Identification of Biomarkers Related to Immune Cell Infiltration in Hepatocellular Carcinoma Using Gene Co-Expression Network

Hepatocellular carcinoma (HCC) is a common cancer with poor prognosis. Due to the lack of effective biomarkers and its complex immune microenvironment, the effects of current HCC therapies are not ideal. In this study, we used the GSE57957 microarray data from Gene Expression Omnibus database to construct a co-expression network. The weighted gene co-expression network analysis and CIBERSORT algorithm, which quantifies cellular composition of immune cells, were used to identify modules related to immune cells. Four hub genes (EFTUD2, GAPDH, NOP56, PA2G4) were identified by co-expression network and protein-protein interactions network analysis. We examined these genes in TCGA database, and found that the four hub genes were highly expressed in tumor tissues in multiple HCC groups, and the expression levels were significantly correlated with patient survival time, pathological stage and tumor progression. On the other hand, methylation analysis showed that the up-regulation of EFTUD2, GAPDH, NOP56 might be due to the hypomethylation status of their promoters. Next, we investigated the correlations between the expression levels of four hub genes and tumor immune infiltration using Tumor Immune Estimation Resource (TIMER). Gene set variation analysis suggested that the four hub genes were associated with numerous pathways that affect tumor progression or immune microenvironment. Overall, our results showed that the four hub genes were closely related to tumor prognosis, and may serve as targets for treatment and diagnosis of HCC. In addition, the associations between these genes and immune infiltration enhanced our understanding of tumor immune environment and provided new directions for the development of drugs and the monitoring of tumor immune status.

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RPL19 Is a Prognostic Biomarker and Promotes Tumor Progression in Hepatocellular Carcinoma

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.686547 ◽

2021 ◽

Vol 9 ◽

Author(s):

Benchen Rao ◽

Jianhao Li ◽

Tong Ren ◽

Jing Yang ◽

Guizhen Zhang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Network Analysis ◽

Tumor Progression ◽

Poor Prognosis ◽

Molecular Mechanisms ◽

The Cancer Genome Atlas ◽

Bile Acid Metabolism ◽

Hub Genes ◽

Immune Infiltration ◽

Tumor Tissues

BackgroundHepatocellular carcinoma (HCC) is one of the most common malignancies, and the therapeutic outcome remains undesirable due to its recurrence and metastasis. Gene dysregulation plays a pivotal role in the occurrence and progression of cancer, and the molecular mechanisms are largely unknown.MethodsThe differentially expressed genes of HCC screened from the GSE39791 dataset were used to conduct weighted gene co-expression network analysis. The selected hub genes were validated in The Cancer Genome Atlas (TCGA) database and 11 HCC datasets from the Gene Expression Omnibus (GEO) database. Then, a tissue microarray comprising 90 HCC specimens and 90 adjacent normal specimens was used to validate the hub genes. Moreover, the Hallmark, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases were used to identify enriched pathways. Then, we conducted the immune infiltration analysis.ResultsA total of 17 co-expression modules were obtained by weighted gene co-expression network analysis. The green, blue, and purple modules were the most relevant to HCC samples. Four hub genes, RPL19, RPL35A, RPL27A, and RPS12, were identified. Interestingly, we found that all four genes were highly expressed in HCC and that their high expression was related to a poor prognosis by analyzing the TCGA and GEO databases. Furthermore, we investigated RPL19 in HCC tissue microarrays and demonstrated that RPL19 was overexpressed in tumor tissues compared with non-tumor tissues (p = 0.016). Moreover, overexpression of RPL19 predicted a poor prognosis in hepatocellular carcinoma (p < 0.0007). Then, enrichment analysis revealed that cell cycle pathways were significantly enriched, and bile acid metabolism-related pathways were significantly down-regulated when RPL19 was highly expressed. Furthermore, immune infiltration analysis showed that immune response was suppressed.ConclusionOur study demonstrates that RPL19 may play an important role in promoting tumor progression and is correlated with a poor prognosis in HCC. RPL19 may serve as a promising biomarker and therapeutic target for the precise diagnosis and treatment of HCC in the future.

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Immune signature driven by ADT-induced immune microenvironment remodeling in prostate cancer is correlated with recurrence-free survival and immune infiltration

Cell Death and Disease ◽

10.1038/s41419-020-02973-1 ◽

2020 ◽

Vol 11 (9) ◽

Author(s):

Xingbo Long ◽

Huimin Hou ◽

Xuan Wang ◽

Shengjie Liu ◽

Tongxiang Diao ◽

...

