Differential expression of hDAB2IPA and hDAB2IPB in normal tissues and promoter methylation of hDAB2IPA in hepatocellular carcinoma

2007 ◽  
Vol 46 (4) ◽  
pp. 655-663 ◽  
Author(s):  
Guo-Hua Qiu ◽  
Huangming Xie ◽  
Nicholas Wheelhouse ◽  
David Harrison ◽  
George G. Chen ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Differential Expression ◽  
Promoter Methylation ◽  
Normal Tissues

scholarly journals Study on the expression of TOP2A in hepatocellular carcinoma and its relationship with patient prognosis

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Jiali Meng ◽  
Yuanchao Wei ◽  
Qing Deng ◽  
Ling Li ◽  
Xiaolong Li
Keyword(s):  
Hepatocellular Carcinoma ◽  
Differential Expression ◽  
Interaction Analysis ◽  
Clinical Sample ◽  
Primary Liver Cancer ◽  
Data Set ◽  
Hub Gene ◽  
Kegg Pathways ◽  
Normal Tissues ◽  
Top2a Gene

Abstract Background Hepatocellular carcinoma (HCC) is a primary liver cancer with a high mortality rate. However, the molecular mechanism of HCC formation remains to be explored and studied. Objective To investigate the expression of TOP2A in hepatocellular carcinoma (HCC) and its prognosis. Methods The data set of hepatocellular carcinoma was downloaded from GEO database for differential gene analysis, and hub gene was identified by Cytoscape. GEPIA was used to verify the expression of HUB gene and evaluate its prognostic value. Then TOP2A was selected as the research object of this paper by combining literature and clinical sample results. Firstly, TIMER database was used to study TOP2A, and the differential expression of TOP2A gene between normal tissues and cancer tissues was analyzed, as well as the correlation between TOP2A gene expression and immune infiltration of HCC cells. Then, the expression of top2a-related antibodies was analyzed using the Human Protein Atlas database, and the differential expression of TOP2A was verified by immunohistochemistry. Then, SRTING database and Cytoscape were used to establish PPI network for TOP2A and protein–protein interaction analysis was performed. The Oncomine database and cBioPortal were used to express and identify TOP2A mutation-related analyses. The expression differences of TOP2A gene were identified by LinkedOmics, and the GO and KEGG pathways were analyzed in combination with related genes. Finally, Kaplan–Meier survival analysis was performed to analyze the clinical and prognosis of HCC patients. Results TOP2A may be a new biomarker and therapeutic target for hepatocellular carcinoma.

The Differential Sperm Protein 17 (Sp17) Expression in Normal Tissues and Myeloma Cells Is Due to Differences in Promoter Methylation.

Blood ◽  
2004 ◽  
Vol 104 (11) ◽  
pp. 4846-4846
Author(s):  
Yana Zhang ◽  
Zhiqing Wang ◽  
Jian Zhang ◽  
William R. Robinson ◽  
Seah H. Lim
Keyword(s):  
Real Time ◽  
Differential Expression ◽  
Promoter Methylation ◽  
Real Time Pcr ◽  
Normal Tissue ◽  
Tissue Expression ◽  
Myeloma Cells ◽  
Normal Tissues ◽  
Normal Testis ◽  
Restriction Digest

