scholarly journals Comprehensive Analysis of the Prognostic Values of the TRIM Family in Hepatocellular Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Weiyu Dai ◽  
Jing Wang ◽  
Zhi Wang ◽  
Yizhi Xiao ◽  
Jiaying Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Survival Analysis ◽  
Risk Score ◽  
Roc Curves ◽  
Genetic Alterations ◽  
Pathway Enrichment Analysis ◽  
Family Risk ◽  
Cox Regression Analysis ◽  
Family Gene

BackgroundAccumulating studies have demonstrated the abnormal expressions and prognostic values of certain members of the tripartite motif (TRIM) family in diverse cancers. However, comprehensive prognostic values of the TRIM family in hepatocellular carcinoma (HCC) are yet to be clearly defined.MethodsThe prognostic values of the TRIM family were evaluated by survival analysis and univariate Cox regression analysis based on gene expression data and clinical data of HCC from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The expression profiles, protein–protein interaction among the TRIM family, prediction of transcription factors (TFs) or miRNAs, genetic alterations, correlations with the hallmarks of cancer and immune infiltrates, and pathway enrichment analysis were explored by multiple public databases. Further, a TRIM family gene-based signature for predicting overall survival (OS) in HCC was built by using the least absolute shrinkage and selection operator (LASSO) regression. TCGA–Liver Hepatocellular Carcinoma (LIHC) cohort was used as the training set, and GSE76427 was used for external validation. Time-dependent receiver operating characteristic (ROC) and survival analysis were used to estimate the signature. Finally, a nomogram combining the TRIM family risk score and clinical parameters was established.ResultsHigh expressions of TRIM family members including TRIM3, TRIM5, MID1, TRIM21, TRIM27, TRIM32, TRIM44, TRIM47, and TRIM72 were significantly associated with HCC patients’ poor OS. A novel TRIM family gene-based signature (including TRIM5, MID1, TRIM21, TRIM32, TRIM44, and TRIM47) was built for OS prediction in HCC. ROC curves suggested the signature’s good performance in OS prediction. HCC patients in the high-risk group had poorer OS than the low-risk patients based on the signature. A nomogram integrating the TRIM family risk score, age, and TNM stage was established. The ROC curves suggested that the signature presented better discrimination than the similar model without the TRIM family risk score.ConclusionOur study identified the potential application values of the TRIM family for outcome prediction in HCC.

scholarly journals Identification of a Novel Gene Model-Based Homologous Recombination Deficiency Score to Improve Survival Prediction of TNBC.

2021 ◽  
Author(s):  
Wenxiang Zhang ◽  
Bolun Ai ◽  
Xiangyi Kong ◽  
Xiangyu Wang ◽  
Jie Zhai ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Homologous Recombination ◽  
Risk Score ◽  
Cox Regression ◽  
Roc Curves ◽  
Cox Regression Analysis ◽  
Homologous Recombination Deficiency ◽  
Ajcc Stage ◽  
Kaplan Meier