Keyword(s):

Prostate Cancer ◽

Immune Cell ◽

Locally Advanced ◽

Underlying Mechanism ◽

Immune Microenvironment ◽

Hub Genes ◽

Free Survival ◽

Immune Signature ◽

Immune Infiltration ◽

Patient Prognosis

Abstract Androgen deprivation therapy (ADT) is a cornerstone treatment for locally advanced or metastatic prostate cancer (PCa). However, its potential effects on the tumor immune microenvironment (TIM) of PCa patients and the underlying mechanism remain largely unclear. To explore the effects of ADT on PCa TIM, RNA sequencing was performed on six paired pre-ADT biopsy and post-ADT PCa lesions, and five paired paracancerous benign tissues from patients receiving neoadjuvant ADT with locally advanced PCa. Bioinformatics methods including ESTIMATE and ssGSEA were used to evaluate the stromal immune score and immune cell infiltration in PCa and paracancerous tissues. Weighted correlation network analysis was used to screen hub genes in the ADT-induced immune remodeling process. The results showed differences exist between PCa and paracancerous tissues in response to ADT. Compared with paracancerous tissues, the immune remodeling effect of ADT in PCa was more intense. ZFP36, JUNB, and SOCS3 served as hub genes in the ADT-induced immune remodeling process and were associated with PSA recurrent-free survival in the TCGA and our neoadjuvant ADT cohort. To investigate the joint action of the above three hub genes, an immune signature score was constructed. The results showed that immune signature score-based immune subtypes reveal the heterogeneity of the immune microenvironment of PCa and showed significant differences in patient prognosis, tumor immune infiltration, mutation burden, and landscape.

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Multi-Omic Analyses of the m5C Regulator ALYREF Reveal Its Essential Roles in Hepatocellular Carcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.633415 ◽

2021 ◽

Vol 11 ◽

Author(s):

Chen Xue ◽

Yalei Zhao ◽

Ganglei Li ◽

Lanjuan Li

Keyword(s):

Gene Expression ◽

Hepatocellular Carcinoma ◽

Immune Cell ◽

The Cancer Genome Atlas ◽

Cancer Tissue ◽

Hub Genes ◽

Specific Expression ◽

Tissue Samples ◽

Expression Levels ◽

Advanced Tumor

The ALYREF protein acts as a crucial epigenetic regulator in several cancers. However, the specific expression levels and functional roles of ALYREF in cancers are largely unknown, including for hepatocellular carcinoma (HCC). In a pan-cancer tissue analysis that included HCC, we assessed the expression of ALYREF compared to normal tissues using The Cancer Genome Atlas database. Associations between ALYREF gene expression and the clinical characteristics of HCC patient samples were assessed using the UALCAN database. Kaplan-Meier plots were performed to assess HCC patient prognosis, and the TIMER database was used to explore associations between ALYREF expression and immune-cell infiltrations. The same methods were used to assess eIF4A3 expression in HCC patient samples. In addition, ALYREF- and elF4A3-related differentially expressed genes (DEGs) were determined using LinkedOmics, associated protein functionalities were predicted for positively associated DEGs, and both the TargetScan and miRDB databases were used to predict potential upstream miRNAs for control of ALYREF and eIF4A3 expression. We found that ALYREF gene expression was dysregulated in several cancers and was significantly elevated in HCC patient tissue samples and HCC cell lines. The overexpression of ALYREF was significantly related to both advanced tumor-node-metastasis stages and poor HCC prognosis. Furthermore, we found that eIF4A3 expression was significantly correlated with ALYREF expression, and that upregulated eIF4A3 was significantly associated with poor HCC patient outcomes. In the protein-protein interaction network, we identified eight hub genes based on the positively associated DEGs in common between ALYREF and eIF4A3, and the high expression levels of these hub genes were positively associated with patient clinical outcomes. In addition, we identified miR-4666a-5p and miR-6124 as potential regulators of ALYREF and eIF4A3 expression. These findings suggest that increased ALYREF expression may function as a novel biomarker for both HCC diagnosis and prognosis predictions.

