scholarly journals Novel Prognostic Markers for Liver Hepatocellular Carcinoma and Their Roles in Immune Infiltration of the Tumor Microenvironment

2021 ◽  
Author(s):  
Meihai Deng ◽  
Hao Liang ◽  
Kunpeng Hu ◽  
Zhaozhong Zhong ◽  
Zhiyong Xiong ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
T Cells ◽  
Tumor Microenvironment ◽  
Immune Cells ◽  
Rna Seq ◽  
Hub Genes ◽  
Novel Biomarkers ◽  
Liver Hepatocellular Carcinoma ◽  
Memory Cd4 T Cells

Abstract Background: Liver hepatocellular carcinoma (LIHC) is one of the most common malignant cancers worldwide, the overall prognosis of LIHC remains unsatisfactory. Valuable prognostic biomarkers are still urgently needed for LIHC. This study aimed to explore hub genes associated with the prognosis of LIHC and tumor microenvironmental immune infiltration, providing potential prognostic biomarker and therapeutic target for LIHC. Methods: RNA-seq counts data for LIHC samples were obtained from TCGA database. RNA-seq counts data for normal liver samples were obtained from GTEx database. Weighted gene co-expression network analysis (WGCNA) was used to cluster differentially expressed genes with similar expression profiles to form modules and significant modules and key genes were screened. Next, these genes was verified by cox analyses and overall survival analysis. Further, CIBERSORT was used to explore the relationship between these genes and tumor infiltrating immune cells. Results: A total number of 2661 significant DEGs were included for consensus WGCNA analysis, which identified 6 modules. Blue module (r=0.85, p<0.0001) showed high relationship with LIHC, which included 400 genes. After the overall survival analyses of hub genes, CDC20, CDCA5, CDCA8, KIF2C and KIFC1 were identified as five potential marker genes, which would result in an unfavorable prognosis in LIHC. Further CIBERSORT analysis showed these novel biomarkers expression levels in LIHC were positively correlated with activated memory CD4+ T cells, follicular helper T cells, regulatory T cells and macrophages M0. While, resting memory CD4+ T cells, monocytes, macrophages M2, resting mast cells showed a negative correlation with the 5 novel biomarkers expression levels. Conclusions: The study screened 5 genes with marked prognostic capability for LIHC and found these genes were correlated with the infiltration of immune cells in LIHC tumor microenvironment. The findings might provide a more detailed molecular mechanism underlying LIHC occurrence and progression, holding promise for acting as potential biomarkers and therapeutic targets.

scholarly journals Expression and prognosis analysis of JMJD5 in human cancers

2020 ◽  
Author(s):  
Hui Li ◽  
Qun Li ◽  
Hong Jing ◽  
Jianghai Zhao ◽  
Hui Zhang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
T Cells ◽  
B Cells ◽  
Lung Adenocarcinoma ◽  
Immune Cells ◽  
Prognostic Value ◽  
Normal Tissues ◽  
Human Cancers ◽  
Stomach Adenocarcinoma ◽  
Liver Hepatocellular Carcinoma

Abstract BackgroundJumonjiC (JmjC) domain-containing protein 5 (JMJD5) plays an important role in cancer metabolism. However, the prognostic value of JMJD5 in most human cancers is still unknown. In this study, we aim to investigate the expression and prognostic value of JMJD5, immune cells infiltration, and the correlations among them. MethodsWe performed a detailed cancer vs. normal analysis of JMJD5 mRNA expression via online Tumor Immune Estimation Resource (TIMER). The protein expressions of JMJD5 in various cancers vs. adjacent normal tissues were examined by immunohistochemistry (IHC) of tissue microarray sections (TMAs). Moreover, the Kaplan-Meier Plotter databases were used to evaluate the prognostic values in above cancers. The correlations between JMJD5 expression level and abundances of six immune infiltrating cells (B cells, CD4+ T cells, CD8+ T cells, macrophages, neutrophils and dendritic cells) were explored by TIMER database in breast cancer (BRCA), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC) and stomach adenocarcinoma (STAD). The prognostic values of tumor- infiltrating immune cells were also investigated by TIMER in above four cancers. Finally, the COX proportional hazards model was used to investigate the correlations among clinical outcome, the abundance of immune cell infiltrates and the expression of JMJD5 in above four cancer types.ResultsThe expression of JMJD5 was significantly lower in human breast carcinoma (BRCA), cholangiocarcinoma (CHOL), liver hepatocellular carcinoma (LIHC) and lung cancer (LUC) but higher in prostate adenocarcinoma (PRAD) and stomach adenocarcinoma (STAD) comparing to their respective normal tissues. And high expression of JMJD5 has better prognosis only in BRCA, LIHC, LUC but the opposite effect in STAD. JMJD5 expression is significant correlation with the abundance of six immune cells infiltration in above four cancers. Both the BRCA or lung adenocarcinoma (LUAD) patients with abundance of B cell and the STAD patients with low level of macrophage have a better cumulative survival. ConclusionsWe provided novel evidence of JMJD5 as an essential prognostic biomarker in cancers through analyses the correlation of the JMJD5 expression, tumor-infiltrating B cells and macrophages and prognostic value. This study offers new perspectives therapeutic target in BRCA, LUAD and STAD.

