Genetic Determinants of Immune Cells and Hepatocellular Carcinoma Risk: A Bioinformatics and Bidirectional Mendelian Randomization Study
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摘要:目的
基于生物信息学及特定算法筛选肝细胞癌的核心靶点并探讨其与免疫细胞的关系，并通过孟德尔随机化方法探讨免疫细胞与肝细胞癌的因果关系。
方法通过GEO和TCGA数据库对肝细胞癌发生的相关基因进行筛选，并通过GSVA和CIBERSORT算法进行免疫浸润分析，随后对免疫细胞与肝细胞癌的因果关系进行双向孟德尔随机化分析。
结果筛选出284个肝癌相关基因，在蛋白互作网络中获取到120个相关基因。孟德尔随机化结果显示：髓系细胞中的HLA DR on CD33+ HLA DR+ CD14dim（OR=1.097，95%CI: 1.002~1.201，P=0.045，PBonferroni=0.091）和调节性T细胞中的CD8 on CD28+ CD45RA+ CD8+ T cell（OR=1.123，95%CI: 1.027~1.228，P=0.011，PBonferroni=0.022）是肝细胞癌的危险因素；肝细胞癌是经典树突状细胞中的HLA DR++ monocyte Absolute Count（OR=0.812，95%CI: 0.702~0.938，P=0.005，PBonferroni=0.139）的保护因素。免疫浸润分析显示，关键基因与交集免疫细胞之间具有较好的相关性。
结论肝癌的发生发展可能与CDK1、CCNB1、CDC20有关，并与Th2 cells、T helper cells及Th17 cells、DC等呈现较高程度的相关性，孟德尔随机化显示HLA DR on CD33+、HLA DR+ CD14dim和CD8 on CD28+、CD45RA+ CD8+ T cell与肝细胞癌的风险增加有关，而肝细胞癌的发生风险与HLA DR++ monocyte Absolute Count的水平降低有关。
Abstract:ObjectiveTo identify core targets of hepatocellular carcinoma (HCC) by using bioinformatics and specific algorithms, explore their relationships with immune cells, and investigate the causal relationships between immune cells and HCC through Mendelian randomization.
MethodsRelevant genes associated with the development of HCC were screened using the GEO and TCGA databases. Immune infiltration analysis was conducted using GSVA and CIBERSORT algorithms. A bidirectional Mendelian randomization analysis was then performed to explore the causal relationships between immune cells and HCC.
ResultsA total of 284 HCC-related genes were identified, with 120 genes recognized within the protein interaction network. Immune infiltration analysis revealed significant correlations between key genes and immune cells. Mendelian randomization results indicated that HLA DR on CD33+ HLA DR+ CD14dim (OR=1.097, 95%CI: 1.002–1.201, P=0.045, PBonferroni=0.091) and CD8 on CD28+ CD45RA+ CD8+ T cell (OR=1.123, 95%CI: 1.027–1.228, P=0.011, PBonferroni=0.022) were the risk factors for HCC. Conversely, HLA DR++ monocyte absolute count was identified as a protective factor for HCC (OR=0.812, 95%CI: 0.702–0.938, P=0.005, PBonferroni=0.139).
ConclusionThe occurrence and development of liver cancer may be related to CDK1, CCNB1, and CDC20, showing a high degree of correlation with Th2 cells, T helper cells, Th17 cells, and DCs. Mendelian randomization shows that HLA DR on CD33+HLA DR+ CD14dim and CD8 on CD28+CD45RA+CD8+T cells are associated with an increased risk of HCC. The risk of hepatocellular carcinoma is associated with a decrease in the level of HLA DR++monocyte absolute count.
