scholarly journals A comprehensive genome-wide profiling comparison between HBV and HCV infected hepatocellular carcinoma

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Suofeng Sun ◽  
Yuan Li ◽  
Shuangyin Han ◽  
Hongtao Jia ◽  
Xiuling Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Copy Number ◽  
Survival Rates ◽  
Gene Expression Pattern ◽  
Asian Population ◽  
Copy Number Variations ◽  
The Cancer Genome Atlas ◽  
Integrated Analysis ◽  
Immune Related Genes

Abstract Background Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide, especially in East Asia. Even with the progress in therapy, 5-year survival rates remain unsatisfied. Chronic infection with the hepatitis B virus (HBV) or hepatitis C virus (HCV) has been epidemiologically associated with HCC and is the major etiology in the East Asian population. The detailed mechanism, especially the changes of DNA methylation and gene expression between the two types of virus-related HCC, and their contributions to the HCC development, metastasis, and recurrence remain largely unknown. Methods In this integrated analysis, we characterized genome-scale profiles of HBV and HCV infected HCC by comparing their gene expression pattern, methylation profiles, and copy number variations from the publicly accessible data of The Cancer Genome Atlas Program (TCGA). Results The HLA-A, STAT1, and OAS2 genes were highly enriched and up-regulated discovered in the HCV-infected HCC. Hypomethylation but not copy number variations might be the major factor for the up-regulation of these immune-related genes in HCV-infected HCC. Conclusions The results indicated the different epigenetic changes of HBV/HCV related hepatocarcinogenesis. The top up-regulated genes in HCV group were significantly clustered in the immune-related and defense response pathways. These findings will help us to understand the pathogenesis of HBV/HCV associated hepatocellular carcinoma.

scholarly journals Identification of factors regulating the gene expression of alcohol dehydrogenases in hepatocellular carcinoma

2021 ◽  
Author(s):  
Jinghe Xie ◽  
Yaqi Qiu ◽  
Shuai Zhang ◽  
Keqing Ma ◽  
Yimeng Ou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Dna Methylation ◽  
Copy Number ◽  
Gene Copy Number ◽  
Copy Number Variations ◽  
The Cancer Genome Atlas ◽  
Gene Copy ◽  
Excessive Alcohol Consumption ◽  
Alcohol Dehydrogenases

Abstract Background Excessive alcohol consumption has been documented to increase the risk of liver hepatocellular carcinoma (HCC) development. Accordingly, a broad interest pointed to alcohol dehydrogenases (ADHs), which display essential roles in alcohol metabolism. Despite the relevance of ADHs expression and the prognosis of HCC has been estimated, so far, limited research concerning the factors that are responsible for the regulation of ADHs expression has been reported. Methods In this study, using The Cancer Genome Atlas (TCGA) and RegNetwork database, we predicted potential factors consisting of DNA methylation, gene copy number variations, transcription factors (TFs) and microRNAs (miRNAs) that might impact ADHs gene expression in HCC. Results We found that DNA methylation induced the down-regulated expression of ADH1B. Of note, our results implicated that gene copy number variation might not have effects on ADHs expression. Regarding TFs, we speculated that NFYA modulated ADH1C, E2F1 and TFAP2A regulated ADH6 expression based on their expression and prognostic value. Moreover, miR-185 and miR-561 might elicit the repression of ADH4, and miR-105 might impair ADH6 expression. Conclusion This study revealed that multiple factors, including DNA methylation, TFs and microRNAs, affect the expression of ADH family members, which provided new insights into discovering promising HCC-suppressive targets.

scholarly journals In Silico Analysis of Gene Expression Change Associated with Copy Number of Enhancers in Pancreatic Adenocarcinoma

2019 ◽  
Vol 20 (14) ◽  
pp. 3582 ◽  
Author(s):  
Rajesh Kumar ◽  
Sumeet Patiyal ◽  
Vinod Kumar ◽  
Gandharva Nagpal ◽  
Gajendra P.S. Raghava
Keyword(s):  
Gene Expression ◽  
Pancreatic Adenocarcinoma ◽  
Copy Number ◽  
Treatment Strategies ◽  
In Silico Analysis ◽  
Copy Number Variations ◽  
Pancreatic Tissue ◽  
Gene Expression Change ◽  
The Cancer Genome Atlas ◽  
Expression Change

