scholarly journals ZCCHC17 Served as a Predictive Biomarker for Prognosis and Immunotherapy in Hepatocellular Carcinoma

2022 ◽  
Vol 11 ◽  
Author(s):  
Fahui Liu ◽  
Jiadong Liang ◽  
Puze Long ◽  
Lilan Zhu ◽  
Wanyun Hou ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Immune Cells ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
High Expression ◽  
Mrna Levels ◽  
Gene Set Enrichment ◽  
Prognostic Evaluation ◽  
Gene Set ◽  
Public Data

Hepatocellular carcinoma (HCC) is one of the common malignant tumors. The prognosis and five-year survival rate of HCC are not promising due to tumor recurrence and metastasis. Exploring markers that contribute to the early diagnosis of HCC, markers for prognostic evaluation of HCC patients, and effective targets for treating HCC patients are in the spotlight of HCC therapy. Zinc Finger CCHC-Type Containing 17 (ZCCHC17) encodes the RNA binding protein ZCCHC17, but its role in HCC is still unclear. Here, 90 paraffin-embedded specimens combined with bioinformatics were used to comprehensively clarify the value of ZCCHC17 in the diagnosis and prognosis of HCC and its potential functions. Paraffin-embedded specimens were used to assess ZCCHC17 protein expression and its correlation with prognosis in 90 HCC patients. the public data sets of HCC patients from TCGA, ICG, and GEO databases were also used for further analysis. It was found that protein and mRNA levels of ZCCHC17 in HCC tissues were significantly higher than those in normal tissues. The abnormally high expression may be related to the abnormal DNA methylation of ZCCHC17 in tumor tissues. The high expression of ZCCHC17 is related to AFP, histologic grade, tumor status, vascular invasion, and pathological stage. Multi-data set analysis showed that patients with high ZCCHC17 expression had a worse prognosis, and multivariate cox regression analysis showed an independent prognostic significance of ZCCHC17. The results of functional analysis, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA), indicate that ZCCHC17 is mainly involved in immune regulation. Subsequently, further single-sample gene set enrichment analysis (ssGSEA) showed that the expression of ZCCHC17 was related to the infiltration of immune cells. Importantly, we also analyzed the relationship between ZCCHC17 and immune checkpoint genes, tumor mutation burden (TMB), microsatellite instability (MSI) and TP53 status in HCC patients and evaluated the role of ZCCHC17 in cancer immunotherapy. In summary, ZCCHC17 is a novel marker for the diagnosis and prognostic evaluation of HCC. Concurrently, it regulates immune cells in the tumor microenvironment (TME) of HCC patients, which has a specific reference value for the immunotherapy of HCC.

scholarly journals Construction and Validation of a Reliable Six-Gene Prognostic Signature Based on the TP53 Alteration for Hepatocellular Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Junyu Huo ◽  
Liqun Wu ◽  
Yunjin Zang
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Prognostic Signature ◽  
Gene Set Enrichment ◽  
Cox Regression Analysis ◽  
Gene Set

BackgroundThe high mutation rate of TP53 in hepatocellular carcinoma (HCC) makes it an attractive potential therapeutic target. However, the mechanism by which TP53 mutation affects the prognosis of HCC is not fully understood.Material and ApproachThis study downloaded a gene expression profile and clinical-related information from The Cancer Genome Atlas (TCGA) database and the international genome consortium (ICGC) database. We used Gene Set Enrichment Analysis (GSEA) to determine the difference in gene expression patterns between HCC samples with wild-type TP53 (n=258) and mutant TP53 (n=116) in the TCGA cohort. We screened prognosis-related genes by univariate Cox regression analysis and Kaplan–Meier (KM) survival analysis. We constructed a six-gene prognostic signature in the TCGA training group (n=184) by Lasso and multivariate Cox regression analysis. To assess the predictive capability and applicability of the signature in HCC, we conducted internal validation, external validation, integrated analysis and subgroup analysis.ResultsA prognostic signature consisting of six genes (EIF2S1, SEC61A1, CDC42EP2, SRM, GRM8, and TBCD) showed good performance in predicting the prognosis of HCC. The area under the curve (AUC) values of the ROC curve of 1-, 2-, and 3-year survival of the model were all greater than 0.7 in each independent cohort (internal testing cohort, n = 181; TCGA cohort, n = 365; ICGC cohort, n = 229; whole cohort, n = 594; subgroup, n = 9). Importantly, by gene set variation analysis (GSVA) and the single sample gene set enrichment analysis (ssGSEA) method, we found three possible causes that may lead to poor prognosis of HCC: high proliferative activity, low metabolic activity and immunosuppression.ConclusionOur study provides a reliable method for the prognostic risk assessment of HCC and has great potential for clinical transformation.

