Identification of a protein signature for predicting overall survival of hepatocellular carcinoma: a study based on data mining
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
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1471-2407
- Extent
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Online-Ressource
- Language
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Englisch
- Notes
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online resource.
- Bibliographic citation
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Identification of a protein signature for predicting overall survival of hepatocellular carcinoma: a study based on data mining ; volume:20 ; number:1 ; day:3 ; month:8 ; year:2020 ; pages:1-9 ; date:12.2020
BMC cancer ; 20, Heft 1 (3.8.2020), 1-9, 12.2020
- Creator
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Wu, Zeng-hong
Yang, Dong-liang
- Contributor
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SpringerLink (Online service)
- DOI
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10.1186/s12885-020-07229-x
- URN
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urn:nbn:de:101:1-2020092822433313969365
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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14.08.2025, 10:45 AM CEST
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Associated
- Wu, Zeng-hong
- Yang, Dong-liang
- SpringerLink (Online service)
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Identification of a six-gene signature predicting overall survival for hepatocellular carcinoma
Eight-gene metabolic signature related with tumor-associated macrophages predicting overall survival for hepatocellular carcinoma
A genomic-clinicopathologic nomogram for predicting overall survival of hepatocellular carcinoma
Four-gene signature predicting overall survival and immune infiltration in hepatocellular carcinoma by bioinformatics analysis with RT‒qPCR validation
Identification and validation of a novel immune-related signature associated with macrophages and CD8 T cell infiltration predicting overall survival for hepatocellular carcinoma
A robust immune-related gene pairs signature for predicting the overall survival of esophageal cancer
Novel m7G-related lncRNA signature for predicting overall survival in patients with gastric cancer
A novel immune-related gene pair prognostic signature for predicting overall survival in bladder cancer
8DEstablishment and validation of a hypoxia-related signature predicting prognosis in hepatocellular carcinoma
A six lipid metabolism related gene signature for predicting the prognosis of hepatocellular carcinoma
Machine learning approach identifies inflammatory gene signature for predicting survival outcomes in hepatocellular carcinoma
An epithelial-mesenchymal transition-related 5-gene signature predicting the prognosis of hepatocellular carcinoma patients
Identification of a six-gene signature predicting overall survival for hepatocellular carcinoma
Eight-gene metabolic signature related with tumor-associated macrophages predicting overall survival for hepatocellular carcinoma
A genomic-clinicopathologic nomogram for predicting overall survival of hepatocellular carcinoma
Four-gene signature predicting overall survival and immune infiltration in hepatocellular carcinoma by bioinformatics analysis with RT‒qPCR validation
Identification and validation of a novel immune-related signature associated with macrophages and CD8 T cell infiltration predicting overall survival for hepatocellular carcinoma
A robust immune-related gene pairs signature for predicting the overall survival of esophageal cancer
Novel m7G-related lncRNA signature for predicting overall survival in patients with gastric cancer
A novel immune-related gene pair prognostic signature for predicting overall survival in bladder cancer
8DEstablishment and validation of a hypoxia-related signature predicting prognosis in hepatocellular carcinoma
A six lipid metabolism related gene signature for predicting the prognosis of hepatocellular carcinoma
Machine learning approach identifies inflammatory gene signature for predicting survival outcomes in hepatocellular carcinoma