Wei Benzun, Zheng Yao, Yu Shuaijun, Wang Aiyun, Lyu Xiao
Department of Hepatobiliary Surgery, Zibo Central Hospital, Zibo, Shandong, China.
Department of Pathology, Zibo Central Hospital, Zibo, Shandong, China.
PLoS One. 2025 Jun 4;20(6):e0325610. doi: 10.1371/journal.pone.0325610. eCollection 2025.
This study aims to identify key genes, biomarkers, and associated signaling pathways involved in liver cancer progression by analyzing differentially expressed genes (DEGs) between normal and cancerous liver tissues, with the goal of establishing diagnostic and prognostic models for liver cancer.
Two datasets, GSE39791 and GSE84402 from GEO, and clinical data from TCGA were selected. Differentially expressed genes (DEGs) were identified using the "limma" package in R, and volcano plots were generated. Functional enrichment of DEGs was performed with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Logistic regression and multivariate Cox regression models were established for diagnostic and prognostic prediction. The immortalized liver cell line THLE-3 and HepG2 cells were used to verify key gene expression via RT-qPCR and Western blot. HepG2 cells were transfected to up- and down-regulate SNAPC2 expression, and cell proliferation, migration, and apoptosis were assessed using CCK-8, colony formation, scratch, transwell migration assays, and flow cytometry with Annexin V-PE/7-AAD staining. Additionally, Gene Set Enrichment Analysis (GSEA) of SNAPC2 revealed its involvement in cancer-related pathways.
Bioinformatics analysis identified 10,961 down-regulated and 3,321 up-regulated genes in the GSE39791 and GSE84402 datasets, and 272 down-regulated and 4,855 up-regulated genes in TCGA data. GO and KEGG analysis revealed 3,820 co-DEGs associated with processes like cell differentiation and morphogenesis. CDCA8, GRPEL2, HAVCR1, MT3, MYCN, NDRG1, PHOSPHO2, SNAPC2, SOCS2, and TXNRD1 were selected to construct prognostic models, and MYCN, NDRG1, TXNRD1, SNAPC2, PHOSPHO2, and CDCA8 for diagnostic models. Western blot validation showed upregulation of CDCA8, GRPEL2, HAVCR1, MYCN, NDRG1, PHOSPHO2, SNAPC2, and TXNRD1 in liver cancer tissues, correlating with poor prognosis. Moreover, reduced SNAPC2 expression in HepG2 cells led to decreased proliferation and migration, and increased apoptosis, suggesting SNAPC2 plays a role in liver cancer progression by promoting cell proliferation and migration.
CDCA8, GRPEL2, HAVCR1, MT3, MYCN, NDRG1, PHOSPHO2, SNAPC2, SOCS2, TXNRD1 were key genes for liver cancer prognosis and diagnosis. Moreover, lowering SNAPC2 expression could improve the prognosis of liver cancer through decreasing proliferation and migration s and increasing apoptosis of cancer cell.
本研究旨在通过分析正常肝组织与癌组织之间的差异表达基因（DEGs），确定参与肝癌进展的关键基因、生物标志物及相关信号通路，以期建立肝癌的诊断和预后模型。
选取来自基因表达综合数据库（GEO）的两个数据集GSE39791和GSE84402以及来自癌症基因组图谱（TCGA）的临床数据。使用R语言中的“limma”软件包识别差异表达基因，并绘制火山图。通过基因本体论（GO）和京都基因与基因组百科全书（KEGG）分析对差异表达基因进行功能富集。建立逻辑回归和多变量Cox回归模型用于诊断和预后预测。使用永生化肝细胞系THLE-3和HepG2细胞，通过逆转录定量聚合酶链反应（RT-qPCR）和蛋白质免疫印迹法（Western blot）验证关键基因的表达。对HepG2细胞进行转染以上调和下调小核核糖核蛋白C2（SNAPC2）的表达，并使用CCK-8法、集落形成实验、划痕实验、Transwell迁移实验以及膜联蛋白V-PE/7-氨基放线菌素D（Annexin V-PE/7-AAD）染色的流式细胞术评估细胞增殖、迁移和凋亡情况。此外，对SNAPC2进行基因集富集分析（GSEA），揭示其参与癌症相关通路。
生物信息学分析在GSE39791和GSE84402数据集中鉴定出10961个下调基因和3321个上调基因，在TCGA数据中鉴定出272个下调基因和4855个上调基因。GO和KEGG分析揭示了3820个与细胞分化和形态发生等过程相关的共同差异表达基因。选择细胞分裂周期相关8（CDCA8）、生长调控蛋白E样蛋白2（GRPEL2）、甲型肝炎病毒细胞受体1（HAVCR1）、金属硫蛋白3（MT3）、原癌基因MYC（MYCN）、N-myc下游调节基因1（NDRG1）、磷酸化酶2（PHOSPHO2）、小核核糖核蛋白C2（SNAPC2）、细胞因子信号转导抑制因子2（SOCS2）和硫氧还蛋白还原酶1（TXNRD1）构建预后模型，选择MYCN、NDRG1、TXNRD1、SNAPC2、PHOSPHO2和CDCA8构建诊断模型。蛋白质免疫印迹法验证显示，肝癌组织中CDCA8、GRPEL2、HAVCR1、MYCN、NDRG1、PHOSPHO2、SNAPC2和TXNRD1上调，与预后不良相关。此外，HepG2细胞中SNAPC2表达降低导致增殖和迁移减少，凋亡增加，提示SNAPC2通过促进细胞增殖和迁移在肝癌进展中发挥作用。
CDCA8、GRPEL2、HAVCR1、MT3、MYCN、NDRG1、PHOSPHO2、SNAPC
