Identification of m6A methyltransferase-related lncRNA signature for predicting immunotherapy and prognosis in patients with hepatocellular carcinoma

N6-methyladenosine (m6A) methyltransferase has been shown to be an oncogene in a variety of cancers. Nevertheless, the relationship between the long non-coding RNAs (lncRNAs) and hepatocellular carcinoma (HCC) remains elusive. We integrated the gene expression data of 371 HCC and 50 normal tissues from The Cancer Genome Atlas (TCGA) database. Differentially expressed protein-coding genes (DE-PCGs)/lncRNAs (DE-lncRs) analysis and univariate regression and Kaplan–Meier (K–M) analysis were performed to identify m6A methyltransferase-related lncRNAs. Three prognostic lncRNAs were selected by univariate and LASSO Cox regression analyses to construct the m6A methyltransferase-related lncRNA signature. Multivariate Cox regression analyses illustrated that this signature was an independent prognostic factor for overall survival (OS) prediction. The Gene Set Enrichment Analysis (GSEA) suggested that the m6A methyltransferase-related lncRNAs were involved in the immune-related biological processes (BPs) and pathways. Besides, we discovered that the lncRNAs signature was correlated with the tumor microenvironment (TME) and the expression of critical immune checkpoints. Tumor Immune Dysfunction and Exclusion (TIDE) analysis revealed that the lncRNAs could predict the clinical response to immunotherapy. Our study had originated a prognostic signature for HCC based on the potential prognostic m6A methyltransferase-related lncRNAs. The present study had deepened the understanding of the TME status of HCC patients and laid a theoretical foundation for the choice of immunotherapy.     

Identification of a prognostic gene signature based on an immunogenomic landscape analysis of bladder cancer

Cancer immune plays a critical role in cancer progression. Tumour immunology and immunotherapy are one of the exciting areas in bladder cancer research. In this study, we aimed to develop an immune‐related gene signature to improve the prognostic prediction of bladder cancer. Firstly, we identified 392 differentially expressed immune‐related genes (IRGs) based on TCGA and ImmPort databases. Functional enrichment analysis revealed that these genes were enriched in inflammatory and immune‐related pathways, including in ‘regulation of signaling receptor activity’, ‘cytokine‐cytokine receptor interaction’ and ‘GPCR ligand binding’. Then, we separated all samples in TCGA data set into the training cohort and the testing cohort in a ratio of 3:1 randomly. Data set {"type":"entrez-geo","attrs":{"text":"GSE13507","term_id":"13507"}}GSE13507 was set as the validation cohort. We constructed a prognostic six‐IRG signature with LASSO Cox regression in the training cohort, including AHNAK, OAS1, APOBEC3H, SCG2, CTSE and KIR2DS4. Six IRGs reflected the microenvironment of bladder cancer, especially immune cell infiltration. The prognostic value of six‐IRG signature was further validated in the testing cohort and the validation cohort. The results of multivariable Cox regression and subgroup analysis revealed that six‐IRG signature was a clinically independent prognostic factor for bladder cancer patients. Further, we constructed a nomogram based on six‐IRG signature and other clinicopathological risk factors, and it performed well in predict patients'' survival. Finally, we found six‐IRG signature showed significant difference in different molecular subtypes of bladder cancer. In conclusions, our research provided a novel immune‐related gene signature to estimate prognosis for patients'' survival with bladder cancer.     

Prognostic value and immune cell infiltration of hypoxic phenotype‐related gene signatures in glioblastoma microenvironment

Glioblastoma (GBM) is a malignant intracranial tumour with the highest proportion and lethality. It is characterized by invasiveness and heterogeneity. However, the currently available therapies are not curative. As an essential environmental cue that maintains glioma stem cells, hypoxia is considered the cause of tumour resistance to chemotherapy and radiation. Growing evidence shows that immunotherapy focusing on the tumour microenvironment is an effective treatment for GBM; however, the current clinicopathological features cannot predict the response to immunotherapy and provide accurate guidance for immunotherapy. Based on the ESTIMATE algorithm, GBM cases of The Cancer Genome Atlas (TCGA) data set were classified into high‐ and low‐immune/stromal score groups, and a four‐gene tumour environment‐related model was constructed. This model exhibited good efficiency at forecasting short‐ and long‐term prognosis and could also act as an independent prognostic biomarker. Additionally, this model and four of its genes (CLECL5A, SERPING1, CHI3L1 and C1R) were found to be associated with immune cell infiltration, and further study demonstrated that these four genes might drive the hypoxic phenotype of perinecrotic GBM, which affects hypoxia‐induced glioma stemness. Therefore, these might be important candidates for immunotherapy of GBM and deserve further exploration.     

