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Abstract：
Background: Liver hepatocellular carcinoma (LIHC) is one of the leading causes of cancer-related death. The prognostic outcomes of advanced LIHC patients are poor. Hence, reliable prognostic biomarkers for LIHC are urgently needed. Methods: Data for vesicle-mediated transport-related genes (VMTRGs) were profiled from 338 LIHC and 50 normal tissue samples downloaded from The Cancer Genome Atlas (TCGA). Univariate Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses were performed to construct and optimize the prognostic risk model. Five GEO datasets were used to validate the risk model. The roles of the differentially expressed genes (DEGs) were investigated via Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses. Differences in immune cell infiltration between the high- and low-risk groups were evaluated using five algorithms. The \"pRRophetic\" was used to calculate the anticancer drug sensitivity of the two groups. Transwell and wound healing assays were performed to assess the role of GDP dissociation inhibitor 2 (GDI2) on LIHC cells. Results: A total of 166 prognosis-associated VMTRGs were identified, and VMTRGs-based risk model was constructed for the prognosis of LIHC patients. Four VMTRGs (GDI2, DYNC1LI1, KIF2C, and RAB32) constitute the principal components of the risk model associated with the clinical outcomes of LIHC. Tumor stage and risk score were extracted as the main prognostic indicators for LIHC patients. The VMTRGs-based risk model was significantly associated with immune responses and high expression of immune checkpoint molecules. High-risk patients were less sensitive to most chemotherapeutic drugs but benefited from immunotherapies. In vitro cellular assays revealed that GDI2 significantly promoted the growth and migration of LIHC cells. Conclusions: A VMTRGs-based risk model was constructed to predict the prognosis of LIHC patients effectively. This risk model was closely associated with the immune infiltration microenvironment. The four key VMTRGs are powerful prognostic biomarkers and therapeutic targets for LIHC.
摘要：
背景：肝细胞癌（LIHC）是癌症相关死亡的主要原因之一。晚期LIHC患者的预后结果较差。因此，目前迫切需要可靠的LIHC预后生物标志物.方法：从338LIHC和从癌症基因组图谱（TCGA）下载的50个正常组织样品中分析囊泡介导的转运相关基因（VMTRG）的数据。进行单变量Cox回归和最小绝对收缩和选择算子（LASSO）回归分析以构建和优化预后风险模型。使用五个GEO数据集来验证风险模型。通过京都基因和基因组百科全书（KEGG）和基因本体论（GO）富集分析研究了差异表达基因（DEG）的作用。使用五种算法评估了高危组和低危组之间免疫细胞浸润的差异。使用“pRrophetic”计算两组的抗癌药物敏感性。进行Transwell和伤口愈合测定以评估GDP解离抑制剂2(GDI2)对LIHC细胞的作用。结果：共有166个与预后相关的VMTRGs被确定，并构建基于VMTRGs的风险模型对LIHC患者的预后进行评估。四个VMTRG(GDI2、DYNC1LI1、KIF2C、和RAB32）构成与LIHC临床结果相关的风险模型的主要成分。提取肿瘤分期和风险评分作为LIHC患者的主要预后指标。基于VMTRGs的风险模型与免疫应答和免疫检查点分子的高表达显著相关。高危患者对大多数化疗药物不太敏感，但受益于免疫疗法。体外细胞测定显示GDI2显著促进LIHC细胞的生长和迁移。结论:基于VMTRGs的风险模型可有效预测LIHC患者的预后。该风险模型与免疫浸润微环境密切相关。四个关键的VMTRG是LIHC的强大预后生物标志物和治疗靶标。