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Key words:
- Mendelian randomization /
- Hepatocellular carcinoma /
- Immune cell /
- Immunophenotype /
- SNPs
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0 引言
胶质母细胞瘤（Glioblastoma, GBM）被认为起源于神经胶质干细胞或祖细胞[1]，是级别较高的恶性胶质瘤（Ⅳ级），也是中枢神经系统（Central nervous system, CNS）最常见的原发性恶性肿瘤，占所有CNS肿瘤的14.5%，占CNS恶性肿瘤的48.6%。GBM患者的中位总生存期（Overall survival, OS）仅为15个月[2-3]。GBM是高度血管化的肿瘤，其生长依赖于新生血管的形成。血管新生是血管内皮细胞（Endothelial cells, ECs）在特定信号刺激下增殖、迁移和分化的复杂过程[4]。内皮细胞除了参与血管生成，进一步为肿瘤提供氧气和营养物质外，还参与肿瘤血管内侵袭，使肿瘤细胞转移到血管腔内，促进肿瘤的侵袭和转移[5]。因此，内皮细胞在GBM的发生、发展中发挥着重要的作用。相较于肿瘤细胞标志物，内皮细胞相关标志物或可以更全面地评估肿瘤进展、反映肿瘤的预后。
寻找可靠的生物标志物来预测GBM患者的预后，并开发新的分子靶向治疗策略，对于目前GBM的治疗至关重要。然而，GBM目前尚缺乏有效的预后标志物。本研究旨在通过多维度生物信息学分析方法，构建GBM内皮细胞风险评分，探讨并验证内皮细胞在GBM中的预后价值。
1 资料与方法
1.1 GBM转录组数据获取与标准化
GBM转录组数据以及相关的临床资料获取于癌症基因组图谱（The Cancer Genome Atlas, TCGA）数据库（TCGA-GBM，包含174名GBM患者），其中转录组数据被统一标准化为每百万条reads的转录本（Transcripts per million reads, TPM）格式，并进行log2转化。中国脑胶质瘤基因组图谱（Chinese Glioma Genome Atlas, CGGA）中GBM相关数据（CGGA-GBM，包含388名GBM患者）获取于GlioVis数据库（http://gliovis.bioinfo.cnio.es）。
1.2 GBM单细胞测序分析
GBM单细胞数据（GSE103224）获取于开源单细胞数据库TSCH2数据库（http://tisch.comp-genomics.org/），相关细胞分群结果直接获取于该数据库。内皮细胞相关基因标志物按照如下标准筛选：在满足P.adj<0.05条件下，按照log2FC的绝对值排序在上调（Up-regulated）和下调（Down-regulated）基因中各取前100进行后续分析。
1.3 单因素Cox回归分析
单因素Cox回归分析获取TCGA-GBM预后相关基因，相关筛选标准：风险比（Hazard ratio, HR）>1且P.adj<0.05或HR<1且P.adj<0.05。
1.4 内皮细胞预后相关标志物获取
使用韦恩图对上述四种标志物取交集，获取的内皮细胞预后相关标志物满足条件：HR>1且为上调基因或HR<1且为下调基因。使用森林图展示相关标志物及其预后相关信息。
1.5 LASSO回归分析
基于TCGA-GBM转录组数据，使用LASSO回归分析进一步筛选内皮细胞预后相关标志物，并构建内皮细胞相关预后风险评分，选取交叉验证误差均值最小对应的lambda值（lambdamin），并获取风险评分中各基因的系数，构成风险评分（Risk score）公式：
$$ Risk\;score = \sum\nolimits_{i = 1}^n \beta i*Expi $$ 其中Exp为基因的表达量，β为基因所对应的系数。
1.6 qPCR实验验证预后相关标志物表达差异
选取在解放军总医院第一医学中心收集的GBM样本组织及瘤周正常脑组织样本各3例进行定量PCR实验，验证用于构建风险评分的4个目的基因（DUSP6、STC1、VWA1和TM4SF1）在GBM组织及瘤周正常脑组织中的表达差异，qPCR引物见表1。收集患者临床病理信息。所有患者及家属均签署知情同意书，本研究符合2013年修订的《赫尔辛基宣言》要求。根据公式整理和计算qPCR实验数据，采用两独立样本t检验进行统计{stats[4.2.1]包以及car[3.1-0]包}，用ggplot2[3.3.6]包对数据进行可视化。
表 1 qPCR实验所用引物Table 1 Primers used in qPCR experimentsGene name Gene ID Forward primer Reverse primer DUSP6 1 848 5′-GAACTGTGGTGTCTTGGTACATT-3′ 5′-GTTCATCGACAGATTGAGCTTCT-3′ STC1 6781 5′-CACGAGCTGACTTCAACAGGA-3′ 5′-GGATGTGCGTTTGATGTGGG-3′ VWA1 64856 5′-GCAGACTCGGGCTACTATGTG-3′ 5′-CACGTTGGACTCAGGCACTA-3′ TM4SF1 4071 5′-TGTGGCAAACGATGTGCGA-3′ 5′-TGACACAGTAGCCAGATCCTG-3′ 1.7 生存分析