scholarly journals As a Newly-Discovered Component in Tumor Microenvironment RUFY4 Participates in Regulation of T cell via Cytokine-Interaction and Predicts Prognosis and Immunotherapy Responsiveness of Clear Cell Renal Carcinoma Patients

2020 ◽  
Author(s):  
Daojia Miao ◽  
Jian Shi ◽  
Zhiyong Xiong ◽  
Changfei Yuan ◽  
Wen Xiao ◽  
...  
Keyword(s):  
Overall Survival ◽  
T Cells ◽  
Tumor Microenvironment ◽  
Immune Cells ◽  
Clear Cell ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Sequencing Data ◽  
Cell Renal Cell Carcinoma ◽  
Immune Infiltration

Abstract Background: clear cell renal cell carcinoma (ccRCC) is one of the most lethal kinds of malignancies in urinary system and the existing immunotherapy have not achieved satisfactory outcomes. Therefore, this study aims to establish a brand-new gene signature for immune-infiltration and clinical outcome (overall survival and immunotherapy responsiveness) of patients with ccRCC. Methods: Based on RNA sequencing data and clinical information in the Cancer Genome Atlas Project (TCGA) database, we investigated proportions of immune cells in 611 samples by an online tool CIBERSORTx. Multivariate survival analysis was used to determine crucial survival-associated immune cells and immune-infiltration-related genes (IIRGs). Next ROC analysis was carried on to evaluate the ability of IIRGs to distinguish patients and functional enrichment analysis were implemented to explore potential interaction network between immune cells and IIRGs. Results: T cells follicular helper (TFHs) and T cells regulatory (Tregs) were highly infiltrated in the tumor microenvironment and their abundance ratios were independent prognostic factors for overall survival. Among IIRGs of TFHs and TREGs, RUFY4 was found to be highly activated in tumor microenvironment and its co-expression network was enriched in regulation of T cells via cytokine-cytokine receptor interactions.Conclusion: These two cells and RUFY4, considered prognostic biomarkers and immunotherapeutic predictors of ccRCC patients, might also simultaneously affect the regulatory network in tumor microenvironment (TME) through cytokine interactions.

scholarly journals Identification of Therapeutic Targets and Prognostic Biomarkers Among Chemokine (C-C Motif) Ligands in the Liver Hepatocellular Carcinoma Microenvironment

2021 ◽  
Vol 9 ◽  
Author(s):  
Zhongyi Jiang ◽  
Changchang Xing ◽  
Pusen Wang ◽  
Xueni Liu ◽  
Lin Zhong
Keyword(s):  
Hepatocellular Carcinoma ◽  
T Cells ◽  
Western Blot ◽  
Signaling Pathway ◽  
Immune Cells ◽  
Stromal Cells ◽  
Prognostic Biomarkers ◽  
Immune Checkpoints ◽  
Immunological Therapy ◽  
Liver Hepatocellular Carcinoma

