Development and validation of a 14-gene signature for prognosis prediction in hepatocellular carcinoma

Worldwide, hepatocellular carcinoma (HCC) remains a crucial medical problem. Precise and concise prognostic models are urgently needed because of the intricate gene variations among liver cancer cells. We conducted this study to identify a prognostic gene signature with biological significance. We applied two algorithms to generate differentially expressed genes (DEGs) between HCC and normal specimens in The Cancer Genome Atlas cohort (training set included) and performed enrichment analyses to expound on their biological significance. A protein-protein interactions network was established based on the STRING online tool. We then used Cytoscape to screen hub genes in crucial modules. A multigene signature was constructed by Cox regression analysis of hub genes to stratify the prognoses of HCC patients in the training set. The prognostic value of the multigene signature was externally validated in two other sets from Gene Expression Omnibus (GSE14520 and GSE76427), and its role in recurrence prediction was also investigated. A total of 2000 DEGs were obtained, including 1542 upregulated genes and 458 downregulated genes. Subsequently, we constructed a 14-gene signature on the basis of 56 hub genes, which was a good predictor of overall survival. The prognostic signature could be replicated in GSE14520 and GSE76427. Moreover, the 14-gene signature could be applied for recurrence prediction in the training set and GSE14520. In summary, the 14-gene signature extracted from hub genes was involved in some of the HCC-related signalling pathways; it not only served as a predictive signature for HCC outcome but could also be used to predict HCC recurrence.     

Prognostic relevance of miR-137 and its liver microenvironment regulatory target gene AFM in hepatocellular carcinoma

MiR-137 has been identified as potential hepatocellular carcinoma (HCC) prognostic biomarkers. Highly relevant HCC prognostic biomarkers may be derived from combinations of miR-137 with its target genes involved in the regulation of liver microenvironment. This study aimed at the discovery of such a combination with improved HCC prognosis performance than miR-137 or its target gene alone in a significantly higher number of HCC patients than previous studies. Analysis of the differentially expressed micro RNAs (miRNAs) between cancer and noncancer tissues reconfirmed miR-137 to be among the most relevant prognostic miRNAs and the data of 375 HCC patients and 50 normal cases were from the Cancer Genome Atlas (TCGA) data sets. Target genes were identified by the established search methods and Kaplan–Meier survival analysis of HCC patients was used to evaluate the overall survival (OS) and recurrence-free survival (RFS). Cox proportional hazards regression indicated that the miR-137 and its target gene AFM combination is an independent prognostic factor for the OS and RFS in HCC. In vitro experiments validated that miR-137 could bind to 3′-untranslated region of the AFM and promote the invasion and metastasis of HCC cell lines. The expressions of miR-137 and its liver microenvironment regulatory target gene AFM in combination significantly correlated with HCC progression in a higher number of patients than in previous studies, which suggested their potential as prognostic biomarkers for HCC.     

CDKN2A is a prognostic biomarker and correlated with immune infiltrates in hepatocellular carcinoma

Cyclin dependent kinase inhibitor 2A (CDKN2A) is an essential regulator of immune cell functionality, but the mechanisms whereby it drives immune infiltration in hepatocellular carcinoma (HCC) remain unclear. In the present study, we studied the association with CDKN2A expression and immune invasion with the risk of developing HCC. A totally of 2207 different genes were found between HCC and adjacent liver tissues from TCGA and GEO databases. CDKN2A was highly expressed in HCC and associated with poorer overall survival and disease-free survival. Notably, CDKN2A expression was positively correlated with infiltrating levels into purity, B cell, CD+8 T cell, CD+4 T cell, macrophage, neutrophil, and dendritic cells in HCC. CDKN2A expression showed strong correlations between diverse immune marker sets in HCC. These findings suggest that CDKN2A expression potentially contributes to regulation of tumor-associated macrophages and can be used as a prognostic biomarker for determining prognosis and immune infiltration in HCC.     