基于内皮细胞相关预后风险评分，获取每例GBM患者的风险评分数值，根据该数值的中位数，将患者分为高风险和低风险组。使用Kaplan-Meier法构建生存曲线以鉴定该预后风险评分的预后效能。上述分析及数据可视化基于软件R（4.2.1），相关R包见表2。
表 2 分析及数据可视化所用R包Table 2 R packages for analysis and data visualizationAnalysis item R package used LASSO regression
analysisglmnet (4.1.7) Single factor Cox
regression analysissurvival (3.3.1) and rms (6.3-0) Survival analysis survival (3.3.1), survminer and
ggplot2 (3.3.6)Venn diagram ggplot2 (3.3.6), VennDiagram (1.7.3) Forest plot ggplot2 (3.3.6) Co-expression heat map ggplot2 (3.3.6) 2 结果
2.1 GBM预后相关基因
单因素Cox回归分析共筛选获得2 115个GBM预后相关基因，其中1 494个满足HR>1且P.adj<0.05，621个满足HR<1且P.adj<0.05。
2.2 GBM单细胞测序分析
基于TSCH2数据库对GBM单细胞数据（GSE103224）进行解析，降维后共获得7群细胞，见图1A，分别为AC样肿瘤细胞（AC-like malignant）、内皮细胞（Endothelial）、单核/巨噬细胞（Mono/Macro）、NB样肿瘤细胞（NB-like malignant）、神经元（Neuron）、OC样肿瘤细胞（OC-like malignant）以及OPC样肿瘤细胞（OPC-like malignant）。相关细胞群标志物如图1B所示。根据内皮细胞差异基因及相应筛选标准，共获取200个内皮细胞相关基因标志物，其中100个为上调基因（CLND5、ITM2A、IGFBP7、IFI27以及ESAM等），100个为下调基因（CRYAB、CRYGS、CDK4、S100A6以及APOE等）。
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图 1 GBM单细胞测序分析及内皮细胞预后相关标志物分析Figure 1 GBM single-cell sequencing analysis and endothelial cell prognostic marker analysisA: seven groups of cells obtained by analyzing GBM single-cell data (GSE103224); B: expression of seven cell-population marker genes; C: six endothelial cell prognostic markers obtained after Venn diagram analysis; D: forest plot showing the prognostic information related to the prognostic markers of endothelial cells; E: quantitative analysis of the violin plot showing that the six markers were significantly highly expressed in endothelial cells. GBM: glioblastoma.2.3 内皮细胞预后相关标志物
韦恩图分析共获取6个满足条件的交集基因，即内皮细胞预后相关标志物，分别为PLXND1、DUSP6、STC1、ESM1、TM4SF1以及VWA1基因，见图1C。森林图显示了内皮细胞预后相关标志物相关（PLXND1、DUSP6、STC1、ESM1、TM4SF以及VWA1）的预后信息，见图1D。定量分析小提琴图显示上述标志物均在内皮细胞中显著高表达，见图1E。
2.4 内皮细胞相关预后风险评分
LASSO回归分析共筛选出4个基因（DUSP6、STC1、VWA1和TM4SF1）用于风险评分的构建，变量系数谱见图2A。风险评分=0.171*DUSP6+0.144*STC1+0.041*VWA1−0.004*TM4SF1。变量轨迹图见图2B。TCGA-GBM发现集生存分析显示，风险评分是GBM患者预后不佳的影响因素（HR=1.56，95%CI：1.10~2.21，P=0.007），见图2C，CGGA-GBM验证集生存分析，风险评分亦是GBM患者预后不佳的影响因素（HR=1.40，95%CI：1.12~1.75，P=0.003）。qPCR实验结果显示DUSP6、STC1、VWA1和TM4SF1在GBM组织与瘤周正常脑组织中的表达均显著上调（P<0.001），见图3。
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图 2 内皮细胞相关预后风险评分Figure 2 Endothelial cell-related prognostic risk scoreA: variable coefficient spectrum of four genes screened by LASSO regression analysis; B: six endothelial cell prognostic markers corresponding with the variable trajectory diagram; C: TCGA-GBM discovery-set survival analysis; D: CGGA-GBM validation-set survival analysis.![]()