Background: Liver hepatocellular carcinoma (LIHC) is the third leading cause of cancer-related death and the sixth most common solid tumor worldwide. In the tumor microenvironment, the cross-talk between cancer cells, immune cells, and stromal cells exerts significant effects on neoplasia and tumor development and is modulated in part by chemokines. Chemokine (C-C motif) ligands (CCL) can directly target tumor cells and stromal cells, and they have been shown to regulate tumor cell proliferation, cancer stem-like cell properties, cancer invasiveness and metastasis, which directly and indirectly affect tumor immunity and influence cancer progression, therapy and patient outcomes. However, the prognostic values of chemokines CCL in LIHC have not been clarified.Methods: In this study, we comprehensively analyzed the relationship between transcriptional chemokines CCL and disease progression of LIHC using the ONCOMINE dataset, GEPIA, UALCAN, STRING, WebGestalt, GeneMANIA, TRRUST, DAVID 6.8, LinkedOmics, TIMER, GSCALite, and Open Targets. We validated the protein levels of chemokines CCL through western blot and immunohistochemistry.Results: The transcriptional levels of CCL5/8/11/13/15/18/20/21/25/26/27/28 in LIHC tissues were significantly elevated while CCL2/3/4/14/23/24 were significantly reduced. A significant correlation was found between the expression of CCL14/25 and the pathological stage of LIHC patients. LIHC patients with low transcriptional levels of CCL14/21 were associated with a significantly poor prognosis. The functions of differentially expressed chemokines CCL were primarily related to the chemokine signaling pathway, cytokine–cytokine receptor interactions, and TNF-α signaling pathway. Our data suggested that RELA/REL, NFKB1, STAT1/3/6, IRF3, SPI1, and JUN were key transcription factors for chemokines CCL. We found significant correlations among the expression of chemokines CCL and the infiltration of six types of immune cells (B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells) and immune checkpoints (PD-1. PD-L1, and CTLA-4). The western blot and immunohistochemistry results showed that protein expression levels of CCL5 and CCL20 were upregulated in LIHC. CCL5 and CCL20 were significantly correlated with the clinical outcome of patients with LIHC, and could be negatively regulated by some drugs or small molecules.Conclusions: Our results may provide novel insights for the potential suitable targets of immunological therapy and prognostic biomarkers for LIHC.

scholarly journals Highly Multiplexed Single Cell Spatial Analysis of the Tumor Microenvironment in Lymphoma Predicts Clinical Outcomes

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 2789-2789 ◽  
Author(s):  
Monirath Hav ◽  
Anthony Colombo ◽  
Erik Gerdtsson ◽  
Mohan Singh ◽  
Denaly Chen ◽  
...  
Keyword(s):  
Overall Survival ◽  
T Cells ◽  
Spatial Analysis ◽  
Tumor Microenvironment ◽  
Single Cell ◽  
Tumor Cells ◽  
Clinical Outcomes ◽  
Immune Cells ◽  
Spatial Interaction ◽  
Spatial Architecture

Background Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of non-Hodgkin lymphoma. Although a majority of patients are cured with standard chemo-immunotherapy, up to 40% of DLBCL patients have refractory disease or develop relapse following R-CHOP regimen, warranting development of novel, more effective therapeutic strategies for this cohort[1]. The composition of immune cells in the tumor microenvironment (TME) and tumor PD-L1 expression have been reported to predict DLBCL outcomes, however PD1 inhibitors demonstrate response rates of less than 10%.[2-5] We hypothesize that a better characterization of spatial architecture of the tumour microenvironment (TME) in lymphoma will help explain why DLBCL has poor responses to PD1 inhibitors and guide future targeted immunotherapies for these patients. Methods Here we characterized the TME in DLBCL using imaging mass cytometry (IMC), which allows high-dimensional, single-cell and spatial analysis of FFPE tissues at sub-cellular resolution [6]. Using a panel of 32 antibodies, IMC was performed 41 tissue microarray cores from 33 DLBCL cases. IMC images were analyzed for relevant immunophenotypes, the spatial architecture of those phenotypes and compared to clinical outcomes to identify immune contexture based biomarkers. Results Phenograph was used to cluster tumor and immune cells based on phenotype. Immune cells represented 33% of the cells broken down to CD4 (36%), CD8 (30%), macrophages (26%) and TREG(8%) (Figure A).Immune cell infiltration in individual tumor samples ranged from 7% to 75% with marked heterogeneity between samples. Analysis of immune marker expression on tumor cells identified co-expression of PD-L1/CCR4/TIM3 to be highly prognostic for overall survival (p=0.003, Figure B-C) To characterize the patterns of spatial interaction in the TME, we developed an unsupervised multivariate model to construct spatial meta-clusters based on average distances from CD8 to the centroids of 5 nearest endothelial cells, TREG, CD4 T cells, macrophages, and tumor cells (Figure D). Spatial analysis revealed 11 meta-clusters for CD8 T cell interactions (Figure E). Each CD8 spatial interaction pattern is distinctive with case to case heterogeneity (Figures F). Risk assessment analyses of spatial clusters 1, 2 and 4 ("hazardous") had almost 3 times higher odds of being identified in refractory cases compared to clusters 3, 5 and 6 ("protective") (Figure G). In the "protective" spatial neighborhoods, we observed the presence of activated CD8, Th1-like CD4, and less suppressive TREGphenotypes, with opposite in "hazardous" areas (Figures H). TIM-3 expression was high both on T cells and tumor cells in the "hazardous" neighborhoods. Finally, we show that sub-setting our analysis of CD8 phenotypes based on their spatial location to other cells improved our ability to predict overall survival in the cohort. Conclusion These results are the first to demonstrate that spatial profiling of immune architecture in DLBCL is associated with clinical outcomes, and that spatial analysis of immune cells should be explored as a potential biomarker for patients treated with immunotherapies. References [1] Coiffier, B. et al.,Blood116, 2040-5 (2010). [2] Lenz, G. et al.,N. Engl. J. Med.359, 2313-2323 (2008). [3] Ansell, S. et al.,J. Clin. Oncol.19, 720-726 (2001). [4] Qiu, L., et al., BMC Cancer19, 273 (2019). [5] Kridel, R., et al., Haematologica100, 143-5 (2015). [6] Giesen, C. et al., Nat. methods |11, 417 (2014). Figure Disclosures Merchant: Agios: Speakers Bureau; Pfizer: Consultancy, Research Funding.