A MiRNA Signature for Defining Aggressive Phenotype and Prognosis in Gliomas

Gliomas represent a disparate group of tumours for which there are to date no cure. Thus, there is a recognized need for new diagnostic and therapeutic approaches based on increased understanding of their molecular nature. We performed the comparison of the microRNA (miRNA) profile of 8 WHO grade II gliomas and 24 higher grade tumours (2 WHO grade III and 22 glioblastomas) by using the Affymetrix GeneChip miRNA Array v. 1.0. A relative quantification method (RT-qPCR) with standard curve was used to confirm the 22 miRNA signature resulted by array analysis. The prognostic performances of the confirmed miRNAs were estimated on the Tumor Cancer Genome Atlas (TCGA) datasets. We identified 22 miRNAs distinguishing grade II gliomas from higher grade tumours. RT-qPCR confirmed the differential expression in the two patients'' groups for 13 out of the 22 miRNAs. The analysis of the Glioblastoma Multiforme (GBM) and Lower Grade Glioma (LGG) datasets from TCGA demonstrated the association with prognosis for 6 of those miRNAs. Moreover, in the GBM dataset miR-21 and miR-210 were predictors of worse prognosis in both univariable and multivariable Cox regression analyses (HR 1.19, p = 0.04, and HR 1.18, p = 0.029 respectively). Our results support a direct contribution of miRNAs to glioma cancerogenesis and suggest that miR-21 and miR-210 may play a role in the aggressive clinical behaviour of glioblastomas.     

Investigation of fatty acid metabolism-related genes in breast cancer: Implications for Immunotherapy and clinical significance

Breast cancer (BRCA) is a major global health issue, characterized by high mortality and low early diagnosis rates. The tumor immune microenvironment (TME) of BRCA is closely linked to fatty acid metabolism (FAM). This study aimed to identify FAM-related subtypes in BRCA based on gene expression and clinical data from the Cancer Genome Atlas (TCGA) database. The study found two distinct FAM-related subtypes, each with unique immune characteristics and prognostic implications. A FAM-related risk score prognostic model was developed and validated using TCGA and International Cancer Genome Consortium (GEO) cohorts, showing potential clinical applications for chemotherapy and immunotherapy. Additionally, a nomogram was established to facilitate clinical use of the risk score. These results highlight the significant correlation between FAM genes and TME in BRCA, and demonstrate the potential clinical utility of the FAM-related risk score in informing treatment decisions for BRCA patients.     

Five genes as a novel signature for predicting the prognosis of patients with laryngeal cancer

In this study, we purpose to investigate a novel five-gene signature for predicting the prognosis of patients with laryngeal cancer. The laryngeal cancer datasets were obtained from The Cancer Genome Atlas (TCGA). Both univariate and multivariate Cox regression analysis was applied to screening for prognostic differential expressed genes (DEGs), and a novel gene signature was obtained. The performance of this Cox regression model was tested by receiver operating characteristic (ROC) curves and area under the curve (AUC). Further survival analysis for each of the five genes was carried out through the Kaplan-Meier curve and Log-rank test. Totally, 622 DEGs were screened from the TCGA datasets in this study. We construct a five-gene signature through Cox survival analysis. Patients were divided into low- and high-risk groups depending on the median risk score, and a significant difference of the 5-year overall survival was found between these two groups (P < .05). ROC curves verified that this five-gene signature had good performance to predict the prognosis of laryngeal cancer (AUC = 0.862, P < .05). In conclusion, the five-gene signature consist of EMP1, HOXB9, DPY19L2P1, MMP1, and KLHDC7B might be applied as an independent prognosis predictor of laryngeal cancer.     