Understanding the gene regulatory network governing cancer initiation and progression is necessary, although it remains largely unexplored. Enhancer elements represent the center of this regulatory circuit. The study aims to identify the gene expression change driven by copy number variation in enhancer elements of pancreatic adenocarcinoma (PAAD). The pancreatic tissue specific enhancer and target gene data were taken from EnhancerAtlas. The gene expression and copy number data were taken from The Cancer Genome Atlas (TCGA). Differentially expressed genes (DEGs) and copy number variations (CNVs) were identified between matched tumor-normal samples of PAAD. Significant CNVs were matched onto enhancer coordinates by using genomic intersection functionality from BEDTools. By combining the gene expression and CNV data, we identified 169 genes whose expression shows a positive correlation with the CNV of enhancers. We further identified 16 genes which are regulated by a super enhancer and 15 genes which have high prognostic potential (Z-score > 1.96). Cox proportional hazard analysis of these genes indicates that these are better predictors of survival. Taken together, our integrative analytical approach identifies enhancer CNV-driven gene expression change in PAAD, which could lead to better understanding of PAAD pathogenesis and to the design of enhancer-based cancer treatment strategies.

scholarly journals Identification of glycolysis related pathways in pancreatic adenocarcinoma and liver hepatocellular carcinoma based on TCGA and GEO datasets

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ji Li ◽  
Chen Zhu ◽  
Peipei Yue ◽  
Tianyu Zheng ◽  
Yan Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Energy Metabolism ◽  
Pancreatic Adenocarcinoma ◽  
Digestive System ◽  
Prognostic Significance ◽  
R Package ◽  
The Cancer Genome Atlas ◽  
System Structure ◽  
Liver Hepatocellular Carcinoma

Abstract Background Abnormal energy metabolism is one of the characteristics of tumor cells, and it is also a research hotspot in recent years. Due to the complexity of digestive system structure, the frequency of tumor is relatively high. We aim to clarify the prognostic significance of energy metabolism in digestive system tumors and the underlying mechanisms. Methods Gene set variance analysis (GSVA) R package was used to establish the metabolic score, and the score was used to represent the metabolic level. The relationship between the metabolism and prognosis of digestive system tumors was explored using the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Volcano plots and gene ontology (GO) analyze were used to show different genes and different functions enriched between different glycolysis levels, and GSEA was used to analyze the pathway enrichment. Nomogram was constructed by R package based on gene characteristics and clinical parameters. qPCR and Western Blot were applied to analyze gene expression. All statistical analyses were conducted using SPSS, GraphPad Prism 7, and R software. All validated experiments were performed three times independently. Results High glycolysis metabolism score was significantly associated with poor prognosis in pancreatic adenocarcinoma (PAAD) and liver hepatocellular carcinoma (LIHC). The STAT3 (signal transducer and activator of transcription 3) and YAP1 (Yes1-associated transcriptional regulator) pathways were the most critical signaling pathways in glycolysis modulation in PAAD and LIHC, respectively. Interestingly, elevated glycolysis levels could also enhance STAT3 and YAP1 activity in PAAD and LIHC cells, respectively, forming a positive feedback loop. Conclusions Our results may provide new insights into the indispensable role of glycolysis metabolism in digestive system tumors and guide the direction of future metabolism–signaling target combined therapy.

scholarly journals Integrated analysis of methylation-driven genes and pretreatment prognostic factors in patients with hepatocellular carcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Dongsheng He ◽  
Shengyin Liao ◽  
Lifang Cai ◽  
Weiming Huang ◽  
Xuehua Xie ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prognostic Factors ◽  
Risk Score ◽  
Prognostic Model ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Integrated Analysis ◽  
Calibration Plot ◽  
Clinical Parameters ◽  
T Stage

