周血单个核细胞(PBMC)诊断候选基因并分析其上游互作微小RNA(miRNA)、长链非编码RNA(lncRNA)、环状(circRNA)和参与的通路,探讨HCC发生、发展过程中的调控机制并寻找可用于临床诊疗的分子靶点。方法:利用GEO数据库筛选HCC患者PBMC中的差异表达基因集,分别进行功能富集及互作分析,继而利用网络模块划分方法寻找差异表达基因中的诊断候选基因,再利用mirDIP、starBase在线工具对诊断候选基因的上游miRNA、lncRNA、circRNA进行预测。结果:获得高可信度的差异表达基因265个,差异表达基因主要富集于增殖调控、代谢调节、细胞通信、炎症疾病等功能,基因本体及KEGG通路富集结果相互关联。筛选获得4个诊断候选基因,包括RNA结合蛋白FUS、C-X-C基序趋化因子配体8、卡林蛋白和RNA聚合酶Ⅱ亚单位H。预测到10个miRNA、1个lncRNA和38个circRNA符合筛选标准,最后构建出一个lncRNA/circRNA-miRNA-mRNA-通路调控网络。结论:本研究基于数据挖掘方法筛选获得HCC患者PBMC中的诊断候选基因及其调控网络,为HCC的早期诊断和合理治疗提供了理论依据,有助于寻找新的肿瘤标志物。

Objective: To identify the differentially expressed genes(DEGs) in peripheral blood mononuclear cells(PBMC) of patients with hepatocellular carcinoma(HCC) and to analyze their regulatory network. Methods: The DEGs in PBMCs of HCC patients were screened based on GEO database. The functional enrichment analysis and interaction analysis were carried out for DEGs. MCODE algorithm was used to screen core genes of DEGs, and the mirDIP and starBase online tools were used to predict upstream miRNAs and lncRNAs of the core genes. Results: A total of 265 DEGs with a high credibility were identified, which were mainly enriched in the biological activity, such as regulation of cell proliferation, metabolic regulation, cell communication and signaling, and inflammatory diseases according to Gene Ontology(GO) analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway, and the two analyses were correlated. Four diagnostic candidate genes were identified, including FUS RNA binding protein, C-X-C motif chemokine ligand 8, cullin 1 and RNA polymerase Ⅱ subunit H. Subsequently, 10 miRNAs, 1 lncRNAs and 38 circRNAs were predicted, and finally a lncRNA/circRNA-miRNA-mRNA-pathway regulatory networks was constructed. Conclusion: The diagnostic candidate genes and its regulatory network in HCC PBMC have been identified based on data mining, which could provide potential tumor biomarkers for early diagnosis and treatment of HCC.