Identification of immune microenvironment subtypes that predicted the prognosis of patients with ovarian cancer

Ovarian cancer (OC) is associated with high mortality rate. However, the correlation between immune microenvironment and prognosis of OC remains unclear. This study aimed to explore prognostic significance of OC tumour microenvironment. The OC data set was selected from the cancer genome atlas (TCGA), and 307 samples were collected. Hierarchical clustering was performed according to the expression of 756 genes. The immune and matrix scores of all immune subtypes were determined, and Kruskal-Wallis test was used to analyse the differences in the immune and matrix scores between OC samples with different immune subtypes. The model for predicting prognosis was constructed based on the expression of immune-related genes. TIDE platform was applied to predict the effect of immunotherapy on patients with OC of different immune subtypes. The 307 OC samples were classified into three immune subtypes A-C. Patients in subtype B had poorer prognosis and lower survival rate. The infiltration of helper T cells and macrophages in microenvironment indicated significant differences between immune subtypes. Enrichment analyses of immune cell molecular pathways showed that JAK–STAT3 pathway changed significantly in subtype B. Furthermore, predictive response to immunotherapy in subtype B was significantly higher than that in subtype A and C. Immune subtyping can be used as an independent predictor of the prognosis of OC patients, which may be related to the infiltration patterns of immune cells in tumour microenvironment. In addition, patients in immune subtype B have superior response to immunotherapy, suggesting that patients in subtype B are suitable for immunotherapy.     

Identification of SLITRK6 as a Novel Biomarker in hepatocellular carcinoma by comprehensive bioinformatic analysis

Hepatocellular carcinoma (HCC) is the most common primary malignancy of the adult liver and morbidity are increasing in recent years, however, there is still no effective strategy to prevent and diagnose HCC. Therefore, it is urgent to research the effective biomarker to predict clinical outcomes of HCC tumorigenesis. In the current study, differentially expressed genes in HCC and normal tissues were investigated using the Gene Expression Omnibus (GEO) dataset GSE144269 and The Cancer Genome Atlas (TCGA). Gene differential expression analysis and weighted correlation network analysis (WGCNA) methods were used to identify nine and 16 key gene modules from the GEO dataset and TCGA dataset, respectively, in which the green module in the GEO dataset and magenta module in TCGA were significantly correlated with HCC occurrence. Third, the enrichment score of gene function annotation results showed that these two key modules focus on the positive regulation of inflammatory response and cell differentiation, etc. Besides, PPI network analysis, mutation analysis, and survival analysis found that SLITRK6 had high connectivity, and its mutation significantly impacted overall survival. In addition, SLITRK6 was found to be low expressed in tumor cells. To summarize, SLITRK6 mutation was found to significantly affect the occurrence and prognosis of HCC. SLITRK6 was confirmed as a new potential gene target for HCC, which may provide a new theoretical basis for personalized diagnosis and chemotherapy of HCC in the future.     

LCMT1 indicates poor prognosis and is essential for cell proliferation in hepatocellular carcinoma

BackgroundHepatocellular carcinoma (HCC) is one of the most malignant type of cancers. Leuci carboxyl methyltransferase 1 (LCMT1) is a protein methyltransferase that plays an improtant regulatory role in both normal and cancer cells. The aim of this study is to evaluate the expression pattern and clinical significance of LCMT1 in HCC.MethodsThe expression pattern and clinical relevance of LCMT1 were determined using the Gene Expression Omnibus (GEO) database, the Cancer Genome Atlas (TCGA) program, and our datasets. Gain-of-function and loss-of-function studies were employed to investigate the cellular functions of LCMT1 in vitro and in vivo. Quantitative real-time polymerase chain reaction (RT-PCR) analysis, western blotting, enzymatic assay, and high-performance liquid chromatography were applied to reveal the underlying molecular functions of LCMT1.ResultsLCMT1 was upregulated in human HCC tissues, which correlated with a “poor” prognosis. The siRNA-mediated knockdown of LCMT1 inhibited glycolysis, promoted mitochondrial dysfunction, and increased intracellular pyruvate levels by upregulating the expression of alani-neglyoxylate and serine-pyruvate aminotransferase (AGXT). The overexpression of LCMT1 showed the opposite results. Silencing LCMT1 inhibited the proliferation of HCC cells in vitro and reduced the growth of tumor xenografts in mice. Mechanistically, the effect of LCMT1 on the proliferation of HCC cells was partially dependent on PP2A.ConclusionsOur data revealed a novel role of LCMT1 in the proliferation of HCC cells. In addition, we provided novel insights into the effects of glycolysis-related pathways on the LCMT1regulated progression of HCC, suggesting LCMT1 as a novel therapeutic target for HCC therapy.     