Abstract Background Triple-negative breast cancer (TNBC) is a specific histological type of breast cancer with a poor prognosis, early recurrence, which lacks durable chemotherapy responses and effective targeted therapies. We aimed to construct an accurate prognostic risk model based on homologous recombination deficiency (HRD) - gene expression profiles for improving prognosis prediction of TNBC. Methods Triple-negative breast cancer RNA sequencing data and sample clinical information were downloaded from the breast invasive carcinoma (BRCA) cohort in the Cancer Genome Atlas (TCGA) database. Combined with the HRD database, tumor samples were divided into two sets. We screened differentially expressed genes (DEGs) and then identified HRD-related prognostic genes using weighted gene co-expression network analysis (WGCNA) and Cox regression analysis. The least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis were used to identifying key prognostic genes. Risk scores were calculated and compared with HRD score, Kaplan–Meier (KM) survival analysis were used to assess its prognostic power. GSE103091 dataset from GEO (Gene Expression Omnibus) database was used to validate the signature. Univariate and multivariate Cox regression were performed to independently verify the prognosis of the risk score. A nomogram was constructed and revealed by time-dependent ROC curves to guide clinical practice. Results We found that HRD tumor samples (HRD score > = 42) in TNBC patients were associated with poor overall survival (p = 0.027). We identified a total of 147 differential genes including 203 up-regulated and 213 down-regulated genes, among which 29 were prognosis-related genes. Through the LASSO method, 6 key prognostic genes ((MUCL1, IVL, FAM46C, CHI3L1, PRR15L, and CLEC3A) were selected and a 6-gene risk score was constructed. We found risk score was negatively associated with homologous recombination deficiency (HRD) scores (r = -0.22, p = 0.019). Compared with the low-risk group, Kaplan-Meier survival analysis shows that the high-risk group has an obvious poorer prognosis (P < 0.0001). Finally, we integrated the risk score model and clinical factors of TNBC (AJCC-stage, HRD score, T stage, and N stage) to construct a compound nomogram. Time-dependent ROC curves showed the risk score performed better in 1-, 3- and 5-year survival predictions compared with AJCC-stage. Conclusions Based on HRD gene expression data, our six HRD-related gene signature and nomogram could be practical and reliable tools for predicting OS in patients with TNBC.

scholarly journals Construction and Validation of a Macrophage-Associated Risk Model for Predicting the Prognosis of Osteosarcoma

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Kang-Wen Xiao ◽  
Zhi-Bo Liu ◽  
Zi-Hang Zeng ◽  
Fei-Fei Yan ◽  
Ling-Fei Xiao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Survival Analysis ◽  
High Risk ◽  
Risk Score ◽  
Risk Model ◽  
Risk Group ◽  
Curve Analysis ◽  
Gene Set Enrichment Analysis ◽  
Data Set ◽  
Cox Regression Analysis

Background. Osteosarcoma is one of the most common bone tumors among children. Tumor-associated macrophages have been found to interact with tumor cells, secreting a variety of cytokines about tumor growth, metastasis, and prognosis. This study aimed to identify macrophage-associated genes (MAGs) signatures to predict the prognosis of osteosarcoma. Methods. Totally 384 MAGs were collected from GSEA software C7: immunologic signature gene sets. Differential gene expression (DGE) analysis was performed between normal bone samples and osteosarcoma samples in GSE99671. Kaplan–Meier survival analysis was performed to identify prognostic MAGs in TARGET-OS. Decision curve analysis (DCA), nomogram, receiver operating characteristic (ROC), and survival curve analysis were further used to assess our risk model. All genes from TARGET-OS were used for gene set enrichment analysis (GSEA). Immune infiltration of osteosarcoma sample was calculated using CIBERSORT and ESTIMATE packages. The independent test data set GSE21257 from gene expression omnibus (GEO) was used to validate our risk model. Results. 5 MAGs (MAP3K5, PML, WDR1, BAMBI, and GNPDA2) were screened based on protein-protein interaction (PPI), DGE, and survival analysis. A novel macrophage-associated risk model was constructed to predict a risk score based on multivariate Cox regression analysis. The high-risk group showed a worse prognosis of osteosarcoma ( p  < 0.001) while the low-risk group had higher immune and stromal scores. The risk score was identified as an independent prognostic factor for osteosarcoma. MAGs model for diagnosis of osteosarcoma had a better net clinical benefit based on DCA. The nomogram and ROC curve also effectively predicted the prognosis of osteosarcoma. Besides, the validation result was consistent with the result of TARGET-OS. Conclusions. A novel macrophage-associated risk score to differentiate low- and high-risk groups of osteosarcoma was constructed based on integrative bioinformatics analysis. Macrophages might affect the prognosis of osteosarcoma through macrophage differentiation pathways and bring novel sights for the progression and prognosis of osteosarcoma.

scholarly journals Transcriptome analysis revealed key prognostic genes and microRNAs in hepatocellular carcinoma