图 3 目的基因在GBM组织与瘤周正常脑组织中的表达差异Figure 3 Difference in the expression of the target gene between GBM tissues and normal brain tissues3 讨论
GBM是成人中脑部最常见、侵袭性最强的原发性恶性肿瘤。尽管GBM患者在接受最大限度的手术切除后可以继续联合放化疗，但预后仍很差，其中位生存期仅约15个月，5年生存率<10%[6-8]。因此我们亟需探索更多的治疗方法，以提高疗效。目前尚缺乏十分可靠的生物标志物来预测当前或新疗法的预后。Sareen等[9]进行的一项系统评价和Meta分析结果显示：O6-甲基鸟嘌呤-DNA甲基转移酶（O6 -methylguanine-DNA methyltransferase, MGMT）启动子甲基化和异柠檬酸脱氢酶1（Isocitrate dehydrogenase 1, IDH1）突变与GBM患者较长的总生存期显著相关，合并风险比分别为1.66（95%CI：1.32~2.09；P<
0.0001 ）和2.37（95%CI：1.81~3.12；P<0.00001 ）。另有研究显示MGMT启动子甲基化水平在预测替莫唑胺（Temozolomide, TMZ）治疗反应中具有重大的临床意义[10]。1p/19q共缺失和第10染色体丢失也是TMZ反应的阳性预测指标。另一方面，错配修复系统（Mismatch repair, MMR）缺陷导致的高突变表型可预测GBM对TMZ具有耐药性。编码H3.3组蛋白的基因H3F3A的突变是儿童患者预后不良的重要标志物。MYB、MN1和MAPK通路的改变则是积极的预后因素[11]。新的研究通过“液体活检”技术在血液或脑脊液中识别GBM特异性的外泌体，从而实现对GBM患者预后的微创预测[12]。另外，细胞因子信号转导抑制因子蛋白通过JAK/STAT通路和NF-κB信号通路调控GBM的生物合成，从而发挥抑癌作用。SOCS蛋白表达的上调与GBM更好的预后相关[13]。理论上，有许多长链非编码RNA（Long non-coding RNA, lncRNA），如HOTAIR、H19和NEAT1可作为GBM的诊断和预后指标。遗憾的是，迄今为止尚未有lncRNAs成功应用于临床[14-15]。Jarmuzek等[16]对炎性反应和免疫标志物作为GBM患者预后因素的最新研究进行了全面的综述和荟萃分析，结果发现中性粒细胞/淋巴细胞比值（NLR）或血小板/淋巴细胞比值（PLR）较高的患者预后更差。同样，细胞死亡过程中释放入血的游离DNA（Cell-free DNA, cfDNA）（HR=2.35，95%CI：1.27~4.36，P<0.01）较高的患者预后更差[16]。在GBM中，一些miRNA的水平降低。研究表明，这些miRNA的过度表达可增强细胞凋亡和抑制肿瘤进展，提示预后较好[17]。国内有学者研究发现固有免疫分子CD58在高级别胶质瘤中的表达高于低级别胶质瘤和非瘤脑组织，其表达差异与胶质瘤生存期相关，故可作为判定胶质瘤的恶性程度及预后的一项指标[18]。但上述生物标志物在GBM预后的临床预测中仍存在一定的局限性。目前还需要探索更有效、更实用的GBM预后标志物。鉴于血管生成在肿瘤生长和转移中的关键作用，以及GBM的高度血管化特性[19]，探究GBM血管内皮细胞相关预后标志物用于构建预后体系已成为新的研究方向。同时，抗血管生成药物也已被广泛探索作为GBM治疗的新选择[5]。GBM细胞分泌大量的促血管生成因子，营造出强烈的促血管新生的肿瘤微环境（Tumor microenvironment, TME）。GBM内皮细胞是血肿瘤屏障（Blood-tumour barrier, BTB）的重要结构基础，使化疗药物难以通过BTB到达病灶[20]。这成为GBM药物治疗的难题。通过应用单细胞测序对BTB进行更深入的了解，并通过生物信息分析方法开发新的内皮细胞生物标志物，从而实现靶向调节GBM内皮细胞的生物学行为，调控GBM的血管新生及BTB的通透性，抑制肿瘤的生长和侵袭，将为GBM提供新的治疗策略[21]。
TME中的免疫细胞在肿瘤生长过程中发挥着重要作用。近年来的多项研究表明，TME中的B细胞与改善临床预后相关。浆细胞是终末分化的B细胞，可产生高度特异性的抗体。关于临床结局与抗体产生之间关系以及Stephanie等的研究数据显示，在不分泌抗体的小鼠中，双重免疫检查点抑制剂治疗对乳腺癌无效，这强烈表明抗体的产生在抗肿瘤免疫反应中具有重要意义[22]。越来越多的证据表明，自然杀伤细胞（Natural killer, NK）细胞的频率、浸润和功能提高了GBM患者的生存率。但GBM细胞可表达独特的MHC-Ⅰ类分子与NK细胞表面的受体结合，从而抑制其功能[23]，且NK细胞受体NCR2高表达可能与GBM预后较差有关[24]。T细胞在机体对恶性肿瘤的适应性免疫应答中发挥着重要作用。调节性T细胞（Regulatory T cells, Tregs）是一类独特的T细胞群，通过免疫抑制措施调节机体的整体免疫稳态。其中表达叉头状转录因子3（Forkhead Box P3, FOXP3）转录因子的Tregs尤为重要。该转录因子可下调NFAT和NFκB信号通路，从而下调IL2等重要效应细胞因子的表达。GBM预后较差与Tregs/T效应细胞比例较高有关[23]。