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Identification of immune-infiltrating cell-related biomarkers in hepatocellular carcinoma based on gene co-expression network analysis

Diagnostic Pathology ◽

10.1186/s13000-021-01118-y ◽

2021 ◽

Vol 16 (1) ◽

Author(s):

Yinghui Hou ◽

Guizhi Zhang

Keyword(s):

Hepatocellular Carcinoma ◽

Network Analysis ◽

Early Diagnosis ◽

The Cancer Genome Atlas ◽

Ppi Network ◽

Sequencing Data ◽

Hub Genes ◽

Survival Prognosis ◽

Expression Levels ◽

Cancer Tissues

Abstract Background Hepatocellular carcinoma (HCC) is often caused by chronic liver infection or inflammation. Searching for potential immunotherapy targets will aid the early diagnosis and treatment of HCC. Methods Firstly, detailed HCC data were downloaded from The Cancer Genome Atlas database. GDCRNATools was used for the comprehensive analysis of RNA sequencing data. Subsequently, the CIBERSORT package was used to estimate infiltration scores of 22 types of immune cells in complex samples. Furthermore, hub genes were identified via weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) network analysis. In addition, multiple databases were used to validate the expression of hub gene in the tumor tissue. Finally, prognostic, diagnostic and immunohistochemical analysis of key hub genes was performed. Results In the present study, 9 hub genes were identified using WGCNA and PPI network analysis. Furthermore, the expression levels of 9 genes were positively correlated with the infiltration levels of CD8-positive T (CD8+ T) cells. In multiple dataset validations, the expression levels of CCL5, CXCR6, CD3E, and LCK were decreased in cancer tissues. In addition, survival analysis revealed that patients with LCK low expression had a poor survival prognosis (P < 0.05). Immunohistochemistry results demonstrated that CCL5, CD3E and LCK were expressed at low levels in HCC cancer tissues. Conclusion The identification of CCL5, CXCR6, CD3E and LCK may be helpful in the development of early diagnosis and therapy of HCC. LCK may be a potential prognostic biomarker for immunotherapy for HCC.

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Exploration of the regulatory mechanism for hepatocellular carcinoma based on ceRNA networks analysis and immune infiltration

10.21203/rs.3.rs-885697/v1 ◽

2021 ◽

Author(s):

Ning Huang ◽

Qiang Chen ◽

Xiaoyi Wang

Keyword(s):

Hepatocellular Carcinoma ◽

T Cells ◽

Network Analysis ◽

Correlation Analysis ◽

Immune Cells ◽

Regulatory Mechanism ◽

Immune Cell ◽

Risk Model ◽

The Cancer Genome Atlas ◽

Ppi Network

Abstract Background Hepatocellular carcinoma (HCC) as malignant cancer has been deeply investigated for its widespread distribution and extremely high mortality rate worldwide. Despite efforts to understand the regulatory mechanism in HCC, it remains largely unknown. Methods The RNA (mRNAs, lncRNAs, and miRNAs) profiles were downloaded from The Cancer Genome Atlas (TCGA) database. Based on the Weighted Gene Co-expression Network Analysis (WGCNA), the hub differentially expressed RNAs (DERNAs) were screened out. The competing endogenous RNA (ceRNA) and Protein and Protein Interaction (PPI) network were constructed based on the hub DERNAs. The Cox and LASSO regression analysis were used to find the independent prognostic ceRNAs. We performed the “CIBERSORT” algorithm estimate the abundance of immune cells. The correlation analysis was applied to determine the relationship between HCC-related immune cells and prognostic ceRNAs. GEPIA and TIMER database were used to explore the association of critical genes with survival and immune cell infiltration, respectively. Results A total of 524 hub RNAs (507 DEmRNAs, 13 DElncRNAs and 4 DEmiRNAs) were identified in the turquoise module (cor = 0.78, P = 4.7e − 198) using WGCNA algorithm. PPI network analysis showed that NDC80, BUB1B and CCNB2 as the critical genes in HCC. Subsequently, survival analysis revealed that the low expression of NDC80 and BUB1B resulted in a longer overall survival (OS) time for HCC patients in GEPIA database. These critical genes and several immune cells were all significantly positive correlated in TIMER database. The ceRNA network were establish, and were incorporated to risk model. Subsequently, ROC curve showed that the area under the curve (AUC) of the 1-, 3-, and 5-year were 0.762, 0.705, and 0.688, respectively. Out of the 22 cell types, T cells CD4 memory resting were identified as the HCC-related immune cells by systematic analysis. The correlation analysis shown that T cells CD4 memory resting is negatively associated with both AL021453.1 (R = − 0.44, P = 0.00049) and CCDC137 (R = − 0.47, P = 2e-04). Conclusion The current study provide potential prognostic signatures and therapeutic targets for HCC.

