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10.3748/wjg.v24.i28.3145 http://scihub22266oqcxt.onion/10.3748/wjg.v24.i28.3145 C6064958!6064958 !30065560     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=30065560 &cmd=llinks): Failed to open stream: HTTP request failed! 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Integrated genomic analysis for prediction of survival for patients with liver cancer using The Cancer Genome Atlas |pdf=|usr=}}{{30065560 }} gab.com Text Integrated genomic analysis for prediction of survival for patients with liver cancer using The Cancer Genome Atlas JO: World J Gastroenterol http://www.kidney.de/pget.php?&tw=1&pmid=30065560 #FOAvip AIM: To evaluate the prognostic power of different molecular data in liver cancer. METHODS: Cox regression screen and least absolute shrinkage and selection operator were performed to select significant prognostic variables. Then the concordance index was calculated to evaluate the prognostic power. For the combination data, based on the clinical cox model, molecular features that better fit the model were combined to calculate the concordance index. Prognostic models were built based on the arithmetic summation of the significant variables. Kaplan-Meier survival curve and log-rank test were performed to compare the survival difference. Then a heatmap was constructed and gene set enrichment analysis was performed for pathway analysis. RESULTS: The mRNA data were the most informative prognostic variables in all kinds of omics data in liver cancer, with the highest concordance index (C-index) of 0.61. For the copy number variation, methylation and miRNA data, the combination of molecular data with clinical data could significantly boost the prediction accuracy of the molecular data alone (P < 0.05). On the other hand, the combination of clinical data with methylation, miRNA and mRNA data could significantly boost the prediction accuracy of the clinical data itself (P < 0.05). Based on the significant prognostic variables, different prognostic models were built. In addition, the heatmap analysis, survival analysis, and gene set enrichment analysis validated the practicability of the prognostic models. CONCLUSION: In all kinds of omics data in liver cancer, the mRNA data might be the most informative prognostic variable. The combination of clinical data with molecular data might be the future direction for cancer prognosis and prediction. Twit Text FOAVip Integrated genomic analysis for prediction of survival for patients with liver cancer using The Cancer Genome Atlas ????World J Gastroenterol http://www.kidney.de/pget.php?&tw=1&pmid=30065560 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=30065560 #FOAvip English Wikipedia <ref>{{Cite pmid|30065560 }}</ref> Integrated genomic analysis for prediction of survival for patients with liver cancer using The Cancer Genome Atlas #MMPMID30065560 Song YZ ; Li X ; Li W ; Wang Z ; Li K ; Xie FL ; Zhang F World J Gastroenterol 2018[Jul]; 24 (28 ): 3145-3154 PMID30065560 show ga AIM: To evaluate the prognostic power of different molecular data in liver cancer. METHODS: Cox regression screen and least absolute shrinkage and selection operator were performed to select significant prognostic variables. Then the concordance index was calculated to evaluate the prognostic power. For the combination data, based on the clinical cox model, molecular features that better fit the model were combined to calculate the concordance index. Prognostic models were built based on the arithmetic summation of the significant variables. Kaplan-Meier survival curve and log-rank test were performed to compare the survival difference. Then a heatmap was constructed and gene set enrichment analysis was performed for pathway analysis. RESULTS: The mRNA data were the most informative prognostic variables in all kinds of omics data in liver cancer, with the highest concordance index (C-index) of 0.61. For the copy number variation, methylation and miRNA data, the combination of molecular data with clinical data could significantly boost the prediction accuracy of the molecular data alone (P < 0.05). On the other hand, the combination of clinical data with methylation, miRNA and mRNA data could significantly boost the prediction accuracy of the clinical data itself (P < 0.05). Based on the significant prognostic variables, different prognostic models were built. In addition, the heatmap analysis, survival analysis, and gene set enrichment analysis validated the practicability of the prognostic models. CONCLUSION: In all kinds of omics data in liver cancer, the mRNA data might be the most informative prognostic variable. The combination of clinical data with molecular data might be the future direction for cancer prognosis and prediction. |*Models, Biological [MESH] |Biomarkers, Tumor/*genetics [MESH] |DNA Copy Number Variations [MESH] |DNA Methylation [MESH] |Databases, Genetic [MESH] |Female [MESH] |Gene Expression Profiling/methods [MESH] |Genomics/*methods [MESH] |Humans [MESH] |Liver Neoplasms/genetics/*mortality [MESH] |Male [MESH] |MicroRNAs/*genetics [MESH] |Middle Aged [MESH] |Predictive Value of Tests [MESH] |Prognosis [MESH] |Proportional Hazards Models [MESH] |RNA, Messenger/genetics [MESH]Integrated genomic analysis for prediction of survival for patients wi(epmc).pdf DeepDyve Pubget Overpricing