Anillin actin-binding protein (ANLN) is crucially involved in cell proliferation and migration. Moreover, ANLN is significantly in tumor progression in several types of human malignant tumors; however, it remains unclear whether ANLN acts through common molecular pathways within different tumor microenvironments, pathogeneses, prognoses and immunotherapy contexts. Therefore, this study aimed to perform bioinformatics analysis to examine the correlation of ANLN with tumor immune infiltration, immune evasion, tumor progression, immunotherapy, and tumor prognosis. We observed increased ANLN expression in multiple tumors, which could be involved in tumor cell proliferation, migration, infiltration, and prognosis. The level of ANLN methylation and genetic alteration was associated with prognosis in numerous tumors. ANLN facilitates tumor immune evasion through different mechanisms, which involve T-cell exclusion in different cancer types and tumor-infiltrating immune cells in colon adenocarcinoma, kidney renal clear cell carcinoma, liver hepatocellular carcinoma, and prostate adenocarcinoma. Additionally, ANLN is correlated with immune or chemotherapeutic outcomes in malignant cancers. Notably, ANLN expression may be a predictive biomarker for the response to immune checkpoint inhibitors. Taken together, our findings suggest that ANLN can be used as an onco-immunological biomarker and could serve as a hallmark for tumor screening, prognosis, individualized treatment design, and follow-up.

Background: Glucose-6-phosphate dehydrogenase (G6PD) plays an important role in the metabolic and immunological aspects of tumors. In hepatocellular carcinoma (HCC), the alteration of tumor microenvironment influences recurrence and metastasis. We extracted G6PD-related data from public databases of HCC tissues and used a bioinformatics approach to explore the correlation between G6PD expression and clinicopathological features and prognosis of immune cell infiltration in HCC. Methods: We extract G6PD expression information from TCGA and GEO databases in liver cancer tissues and normal tissues, validated by immunohistochemistry, and the correlation between G6PD expression and clinical features is analyzed, and the clinical significance of G6PD in liver cancer is assessed by Kaplan-Meier, Cox regression and prognostic line graph models. Functional enrichment analysis is performed by protein-protein interaction (PPI) network, GO/KEGG, GSEA and G6PD-associated differentially expressed genes (DEGs). TIMER and ssGSEA packages are used to assess the correlation between expression and the level of immune cell infiltration. Results: Our results show that G6PD expression is significantly upregulated in hepatocellular carcinoma tissues ( P < 0.001). G6PD expression is associated with histological grade, pathological stage, T-stage, vascular infiltration and AFP level ( P < 0.05); HCC patients in the low G6PD expression group had longer overall survival and better prognosis compared with the high G6PD expression group ( P < 0.05). The level of G6PD expression also affects the levels of macrophages, unactivated dendritic cells, B cells, and follicular helper T cells in the tumor microenvironment. Conclusion : High expression of G6PD is a potential biomarker for poor prognosis of hepatocellular carcinoma, and G6PD may be a target for immunotherapy of HCC.

目的 基于肿瘤基因组图谱(TCGA)数据库,探索肝细胞癌中差异表达铁死亡相关基因与肿瘤预后的关系,构建肝细胞癌患者预后风险模型.方法 从TCGA数据库中下载肝细胞癌转录组和患者对应的临床数据,筛选出癌组织中的差异表达基因,同时结合FerrDb数据库,筛选出与肝细胞癌患者总生存期密切相关的铁死亡基因,采用Losso回归分析和Cox回归分析确认影响预后的关键基因并筛选出与预后相关的差异表达基因.COX回归分析用于构建评估预后的风险评分模型,用诺莫列线图及校准曲线评价模型的预测能力.结果 22个铁死亡相关基因在肝细胞癌组织与正常组织中表达差异有统计学意义(P＜0.05);GO和KEGG分析显示,差异基因在肿瘤代谢等生物学过程显著富集(P＜0.05);Lasso回归分析确认CARS1、FANCD2、SLC1A5、SLC7A11为影响预后的关键基因,成功构建基于铁死亡相关基因的预后模型,模型将所有患者分为高风险与低风险组且提示高风险组的总体生存率显著低于低分险组(P＜0.05);ROC曲线显示,1年、3年和5年生存率的曲线下面积分别为0.765、0.673和0.687;多因素COX回归分析提示,风险评分是影响肝细胞癌预后的独立危险因素(HR=1.833,95％CI为1.257～2.673;P＜0.05);诺莫列线图及校准曲线提示,预后模型具有良好的预测能力.结论 此研究成功构建了铁死亡相关基因的肝细胞癌预后模型,该模型可以对肝细胞癌患者的个体化治疗提供理论依据,并提高肝细胞癌患者的个体化预后预测结果的准确度.

