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Hepatocellular carcinoma:An analysis of the expression status of stress granules and their prognostic value
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作者 Qing-Shuai Ren Qiu Sun +2 位作者 Shu-Qin Cheng Li-Ming Du Ping-Xuan Guo 《World Journal of Gastrointestinal Oncology》 SCIE 2024年第6期2571-2591,共21页
BACKGROUND Hepatocellular carcinoma(HCC)is a global popular malignant tumor,which is difficult to cure,and the current treatment is limited.AIM To analyze the impacts of stress granule(SG)genes on overall survival(OS)... BACKGROUND Hepatocellular carcinoma(HCC)is a global popular malignant tumor,which is difficult to cure,and the current treatment is limited.AIM To analyze the impacts of stress granule(SG)genes on overall survival(OS),survival time,and prognosis in HCC.METHODS The combined The Cancer Genome Atlas-Liver Hepatocellular Carcinoma(TCGA-LIHC),GSE25097,and GSE36376 datasets were utilized to obtain genetic and clinical information.Optimal hub gene numbers and corresponding coefficients were determined using the Least absolute shrinkage and selection operator model approach,and genes for constructing risk scores and corresponding correlation coefficients were calculated according to multivariate Cox regression,respectively.The prognostic model’s receiver operating characteristic(ROC)curve was produced and plotted utilizing the time ROC software package.Nomogram models were constructed to predict the outcomes at 1,3,and 5-year OS prognostications with good prediction accuracy.RESULTS We identified seven SG genes(DDX1,DKC1,BICC1,HNRNPUL1,CNOT6,DYRK3,CCDC124)having a prognostic significance and developed a risk score model.The findings of Kaplan-Meier analysis indicated that the group with a high risk exhibited significantly reduced OS in comparison with those of the low-risk group(P<0.001).The nomogram model’s findings indicate a significant enhancement in the accuracy of OS prediction for individuals with HCC in the TCGA-HCC cohort.Gene Ontology and Gene Set Enrichment Analysis suggested that these SGs might be involved in the cell cycle,RNA editing,and other biological processes.CONCLUSION Based on the impact of SG genes on HCC prognosis,in the future,it will be used as a biomarker as well as a unique therapeutic target for the identification and treatment of HCC. 展开更多
关键词 Stress granule genes Hepatocellular carcinoma Gastrointestinal neoplasms bioinformatics prognosis Prognostic value
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The m6A methylation landscape stratifies hepatocellular carcinoma into 3 subtypes with distinct metabolic characteristics 被引量:2
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作者 Xiaotian Shen Beiyuan Hu +5 位作者 Jing Xu Wei Qin Yan Fu Shun Wang Qiongzhu Dong Lunxiu Qin 《Cancer Biology & Medicine》 SCIE CAS CSCD 2020年第4期937-952,共16页
Objective:Epigenetic aberration plays an important role in the development and progression of hepatocellular carcinoma(HCC).However,the alteration of RNA N6-methyladenosine(m6A)modifications and its role in HCC progre... Objective:Epigenetic aberration plays an important role in the development and progression of hepatocellular carcinoma(HCC).However,the alteration of RNA N6-methyladenosine(m6A)modifications and its role in HCC progression remain unclear.We therefore aimed to provide evidence using bioinformatics analysis.Methods:We comprehensively analyzed the m6A regulator modification patterns of 605 HCC samples and correlated them with metabolic alteration characteristics.We elucidated 390 gene-based m6A-related signatures and defined an m6Ascore to quantify m6A modifications.We then assessed their values for predicting prognoses and therapeutic responses in HCC patients.Results:We identified 3 distinct m6A modification patterns in HCC,and each pattern had distinct metabolic characteristics.The evaluation of m6A modification patterns using m6Ascores could predict the prognoses,tumor stages,and responses to sorafenib treatments of HCC patients.A nomogram based on m6Ascores showed high accuracy in predicting the overall survival of patients.The area under the receiver operating characteristic curve of predictions of 1,3,and 5-year overall survivals were 0.71,0.69,and 0.70 in the training cohort,and in the test cohort it was 0.74,0.75,and 0.71,respectively.M6Acluster C1,which corresponded to hypoactive mRNA methylation,lower expression of m6A regulators,and a lower m6Ascore,was characterized by metabolic hyperactivity,lower tumor stage,better prognosis,and lower response to sorafenib treatment.In contrast,m6Acluster C3 was distinct in its hyperactive mRNA methylations,higher expression of m6A regulators,and higher m6Ascores,and was characterized by hypoactive metabolism,advanced tumor stage,poorer prognosis,and a better response to sorafenib.The m6Acluster,C2,was intermediate between C1 and C3.Conclusions:HCCs harbored distinct m6A regulator modification patterns that contributed to the metabolic heterogeneity and diversity of HCC.Development of m6A gene signatures and the m6Ascore provides a more comprehensive understanding of m6A modifications in HCC,and helps predict the prognosis and treatment response. 展开更多
关键词 Hepatocellular carcinoma RNA N6-methyladenosine metabolism bioinformatics prognosis
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Prognostic value of potential biomarkers in prostate cancer via bioinformatic analysis
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作者 Shan-Qi Guo Hao Deng +2 位作者 Ying-Jie Jia Xiao-Jiang Li Xing-Kang Jiang 《TMR Integrative Medicine》 2021年第15期1-11,共11页
Background:The Genotype-Tissue Expression was used to expanded normal tissue of the Cancer Genome Atlas database.This study aimed to investigate genes associated with the pathogenesis and prognosis of prostate cancer.... Background:The Genotype-Tissue Expression was used to expanded normal tissue of the Cancer Genome Atlas database.This study aimed to investigate genes associated with the pathogenesis and prognosis of prostate cancer.Methods:We conducted prognostic related genes for prostate cancer by using transcriptome data from the Genotype-Tissue Expression Project and the Cancer Genome Atlas data sources,which were analyzed using an integrated bioinformatics strategy.Clinically significant modules were distinguished,and GO and KEGG analysis were used to Database for Annotation,Visualization and Integrated Discovery.Further annotation was performed through Gene set enrichment analysis.Logistic regression was carried out to analyze the associations between clinicopathologic characteristics and the hub genes.Logistic regression model and survival analysis were performed.Results:By using data available from the Cancer Genome Atlas and the Genotype-Tissue Expression databases,we here show that 53 differential expression genes were identified.Through GO and KEGG analysis a prognostic related gene signature consisted of GOLM1,EIF4A1,ABCC4,RPL7P16,NPIPB12 and PCA3 was constructed with a good performance in predicting overall survivals.The majority of the six hub genes were associated with clinical characteristics of prostate cancer.Conclusion:These genes might be considered as new targets for further investigating the diagnostic and prognostic biomarkers to facilitate the molecular targeting therapy since they showed differently expressed in prostate cancer and correlate with overall survival prognosis. 展开更多
关键词 Prostate cancer prognosis bioinformatic analysis the Cancer Genome Atlas Genotype-Tissue Expression
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