scholarly journals SNHG3/miR-148a-3p Axis–mediated High Expression of DNMT1 Correlates with Poor Prognosis and Tumor Immune Infiltration of Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Ninghua Yao ◽  
Wei Jiang ◽  
Jie Sun ◽  
Chen Yang ◽  
Wenjie Zheng ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Immune Cell ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Immune Cell Infiltration ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Immune Infiltration ◽  
Computer Analyses ◽  
Dnmt1 Expression

Abstract Background Epigenetic reprogramming plays an important role in the occurrence, development, and prognosis of hepatocellular carcinoma (HCC). DNA methylation is a key epigenetic regulatory mechanism, and DNA methyltransferase 1 (DNMT1) is the major enzyme responsible for maintenance methylation. Nevertheless, the role and mechanism of DNMT1 in HCC remains poorly defined. Methods In the current study, we conducted pan-cancer analysis for DNMT1’s expression and prognosis using The Cancer Genome Atlas (TCGA) data set. We conducted gene Set Enrichment Analysis (GSEA) between high-and-low DNMT1 expression groups to identify DNMT1-related functional significance. We also investigated the relationship between DNMT1 expression and tumor immune microenvironment, including immune cell infiltration and the expression of immune checkpoints. Through a combination series of computer analyses (including expression analyses, correlation analyses, and survival analyses), the noncoding RNAs (ncRNAs) that contribute to the overexpression of DNMT1 were ultimately identified. Results We found that DNMT1 was upregulated in 16 types of human carcinoma including HCC, and DNMT1 might be a biomarker predicting unfavorable prognosis in HCC patients. DNMT1 mRNA expression was statistically associated with age, histological grade, and the level of serum AFP. Moreover, DNMT1 level was significantly and positively linked to tumor immune cell infiltration, immune cell biomarkers, and immune checkpoint expression. Meanwhile, Gene Set Enrichment Analysis (GSEA) revealed that high-DNMT1 expression was associated with epithelial mesenchymal transition (EMT), E2F target, G2M checkpoint, and inflammatory response. Finally, through a combination series of computer analyses the SNHG3/hsa-miR-148a-3p/DNMT1 axis was confirmed as the potential regulatory pathway in HCC. Conclusion SNHG3/miR-148a-3p axis upregulation of DNMT1 may be related to poor outcome, tumor immune infiltration, and regulated malignant properties in HCC.

scholarly journals Condition-specific series of metabolic sub-networks and its application for gene set enrichment analysis

2018 ◽  
Vol 35 (13) ◽  
pp. 2258-2266 ◽  
Author(s):  
Van Du T Tran ◽  
Sébastien Moretti ◽  
Alix T Coste ◽  
Sara Amorim-Vaz ◽  
Dominique Sanglard ◽  
...  
Keyword(s):  
Fitness Function ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Supplementary Information ◽  
Optimal Order ◽  
Gene Set Enrichment ◽  
Experimental Conditions ◽  
Gene Set ◽  
Transcriptomic Data ◽  
Public Data