综上所述，内皮细胞基因相关标志物可在一定程度上反映GBM的恶性程度及预后。本研究通过qPCR实验进一步验证了GBM中内皮细胞基因相关标志物（DUSP6、STC1、VWA1 和TM4SF1）在GBM组织中的表达均较瘤周正常脑组织显著上调，并通过采用多基因构建风险评分，且验证集生存分析显示风险评分是GBM患者预后不佳的影响因素，进一步完善了GBM预后分析体系。未来需进一步进行大规模临床分析研究。
Competing interests: The authors declare that they have no competing interests.利益冲突声明：所有作者均声明不存在利益冲突。作者贡献：吴 桐：研究方法和数据分析、论文撰写高 菲：研究方法的完善和修改、数据分析滕 飞：数据检索和下载、数据分析张巧丽：论文审阅和修改 -
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图 3 基于肝细胞癌关键基因的GO和KEGG生物学功能富集分析
Figure 3 Biological function enrichment analysis of GO and KEGG based on key genes of hepatocellular carcinoma
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图 4 PPI网络（节点大小和颜色反映了该节点在网络中的重要性）
Figure 4 PPI network (node size and color reflect the importance of the node in the network)
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图 8 关键基因与孟德尔随机化研究中发现的免疫细胞相关性
Figure 8 Correlation between key genes and immune cells discovered in Mendelian randomization studies
表 1 正向MR敏感性分析
Table 1 Positive MR sensitivity analysis
Panel Trait IVW MR-Egger Q P Intercept P Blood protein measurement HLA DR on CD33+ HLA DR+ CD14dim 12.0333 0.3611 0.0305 0.3822 Blood protein measurement CD8 on CD28+ CD45RA+ CD8+ T cell 23.9265 0.0911 − 0.0059 0.8276 
下载: 导出CSV
表 2 反向MR敏感性分析
Table 2 Reverse MR sensitivity analysis
Panel Trait IVW MR-Egger Q P Intercept P Lymphocyte count CD20- CD38- B cell %B cell 0.2154 0.8979 0.0304 0.7262 Leukocyte count HLA DR++ monocyte %leukocyte 1.0918 0.5793 − 0.0504 0.6030 Leukocyte count HLA DR++ monocyte absolute count 1.8067 0.4052 − 0.0773 0.4596 Myeloid white cell count Monocytic myeloid-derived suppressor cells absolute count 0.8472 0.6547 0.0712 0.5785 Lymphocyte count Transitional B cell %lymphocyte 1.2605 0.5325 − 0.0634 0.5193 Blood protein measurement BAFF-R on IgD+ CD38+ B cell 1.3778 0.5021 − 0.0299 0.7616 Blood protein measurement BAFF-R on transitional B cell 0.1478 0.9288 − 0.0059 0.9462 Blood protein measurement CD24 on IgD+ CD38+ B cell 0.7720 0.6798 − 0.0293 0.7390 Blood protein measurement CD27 on IgD+ CD38- unswitched memory B cell 0.4226 0.8095 0.0097 0.9333 Blood protein measurement CD38 on CD20- B cell 2.2254 0.3287 0.0506 0.6504 Blood protein measurement IgD on IgD+ CD38- B cell 2.2311 0.3277 0.0958 0.3954 Blood protein measurement CD3 on Effector Memory CD8+ T cell 0.4769 0.7878 0.0160 0.8661 Blood protein measurement CD3 on HLA DR+ CD4+ T cell 0.3367 0.8450 − 0.0239 0.7976 Blood protein measurement CD86 on granulocyte 