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8930 ◽  
Author(s):  
Xi Ma ◽  
Lin Zhou ◽  
Shusen Zheng
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Survival Analysis ◽  
Poor Prognosis ◽  
Bioinformatics Analysis ◽  
Expression Profiles ◽  
Pathway Enrichment Analysis ◽  
Cox Regression Analysis ◽  
Key Genes ◽  
Overall Survival Analysis

Background Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide. However, the molecular mechanisms involved in HCC remain unclear and are in urgent need of elucidation. Therefore, we sought to identify biomarkers in the prognosis of HCC through an integrated bioinformatics analysis. Methods Messenger RNA (mRNA) expression profiles were obtained from the Gene Expression Omnibus database and The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) for the screening of common differentially expressed genes (DEGs). Function and pathway enrichment analysis, protein-protein interaction network construction and key gene identification were performed. The significance of key genes in HCC was validated by overall survival analysis and immunohistochemistry. Meanwhile, based on TCGA data, prognostic microRNAs (miRNAs) were decoded using univariable and multivariable Cox regression analysis, and their target genes were predicted by miRWalk. Results Eleven hub genes (upregulated ASPM, AURKA, CCNB2, CDC20, PRC1 and TOP2A and downregulated AOX1, CAT, CYP2E1, CYP3A4 and HP) with the most interactions were considered as potential biomarkers in HCC and confirmed by overall survival analysis. Moreover, AURKA, PRC1, TOP2A, AOX1, CYP2E1, and CYP3A4 were considered candidate liver-biopsy markers for high risk of developing HCC and poor prognosis in HCC. Upregulation of hsa-mir-1269b, hsa-mir-518d, hsa-mir-548aq, hsa-mir-548f-1, and hsa-mir-6728, and downregulation of hsa-mir-139 and hsa-mir-4800 were determined to be risk factors of poor prognosis, and most of these miRNAs have strong potential to help regulate the expression of key genes. Conclusions This study undertook the first large-scale integrated bioinformatics analysis of the data from Illumina BeadArray platforms and the TCGA database. With a comprehensive analysis of transcriptional alterations, including mRNAs and miRNAs, in HCC, our study presented candidate biomarkers for the surveillance and prognosis of the disease, and also identified novel therapeutic targets at the molecular and pathway levels.

scholarly journals Identification of Potential Biomarkers From Hepatocellular Carcinoma With MT1 Deletion

2021 ◽  
Vol 27 ◽  
Author(s):  
Ruohao Zhang ◽  
Miao Huang ◽  
Hong Wang ◽  
Shengming Wu ◽  
Jiali Yao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Prognostic Factor ◽  
Cox Regression ◽  
Independent Prognostic Factor ◽  
Comparative Genomic ◽  
Pathway Enrichment Analysis ◽  
Hub Genes ◽  
Cox Regression Analysis ◽  
Potential Biomarkers

Background: Hepatocellular carcinoma (HCC) is one of the deadliest cancers worldwide. Metallothioneins (MTs) are metal-binding proteins involved in multiple biological processes such as metal homeostasis and detoxification, as well as in oncogenesis. Copy number variation (CNV) plays a vital role in pathogenesis and carcinogenesis. Nevertheless, there is no study on the role of MT1 CNV in HCC.Methods: Array-based Comparative Genomic Hybridization (aCGH) analysis was performed to obtain the CNV data of 79 Guangxi HCC patients. The prognostic effect of MT1-deletion was analyzed by univariate and multivariate Cox regression analysis. The differentially expressed genes (DEGs) were screened based on The Gene Expression Omnibus database (GEO) and the Liver Hepatocellular Carcinoma of The Cancer Genome Atlas (TCGA-LIHC). Then function and pathway enrichment analysis, protein-protein interaction (PPI) and hub gene selection were applied on the DEGs. Lastly, the hub genes were validated by immunohistochemistry, tissue expression and prognostic analysis.Results: The MT1-deletion was demonstrated to affect the prognosis of HCC and can act as an independent prognostic factor. 147 common DEGs were screened. The most significant cluster of DEGs identified by Molecular Complex Detection (MCODE) indicated that the expression of four MT1s were down-regulated. MT1X and other five hub genes (TTK, BUB1, CYP3A4, NR1I2, CYP8B1) were associated with the prognosis of HCC. TTK, could affect the prognosis of HCC with MT1-deletion and non-deletion. NR1I2, CYP8B1, and BUB1 were associated with the prognosis of HCC with MT1-deletion.Conclusions: In the current study, we demonstrated that MT1-deletion can be an independent prognostic factor in HCC. We identified TTK, BUB1, NR1I2, CYP8B1 by processing microarray data, for the first time revealed the underlying function of MT1 deletion in HCC, MT1-deletion may influence the gene expression in HCC, which may be the potential biomarkers for HCC with MT1 deletion.