Identification of Druggable Cancer Driver Genes Amplified across TCGA Datasets

The Cancer Genome Atlas (TCGA) projects have advanced our understanding of the driver mutations, genetic backgrounds, and key pathways activated across cancer types. Analysis of TCGA datasets have mostly focused on somatic mutations and translocations, with less emphasis placed on gene amplifications. Here we describe a bioinformatics screening strategy to identify putative cancer driver genes amplified across TCGA datasets. We carried out GISTIC2 analysis of TCGA datasets spanning 14 cancer subtypes and identified 461 genes that were amplified in two or more datasets. The list was narrowed to 73 cancer-associated genes with potential “druggable” properties. The majority of the genes were localized to 14 amplicons spread across the genome. To identify potential cancer driver genes, we analyzed gene copy number and mRNA expression data from individual patient samples and identified 40 putative cancer driver genes linked to diverse oncogenic processes. Oncogenic activity was further validated by siRNA/shRNA knockdown and by referencing the Project Achilles datasets. The amplified genes represented a number of gene families, including epigenetic regulators, cell cycle-associated genes, DNA damage response/repair genes, metabolic regulators, and genes linked to the Wnt, Notch, Hedgehog, JAK/STAT, NF-KB and MAPK signaling pathways. Among the 40 putative driver genes were known driver genes, such as EGFR, ERBB2 and PIK3CA. Wild-type KRAS was amplified in several cancer types, and KRAS-amplified cancer cell lines were most sensitive to KRAS shRNA, suggesting that KRAS amplification was an independent oncogenic event. A number of MAP kinase adapters were co-amplified with their receptor tyrosine kinases, such as the FGFR adapter FRS2 and the EGFR family adapter GRB7. The ubiquitin-like ligase DCUN1D1 and the histone methyltransferase NSD3 were also identified as novel putative cancer driver genes. We discuss the patient tailoring implications for existing cancer drug targets and we further discuss potential novel opportunities for drug discovery efforts.     

CFHR3 is a potential novel biomarker for hepatocellular carcinoma

Complement factor H-related 3 (CFHR3) is a protein-coding gene acting in various diseases. However, its prognostic values of CFHR3 in hepatocellular carcinoma (HCC) are not understandable. Therefore, we present a further study on CFHR3 in HCC. CFHR3 expression data were acquired from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC). We compared the differential expression of CFHR3 between the low-stage (stage I and II) and high-stage (stage III and IV) patients with HCC in the TCGA and ICGC cohorts. Furthermore, we assessed the CFHR3 expression as a prognostic marker using the Kaplan-Meier survival analysis, univariate, and multivariate analysis. The Kaplan-Meier analysis declared that CFHR3 overexpression was correlated with a good prognosis for HCC patients. Multivariate analysis proved the prognostic significance of CFHR3 expression levels (P < .001 and .003 for TCGA and ICGC, respectively). Immune-related scores in low-risk cohorts were higher than high-risk cohorts. Gene set enrichment analysis implied that the low CFHR3 expression phenotype was significantly enriched in critical biological functions and pathways and was associated with tumorigenesis, such as regulation of cell activation cycle, and the WNT and NOTCH signal pathway. Above all, CFHR3 could be a novel prognostic biomarker and therapeutic target for HCC.     

Hypermethylated and downregulated MEIS2 are involved in stemness properties and oxaliplatin-based chemotherapy resistance of colorectal cancer

The resistance against oxaliplatin (L-OHP) based regimens remains a major obstacle for its efficient usage in treating metastatic colorectal cancer (mCRC). In this study, we performed weighted gene coexpression network analysis (WGCNA) to systematically screen the relevant hub genes for L-OHP resistance using the raw microarray data of 30 consecutive mCRC samples from our earlier study (GSE69657). The results were further confirmed through datasets from Gene Expression Omnibus (GEO). From L-OHP resistance module, nine genes in both the coexpression and protein–protein interaction networks were chosen as hub genes. Among these genes, Meis Homeobox 2 (MEIS2) had the highest correlation with L-OHP resistance (r = −0.443) and was deregulated in L-OHP resistant tissues compared with L-OHP sensitive tissues in both our own dataset and GSE104645 testing dataset. The receiver operating characteristic curve validated that MEIS2 had a good ability in predicting L-OHP response in both our own dataset (area under the curve [AUC] = 0.802) and GSE104645 dataset (AUC = 0.746). Then, the down expression of MEIS2 was observed in CRC tissue compared with normal tissue in 12 GEO-sourced datasets and The Cancer Genome Atlas (TCGA) and was correlated with poor event-free survival. Furthermore, analyzing methylation data from TCGA showed that MEIS2 had increased promoter hypermethylation. In addition, MEIS2 expression was significantly decreased in CRC stem cells compared with nonstem cells in two GEO datasets (GSE14773 and GSE24747). Further methylation analysis from GSE104271 demonstrated that CRC stem cells had higher MEIS2 promoter methylation levels in cg00366722 and cg00610348 sites. Gene set enrichment analysis showed that MEIS2 might be involved in the Wnt/β-catenin pathway. In the overall view, MEIS2 had increased promoter hypermethylation and was downregulated in poor L-OHP response mCRC tissues. MEIS2 might be involved in the Wnt/β-catenin pathway to maintain CRC stemness, which leads to L-OHP resistance.     