A dual immune signature of CD8+ T cells and MMP9 improves the survival of patients with hepatocellular carcinoma

The 5-year survival of hepatocellular carcinoma (HCC) is difficult due to the high recurrence rate and metastasis. Tumor infiltrating immune cells (TICs) and immune-related genes (IRGs) bring hope to improve survival and treatment of HCC patients. However, there are problems in predicting immune signatures and identifying novel therapeutic targets. In the study, the CIBERSORT algorithm was used to evaluate 22 immune cell infiltration patterns in gene expression omnibus (GEO) and the cancer genome atlas (TCGA) data. Eight immune cells were found to have significant infiltration differences between the tumor and normal groups. The CD8+ T cells immune signature was constructed by least absolute shrinkage and selection operator (LASSO) algorithm. The high infiltration level of CD8+ T cells could significantly improve survival of patients. The weighted gene co-expression network analysis (WGCNA) algorithm identified MMP9 was closely related to the overall survival of HCC patients. K-M survival and tROC analysis confirmed that MMP9 had an excellent prognostic prediction. Cox regression showed that a dual immune signature of CD8+ T cells and MMP9 was independent survival factor in HCC. Therefore, a dual prognostic immune signature could improve the survival of patient and may provide a new strategy for the immunotherapy of HCC.     

Diagnostic and prognostic value of circular RNAs in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the sixth most common malignant tumour, which has posed a heavy health and financial burden worldwide. Due to limited symptoms at the early stage and the limitation in current biomarkers, HCC patients are usually diagnosed at the advanced stage with a pessimistic overall survival rate. Circular RNAs (circRNAs) are a subclass of single-stranded RNAs characterized by a covalently closed loop structure without 3’- or 5’-end. With advances in high-throughput sequencing technology and bioinformatics, accumulating studies have demonstrated the promotor or suppressor roles of circRNAs in the carcinogenesis, progression, and metastasis of HCC. Moreover, circRNAs are characteristic of higher abundance, stability and conservation compared with linear RNAs. Therefore, circRNAs have emerged as one of the most promising diagnostic and prognostic biomarkers for HCC with reliable accuracy, sensitivity and specificity. In this review, we briefly introduce the characteristics of circRNAs and summarize the roles of circRNAs in the biological procedures of HCC. Furthermore, we provide an overview on the potential diagnostic and prognostic value of circRNAs as biomarkers for patients with HCC. Finally, we discuss future perspectives of circRNAs in cancer research.     

环氧化物水解酶2在肝细胞癌中的表达水平与功能作用

本研究通过公共数据和实验数据,全面分析环氧化物水解酶2(epoxide hydrolase 2, EPHX2)在肝细胞癌中的表达情况、功能作用以及预后意义。利用GEO和MitoCarta数据集,筛选肝细胞癌中呈差异表达的线粒体相关基因;利用TCGA数据库分析EPHX2及其相关基因在肝细胞癌中的表达水平;运行R包绘制Kaplan-Meier生存曲线和功能富集分析;基于STRING和GSEA构建蛋白质互作网络和基因集富集分析;荧光定量PCR和GEO数据集验证EPHX2在肝细胞癌中的表达水平。本研究共筛选得到15个在肝细胞癌中呈差异表达的线粒体相关基因。EPHX2在肝细胞癌组织中的表达水平显著降低(P<0.01)。EPHX2表达水平与肝癌患者性别、分期和级别有关,而与年龄、T分期等因素无关。与EPHX2低表达组肝癌患者相比,EPHX2高表达组肝癌患者预后较好。功能富集结果显示,EPHX2与补体途径、脂肪酸降解等信号通路有关。蛋白质互作网络结果显示,EPHX2与HAO1、AGXT、ACOX1、GSTκ1、SCP-2、CAT、CYP2C8,CYP2C9,CYP2B6,和CYP2J2等密切相关。GSEA结果显示,EPHX2低表达组与肝癌细胞增殖、肝癌复发等基因集正相关。荧光定量PCR和GEO数据集验证结果显示,EPHX2在肝细胞癌组织和肝癌细胞株中呈显著低表达。EPHX2在肝细胞癌中呈显著低表达,提示其可能在肝细胞癌发生发展过程中发挥抑癌基因作用,但具体作用机制还需进一步验证。     

多基因模型在肝细胞癌预后中的应用

利用较少分子信息预测肝细胞癌类型对患者的个性化治疗十分关键。探索已知的与肝细胞癌预后相关的信号通路,共发现41个关键基因。随后,运用机器学习的方法对其构建风险预测模型,并在4个肝细胞癌数据集上进行验证。结果显示,该模型能将肝细胞癌患者分成两个预后差异显著的类型:癌症基因图谱(The cancer genome atlas,TCGA)数据集交叉验证的平均log rank P值为0.03;其他测试数据集的log rank P 值分别为0.000 38、0.002 1和0.01。生物信息学分析显示肝细胞癌的预后与细胞周期等信号通路显著相关,并筛选出12个潜在的肝细胞癌分子标志物。研究结果表明,基于41个基因构建的肝细胞癌预后模型具有较好的稳健性和准确的风险预测能力。     