Resting memory CD4+ T cells and plasma cells in gastric cancer may be potential immune biomarkers.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e16523-e16523
Author(s):  
Jingde Chen ◽  
Bei Zhang ◽  
Yifan Zhou ◽  
Xiaochen Zhao ◽  
Yuezong Bai
Keyword(s):  
Gastric Cancer ◽  
Overall Survival ◽  
T Cells ◽  
Survival Analysis ◽  
Immune Cells ◽  
Plasma Cells ◽  
Immune Cell ◽  
Expression Data ◽  
Immune Biomarkers ◽  
Memory Cd4 T Cells

e16523 Background: Previous studies revealed the CD8+T cells deeply involved in tumor progress and reaction to immunotherapy. Now we used expression data and in silicon algorithm to analyze immune cell composition in gastric cancer and explored other subsets of immune cells that may be the potential immune biomarkers. Methods: CIBERSORT quantified 22 immune cell subtypes using four GEO gastric cancer cohorts (GSE15459, GSE26253, GSE29272, GSE57303) and TCGA-STAD, TCGA-PAAD gene expression data, and only CIBERSORT P- value of < 0.05 were included in the survival analysis. Immune cell high was defined as ≥median cells proportion individually and were computed for survival analysis and hazard ratios. Wilcox test was applied to analyze the differences between normal and tumor tissues. Log-rank Mantel-Cox test was applied to compare the survival curves between the patient groups. Statistical analyses were conducted using R v3.3.2. Results: Unsupervised hierarchical analysis of four GEO gastric tumor cohort using 22 immune cells proportions identifies three subclasses. In the subclass with the best overall survival performance, we found enriched resting CD4 T memory cells. Then in TCGA-STAD (P = 0.03) and TCGA-PAAD (P = 0.03) cohort, we observed that normal tissue obtained higher fraction of resting CD4 T memory cells than tumor. On the other hand, in patients who administrated chemotherapy in TCGA-STAD, by comparing immune cell high and low subgroup, we found that plasma cells (P = 0.02), T cells CD8(P = 0.03) were associated with improved overall survival. while, neutrophils(P = 0.05), NK cells resting(P = 0.04), were correlated with decreased overall survival. Also we proved one similar result in GEO cohort, that plasma cells-high (above median) subgroup provided increased overall survival (P = 0.04, HR = 0.76). In contrast, other three immune cells were not significantly associated with survival benefits. Conclusions: Together, these results indicated that memory CD4 T cells and plasma cells infiltration in gastric cancer have important clinical meanings and may be potential immune biomarkers. The continued integration of observations from a variety of experimental models will be required to further understand and utilize the full potential of immune cells.

scholarly journals A prognostic model based on seven immune-related genes predicts the overall survival of patients with hepatocellular carcinoma