scholarly journals A novel DNA methylation-based model that effectively predicts prognosis in hepatocellular carcinoma

2021 ◽  
Vol 41 (3) ◽  
Author(s):  
Xiang-Yong Hao ◽  
An-Qiang Li ◽  
Hao Shi ◽  
Tian-Kang Guo ◽  
Yan-Fei Shen ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Dna Methylation ◽  
Predictive Model ◽  
Risk Score ◽  
Methylation Level ◽  
Pathway Enrichment Analysis ◽  
Validation Group ◽  
Cox Regression Analysis ◽  
Differentially Methylated Genes ◽  
Cox Analysis

Abstract Purpose: To build a novel predictive model for hepatocellular carcinoma (HCC) patients based on DNA methylation data. Methods: Four independent DNA methylation datasets for HCC were used to screen for common differentially methylated genes (CDMGs). Gene Ontology (GO) enrichment, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were used to explore the biological roles of CDMGs in HCC. Univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) Cox analysis were performed to identify survival-related CDMGs (SR-CDMGs) and to build a predictive model. The importance of this model was assessed using Cox regression analysis, propensity score-matched (PSM) analysis and stratification analysis. A validation group from the Cancer Genome Atlas (TCGA) was constructed to further validate the model. Results: Four SR-CDMGs were identified and used to build the predictive model. The risk score of this model was calculated as follows: risk score = (0.01489826 × methylation level of WDR69) + (0.15868618 × methylation level of HOXB4) + (0.16674959 × methylation level of CDKL2) + (0.16689301 × methylation level of HOXA10). Kaplan–Meier analysis demonstrated that patients in the low-risk group had a significantly longer overall survival (OS; log-rank P-value =0.00071). The Cox model multivariate analysis and PSM analysis identified the risk score as an independent prognostic factor (P&lt;0.05). Stratified analysis results further confirmed this model performed well. By analyzing the validation group, the results of receiver operating characteristic (ROC) curve analysis and survival analysis further validated this model. Conclusion: Our DNA methylation-based prognosis predictive model is effective and reliable in predicting prognosis for patients with HCC.

scholarly journals Cancer-Testis Gene Expression in Hepatocellular Carcinoma: Identification of Prognostic Markers and Potential Targets for Immunotherapy

2020 ◽  
Vol 19 ◽  
pp. 153303382094427
Author(s):  
Yan-Peng Zhang ◽  
Zhi-Wei Bao ◽  
Jing-Bang Wu ◽  
Yun-Hao Chen ◽  
Jun-Ru Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Survival Analysis ◽  
T Cell ◽  
Prognostic Markers ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Pathway Enrichment ◽  
T Cell Infiltration ◽  
Cancer Testis