1.2054 0.5473 0.0699 0.5245 Blood protein measurement CD33 on CD33+ HLA DR+ CD14dim 0.8884 0.6413 0.0914 0.5288 Blood protein measurement CD33 on Monocytic Myeloid-Derived Suppressor Cells 1.5625 0.4578 0.0829 0.5587 Blood protein measurement CD33 on CD33dim HLA DR- 0.5776 0.7492 0.0729 0.5994 Blood protein measurement CD33 on basophil 0.5990 0.7412 0.0696 0.6138 Blood protein measurement CD33 on CD33+ HLA DR+ 0.9939 0.6084 0.0969 0.5098 Blood protein measurement CD33 on CD33+ HLA DR+ CD14- 1.0684 0.5861 0.0990 0.5034 Blood protein measurement CD4 on HLA DR+ CD4+ T cell 0.2144 0.8984 0.0294 0.7503 Blood protein measurement FSC-A on monocyte 1.5506 0.4606 0.0926 0.4311 Blood protein measurement CD64 on CD14- CD16+ monocyte 0.6259 0.7313 0.0311 0.7254 Blood protein measurement CCR2 on CD14+ CD16+ monocyte 0.2206 0.8956 0.0304 0.7223 Blood protein measurement CD4 on Effector Memory CD4+ T cell 0.2212 0.8953 0.0107 0.9076 Blood protein measurement CD8 on Effector Memory CD8+ T cell 1.1106 0.5739 − 0.0772 0.4850 Blood protein measurement CD8 on Terminally Differentiated CD8+ T cell 3.0539 0.2172 0.0729 0.6119 Blood protein measurement SSC-A on myeloid Dendritic Cell 1.5909 0.4514 0.0678 0.5353 Blood protein measurement SSC-A on monocyte 1.4362 0.4877 0.0904 0.4439
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[1] Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2024, 74(3): 229-263. doi: 10.3322/caac.21834
[2] Llovet JM, Kelley RK, Villanueva A, et al. Hepatocellular carcinoma[J]. Nat Rev Dis Primers, 2021, 7(1): 6.
[3] Thomas London W, Petrick JL, et al. Liver cancer. In: M Thun, MS Linet, JR Cerhan, CA Haiman, D Schottenfeld, eds. Cancer Epidemiology and Prevention[M]. 4th ed. Oxford University Press, 2018: 635-660.
[4] 中华人民共和国国家卫生健康委员会. 原发性肝癌诊疗指南(2024年版)[J]. 临床肝胆病杂志, 2024, 40(5): 893-918. [National Health Commission of the People's Republic of China. Primary liver cancer Diagnosis and treatment guidelines (2024 edition)[J]. Lin Chuang Gan Dan Bing Za Zhi, 2024, 40(5): 893-918.] doi: 10.12449/JCH240508 National Health Commission of the People's Republic of China. Primary liver cancer Diagnosis and treatment guidelines (2024 edition)[J]. Lin Chuang Gan Dan Bing Za Zhi, 2024, 40(5): 893-918. doi: 10.12449/JCH240508
[5] Massarweh NN, El-Serag HB. Epidemiology of hepatocellular carcinoma and intrahepatic cholangiocarcinoma[J]. Cancer Control, 2017, 24(3): 1073274817729245.