Prediction of platinum-resistance patients of gastric cancer using bioinformatics

Lack of guidelines for personalized chemotherapy treatment after surgery has caused gastric cancer (GC) patients' unnecessary exposure to toxicity and the financial burden of chemotherapy treatments. In our study, we aimed to identify potential biomarkers to predict GC patients' susceptibility to platinum-based on Gene Expression Omnibus (GEO) data sets. A total of 603 differentially expressed genes (DEGs) were identified between platinum-resistant cell lines and platinum-sensitive cell lines based on the Cancer Cell Line Encyclopedia (CCLE) data sets. A total of 253 patients who had accepted radical gastrectomy were recruited, of which 97 received platinum-based chemotherapy and 156 were untreated. Three biomarkers (BRMS1, ND6, SRXN1) were then selected by univariate and multivariate Cox regression analysis to establish the predictive models using nomogram. Then this model was further validated through the GEO data set (GSE62254) which showed that this model could precisely predict the disease-free survival and overall survival of patients treated with platinum-based chemotherapy after surgery compared with untreated GC patients (P < 0.0001). This predictive model might provide helpful messages about the patients' susceptibility to platinum to guide personalized chemotherapy.     

肝癌预后miRNA风险评分模型的鉴定和分析

肝细胞癌(hepatocellular carcinoma,HCC)是世界上高发病率和高死亡率的恶性肿瘤之一.研究目的是寻找HCC相关的mi RNA预后生物学标志物,预测HCC患者的风险程度和生存时间,为他们提供有效的预后信息.使用4种方法从TCGA中识别差异表达的mi RNAs(DEMs).并用Kaplan-Meier生存曲线、单因素和多因素Cox回归分析从DEMs中筛选肝癌预后相关的mi RNA.最终4个HCC的预后mi RNA生物学标志物(hsa-mi R-132-3p、hsa-mi R-139-5p、hsa-mi R-3677-3p、hsa-mi R-500a-3p)被筛选出来组合成一个风险评分模型.目前还没有实验证据表明组合中的hsa-mir-3677-3p与HCC相关,是本研究新发现的mi RNA.生存曲线、ROC曲线、卡方检验等多种生物信息学方法的评价结果均表明,该模型计算出的风险分值能有效预测患者的风险程度(P<0.000,风险比=2.551,95%置信区间=1.751-3.717).低风险组HCC患者1-5年生存率比高风险组高20%-30%.通过与临床数据分析发现,组合的生物学标志物较其他临床指标相比具有更好的预后效果,也可以作为独立的预后因子.最后,预测了4种mi RNA的靶基因,包括AGO2、FOXO1、ROCK2、RAP1B、CYLD等,并在细胞增殖、迁移、凋亡、免疫应答等生物学过程中富集.     

Identification and Validation of a Prognostic Model Based on Three MVI-Related Genes in Hepatocellular Carcinoma

MVI has significant clinical value for treatment selection and prognosis evaluation in hepatocellular carcinoma (HCC). We aimed to construct a model based on MVI-Related Genes (MVIRGs) for risk assessment and prognosis prediction in patients with HCC. This study utilized various statistical analysis methods for prognostic model construction and validation in the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) cohorts, respectively. In addition, immunohistochemistry and qRT-PCR were used to analyze and identify the value of the model in our cohort. After the analyses, 153 differentially expressed MVIRGs were identified, and three key genes were selected to construct a prognostic model. The high-risk group showed significantly lower overall survival (OS), and this trend was observed in all subgroups: different age groups, genders, stages, and grades. Risk score was a risk factor independent of age, gender, stage, and grade. Moreover, the ICGC cohort validated the prognostic value of the model corresponding to the TCGA. In our cohort, qRT-PCR and immunohistochemistry showed that all three genes had higher expression levels in HCC samples than in normal controls. High expression levels of genes and high-risk scores showed significantly lower recurrence-free survival (RFS) and OS, especially in MVI-positive HCC samples. Therefore, the prognostic model constructed by three MVIRGs can reliably predict the RFS and OS of patients with HCC and is valuable for guiding clinical treatment selection and prognostic assessment of HCC.     