Abstract Motivation Genome-scale metabolic networks and transcriptomic data represent complementary sources of knowledge about an organism’s metabolism, yet their integration to achieve biological insight remains challenging. Results We investigate here condition-specific series of metabolic sub-networks constructed by successively removing genes from a comprehensive network. The optimal order of gene removal is deduced from transcriptomic data. The sub-networks are evaluated via a fitness function, which estimates their degree of alteration. We then consider how a gene set, i.e. a group of genes contributing to a common biological function, is depleted in different series of sub-networks to detect the difference between experimental conditions. The method, named metaboGSE, is validated on public data for Yarrowia lipolytica and mouse. It is shown to produce GO terms of higher specificity compared to popular gene set enrichment methods like GSEA or topGO. Availability and implementation The metaboGSE R package is available at https://CRAN.R-project.org/package=metaboGSE. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Uncovering of the Association Between m5C Regulator-Mediated Methylation Modification Patterns and Tumor Microenvironment Infiltration Characteristics in Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Xinyu Gu ◽  
Haibo Zhou ◽  
Qingfei Chu ◽  
Qiuxian Zheng ◽  
Jing Wang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Immune Cells ◽  
Immune Cell ◽  
Enrichment Analysis ◽  
Tumour Microenvironment ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Cell Infiltration ◽  
Gene Set ◽  
Normal Tissues

Abstract Background: 5-Methylcytosine (m5C) plays essential roles in hepatocellular carcinoma (HCC), but the association between m5C regulation and immune cell infiltration in HCC has not yet been clarified.Methods: In this study, we analysed 371 patients with HCC from The Cancer Genome Atlas (TCGA) database, and the expression of 13 m5C regulators was investigated. Additionally, gene set variation analysis (GSVA), unsupervised clustering analysis, single-sample gene set enrichment analysis (ssGSEA), correlation analysis, and immunohistochemical (IHC) staining were performed.Results: Among the 371 patients, 41 had mutations in m5C regulators, the frequency of which was 11.26%. Then, we identified three m5C modification patterns that had obvious tumour microenvironment (TME) cell infiltration characteristics. Cluster-1 had an immune rejection phenotype; Cluster-2 had an immunoinflammatory phenotype; and Cluster-3 had an immune desert phenotype. In addition, we found that DNMT1 was highly expressed in tumour tissues compared with normal tissues in a tissue microarray (TMA) and that it was positively correlated with many TME-infiltrating immune cells. High expression of the m5C regulator DNMT1 was related to a poor prognosis in patients with HCC. Furthermore, we developed three Immu-clusters that were consistent with the immune characteristics of the m5C methylation modification patterns. We also discovered differences in the levels of immune cells and expression of chemokines and cytokines among the three Immu-clusters.Conclusions: Our work revealed the association between m5C modification and immune regulators in the TME. These findings also suggest that DNMT1 has great potential as a prognostic biomarker and therapeutic target for HCC.

scholarly journals Identification METTL18 as a Potential Prognosis Biomarker and Associated With Immune Infiltrates in Hepatocellular Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Tian-Hao Li ◽  
Cheng Qin ◽  
Bang-Bo Zhao ◽  
Hong-Tao Cao ◽  
Xiao-Ying Yang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Multivariate Analysis ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Wilcoxon Test ◽  
Hepatic Tissue ◽  
Gene Set Enrichment ◽  
Poor Overall Survival ◽  
Gene Set

Methyltransferase-like 18 (METTL18), a METTL family member, is abundant in hepatocellular carcinoma (HCC). Studies have indicated the METTL family could regulate the progress of diverse malignancies while the role of METTL18 in HCC remains unclear. Data of HCC patients were acquired from the cancer genome atlas (TCGA) and gene expression omnibus (GEO). The expression level of METTL18 in HCC patients was compared with normal liver tissues by Wilcoxon test. Then, the logistic analysis was used to estimate the correlation between METTL18 and clinicopathological factors. Besides, Gene Ontology (GO), Gene Set Enrichment Analysis (GSEA), and single-sample Gene Set Enrichment Analysis (ssGSEA) were used to explore relevant functions and quantify the degree of immune infiltration for METTL18. Univariate and Multivariate Cox analyses and Kaplan–Meier analysis were used to estimate the association between METTL18 and prognosis. Besides, by cox multivariate analysis, a nomogram was conducted to forecast the influence of METTL18 on survival rates. METTL18-high was associated with Histologic grade, T stage, Pathologic stage, BMI, Adjacent hepatic tissue inflammation, AFP, Vascular invasion, and TP53 status (P < 0.05). HCC patients with METTL18-high had a poor Overall-Survival [OS; hazard ratio (HR): 1.87, P < 0.001), Disease-Specific Survival (DSS, HR: 1.76, P = 0.015), and Progression-Free Interval (PFI, HR: 1.51, P = 0.006). Multivariate analysis demonstrated that METTL18 was an independent factor for OS (HR: 2.093, P < 0.001), DSS (HR: 2.404, P = 0.015), and PFI (HR: 1.133, P = 0.006). Based on multivariate analysis, the calibration plots and C-indexes of nomograms showed an efficacious predictive effect for HCC patients. GSEA demonstrated that METTL18-high could activate G2M checkpoint, E2F targets, KRAS signaling pathway, and Mitotic Spindle. There was a positive association between the METTL18 and abundance of innate immunocytes (T helper 2 cells) and a negative relation to the abundance of adaptive immunocytes (Dendritic cells, Cytotoxic cells etc.). Finally, we uncovered knockdown of METTL18 significantly suppressed the proliferation, invasion, and migration of HCC cells in vitro. This research indicates that METTL18 could be a novel biomarker to evaluate HCC patients’ prognosis and an important regulator of immune responses in HCC.