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。     

Translational Validation of Personalized Treatment Strategy Based on Genetic Characteristics of Glioblastoma

Glioblastoma (GBM) heterogeneity in the genomic and phenotypic properties has potentiated personalized approach against specific therapeutic targets of each GBM patient. The Cancer Genome Atlas (TCGA) Research Network has been established the comprehensive genomic abnormalities of GBM, which sub-classified GBMs into 4 different molecular subtypes. The molecular subtypes could be utilized to develop personalized treatment strategy for each subtype. We applied a classifying method, NTP (Nearest Template Prediction) method to determine molecular subtype of each GBM patient and corresponding orthotopic xenograft animal model. The models were derived from GBM cells dissociated from patient''s surgical sample. Specific drug candidates for each subtype were selected using an integrated pharmacological network database (PharmDB), which link drugs with subtype specific genes. Treatment effects of the drug candidates were determined by in vitro limiting dilution assay using patient-derived GBM cells primarily cultured from orthotopic xenograft tumors. The consistent identification of molecular subtype by the NTP method was validated using TCGA database. When subtypes were determined by the NTP method, orthotopic xenograft animal models faithfully maintained the molecular subtypes of parental tumors. Subtype specific drugs not only showed significant inhibition effects on the in vitro clonogenicity of patient-derived GBM cells but also synergistically reversed temozolomide resistance of MGMT-unmethylated patient-derived GBM cells. However, inhibitory effects on the clonogenicity were not totally subtype-specific. Personalized treatment approach based on genetic characteristics of each GBM could make better treatment outcomes of GBMs, although more sophisticated classifying techniques and subtype specific drugs need to be further elucidated.     

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.     

Identification of core genes and outcomes in hepatocellular carcinoma by bioinformatics analysis

Hepatocellular carcinoma (HCC) is the most common malignant liver disease in the world. However, the mechanistic relationships among various genes and signaling pathways are still largely unclear. In this study, we aimed to elucidate potential core candidate genes and pathways in HCC. The expression profiles GSE14520, GSE25097, GSE29721, and GSE62232, which cover 606 tumor and 550 nontumour samples, were downloaded from the Gene Expression Omnibus (GEO) database. Furthermore, HCC RNA-seq datasets were also downloaded from the Cancer Genome Atlas (TCGA) database. The differentially expressed genes (DEGs) were filtered using R software, and we performed gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis using the online databases DAVID 6.8 and KOBAS 3.0. Furthermore, the protein-protein interaction (PPI) network complex of these DEGs was constructed by Cytoscape software, the molecular complex detection (MCODE) plug-in and the online database STRING. First, a total of 173 DEGs (41 upregulated and 132 downregulated) were identified that were aberrantly expressed in both the GEO and TCGA datasets. Second, GO analysis revealed that most of the DEGs were significantly enriched in extracellular exosomes, cytosol, extracellular region, and extracellular space. Signaling pathway analysis indicated that the DEGs had common pathways in metabolism-related pathways, cell cycle, and biological oxidations. Third, 146 nodes were identified from the DEG PPI network complex, and two important modules with a high degree were detected using the MCODE plug-in. In addition, 10 core genes were identified, TOP2A, NDC80, FOXM1, HMMR, KNTC1, PTTG1, FEN1, RFC4, SMC4, and PRC1. Finally, Kaplan-Meier analysis of overall survival and correlation analysis were applied to these genes. The abovementioned findings indicate that the identified core genes and pathways in this bioinformatics analysis could significantly enrich our understanding of the development and recurrence of HCC; furthermore, these candidate genes and pathways could be therapeutic targets for HCC treatment.     

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.