Abstract Sperm protein 17 (Sp17) is a spermatozoa protein that we have previously identified to be aberrantly expressed in myeloma cells. Using a combination of RT-PCR and Northern blot analysis, we demonstrated that Sp17 showed a very restricted normal tissue expression, being detected only in normal testis, suggesting that Sp17 is a novel Cancer-Testis antigen in multiple myeloma. We subsequently showed that Sp17 expression in myeloma cell lines was regulated through promoter methylation and could be upregulated by demethylating agent such as 5-azacytidine. In the present study, we have used a combination of real time PCR and immunohistochemistry on a large panel of normal tissues to determine the pattern of differential expression of Sp17 in normal tissues and in myeloma cells. We also investigated, using restriction digest/PCR, whether or not any differential expression of Sp17 in these normal tissues is also regulated by promoter methylation. Using real time PCR, we found that Sp17 transcripts could be detected in some normal tissues. However, the levels of expression were less than 2% of those in normal testis. In contrast, Sp17+ myeloma cells expressed 3–18% of normal testis levels of Sp17 transcripts. This results, therefore, are similar to those obtained by real time PCR in some other CT antigens in which levels of <3% were detected in some normal tissues. Immunohistochemical evaluation using two Sp17 murine monoclonal antibodies, each directed at a non-overlapping B-cell epitope, showed Sp17 protein to be detected only in testis and not any other normal tissues. Specificity of binding of the antibodies to testis was also confirmed in competitive binding assays. These combined results therefore point to a differential expression of Sp17 in normal tissues and over-expression (compared to some normal tissues) of Sp17 in some myeloma cells. We next determined if the differential expression of Sp17 in normal tissue was in fact regulated through promoted methylation. We previously identified that the HpaII sites at -359 and −350 of the Sp17 gene were involved in the regulation of Sp17 gene expression. Demethylation at these sites resulted in Sp17 gene expression. We have, therefore, used HpaII digest/PCR across these restriction sites in six normal tissues, i.e. kidney, blood, pancreas, skeletal muscle, spleen and testis. Normal testis consistently failed to produce any PCR products when amplified across the HpaII sites after restriction digest, suggesting that these HpaII sites were demethylated. In contrast, the corresponding HpaII sites in kidney, blood, pancreas, skeletal muscles and spleen consistently produced PCR products of the expected size, indicating methylation and insensitivity to HpaII restriction digest of the gene sequence within the Sp17 promoter. Control amplification across another gene segments in all normal tissues were positive. These results therefore provide evidence that the myeloma CT antigen, Sp17, exhibit a differential normal tissue expression that is regulated through promoter methylation.

Identifying Breast Cancer-induced gene perturbations and its application in guiding drug repurposing

2020 ◽  
Vol 15 ◽  
Author(s):  
Jujuan Zhuang ◽  
Shuang Dai ◽  
Lijun Zhang ◽  
Pan Gao ◽  
Yingmin Han ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Differential Expression ◽  
Enrichment Analysis ◽  
Cancer Tissue ◽  
Connectivity Analysis ◽  
Cancer Drugs ◽  
Normal Tissues ◽  
Differential Gene ◽  
Differential Modules ◽  
Differential Connectivity

Background: Breast cancer is a complex disease with high prevalence in women, the molecular mechanisms of which are still unclear at present. Most transcriptomic studies on breast cancer focus on differential expression of each gene between tumor and the adjacent normal tissues, while the other perturbations induced by breast cancer including the gene regulation variations, the changes of gene modules and the pathways, which might be critical to the diagnosis, treatment and prognosis of breast cancer are more or less ignored. Objective: We presented a complete process to study breast cancer from multiple perspectives, including differential expression analysis, constructing gene co-expression networks, modular differential connectivity analysis, differential gene connectivity analysis, gene function enrichment analysis key driver analysis. In addition, we prioritized the related anti-cancer drugs based on enrichment analysis between differential expression genes and drug perturbation signatures. Methods: The RNA expression profiles of 1109 breast cancer tissue and 113 non-tumor tissues were downloaded from The Cancer Genome Atlas (TCGA) database. Differential expression of RNAs was identified using the “DESeq2” bioconductor package in R, and gene co-expression networks was constructed using the weighted gene co-expression network analysis (WGCNA). To compare the module changes and gene co-expression variations between tumor and the adjacent normal tissues, modular differential connectivity (MDC) analysis and differential gene connectivity analysis (DGCA) were performed. Results: Top differential genes like MMP11 and COL10A1 were known to be associated with breast cancer. And we found 23 modules in the tumor network had significantly different co-expression patterns. The top differential modules were enriched in Goterms related to breast cancer like MHC protein complex, leukocyte activation, regulation of defense response and so on. In addition, key genes like UBE2T driving the top differential modules were significantly correlated with the patients’ survival. Finally, we predicted some potential breast cancer drugs, such as Eribulin, Taxane, Cisplatin and Oxaliplatin. Conclusion: As an indication, this framework might be useful in understanding the molecular pathogenesis of diseases like breast cancer and inferring useful drugs for personalized medication

scholarly journals PPM1G promotes the progression of hepatocellular carcinoma via phosphorylation regulation of alternative splicing protein SRSF3

2021 ◽  
Vol 12 (8) ◽  
Author(s):  
Dawei Chen ◽  
Zhenguo Zhao ◽  
Lu Chen ◽  
Qinghua Li ◽  
Jixue Zou ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Alternative Splicing ◽  
Protein Function ◽  
Vital Role ◽  
Normal Tissues ◽  
Mechanistic Analysis ◽  
Highly Correlated ◽  
Splicing Patterns