Correlation of zinc finger protein 2, a prognostic biomarker,with immune infiltrates in liver cancer

Purpose: The expression and clinical value of zinc finger protein 2 gene (ZIC2) in hepatocellular carcinoma (HCC) were analyzed by mining gene information from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases.Methods: Gene chip data sets were retrieved from GEO and TCGA and screened for differentially expressed genes in HCC. Gene expression profile interaction analysis (GEPIA) and Kaplan–Meier curves were used to analyze the relationship between differentially expressed genes (DEGs) and survival and prognosis in patients with HCC. Moreover, the Genecards database was used to extract ZIC2-related proteins and to analyze the physiological process of protein enrichment. Furthermore, the relationships between ZIC2 gene and tumor cell immune invasion and that between immune cell infiltration and the 5-year survival rate were studied using the tumor immune evaluation resource (TIMER) database.Results: Datasets from GEO and TCGA revealed that ZIC2 was differentially expressed in HCC tissues and normal tissues (P<0.05). High ZIC2 expression was associated with overall survival (OS) and progress-free survival in HCC patients. Overall, 25 ZIC2 related proteins, including Gli3, PRKDC, and rnf180 were identified and protein enrichment analysis indicated these were associated with four types of cell components, six types of cell functions, and eight types of biological processes. ZIC2 was positively correlated with immune infiltration cells in patients with HCC, and higher expression of ZIC2 mRNA CD4+T cells is associated with a better 5-year survival.Conclusion: ZIC2 gene may be used as an immune response marker in liver cancer to predict the prognosis of HCC.     

Proteomic analysis of HBV-associated HCC: Insights on mechanisms of disease onset and biomarker discovery

The development of hepatocellular carcinoma (HCC) can be considered as an end-stage outcome of chronic hepatitis B virus (HBV) infection. Early prognostic markers are needed to allow effective treatments and prevent HCC from developing. Proteomics analysis has been used to identify markers from clinical samples from HCC patients. This approach can be further improved by identifying early biomarkers before the onset of HCC. One way would be to use the cell-based HBV replication system, which is reflective of the early stage of virus infection and thus secreted proteins identified at this stage may have relevance in HCC prognosis. In this review, we focus the discussion on the current status of proteomics analysis of cellular proteins and HCC biomarker identification, with a special highlight on the potential of the cell-based HBV replication system for the identification of prognostic HCC biomarkers.     

肺腺癌诊断标志物筛选及免疫细胞浸润分析

用生物信息学方法筛选肺腺癌(Lung adenocarcinoma,LUAD)的诊断生物标志物,并分析肺腺癌中免疫细胞浸润情况。从GEO和TCGA数据库下载肺腺癌的表达数据集,利用R软件筛选肺腺癌与正常肺组织间的差异表达基因(DEGs),使用DAVID网站对DEGs进行GO及KEGG富集分析,使用STRING及Cytoscape等工具对DEGs构建蛋白相互作用网络并筛选hub基因;利用Kaplan-Meier法对DEGs进行生存分析,并对hub基因进行ROC分析筛选诊断生物标志物,利用GSEA预测有预后价值的基因参与的信号通路;并用Cibersort软件反卷积算法分析肺腺癌中免疫细胞浸润情况。共得到肺腺癌的234个DEGs,这些基因主要参与信号转导、物质代谢、免疫反应等相关信号通路;构建PPI网络筛选出的20个hub基因中8个存在预后价值(CCNA2、DLGAP5、HMMR、MMP1、MMP9、MMP13、SPP1、TOP2A),ROC分析中DLGAP5、SPP1值分别是0.703、0.706;DLGAP5、SPP1基因表达水平与肺腺癌组织浆细胞、未活化的CD4⁺记忆细胞、调节T细胞、巨噬细胞M0、M1、M2及中性粒细胞浸润密切相关(P＜0.05)。肺腺癌中DLGAP5、SPP1具有较高诊断价值且参与肺腺癌组织免疫细胞浸润;DLGAP5、SPP1基因可作为肺腺癌诊断的生物标志物,可为肺腺癌的靶向治疗研究提供新思路。     