[6] 刘宗超, 李哲轩, 张阳, 等. 2020全球癌症统计报告解读[J]. 肿瘤综合治疗电子杂志, 2021, 7(2): 1-14. [Liu ZC, Li ZX, Zhang Y, et al. Interpretation of 2020 global cancer statistics report[J]. Zhong Liu Zong He Zhi Liao Dian Zi Za Zhi, 2019, 7(2): 1-14.] Liu ZC, Li ZX, Zhang Y, et al. Interpretation of 2020 global cancer statistics report[J]. Zhong Liu Zong He Zhi Liao Dian Zi Za Zhi, 2019, 7(2): 1-14.
[7] de Visser KE, Joyce JA. The evolving tumor microenvironment: From cancer initiation to metastatic outgrowth[J]. Cancer Cell, 2023, 41(3): 374-403. doi: 10.1016/j.ccell.2023.02.016
[8] Yasuoka H, Asai A, Ohama H, et al. Increased both PD-L1 and PD-L2 expressions on monocytes of patients with hepatocellular carcinoma was associated with a poor prognosis[J]. Sci Rep, 2020, 10(1): 10377. doi: 10.1038/s41598-020-67497-2
[9] Cao D, Chen MK, Zhang QF, et al. Identification of immunological subtypes of hepatocellular carcinoma with expression profiling of immune-modulating genes[J]. Aging (Albany NY), 2020, 12(12): 12187-12205.
[10] Liu F, Qin L, Liao Z, et al. Microenvironment characterization and multi-omics signatures related to prognosis and immunotherapy response of hepatocellular carcinoma[J]. Exp Hematol Oncol., 2020, 9: 10. doi: 10.1186/s40164-020-00165-3
[11] 黄燕妮, 蓝雪灵, 朱敏敏, 等. PD-1/PD-L1抑制剂联合抗血管内皮生长因子药物免疫治疗晚期肝癌的研究进展[J]. 中国药理学通报, 2024, (8): 1429-1436. [Huang Yanni, LAN Xueling, Zhu Minmin, et al. Research progress of PD-1/PD-L1 inhibitors combined with anti-vascular endothelial growth factor drugs in immunotherapy of advanced liver cancer[J]. Zhongguo Yao Li Xue Tong Bao, 2024, (8): 1429-1436.] Huang Yanni, LAN Xueling, Zhu Minmin, et al. Research progress of PD-1/PD-L1 inhibitors combined with anti-vascular endothelial growth factor drugs in immunotherapy of advanced liver cancer[J]. Zhongguo Yao Li Xue Tong Bao, 2024, (8): 1429-1436.
[12] Luo X, Huang W, Li S, et al. SOX12 Facilitates Hepatocellular Carcinoma Progression and Metastasis through Promoting Regulatory T-cells Infiltration and Immunosuppression[J]. Adv Sci (Weinh), 2024, 11(36): e2310304.
[13] Haycock PC, Burgess S, Wade KH, et al. Best (but oft-forgotten) practices: the design, analysis, and interpretation of Mendelian randomization studies[J]. Am J Clin Nutr, 2016, 103(4): 965-978. doi: 10.3945/ajcn.115.118216
[14] Sanderson E, Glymour MM, Holmes MV, et al. Mendelian randomization[J]. Nat Rev Methods Primers, 2022, 2: 6. doi: 10.1038/s43586-021-00092-5
[15] Orrù V, Steri M, Sidore C, et al. Complex genetic signatures in immune cells underlie autoimmunity and inform therapy[J]. Nat Genet, 2020, 52(10): 1036-1045. doi: 10.1038/s41588-020-0684-4
[16] Birney E. Mendelian Randomization[J]. Cold Spring Harb Perspect Med, 2022, 12(4): a041302.
[17] Pierce BL, Ahsan H, Vanderweele TJ. Power and instrument strength requirements for Mendelian randomization studies using multiple genetic variants[J]. Int J Epidemiol, 2011, 40(3): 740-752. doi: 10.1093/ije/dyq151
[18] Kamat MA, Blackshaw JA, Young R, et al. PhenoScanner V2: an expanded tool for searching human genotype-phenotype associations[J]. Bioinformatics, 2019, 35(22): 4851-4853. doi: 10.1093/bioinformatics/btz469
[19] Burgess S, Thompson SG. Interpreting findings from mendelian randomization using the MR-Egger method[J]. Eur J Epidemiol, 2017, 32(5): 377-389. doi: 10.1007/s10654-017-0255-x
[20] Wu F, Huang Y, Hu J, et al. Mendelian randomization study of inflammatory bowel disease and bone mineral density[J]. BMC Med, 2020, 18(1): 312. doi: 10.1186/s12916-020-01778-5
[21] Hao X, Ren C, Zhou H, et al. Association between circulating immune cells and the risk of prostate cancer: a Mendelian randomization study[J]. Front Endocrinol (Lausanne), 2024, 15: 1358416.