目的:建立一项更易于临床操作、推广的关于原发性肝细胞癌伴微血管侵犯(MVI)术前早期诊断列线图预测模型，通过内部验证及外部验证对模型进行评估。方法:回顾性分析2017年1月至2020年12月期间宁夏医科大学总医院收治的294例肝细胞癌患者的临床资料。依据就诊时间分为两组：建模组( n＝231)和验证组( n＝63)。根据既往文献和相关临床经验且易于术前获取的原则，初步选取γ-谷氨酰基转移酶(GGT)、血小板计数/淋巴细胞计数比值(PLR)、纤维蛋白原/白蛋白比值(FAR)、淋巴细胞计数/单核细胞计数比值(LMR)、天门冬氨酸氨基转移酶/血小板计数比值(APRI)等指标进行考察，筛选确定肝细胞癌伴MVI的独立危险因素，并以此构建列线图预测模型，将验证组应用于模型进行外部验证。 结果:本研究共纳入294例患者，其中男性223例，女性71例，年龄(55.1±10.9)岁。建模组中MVI阳性95例，MVI阴性136例；验证组中MVI阳性38例，MVI阴性25例。多因素logistic回归分析结果显示，FAR>0.06、GGT>50 U/L、APRI>0.16、肿瘤长径>5 cm、LMR>3.57和PLR>98.75是肝细胞癌发生MVI的独立危险因素( P<0.05)。以此构建的列线图模型的实际预测结果与理想结果相接近，具有良好的预测性能，C指数均在0.71~0.90之间。运用决策曲线分析评估预测模型肝细胞癌术前MVI风险的临床净获益情况，结果显示，当净获益率>0时，预测模型阈值为4%~77%，表明该模型具有良好的临床应用价值。 结论:根据术前临床指标GGT、APRI、LMR、PLR、FAR及肿瘤长径构建列线图模型，可以简单、准确地对原发性肝癌伴MVI进行预测。

目的 探究肝细胞癌癌旁组织CCN2(connective tissue growth factor,CTGF/CCN2)的表达与临床病理特征及预后的相关性.方法 采用免疫组织化学法检测肝细胞癌癌旁组织CCN2的表达程度;回顾性分析癌旁组织CCN2的表达与肝细胞癌患者临床病理特征及预后的相关性.结果 肝细胞癌癌旁组织中CCN2表达水平与性别、白蛋白水平、肿瘤侵犯肝包膜、肿瘤直径、T分期、远处转移及肝内早期复发有关(P均<0.05).Spearman秩相关分析显示,CCN2表达水平与性别(rs=0.250)、侵犯肝包膜(rs=0.242)、肿瘤直径(rs=0.169)、T分期(rs=0.190)、远处转移(rs=0.267)及肝内早期复发(rs=0.285)均呈正相关关系(P均<0.05).Kaplan-Meier生存曲线显示,CCN2低表达组患者总生存期(overall survival,OS)和无病生存期(disease-free survival,DFS)均高于CCN2高表达组患者(P均<0.05).单因素和多因素Cox回归分析显示,癌旁组织CCN2高表达是肝细胞癌患者预后的危险因素(P<0.05).结论 癌旁组织CCN2可作为肝细胞癌预后评估的临床标记物.