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Qian Yan ◽  
Wenjiang Zheng ◽  
Boqing Wang ◽  
Baoqian Ye ◽  
Huiyan Luo ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
High Risk ◽  
Risk Score ◽  
Immune Cells ◽  
Prognostic Model ◽  
Risk Groups ◽  
Low Risk ◽  
Prognostic Performance ◽  
Immune Related Genes

Abstract Background Hepatocellular carcinoma (HCC) is a disease with a high incidence and a poor prognosis. Growing amounts of evidence have shown that the immune system plays a critical role in the biological processes of HCC such as progression, recurrence, and metastasis, and some have discussed using it as a weapon against a variety of cancers. However, the impact of immune-related genes (IRGs) on the prognosis of HCC remains unclear. Methods Based on The Cancer Gene Atlas (TCGA) and Immunology Database and Analysis Portal (ImmPort) datasets, we integrated the ribonucleic acid (RNA) sequencing profiles of 424 HCC patients with IRGs to calculate immune-related differentially expressed genes (DEGs). Survival analysis was used to establish a prognostic model of survival- and immune-related DEGs. Based on genomic and clinicopathological data, we constructed a nomogram to predict the prognosis of HCC patients. Gene set enrichment analysis further clarified the signalling pathways of the high-risk and low-risk groups constructed based on the IRGs in HCC. Next, we evaluated the correlation between the risk score and the infiltration of immune cells, and finally, we validated the prognostic performance of this model in the GSE14520 dataset. Results A total of 100 immune-related DEGs were significantly associated with the clinical outcomes of patients with HCC. We performed univariate and multivariate least absolute shrinkage and selection operator (Lasso) regression analyses on these genes to construct a prognostic model of seven IRGs (Fatty Acid Binding Protein 6 (FABP6), Microtubule-Associated Protein Tau (MAPT), Baculoviral IAP Repeat Containing 5 (BIRC5), Plexin-A1 (PLXNA1), Secreted Phosphoprotein 1 (SPP1), Stanniocalcin 2 (STC2) and Chondroitin Sulfate Proteoglycan 5 (CSPG5)), which showed better prognostic performance than the tumour/node/metastasis (TNM) staging system. Moreover, we constructed a regulatory network related to transcription factors (TFs) that further unravelled the regulatory mechanisms of these genes. According to the median value of the risk score, the entire TCGA cohort was divided into high-risk and low-risk groups, and the low-risk group had a better overall survival (OS) rate. To predict the OS rate of HCC, we established a gene- and clinical factor-related nomogram. The receiver operating characteristic (ROC) curve, concordance index (C-index) and calibration curve showed that this model had moderate accuracy. The correlation analysis between the risk score and the infiltration of six common types of immune cells showed that the model could reflect the state of the immune microenvironment in HCC tumours. Conclusion Our IRG prognostic model was shown to have value in the monitoring, treatment, and prognostic assessment of HCC patients and could be used as a survival prediction tool in the near future.

Prognostic value of peripheral naive CD8+ T cells in oligometastatic non-small-cell lung cancer

2021 ◽  
Author(s):  
Xin Zhao ◽  
Yan Zhang ◽  
Zhenlin Gao ◽  
Yaguang Han
Keyword(s):  
Lung Cancer ◽  
Overall Survival ◽  
T Cells ◽  
Small Cell Lung Cancer ◽  
Immune Cells ◽  
Prognostic Value ◽  
Progression Free Survival ◽  
Small Cell ◽  
Small Cell Lung ◽  
Free Survival

Aim: This study aimed to investigate the prognostic value of peripheral naive and memory CD8+ and CD4+ T cells and other immune cells in patients with oligometastatic non-small-cell lung cancer (NSCLC) undergoing radiotherapy (RT). Methods: A total of 142 patients with oligometastatic NSCLC treated with RT were enrolled, and their blood samples were collected within 3 days before RT. Immune cells were identified by flow cytometry. Results: Patients with high levels of naive CD8+ T cells had longer overall survival (p = 0.004) and progression-free survival (p = 0.001) than those with low levels of naive CD8+ T cells. Multivariate analyses revealed that naive CD8+ T cells were independently correlated with overall survival (p = 0.019) and progression-free survival (p = 0.024). Conclusion: The results suggest that peripheral naive CD8+ T cells may be an independent prognostic indicator for patients with oligometastatic NSCLC undergoing RT.

