| 摘要: |
| 目的 利用生物信息学技术筛选与肝癌复发相关的核心基因及信号通路，为探索肝癌复发的分子机制提供理论依据。方法 从癌症基因组图谱（TCGA）和基因表达汇编（GEO）数据库下载肝癌相关数据集，利用edgeR算法筛选差异表达基因。对差异表达基因进行基因本体（GO）及京都基因与基因组百科全书（KEGG）富集分析。通过蛋白质-蛋白质相互作用（PPI）网络构建确定肝癌复发相关核心基因。选择核心基因后，应用最小绝对收缩和选择算子（LASSO）-logistic回归分析构建肝癌复发预测模型。应用单样本基因集富集分析（ssGSEA）、使用表达数据估计恶性肿瘤组织中的基质细胞和免疫细胞（ESTIMATE）及微环境细胞种群计数器（MCP）方法评估免疫浸润细胞。结果 共筛选出343个肝癌复发相关差异表达基因。GO及KEGG富集分析结果表明，差异表达基因主要富集于前脑发育、化学突触传递的调节等生物过程及神经激活配体-受体相互作用等信号通路。PPI分析筛选出12个核心基因。通过对12个核心基因进行LASSO-logistic 回归分析，确定的独立风险评分模型为风险评分＝22.1－生长抑素（SST）表达值×0.21－多巴胺 D2 受体（DRD2）表达值×1.94－钙结合蛋白 2（CALB2）表达值×1.153。该风险评分模型显示出较好的预测能力，AUC值为0.683。CALB2在肿瘤组织中低表达，与复发性肝癌患者肿瘤微环境中的免疫细胞浸润呈正相关。结论 肝癌复发风险评分模型可以提供较好的复发预测能力，其中的核心基因CALB2可以在肝癌复发中发挥关键作用。 |
| 关键词: 肝肿瘤 复发 核心基因 预测模型 癌症基因组图谱 基因表达汇编 |
| DOI：10.16781/j.CN31-2187/R.20210889 |
| 投稿时间：2021-09-07修订日期：2021-12-06 |
| 基金项目:国家自然科学基金（81803450）. |
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| Effect of CALB2 mediated immune cell infiltration in tumor microenvironment for the recurrence of liver cancer:a mechanism study based on bioinformatics |
| GE Ji-yun,YAO Chen,ZHANG Jing,WANG Li-ling,XIE Fang-yuan,BAO Lei-lei,HUANG Yu-feng* |
(Department of Pharmacy, The Third Affiliated Hospital of Naval Medical University (Second Military Medical University), Shanghai 200438, China
*Corresponding author) |
| Abstract: |
| Objective To analyze and screen the hub genes and signal pathways related to the recurrence of liver cancer based on bioinformatics, so as to provide a theoretical basis for exploring the molecular mechanism of liver cancer recurrence. Methods Datasets related to liver cancer were downloaded from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database, and differential expression genes were screened by edgeR algorithm, and then they were evaluated by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Protein-protein interaction (PPI) network was constructed to determine the hub genes related to liver cancer recurrence. After the hub genes were selected, least absolute shrinkage and selection operator (LASSO)-logistic regression analysis was used to construct the recurrence prediction model. Single sample gene set enrichment analysis (ssGSEA), estimation of stromal and immune cells in malignant tumour tissues using expression data (ESTIMATE) and microenvironment cell populations-counter (MCP) algorithms were used to evaluate immune infiltrating cells. Results A total of 343 recurrence related differentially expressed genes were screened. Go and KEGG enrichment analyses indicated that most of the genes were enriched in biological processes such as forebrain development, regulation of chemical synaptic transmission and signaling pathways such as neuroactive ligand-receptor interaction. PPI network analysis screened 12 hub genes. An independent risk-score model was determined by using LASSO-logistic regression analysis based on 12 hub genes, with a calculation formula:risk score=22.1-somatostatin (SST) expression value×0.21-dopamine receptor D2 (DRD2) expression value×1.94-calbindin 2 (CALB2) expression value×1.153. The risk model showed a good predictive ability with an area under curve of 0.683. Moreover, it was proved that CALB2 was lowly expressed in tumor tissue and positively correlated with immune infiltrating cells in tumor microenvironment of recurrent liver cancer. Conclusion The recurrence related gene model can provide better efficacy to predict the recurrence of patients, and the hub gene CALB2 plays a crucial role in the recurrence of liver cancer. |
| Key words: liver neoplasms recurrence hub genes prediction model The Cancer Genome Atlas Gene Expression Omnibus |
