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Zhun Wu
The First Affiliated Hospital of Xiamen University
Corresponding Author
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Hepatocellular carcinoma (HCC) is the seventh most commonly occurring cancer and the second most common cause of cancer-related death worldwide. Despite improvements in early detection and treatment, the morbidity and mortality remain high because of complex molecular mechanisms and cellular heterogeneity in HCC. Immunotherapy therapies have been identified to be an effective treatment strategy for HCC. However, novel model is still needed to predict the survival and clinical immunotherapy response in HCC. Here we established a gene signature using iron metabolism-related genes form the Cancer Genome Atlas (TCGA), and the survival outcomes were validated from International Cancer Genome Consortium(ICGC). Distinct subtypes (high- and low-risk subtypes) were characterized by different clinical outcomes. The high-risk subtype was featured by better survival outcomes, upregulation of immune checkpoints expression, including programmed death-ligand 1 (PD-L1) expression, cytotoxic T-lymphocyte associated protein 4(CTLA-4) expression, T-cell immunoglobulin mucin 3(TIM-3) expression and T cell Ig and ITIM domain (TIGIT) expression, upregulated cell cycle relevant pathways and better response for immunotherapy. Thus our finding suggested that the novel model may be useful as a biomarker for prognostic predication, immunotherapy and cell cycle inhibitors may be efficacious for high-risk subtype of HCC patients.
Keywords
Hepatocellular carcinoma, Iron metabolism, Gene signature, Overall survival, Immune status
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This preprint is under consideration at Journal of Translational Medicine. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
Zhun Wu
The First Affiliated Hospital of Xiamen University
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 04 Mar, 2021
No community comments so far
Editor assigned
On 28 Feb, 2021
Submission checks complete
On 27 Feb, 2021
First submitted
On 21 Feb, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Hepatocellular carcinoma (HCC) is the seventh most commonly occurring cancer and the second most common cause of cancer-related death worldwide. Despite improvements in early detection and treatment, the morbidity and mortality remain high because of complex molecular mechanisms and cellular heterogeneity in HCC. Immunotherapy therapies have been identified to be an effective treatment strategy for HCC. However, novel model is still needed to predict the survival and clinical immunotherapy response in HCC. Here we established a gene signature using iron metabolism-related genes form the Cancer Genome Atlas (TCGA), and the survival outcomes were validated from International Cancer Genome Consortium(ICGC). Distinct subtypes (high- and low-risk subtypes) were characterized by different clinical outcomes. The high-risk subtype was featured by better survival outcomes, upregulation of immune checkpoints expression, including programmed death-ligand 1 (PD-L1) expression, cytotoxic T-lymphocyte associated protein 4(CTLA-4) expression, T-cell immunoglobulin mucin 3(TIM-3) expression and T cell Ig and ITIM domain (TIGIT) expression, upregulated cell cycle relevant pathways and better response for immunotherapy. Thus our finding suggested that the novel model may be useful as a biomarker for prognostic predication, immunotherapy and cell cycle inhibitors may be efficacious for high-risk subtype of HCC patients.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
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