Abstract Background The potential reversibility of aberrant DNA methylation indicates an opportunity for oncotherapy. This study aimed to integrate methylation-driven genes and pretreatment prognostic factors and then construct a new individual prognostic model in hepatocellular carcinoma (HCC) patients. Methods The gene methylation, gene expression dataset and clinical information of HCC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Methylation-driven genes were screened with a Pearson’s correlation coefficient less than − 0.3 and a P value less than 0.05. Univariable and multivariable Cox regression analyses were performed to construct a risk score model and identify independent prognostic factors from the clinical parameters of HCC patients. The least absolute shrinkage and selection operator (LASSO) technique was used to construct a nomogram that might act to predict an individual’s OS, and then C-index, ROC curve and calibration plot were used to test the practicability. The correlation between clinical parameters and core methylation-driven genes of HCC patients was explored with Student’s t-test. Results In this study, 44 methylation-driven genes were discovered, and three prognostic signatures (LCAT, RPS6KA6, and C5orf58) were screened to construct a prognostic risk model of HCC patients. Five clinical factors, including T stage, risk score, cancer status, surgical method and new tumor events, were identified from 13 clinical parameters as pretreatment-independent prognostic factors. To avoid overfitting, LASSO analysis was used to construct a nomogram that could be used to calculate the OS in HCC patients. The C-index was superior to that from previous studies (0.75 vs 0.717, 0.676). Furthermore, LCAT was found to be correlated with T stage and new tumor events, and RPS6KA6 was found to be correlated with T stage. Conclusion We identified novel therapeutic targets and constructed an individual prognostic model that can be used to guide personalized treatment in HCC patients.

scholarly journals Copy number variations in East-Asian population and their evolutionary and functional implications

2009 ◽  
Vol 19 (6) ◽  
pp. 1001-1008 ◽  
Author(s):  
S.-H. Yim ◽  
T.-M. Kim ◽  
H.-J. Hu ◽  
J.-H. Kim ◽  
B.-J. Kim ◽  
...  
Keyword(s):  
Copy Number ◽  
East Asian ◽  
Asian Population ◽  
Copy Number Variations ◽  
East Asian Population ◽  
Functional Implications

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.

scholarly journals Identification and Validation of a Predictive Immune-related Genes Signature for the Prognosis of Cholangiocarcinoma

2020 ◽  
Author(s):  
Rui Zhang ◽  
Chen Chen ◽  
Qi Li ◽  
Jialu Fu ◽  
Dong Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
High Risk ◽  
Risk Score ◽  
Risk Model ◽  
Predictive Accuracy ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Immune Related Genes

Abstract Background: Immune-related genes (IRGs) play a crucial role in the initiation and progression of cholangiocarcinoma (CCA). However, immune signatures have rarely been used to predict prognosis of CCA. The aim of this study was to construct a novel model for CCA to predict survival based on IRGs expression data.Methods: The gene expression profiles and clinical data of CCA patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database were integrated to establish and validate prognostic IRG signatures. Differentially expressed immune-related genes were screened. Univariate and multivariate Cox analysis were performed to identify prognostic IRGs, and the risk model that predicts outcomes was constructed. Furthermore, receiver operating characteristic (ROC) and Kaplan-Meier curve were plotted to examine predictive accuracy of the model, and a nomogram was constructed based on IRGs signature, combining with other clinical characteristics. Finally, CIBERSORT was used to analyze the association of immune cells infiltration with risk score.Results: We identified that 223 IRGs were significantly dysregulated in patients with CCA, among which five IRGs (AVPR1B, CST4, TDGF1, RAET1E and IL9R) were identified as robust indicators for overall survival (OS), and a prognostic model was built based on the IRGs signature. Meanwhile, patients with high risk had worse OS in training and validation cohort, and the area under the ROC was 0.898 and 0.846, respectively. Nomogram demonstrated that immune risk score contributed much more points than other clinicopathological variables, with a C-index of 0.819 (95% CI, 0.727-0.911). Finally, we found that IRGs signature was positively correlated with the proportion of CD8+ T cells, neurophils and T gamma delta, while negatively with that of CD4+ memory resting T cells.Conclusions: We established and validated an effective five IRGs-based prediction model for CCA, which could accurately classify patients into groups with low and high risk of poor prognosis.

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