AbstractEmerging evidence has demonstrated that alternative splicing has a vital role in regulating protein function, but how alternative splicing factors can be regulated remains unclear. We showed that the PPM1G, a protein phosphatase, regulated the phosphorylation of SRSF3 in hepatocellular carcinoma (HCC) and contributed to the proliferation, invasion, and metastasis of HCC. PPM1G was highly expressed in HCC tissues compared to adjacent normal tissues, and higher levels of PPM1G were observed in adverse staged HCCs. The higher levels of PPM1G were highly correlated with poor prognosis, which was further validated in the TCGA cohort. The knockdown of PPM1G inhibited the cell growth and invasion of HCC cell lines. Further studies showed that the knockdown of PPM1G inhibited tumor growth in vivo. The mechanistic analysis showed that the PPM1G interacted with proteins related to alternative splicing, including SRSF3. Overexpression of PPM1G promoted the dephosphorylation of SRSF3 and changed the alternative splicing patterns of genes related to the cell cycle, the transcriptional regulation in HCC cells. In addition, we also demonstrated that the promoter of PPM1G was activated by multiple transcription factors and co-activators, including MYC/MAX and EP300, MED1, and ELF1. Our study highlighted the essential role of PPM1G in HCC and shed new light on unveiling the regulation of alternative splicing in malignant transformation.

scholarly journals Cyclin-Dependent Kinase Regulatory Subunit 2 Indicated Poor Prognosis and Facilitated Aggressive Phenotype of Hepatocellular Carcinoma

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Jie Zhang ◽  
Qianqian Song ◽  
Jinxia Liu ◽  
Lina Lu ◽  
Yuqing Xu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Poor Prognosis ◽  
Disease Free Survival ◽  
Normal Liver ◽  
Regulatory Subunit ◽  
Mrna Levels ◽  
Cyclin Dependent Kinase ◽  
Normal Tissues ◽  
Hcc Cells

Cyclin-dependent kinase regulatory subunit 2 (CKS2) is a member of the cell cycle-dependent protein kinase subunit family, which is implicated as an oncogene in various malignancies. However, the clinical significance, oncogenic functions, and related mechanisms of CKS2 in hepatocellular carcinoma (HCC) remain largely unclear. In the present study, expression features and prognostic value of CKS2 were evaluated in the bioinformatic databases and HCC tissues. The effects of CKS2 on the malignant phenotypes of HCC cells were explored in vitro. According to the analyses of three bioinformatic databases, mRNA levels of CKS2 were elevated in HCC tissues compared with the normal tissues. Immunohistochemical assays found that high CKS2 expression was closely associated with liver cirrhosis (P=0.019), poor differentiation (P=0.02), portal vein invasion (P<0.001), TNM stage (P=0.019), tumor metastasis (P=0.008), and recurrence (P=0.003). The multivariate regression analyses suggested that CKS2 was an independent prognostic factor for overall survival (HR=2.088, P=0.014) and disease-free survival (HR=2.511, P=0.002) of HCC patients. Moreover, the bioinformatic analyses indicated that CKS2 might be associated with the malignant phenotypes in HCC progression. In addition, in vitro assays showed that CKS2 expression was higher in HCC cell lines than in normal liver cells. Knockdown of CKS2 remarkably repressed the proliferation, colony formation (P=0.0003), chemoresistance, migration (P=0.0047), and invasion (P=0.0012) of HCC cells. Taken together, overexpression of CKS2 was significantly correlated with poor prognosis of HCC patients and the malignant phenotypes of HCC cells, suggesting that it was a novel prognostic biomarker and potential target of HCC.

scholarly journals APC promoter methylation and protein expression in hepatocellular carcinoma

2007 ◽  
Vol 134 (5) ◽  
pp. 579-589 ◽  
Author(s):  
Antal Csepregi ◽  
Christoph Röcken ◽  
Juliane Hoffmann ◽  
Ping Gu ◽  
Stephan Saliger ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Protein Expression ◽  
Promoter Methylation

scholarly journals Differential expression of APE1 in hepatocellular carcinoma and the effects on proliferation and apoptosis of cancer cells

2018 ◽  
Vol 12 (5) ◽  
pp. 456-462 ◽  
Author(s):  
Zhipeng Sun ◽  
Yubing Zhu ◽  
Aminbuhe ◽  
Qing Fan ◽  
Jirun Peng ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cancer Cells ◽  
Differential Expression ◽  
Proliferation And Apoptosis

scholarly journals Promoter Methylation ofSFRP3Is Frequent in Hepatocellular Carcinoma