scholarly journals High expression of PIMREG predicts poor survival outcomes and is correlated with immune infiltrates in lung adenocarcinoma

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11697
Author(s):  
Feng Jiang ◽  
Min Liang ◽  
Xiaolu Huang ◽  
Wenjing Shi ◽  
Yumin Wang
Keyword(s):  
Prognostic Value ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
High Expression ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Kaplan Meier ◽  
Cancer Genome Atlas

Background PIMREG is upregulated in multiple cancer types. However, the potential role of PIMREG in lung adenocarcinoma (LUAD) remains unclear. The present study aimed to explore its clinical significance in LUAD. Methods Using the Cancer Genome Atlas (TCGA) databases, we obtained 513 samples of LUAD and 59 normal samples from the Cancer Genome Atlas (TCGA) databases to analyze the relationship between PIMREG and LUAD. We used t and Chi-square tests to evaluate the level of expression of PIMREG and its clinical implication in LUAD. The prognostic value of PIMREG in LUAD was identified through the Kaplan–Meier method, Cox regression analysis, and nomogram. Gene set enrichment analysis (GSEA) and single-sample gene set enrichment analysis (ssGSEA) were performed to screen biological pathways and analyze the correlation of the immune infiltrating level with the expression of PIMREG in LUAD. Results PIMREG was highly expressed in patients with LUAD. Specifically, the level of PIMREG gradually increased from pathological stage I to IV. Further, we validated the higher expression of PIMREG expressed in LUAD cell lines. Moreover, PIMREG had a high diagnostic value, with an -AUC of 0.955. Kaplan–Meier survival and Cox regression analyses revealed that the high expression of PIMREG was independently associated with poor clinical outcomes. In our prognostic nomogram, the expression of PIMREG implied a significant prognostic value. Gene set enrichment analysis (GSEA) identified that the high expression PIMREG phenotype was involved in the mitotic cell cycle, mRNA splicing, DNA repair, Rho GTPase signaling, TP53 transcriptional regulation, and translation pathways. Next, we also explored the correlation of PIMREG and tumor-immune interactions and found a negative correlation between PIMREG and the immune infiltrating level of T cells, macrophages, B cells, dendritic cells (DCs) , and CD8+ T cells in LUAD. Conclusions High levels of PIMREG correlated with poor prognosis and immune infiltrates in LUAD.

scholarly journals Condition-specific series of metabolic sub-networks and its application for gene set enrichment analysis

2017 ◽  
Author(s):  
Van Du T. Tran ◽  
Sébastien Moretti ◽  
Alix T. Coste ◽  
Sara Amorim-Vaz ◽  
Dominique Sanglard ◽  
...  
Keyword(s):  
Metabolic Networks ◽  
Fitness Function ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Optimal Order ◽  
Gene Set Enrichment ◽  
Experimental Conditions ◽  
Gene Set ◽  
Transcriptomic Data ◽  
Public Data