Background: Cancer-testis genes can serve as prognostic biomarkers and valuable targets for immunotherapy in multiple tumors because of their restricted expression in testis and cancer. However, their expression pattern in hepatocellular carcinoma is still not well understood. The purpose is to comprehensively characterize the cancer-testis gene expression in hepatocellular carcinoma as well as identify prognostic markers and potential targets for immunotherapy. Methods: Cancer-testis database and publicly available data sets reporting new cancer-testis genes were integrated, and then restricted them in a testis and hepatocellular carcinoma expression pattern. Pathway enrichment analysis and survival analysis were conducted to evaluate the biological function and prognostic effect of cancer-testis genes. Clustering analysis and coexpression analysis were performed to illustrate cancer-testis gene expression patterns in hepatocellular carcinoma. The association of gene expression of each cancer-testis gene to the corresponding methylation status was detected. Finally, we explored the associations between cancer-testis genes and CD8+ T-cell infiltration in hepatocellular carcinoma by TISIDB, and then validated it in an independent hepatocellular carcinoma cohort with 72 patients. Results: A total of 59 testis-specific genes were identified highly expressed in hepatocellular carcinoma. Pathway enrichment analysis revealed that cancer-testis genes in hepatocellular carcinoma significantly involves in the process of cell cycle regulation. Most of the cancer-testis genes were coexpressed, and cluster analysis suggested that cancer-testis gene expressed in hepatocellular carcinoma is independent of sex, hepatitis status, and histology type. We also found that demethylation might be a regulatory mechanism of cancer-testis gene expression in hepatocellular carcinoma. Survival analysis indicated that cancer-testis genes could predict the prognosis of patients with hepatocellular carcinoma. Furthermore, BUB1B was identified contributing to the resistance of CD8+ T-cell infiltration in hepatocellular carcinoma and was an independent prognostic factor both for overall survival and disease-free survival. Conclusions: Our analysis enables better understanding of cancer-testis genes in hepatocellular carcinoma and provides potential targets for hepatocellular carcinoma treatment. Experimental and clinical studies are needed for further validations.

scholarly journals Identification of lncRNA prognostic signature and analysis of their functions for HCV-related hepatocellular carcinoma

2020 ◽  
Author(s):  
Kun Wang ◽  
Wenxin Li ◽  
Yefu Liu ◽  
Zhiqiang Hao ◽  
Xiangdong Hua ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Risk Score ◽  
Cox Regression ◽  
Roc Curves ◽  
The Cancer Genome Atlas ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Clinical Variables ◽  
Kaplan Meier ◽  
Underlying Mechanisms

Abstract Background Hepatitis C virus (HCV) infection is a main contribution to the increase in hepatocellular carcinoma (HCC) incidence and patients’ death recently, but prognostic biomarkers for HCV-related HCC remain rarely reported. This study was to identify an lncRNA prognostic signature for HCV-HCC patients and explore their underlying function mechanisms. Methods In total, 102 HCV-HCC samples and 50 normal control samples were obtained from The Cancer Genome Atlas (TCGA) database. Univariate and multivariate Cox regression analysis were conducted to screen an lncRNA signature that could predict overall survival (OS) and then, the risk score was calculated using this signature. The prognostic potential of this risk score was evaluated by drawing Kaplan-Meier, receiver operating characteristic (ROC) curves and performing multivariate Cox regression analyses with clinical variables. Furthermore, a co-expression and competing endogenous RNA (ceRNA) networks were constructed to explore the functional mechanisms of lncRNAs. Results Multivariate Cox regression showed six lncRNAs (SLC16A1-AS1, ZFPM2-AS1, JARID2-AS1, LINC01426, USP3-AS1 and LYPLAL1-AS1) were significantly associated with OS of HCV-HCC patients. These six lncRNAs were used to establish a risk score model, which displayed a higher prognosis prediction accuracy [area under the ROC curve (AUC) = 0.95 for training set; AUC = 0.885 for testing; AUC = 0.907 for entire set]. Also, this was independent of various clinical variables. The crucial co-expression (LINC01426/SLC16A1-AS1-AURKA/SFN/CCNB1, ZFPM2-AS1/LYPLAL1-AS1/JARID2-AS1-TSSK6) or ceRNA (USP3-AS1-hsa-miR-383-SFN) interaction axes were identified. Conclusion Our study identified a novel six-lncRNA prognosis signature for HCV-HCC patients and indicated their underlying mechanisms for HCC progression.