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Identification of Hub Genes Associated with Immune Cells in Dilated Cardiomyopathy via Weighted Gene Co-expression Network Analysis

10.21203/rs.3.rs-652198/v1 ◽

2021 ◽

Author(s):

Jielin Deng ◽

Yunqiu Jiang ◽

Changjin Deng ◽

hong jiang

Keyword(s):

Network Analysis ◽

Dilated Cardiomyopathy ◽

Immune Cells ◽

Bioinformatics Analysis ◽

Immune Cell ◽

Gene Profiling ◽

Functional Enrichment ◽

Pathophysiological Mechanism ◽

Hub Genes ◽

Key Genes

Abstract Background: Dilated cardiomyopathy (DCM) is the most common cardiomyopathy which account for a majority of heart failure. Although massive clinic experiments and gene profiling analyses on DCM have been conducted, the molecular mechanism of DCM associated with immune cells has not been fully elucidated. This study was designed to discover the immune mechanism of DCM using integrative bioinformatics analysis and provide new insights into the pathophysiology of DCM. Methods: The GSE29819 dataset was downloaded, and Cibersort was applied to estimate the relative expression of 22 kinds of immune cells based on 14 samples of 7 DCM patients. Weighted gene co-expression network analysis (WGCNA) was performed to cluster the 2500 genes with the highest average expression into different modules and explore relationships between modules and immune cell types. Functional enrichment analysis was performed on key genes in significant modules identified by WGCNA and Cibersort. Key genes were then applied to Cytoscape to construct protein-protein interaction (PPI) network. Differentially expressed genes (DEGs) were identified based on DCM and normal controls in GSE29819 through R language. Hub genes were selected based on the DEGs and the genes identified by PPI and then verified via public GEO databases. Results: The yellow and tan modules with 163 genes were identified as the key modules based on top 2500 DCM microarrays, significantly correlated with M1 and M2 macrophages. The intersection of newly screened 17 genes based on 163 key genes through Cytoscape and 2682 DEGs were defined as hub genes including CCT2, CCL2, and TXN. The results were finally verified via GSE116250 datasets.Conclusions: The three hub genes associated with two immune cells identified by comprehensive bioinformatics analysis may play crucial roles in the pathophysiological mechanism of DCM, which provided potential immunological therapeutic targets and new insights into the treatment of DCM.

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Identification of Hub Genes and Pathways With Immune Cell Infiltration in Cholangiocarcinoma by Weighted Gene Co-expression Network Analysis

10.21203/rs.3.rs-135007/v1 ◽

2020 ◽

Author(s):

Ye Yan ◽

Lihao Zhao ◽

Huafang Su ◽

Gang Li ◽

Sujing Jiang

Keyword(s):