scholarly journals Identification of Immune-Related Prognostic mRNA and lncRNA in Patients with Hepatocellular Carcinoma

2022 ◽  
Vol 2022 ◽  
pp. 1-16
Author(s):  
Dan Chen ◽  
Xiaoting Li ◽  
Hui Li ◽  
Kai Wang ◽  
Xianghua Tian
Keyword(s):  
Hepatocellular Carcinoma ◽  
Flow Cytometry ◽  
T Cells ◽  
Regression Analysis ◽  
Survival Analysis ◽  
Immune Cells ◽  
Cox Regression ◽  
Differentially Expressed ◽  
Cox Regression Analysis ◽  
Immune Related Genes

Background. As the most common hepatic malignancy, hepatocellular carcinoma (HCC) has a high incidence; therefore, in this paper, the immune-related genes were sought as biomarkers in liver cancer. Methods. In this study, a differential expression analysis of lncRNA and mRNA in The Cancer Genome Atlas (TCGA) dataset between the HCC group and the normal control group was performed. Enrichment analysis was used to screen immune-related differentially expressed genes. Cox regression analysis and survival analysis were used to determine prognostic genes of HCC, whose expression was detected by molecular experiments. Finally, important immune cells were identified by immune cell infiltration and detected by flow cytometry. Results. Compared with the normal group, 1613 differentially expressed mRNAs (DEmRs) and 1237 differentially expressed lncRNAs (DElncRs) were found in HCC. Among them, 143 immune-related DEmRs and 39 immune-related DElncRs were screened out. These genes were mainly related to MAPK cascade, PI3K-AKT signaling pathway, and TGF-beta. Through Cox regression analysis and survival analysis, MMP9, SPP1, HAGLR, LINC02202, and RP11-598F7.3 were finally determined as the potential diagnostic biomarkers for HCC. The gene expression was verified by RT-qPCR and western blot. In addition, CD4 + memory resting T cells and CD8 + T cells were identified as protective factors for overall survival of HCC, and they were found highly expressed in HCC through flow cytometry. Conclusion. The study explored the dysregulation mechanism and potential biomarkers of immune-related genes and further identified the influence of immune cells on the prognosis of HCC, providing a theoretical basis for the prognosis prediction and immunotherapy in HCC patients.

scholarly journals Identification of a five-gene signature in association with overall survival for hepatocellular carcinoma

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11273
Author(s):  
Lei Yang ◽  
Weilong Yin ◽  
Xuechen Liu ◽  
Fangcun Li ◽  
Li Ma ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cell Cycle ◽  
Overall Survival ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
Enrichment Score ◽  
Hub Genes ◽  
Cox Regression Analysis

Background Hepatocellular carcinoma (HCC) is considered to be a malignant tumor with a high incidence and a high mortality. Accurate prognostic models are urgently needed. The present study was aimed at screening the critical genes for prognosis of HCC. Methods The GSE25097, GSE14520, GSE36376 and GSE76427 datasets were obtained from Gene Expression Omnibus (GEO). We used GEO2R to screen differentially expressed genes (DEGs). A protein-protein interaction network of the DEGs was constructed by Cytoscape in order to find hub genes by module analysis. The Metascape was performed to discover biological functions and pathway enrichment of DEGs. MCODE components were calculated to construct a module complex of DEGs. Then, gene set enrichment analysis (GSEA) was used for gene enrichment analysis. ONCOMINE was employed to assess the mRNA expression levels of key genes in HCC, and the survival analysis was conducted using the array from The Cancer Genome Atlas (TCGA) of HCC. Then, the LASSO Cox regression model was performed to establish and identify the prognostic gene signature. We validated the prognostic value of the gene signature in the TCGA cohort. Results We screened out 10 hub genes which were all up-regulated in HCC tissue. They mainly enrich in mitotic cell cycle process. The GSEA results showed that these data sets had good enrichment score and significance in the cell cycle pathway. Each candidate gene may be an indicator of prognostic factors in the development of HCC. However, hub genes expression was weekly associated with overall survival in HCC patients. LASSO Cox regression analysis validated a five-gene signature (including CDC20, CCNB2, NCAPG, ASPM and NUSAP1). These results suggest that five-gene signature model may provide clues for clinical prognostic biomarker of HCC.

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