[22] Chen W, Zheng R, Baade PD, et al. Cancer statistics in China, 2015[J]. CA Cancer J Clin, 2016, 66(2): 115-132. doi: 10.3322/caac.21338
[23] Liao S, Wang K, Zhang L, et al. PRC1 and RACGAP1 are Diagnostic Biomarkers of Early HCC and PRC1 Drives Self-Renewal of Liver Cancer Stem Cells[J]. Front Cell Dev Biol, 2022, 10: 864051. doi: 10.3389/fcell.2022.864051
[24] Zongyi Y, Xiaowu L. Immunotherapy for hepatocellular carcinoma[J]. Cancer Lett, 2020, 470: 8-17. doi: 10.1016/j.canlet.2019.12.002
[25] Malumbres M, Barbacid M. Mammalian cyclin-dependent kinases[J]. Trends Biochem Sci, 2005, 30(11): 630-641.
[26] Zou Y, Ruan S, Jin L, et al. CDK1, CCNB1, and CCNB2 are Prognostic Biomarkers and Correlated with Immune Infiltration in Hepatocellular Carcinoma[J]. Med Sci Monit, 2020, 26: e925289.
[27] Wang Z, Wan L, Zhong J, et al. Cdc20: a potential novel therapeutic target for cancer treatment[J]. Curr Pharm Des, 2013, 19(18): 3210-3214.
[28] Wang J, Amin A, Cheung MH, et al. Targeted inhibition of the expression of both MCM5 and MCM7 by miRNA-214 impedes DNA replication and tumorigenesis in hepatocellular carcinoma cells[J]. Cancer Lett, 2022, 539: 215677. doi: 10.1016/j.canlet.2022.215677
[29] Ahmed R, Gray D. Immunological memory and protective immunity: understanding their relation[J]. Science, 1996, 272(5258): 54-60. doi: 10.1126/science.272.5258.54
[30] Hamann D, Baars PA, Rep MH, et al. Phenotypic and functional separation of memory and effector human CD8 T cells[J]. J Exp Med, 1997, 186(9): 1407-1418. doi: 10.1084/jem.186.9.1407
[31] Pilch H, Hoehn H, Schmidt M, et al. CD8+CD45RA+CD27-CD28-T-cell subset in PBL of cervical cancer patients representing CD8+T-cells being able to recognize cervical cancer associated antigens provided by HPV 16 E7[J]. Zentralbl Gynakol, 2002, 124(8-9): 406-412.
[32] Höhn H, Jülch M, Pilch H, et al. Definition of the HLA-A2 restricted peptides recognized by human CD8+ effector T cells by flow-assisted sorting of the CD8+ CD45RA+ CD28- T cell subpopulation[J]. Clin Exp Immunol, 2003, 131(1): 102-110. doi: 10.1046/j.1365-2249.2003.02036.x
[33] Wu Z, Shi H, Zhang L, et al. Comparative analysis of monocyte-derived dendritic cell phenotype and T cell stimulatory function in patients with acute-on-chronic liver failure with different clinical parameters[J]. Front Immunol, 2023, 14: 1290445. doi: 10.3389/fimmu.2023.1290445
[34] Mengos AE, Gastineau DA, Gustafson MP. The CD14+HLA-DRlo/neg Monocyte: An Immunosuppressive Phenotype That Restrains Responses to Cancer Immunotherapy[J]. Front Immunol, 2019, 10: 1147. doi: 10.3389/fimmu.2019.01147
[35] Gustafson MP, Lin Y, Bleeker JS, et al. Intratumoral CD14+ Cells and Circulating CD14+HLA-DRlo/neg Monocytes Correlate with Decreased Survival in Patients with Clear Cell Renal Cell Carcinoma[J]. Clin Cancer Res, 2015, 21(18): 4224-4233.
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