scholarly journals Constructing the Logical Regression Model to Predict the Target of Jianpi Jiedu Decoction in the Treatment of Hepatocellular Carcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Rongjie Zhang ◽  
Yan Chen ◽  
Ge Zhou ◽  
Baoguo Sun ◽  
Yue Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Survival Analysis ◽  
Regression Model ◽  
Target Genes ◽  
Cox Regression ◽  
Risk Group ◽  
Cox Regression Analysis ◽  
Expression Levels ◽  
Prognostic Analysis ◽  
Significant Difference

Objectives. The purpose of this study was to identify the molecular mechanism and prognosis-related genes of Jianpi Jiedu decoction in the treatment of hepatocellular carcinoma. Methods. The gene expression data of hepatocellular carcinoma samples and normal tissue samples were downloaded from TCGA database, and the potential targets of drug composition of Jianpi Jiedu decoction were obtained from TCMSP database. The genes were screened out in order to obtain the expression of these target genes in patients with hepatocellular carcinoma. The differential expression of target genes was analyzed by R software, and the genes related to prognosis were screened by univariate Cox regression analysis. Then, the LASSO model was constructed for risk assessment and survival analysis between different risk groups. At the same time, independent prognostic analysis, GSEA analysis, and prognostic analysis of single gene in patients with hepatocellular carcinoma were performed. Results. 174 compounds of traditional Chinese medicine were screened by TCMSP database, corresponding to 122 potential targets. 39 upregulated genes and 9 downregulated genes were screened out. A total of 20 candidate prognostic related genes were screened out by univariate Cox analysis, of which 12 prognostic genes were involved in the construction of the LASSO regression model. There was a significant difference in survival time between the high-risk group and low-risk group ( p < 0.05 ). Among the genes related to prognosis, the expression levels of CCNB1, NQO1, NUF2, and CHEK1 were high in tumor tissues ( p < 0.05 ). Survival analysis showed that the high expression levels of these four genes were significantly correlated with poor prognosis of HCC ( p < 0.05 ). GSEA analysis showed that the main KEGG enrichment pathways were lysine degradation, folate carbon pool, citrate cycle, and transcription factors. Conclusions. In the study, we found that therapy target genes of Jianpi Jiedu decoction were mainly involved in metabolism and apoptosis in hepatocellular carcinoma, and there was a close relationship between the prognosis of hepatocellular carcinoma and the genes of CCNB1, NQO1, NUF2, and CHEK1.

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 Exploration and validation of a novel prognostic signature based on comprehensive bioinformatics analysis in hepatocellular carcinoma

2020 ◽  
Vol 40 (11) ◽  
Author(s):  
Xiaofei Wang ◽  
Jie Qiao ◽  
Rongqi Wang
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Risk Score ◽  
Expression Profiles ◽  
Univariate Analysis ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Good Prediction ◽  
Expression Levels

Abstract The present study aimed to construct a novel signature for indicating the prognostic outcomes of hepatocellular carcinoma (HCC). Gene expression profiles were downloaded from Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) databases. The prognosis-related genes with differential expression were identified with weighted gene co-expression network analysis (WGCNA), univariate analysis, the least absolute shrinkage and selection operator (LASSO). With the stepwise regression analysis, a risk score was constructed based on the expression levels of five genes: Risk score = (−0.7736* CCNB2) + (1.0083* DYNC1LI1) + (−0.6755* KIF11) + (0.9588* SPC25) + (1.5237* KIF18A), which can be applied as a signature for predicting the prognosis of HCC patients. The prediction capacity of the risk score for overall survival was validated with both TCGA and ICGC cohorts. The 1-, 3- and 5-year ROC curves were plotted, in which the AUC was 0.842, 0.726 and 0.699 in TCGA cohort and 0.734, 0.691 and 0.700 in ICGC cohort, respectively. Moreover, the expression levels of the five genes were determined in clinical tumor and normal specimens with immunohistochemistry. The novel signature has exhibited good prediction efficacy for the overall survival of HCC patients.

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