T Cell ◽

Network Analysis ◽

Protein Interactions ◽

Immune Cell ◽

The Cancer Genome Atlas ◽

Prognostic Biomarkers ◽

Cd4 T Cell ◽

Cell Infiltration ◽

Hub Genes ◽

Kegg Analysis

Abstract Background: Cholangiocarcinoma (CCA) is the most frequent tumor in biliary tract and the second most common primary tumor of the liver. However, the molecular biomarkers in tumorigenesis of CCA remain unclear. Therefore, we aim to explore the underlying mechanisms of progression and screen for novel prognostic biomarkers and therapeutic targets.Method: The genes expression sequencing of normal and CCA samples were selected from the Gene Expression Omnibus database (GEO) and the Cancer Genome Atlas (TCGA). The weighted gene co-expression network analysis (WGCNA) was used to build the co-expression network. Gene ontology (GO) as well as Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were applied for the selected genes. The protein-protein interactions of these modules are visualized using cytoscape. Furthermore, the significance of these genes was confirmed by survival analysis. The tumor immune estimation resource (TIMER) was presented to investigate assess the relationship between the hub genes and the immune cells infiltration.Results: Ten hub genes with CCA development were identified in this study containing CDC20, CCNA2, TOP2A, AURKA, CCNB2, UBE2C, NUSAP1, PRC1, PTTG1 and MCM4. Key genes of CCNB2 and PTTG1 might be potential prognostic biomarkers for CCA. GO analysis indicated the enrichment terms of nuclear division, collagen-containing extracellular matrix and cell adhesion molecule binding. KEGG analysis demonstrated that the cell cycle pathway was the significantly altered pathway. There was a negative correlation between TOP2A, AURKA, CCNB2, PRC1 expression and the infiltration of CD4+T cell, while MCM4 expression was positively associated with the infiltration of neutrophil cells. No significant association between CDC20 levels and CD4+T cell, CD8+T cell, B cell, neutrophil, macrophage, or dendritic cell infiltration in CCA, the same as CCNA2, UBE2C, NUSAP1, PTTG1 respectively.Conclusion: These candidate genes may involve in the development of CCA. Our results offer novel insights into the etiology, prognosis, and treatment of CCA.

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Identification of Prognostic Biomarkers and Correlation With Immune Infiltrates in Hepatocellular Carcinoma Based on a Competing Endogenous RNA Network

Frontiers in Genetics ◽

10.3389/fgene.2021.591623 ◽

2021 ◽

Vol 12 ◽

Author(s):

Zhangya Pu ◽

Yuanyuan Zhu ◽

Xiaofang Wang ◽

Yun Zhong ◽

Fang Peng ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

T Cells ◽

Survival Analysis ◽

Immune Cell ◽

Cox Regression ◽

Quantitative Polymerase Chain Reaction ◽

Prognostic Biomarkers ◽

Hub Genes ◽

Immune Infiltration ◽

Cerna Network

BackgroundHepatocellular carcinoma (HCC) is one of the most common malignant tumors worldwide. Recently, competing endogenous RNAs (ceRNA) have revealed a significant role in the progression of HCC. Herein, we aimed to construct a ceRNA network to identify potential biomarkers and illustrate its correlation with immune infiltration in HCC.MethodsRNA sequencing data and clinical traits of HCC patients were downloaded from TCGA. The limma R package was used to identify differentially expressed (DE) RNAs. The predicted prognostic model was established using univariate and multivariate Cox regression. A K-M curve, TISIDB and GEPIA website were utilized for survival analysis. Functional annotation was determined using Enrichr and Reactome. Protein-to-protein network analysis was implemented using SRTNG and Cytoscape. Hub gene expression was validated by quantitative polymerase chain reaction, Oncomine and the Hunan Protein Atlas database. Immune infiltration was analyzed by TIMMER, and Drugbank was exploited to identify bioactive compounds.ResultsThe predicted model that was established revealed significant efficacy with 3- and 5-years of the area under ROC at 0.804 and 0.744, respectively. Eleven DEmiRNAs were screened out by a K-M survival analysis. Then, we constructed a ceRNA network, including 56 DElncRNAs, 6 DEmiRNAs, and 28 DEmRNAs. The 28 DEmRNAs were enriched in cancer-related pathways, for example, the TNF signaling pathway. Moreover, six hub genes, CEP55, DEPDC1, KIF23, CLSPN, MYBL2, and RACGAP1, were all overexpressed in HCC tissues and independently correlated with survival rate. Furthermore, expression of hub genes was related to immune cell infiltration in HCC, including B cells, CD8+ T cells, CD4+ T cells, monocytes, macrophages, neutrophils, and dendritic cells.ConclusionThe findings from this study demonstrate that CEP55, DEPDC1, KIF23, CLSPN, MYBL2, and RACGAP1 are closely associated with prognosis and immune infiltration, representing potential therapeutic targets or prognostic biomarkers in HCC.

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Identification of Hub Genes Associated With Immune Infiltration and Predict Prognosis in Hepatocellular Carcinoma via Bioinformatics Approaches

Frontiers in Genetics ◽

10.3389/fgene.2020.575762 ◽

2021 ◽

Vol 11 ◽

Author(s):

Huaping Chen ◽

Junrong Wu ◽

Liuyi Lu ◽

Zuojian Hu ◽

Xi Li ◽

...

