Promoter methylation and H3K27 deacetylation regulate the transcription of VIPR1 in hepatocellular carcinoma

Lu, Sicong; Lu, Huazhong; Jin, Rongzhong; Mo, Zhijing

doi:10.1016/j.bbrc.2018.12.129

Cited by 13 publications

(13 citation statements)

References 17 publications

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“…When overexpressed, it is found to significantly inhibit the growth, migration, and invasion of lung cancer cells (Zhao et al, 2019). This study showed that LUAD, KIRC and LIHC patients with low VIPR1 expression had poor prognosis ( Supplementary Figure S2A), which was consistent with the findings in liver cancer and cervical cancer (Kim et al, 2013;Lu et al, 2019). Fibronectin 3 (FCN3), encoded by the FCN3 gene, is a recognition molecule in the lectin pathway of the complement system (Munthe-Fog et al, 2009).…”

Section: Discussionsupporting

confidence: 79%

Identification of Key Genes in Lung Adenocarcinoma and Establishment of Prognostic Mode

Xing

Zhang

et al. 2020

Front. Mol. Biosci.

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Background: The development of human tumors is associated with the abnormal expression of various functional genes, and a massive tumor-based database needs to be deeply mined. Based on a multigene prediction model, access to urgent prognosis of patients has become possible. Materials and Methods: We selected three RNA expression profiles (GSE32863, GSE10072, and GSE43458) from the lung adenocarcinoma (LUAD) database of the Gene Expression Omnibus (GEO) and analyzed the differentially expressed genes (DEGs) between tumor and normal tissue using GEO2R program. After that, we analyzed the transcriptome data of 479 LUAD samples (54 normal tissue samples and 425 cancer tissue samples) and their clinical follow-up data from the (TCGA) database. Kaplan-Meier (KM) curve and receiver operating characteristic (ROC) were used to assess the prediction model. Multivariate Cox analysis was used to identify independent predictors. TCGA pancreatic adenocarcinoma datasets were used to establish a nomogram model. Results: We found 98 significantly prognosis-related genes using KM and COX analysis, among which six genes were found to be the DEGs in GEO. Using multivariate analysis, it was found that a single gene could not be used as an independent predictor of prognosis. However, the risk score calculated by weighting these six genes could serve as an independent prognosis predictor. COX analysis performed with multiple covariates such as age, gender, tumor stage, and TNM typing showed that risk score could still be utilized as an independent risk factor for patient survival rate (p = 0.013) and had an applicable reliability (area under the curve, AUC = 0.665). By combining risk score and various clinical features, the nomogram model was constructed, which had been proven to have high consistency for the prediction of 3-and 5-year survival rate (concordance = 0.751) and high accuracy as tested by ROC (AUC = 0.71;AUC = 0.708). Conclusion: We proposed a method to predict the prognosis of LUAD by weighting multiple genes and constructed a nomogram model suitable for the prognostic evaluation of LUAD, which could provide a new tool for the identification of therapeutic targets and the efficacy evaluation of LUAD.

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Section: Discussionsupporting

confidence: 79%

Identification of Key Genes in Lung Adenocarcinoma and Establishment of Prognostic Mode

Xing

Zhang

et al. 2020

Front. Mol. Biosci.

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“…While in HCC-favorable gene set, many genes have been identified to be associated with HCC, such as VIPR1, CPEB3, HTR2A-AS1, ACSM3, ADRA1A, AKR1D1, BHMT, CD226, CD5L, CYP3A4, CYP3A43, DMGDH, ESR1, GLYATL1 and RDH16. Low expression of VIPR1 had an adverse prognostic impact on HCC [20], loss of ACSM3 expression was found to correlate with advanced HCC stages and a poor survival [21], down-regulation of BHMT in HCC associates with poor prognosis [22], low-expressed of CD226 could promote proliferative, migrating, and invasive activities of HCC cells [23], down-regulation of CYP3A4 gene is an independent predictor of early recurrence of HCC [24]. The results of the previous study were consistent with our findings.…”

Section: Discussionmentioning

confidence: 97%

Identification and validation of a ten-gene set variation score as a diagnostic and prognostic stratification tools in hepatocellular carcinoma

Zhao

Lin

et al. 2020

Preprint

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We aimed to identify the progress of hepatocellular carcinoma (HCC)-specific gene set. Using the HCC data set from The Cancer Genome Atlas, we found that 10 genes were gradually up-graduated with the progress of HCC and associated with survival and classed as HCC-unfavorable gene set, while 29 genes were gradually down-graduated and associated with survival and classed as HCC-favorable gene set. Gene Set Variation Analysis (GSVA) was used to score individual samples against the two gene sets. ROC curve analysis showed both of HCC-unfavorable GSVA score and HCC-favorable GSVA score were reliable biomarkers for diagnosing HCC, tROC curve analysis and univariate/multivariate Cox proportional hazards analyses indicated that HCC-unfavorable GSVA score was an independently prognostic biomarkers. Moreover, the results were validated in an external independent data set. In addition, according to mutation and methylation analysis, we proposed that the aberrant expression of HCC-unfavorable gene may be driven by hypomethylation, not mutation.

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“…VIPR1 is widely expressed among various tissues and plays key roles in many physiological functions including immune regulation, glycogen metabolism, etc. ( 78 ). Previous studies have found that VIPR1 was differentially expressed between cancer and normal tissues in many malignancies ( 78 ).…”

Section: Discussionmentioning

confidence: 99%

Systematically integrative analysis identifies diagnostic and prognostic candidates and small-molecule drugs for lung adenocarcinoma

Chen¹,

Wang²,

Hu³

2021

Transl Cancer Res TCR

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Background: Lung adenocarcinoma (LUAD) is the most common histological subtype of lung cancer (LC).However, the early-stage diagnostic rate is still low, and the 5-year overall survival (OS) rate remains poor. The present study aimed to identify critical genes as diagnostic and prognostic markers and small-molecule drugs for combating LUAD using a systematic bioinformatics analysis.Methods: Five gene expression profiling datasets were systematically integrated and analyzed. First, gene coexpression modules were identified, and differentially expressed genes (DEGs) were screened. Second, the functional changes of these DEGs were systematically investigated. Third, the protein-protein interaction network, high correlation module and key genes were identified. Fourth, prognosis and diagnostic analyses were performed. Fifth, small-molecule drugs were predicted for guiding LUAD therapy.Results: Finally, 12-gene and 2-gene signatures were identified as diagnostic and prognostic markers.The areas under the curves (AUCs) of two signatures associated with 3-year survival were 0.686 and 0.603, respectively. The AUCs of two signatures were over 95% and 94% in diagnostic model, separately. Eleven small-molecule drugs were found and irinotecan was simultaneously predicted in three drug databases. Conclusions:The present study identified some key dysregulated genes involved in LUAD and potential drugs by a comprehensive analysis, which provides novel insights into the pathological mechanism involved in LUAD and may shed light on the diagnosis, prognosis and treatment of LUAD patients.

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Promoter methylation and H3K27 deacetylation regulate the transcription of VIPR1 in hepatocellular carcinoma

Cited by 13 publications

References 17 publications

Identification of Key Genes in Lung Adenocarcinoma and Establishment of Prognostic Mode

Identification of Key Genes in Lung Adenocarcinoma and Establishment of Prognostic Mode

Identification and validation of a ten-gene set variation score as a diagnostic and prognostic stratification tools in hepatocellular carcinoma

Systematically integrative analysis identifies diagnostic and prognostic candidates and small-molecule drugs for lung adenocarcinoma

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