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Ya-Wen Lin ◽  
Yu-Lueng Shih ◽  
Gi-Shih Lien ◽  
Fat-Moon Suk ◽  
Chung-Bao Hsieh ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Chronic Hepatitis ◽  
Mrna Expression ◽  
Cell Lines ◽  
Promoter Methylation ◽  
Promoter Hypermethylation ◽  
Polymerase Chain ◽  
Related Proteins ◽  
And Control

Oncogenic activation of the Wnt/β-catenin signaling pathway is common in human cancers. The secreted frizzled-related proteins (SFRPs) function as negative regulators of Wnt signaling and have important implications in carcinogenesis. Because there have been no reports about the role ofSFRP3in hepatocellular carcinoma (HCC), we investigated the level of methylation and transcription ofSFRP3. Four HCC cell lines, 60 HCCs, 23 cirrhosis livers, 37 chronic hepatitis livers, and 30 control livers were prescreened forSFRP3promoter methylation by methylation-specific polymerase chain reaction (MS-PCR) and bisulfite sequencing.SFRP3promoter methylation was observed in 100%, 60%, 39.1%, 16.2%, and 0% in HCC cell lines, primary HCCs, cirrhosis livers, chronic hepatitis livers, and control livers, respectively. Demethylation treatment with 5-aza-2′-deoxycytidine in HCC cells restored or increased theSFRP3mRNA expression. We next used quantitative MS-PCR (QMSP) to analyze the methylation level ofSFRP3in 60 HCCs and their corresponding nontumor tissues. Methylation ofSFRP3promoter region in HCCs increased significantly compared with control tissues. There is a positive correlation between promoter hypermethylation andSFRP3mRNA downregulation. Our data suggest that promoter hypermethylation ofSFRP3is a common event in HCCs and plays an important role in regulation ofSFRP3mRNA expression.

scholarly journals Machine Learning for Building Immune Genetic Model in Hepatocellular Carcinoma Patients

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Jun Liu ◽  
Zheng Chen ◽  
Wenli Li
Keyword(s):  
Hepatocellular Carcinoma ◽  
Risk Score ◽  
Risk Model ◽  
Cox Model ◽  
Cancer Genome ◽  
Algorithm Analysis ◽  
Immune Genes ◽  
Normal Tissues ◽  
Model Based ◽  
Tumor Stages

Background. Hepatocellular carcinoma (HCC) is the leading liver cancer with special immune microenvironment, which played vital roles in tumor relapse and poor drug responses. In this study, we aimed to explore the prognostic immune signatures in HCC and tried to construct an immune-risk model for patient evaluation. Methods. RNA sequencing profiles of HCC patients were collected from the cancer genome Atlas (TCGA), international cancer genome consortium (ICGC), and gene expression omnibus (GEO) databases (GSE14520). Differentially expressed immune genes, derived from ImmPort database and MSigDB signaling pathway lists, between tumor and normal tissues were analyzed with Limma package in R environment. Univariate Cox regression was performed to find survival-related immune genes in TCGA dataset, and in further random forest algorithm analysis, significantly changed immune genes were used to generate a multivariate Cox model to calculate the corresponding immune-risk score. The model was examined in the other two datasets with recipient operation curve (ROC) and survival analysis. Risk effects of immune-risk score and clinical characteristics of patients were individually evaluated, and significant factors were then used to generate a nomogram. Results. There were 52 downregulated and 259 upregulated immune genes between tumor and relatively normal tissues, and the final immune-risk model (based on SPP1, BRD8, NDRG1, KITLG, HSPA4, TRAF3, ITGAV and MAP4K2) can better differentiate patients into high and low immune-risk subpopulations, in which high score patients showed worse outcomes after resection ( p < 0.05 ). The differentially enriched pathways between the two groups were mainly about cell proliferation and cytokine production, and calculated immune-risk score was also highly correlated with immune infiltration levels. The nomogram, constructed with immune-risk score and tumor stages, showed high accuracy and clinical benefits in prediction of 1-, 3- and 5-year overall survival, which is useful in clinical practice. Conclusion. The immune-risk model, based on expression of SPP1, BRD8, NDRG1, KITLG, HSPA4, TRAF3, ITGAV, and MAP4K2, can better differentiate patients into high and low immune-risk groups. Combined nomogram, using immune-risk score and tumor stages, could make accurate prediction of 1-, 3- and 5-year survival in HCC patients.

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