Keyword(s):

Gene Expression ◽

Hepatocellular Carcinoma ◽

Cell Division ◽

Immune Cell ◽

Cox Regression ◽

Close Association ◽

Functional Enrichment ◽

Microtubule Assembly ◽

Hub Genes ◽

Immune Infiltration

AimsIn the cancer-related research field, there is currently a major need for a greater number of valuable biomarkers to predict the prognosis of hepatocellular carcinoma (HCC). In this study, we aimed to screen hub genes related to immune cell infiltration and explore their prognostic value for HCC.MethodsWe analyzed five datasets (GSE46408, GSE57957, GSE74656, GSE76427, and GSE87630) from the Gene Expression Omnibus database to screen the differentially expressed genes (DEGs). A protein–protein interaction network of the DEGs was constructed using the Search Tool for the Retrieval of Interacting Genes; then, the hub genes were identified. Functional enrichment of the genes was performed on the Metascape website. Next, the expression of these hub genes was validated in several databases, including Oncomine, Gene Expression Profiling Interactive Analysis 2 (GEPIA2), and Human Protein Atlas. We explored the correlations between the hub genes and infiltrated immune cells in the TIMER2.0 database. The survival curves were generated in GEPIA2, and the univariate and multivariate Cox regression analyses were performed using TIMER2.0.ResultsThe top ten hub genes [DNA topoisomerase II alpha (TOP2A), cyclin B2 (CCNB2), protein regulator of cytokinesis 1 (PRC1), Rac GTPase-activating protein 1 (RACGAP1), aurora kinase A (AURKA), cyclin-dependent kinase inhibitor 3 (CDKN3), nucleolar and spindle-associated protein 1 (NUSAP1), cell division cycle-associated 5 (CDCA5), abnormal spindle microtubule assembly (ASPM), and non-SMC condensin I complex subunit G (NCAPG)] were identified in subsequent analysis. These genes are most markedly enriched in cell division, suggesting their close association with tumorigenesis. Multi-database analyses validated that the hub genes were upregulated in HCC tissues. All hub genes positively correlated with several types of immune infiltration, including B cells, CD4+ T cells, macrophages, and dendritic cells. Furthermore, these hub genes served as independent prognostic factors, and the expression of these hub genes combing with the macrophage levels could help predict an unfavorable prognosis of HCC.ConclusionIn sum, these hub genes (TOP2A, CCNB2, PRC1, RACGAP1, AURKA, CDKN3, NUSAP1, CDCA5, ASPM, and NCAPG) may be pivotal markers for prognostic prediction as well as potentially work as targets for immune-based intervention strategies in HCC.

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Identification of Hub Genes and Their Correlation With Immune Infiltration Cells in Hepatocellular Carcinoma Based on GEO and TCGA Databases

Frontiers in Genetics ◽

10.3389/fgene.2021.647353 ◽

2021 ◽

Vol 12 ◽

Author(s):

Rui Huang ◽

Jinying Liu ◽

Hui Li ◽

Lierui Zheng ◽

Haojun Jin ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Immune Cell ◽

Primary Liver Cancer ◽

Cell Infiltration ◽

Regulation Mechanism ◽

Immune Cell Infiltration ◽

Hub Genes ◽

Rank Analysis ◽

Immune Infiltration ◽

Follicular Helper

Hepatocellular carcinoma (HCC) is a primary liver cancer with extremely high mortality in worldwide. HCC is hard to diagnose and has a poor prognosis due to the less understanding of the molecular pathological mechanisms and the regulation mechanism on immune cell infiltration during hepatocarcinogenesis. Herein, by performing multiple bioinformatics analysis methods, including the RobustRankAggreg (RRA) rank analysis, weighted gene co-expression network analysis (WGCNA), and a devolution algorithm (CIBERSORT), we first identified 14 hub genes (NDC80, DLGAP5, BUB1B, KIF20A, KIF2C, KIF11, NCAPG, NUSAP1, PBK, ASPM, FOXM1, TPX2, UBE2C, and PRC1) in HCC, whose expression levels were significantly up-regulated and negatively correlated with overall survival time. Moreover, we found that the expression of these hub genes was significantly positively correlated with immune infiltration cells, including regulatory T cells (Treg), T follicular helper (TFH) cells, macrophages M0, but negatively correlated with immune infiltration cells including monocytes. Among these hub genes, KIF2C and UBE2C showed the most significant correlation and were associated with immune cell infiltration in HCC, which was speculated as the potential prognostic biomarker for guiding immunotherapy.

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