人肝细胞癌预后不良相关基因的生物信息学分析及其临床意义

目的:筛选肝细胞癌(HCC)预后不良相关基因,并探讨其临床意义。方法:在基因表达综合数据库(GEO)中获取符合分析条件的肝细胞癌全基因组表达谱数据并分析得到差异表达基因(DEGs),再运用生物学信息注释及可视化数据库 (DAVID) 和蛋白相互作用数据库 (String) 分别进行功能富集分析和蛋白质互作用网络的构建。利用癌症基因组图谱数据库(TCGA)和Cox比例风险回归模型对相关差异基因进行预后分析。结果:找到一个符合条件的人类HCC数据库 (GSE84402),共筛选出1141个差异表达基因(DEGs),其中上调基因720个,下调基因421个。基因功能富集分析和蛋白质互作用分析结果显示CDK1、CDC6、CCNA2、CHEK1、CENPE 、PIK3R1、RACGAP1、BIRC5、KIF11和CYP2B6为HCC预后的关键基因。TCGA数据库和Cox回归模型分析显示CDC6、PIK3R1、RACGAP1和KIF11的表达升高,CENPE的表达降低与HCC预后不良密切相关。结论:CDC6、CENPE、PIK3R1、RACGAP1和KIF11可能和HCC的预后不良相关,可作为未来HCC预后研究的参考标志物。     

Hub genes identification and association of key pathways with hypoxia in cancer cells: A bioinformatics analysis

Three human cancer cell lines (A549, HCT116, and HeLa) were used to investigate the molecular mechanisms and potential prognostic biomarkers associated with hypoxia. We obtained gene expression data from Gene Expression Omnibus (GEO) datasets GSE11704, GSE147384, and GSE38061, which included 5 hypoxic and 8 control samples. Using the GEO2R tool and Venn diagram software, we identified common differentially expressed genes (cDEGs). The cDEGs were then subjected to Gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis by employing DAVID. The hub genes were identified from critical PPI subnetworks through CytoHuba plugin and these genes' prognostic significance and expression were verified using Kaplan-Meier analysis and Gene Expression Profiling Interactive Analysis (GEPIA), respectively. The research showed 676 common DEGs (cDEGs), with 207 upregulated and 469 downregulated genes. The STRING analysis showed 673 nodes and 1446 edges in the PPI network. We identified 4 significant modules and 19 downregulated hub genes. GO analysis revealed all of them were majorly involved in ribosomal large subunit assembly and biogenesis, rRNA processing, ribosome biogenesis, translation, RNA & protein binding frequently at the sites of nucleolus and nucleoplasm while 11 were significantly associated with a better prognosis of hypoxic tumors. Our research sheds light on the molecular mechanisms that underpin hypoxia in human cancer cell lines and identifies potential prognostic biomarkers for hypoxic tumors.     

Systematic construction and validation of an immune prognostic model for lung adenocarcinoma

Lung adenocarcinoma (LUAD), the most common non‐small‐cell lung cancer, is characterized by a dense lymphocytic infiltrate, which indicates that the immune system plays an active role in the development and growth of this cancer. However, no investigations to date have proposed robust models for predicting survival outcome for patients with LUAD in terms of tumour immunology. A total of 761 LUAD patients were included in this study, in which the database of The Cancer Genome Atlas (TCGA) was utilized for discovery, and the Gene Expression Omnibus (GEO) database was utilized for validation. Bioinformatics analysis and R language tools were utilized to construct an immune prognostic model and annotate biological functions. Lung adenocarcinoma showed a weakened immune phenotype compared with adjacent normal tissues. Immune‐related gene sets were profiled, an immune prognostic model based on 2 immune genes (ANLN and F2) was developed with the TCGA database to distinguish cases as having a low or high risk of unfavourable prognosis, and the model was verified with the GEO database. The model was prognostically significant in stratified cohorts, including stage I‐II, stage III‐IV and epidermal growth factor receptor (EGFR) mutant subsets, and was considered to be an independent prognostic factor for LUAD. Furthermore, the low‐ and high‐risk groups showed marked differences in tumour‐infiltrating leucocytes, tumour mutation burden, aneuploidy and PD‐L1 expression. In conclusion, an immune prognostic model was proposed for LUAD that is capable of independently identifying patients at high risk for poor survival, suggesting a relationship between local immune status and prognosis.