AbstractMotivationGenome-scale metabolic networks and transcriptomic data represent complementary sources of knowledge about an organism’s metabolism, yet their integration to achieve biological insight remains challenging.ResultsWe investigate here condition-specific series of metabolic sub-networks constructed by successively removing genes from a comprehensive network. The optimal order of gene removal is deduced from transcriptomic data. The sub-networks are evaluated via a fitness function, which estimates their degree of alteration. We then consider how a gene set, i.e. a group of genes contributing to a common biological function, is depleted in different series of sub-networks to detect the difference between experimental conditions. The method, named metaboGSE, is validated on public data for Yarrowia lipolytica and mouse. It is shown to produce GO terms of higher specificity compared to popular gene set enrichment methods like GSEA or topGO.AvailabilityThe metaboGSE R package is available at https://cran.r-project.org/web/packages/metaboGSE.

scholarly journals Identification and analysis of immune-related subtypes of hepatocellular carcinoma

2020 ◽  
pp. 153537022097013
Author(s):  
Qimeng Wang ◽  
Jin Huang ◽  
Huihua Zhang ◽  
Huan Liu ◽  
Min Yu
Keyword(s):  
Hepatocellular Carcinoma ◽  
Immune Cells ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Expression Data ◽  
Ppi Network ◽  
Gene Set Enrichment ◽  
Genes Expression ◽  
Connectivity Degree ◽  
Checkpoint Genes

Hepatocellular carcinoma is a malignance that remains difficult to cure. Immunotherapy has shown its potential application in a variety of refractory malignancies. Due to the complexity of immune microenvironment of hepatocellular carcinoma, the efficacy of immunotherapy for hepatocellular carcinoma is not as effective as expected. Expression data of hepatocellular carcinoma from the TCGA and ICGC databases were used for classification and verification of hepatocellular carcinoma subtypes. The immune-related functions and pathways were identified via gene set enrichment analysis, while the sections denoting the subsets of the immune cells were estimated using the CIBERSORT algorithm. Immunity low (Immunity_L), immunity medium (Immunity_M), and immunity high (Immunity_H) were specified as the three immune-related subtypes of hepatocellular carcinoma. The quantity of stromal and immune cells was the most substantial in Immunity_H, compared to the other subtypes. Interestingly, the proportion of M0 macrophages decreased from Immunity_L to Immunity_H, while the proportion of CD8 T cells increased. Furthermore, the HLA genes expression levels, as well as those of six immune checkpoint genes were substantially lower in Immunity_L than in Immunity_H. Functional analysis was performed for 1512 differentially expressed genes between Immunity_L and Immunity_H. Finally, the PPI network was constructed with 118 nodes. The highest connectivity degree nodes were B2M, HLA-DRA, and HLA-DRB1. The above results were verified in ICGC-JP and ICGC-FR databases with a consistent trend. In this study, we divided hepatocellular carcinoma into three subtypes and explored the immune-related characteristics of these subtypes. These results may provide new insights for immunotherapy of hepatocellular carcinoma.

Expression of NOTCH receptors and ligands and prognosis of hepatocellular carcinoma

2020 ◽  
Vol 14 (17) ◽  
pp. 1631-1639
Author(s):  
Binyu Zhao ◽  
Shanshan Hu ◽  
Qingqing Xiao ◽  
Sinuo Fan ◽  
Xizhi Yu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prognostic Significance ◽  
Enrichment Analysis ◽  
Notch Receptor ◽  
Gene Set Enrichment Analysis ◽  
Rank Test ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Notch Receptors ◽  
Tumor Node Metastasis Stage

Aim: To elucidate potential prognostic significance of NOTCH receptor and ligand expression in hepatocellular carcinoma. Materials & methods: NOTCH receptors and ligands were divided into increased and decreased expression groups by X-tile program. The association between NOTCH receptors/ligands and prognosis was analyzed by Kaplan–Meier method and log-rank test. Gene set enrichment analysis was performed to explore NOTCH receptors/ligands-related pathways via gsea-3.0. Results: DLL3 and DLL4 were independent prognostic factors for overall survival. Further studies showed that only DLL3 was significantly associated with tumor, node, metastasis stage. Gene set enrichment analysis analysis demonstrated that retinol metabolism, drug metabolism cytochrome P450 and tryptophan metabolism were significantly enriched in DLL3 expression phenotype. Conclusion: We demonstrate that DLL3 may be a prognostic biomarker in hepatocellular carcinoma.

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