Abstract
Purpose
Cancer stem cells are associated with unfavorable prognosis in hepatocellular carcinoma (HCC). However, existing stemness-related biomarkers and prognostic models are limited.
Methods
The stemness-related signatures were derived from taking the union of the results obtained by performing WGCNA and CytoTRACE analysis at the bulk RNA-seq and scRNA-seq levels, respectively. Univariate Cox regression and the LASSO were applied for filtering prognosis-related signatures and selecting variables. Finally, ten gene signatures were identified to construct the prognostic model. We evaluated the differences in survival, genomic alternation, biological processes, and degree of immune cell infiltration in the high- and low-risk groups. pRRophetic and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms were utilized to predict chemosensitivity and immunotherapy response. Human Protein Atlas (HPA) database was used to evaluate the protein expressions.
Results
A stemness-related prognostic model was constructed with ten genes including YBX1, CYB5R3, CDC20, RAMP3, LDHA, MTHFS, PTRH2, SRPRB, GNA14, and CLEC3B. Kaplan–Meier and ROC curve analyses showed that the high-risk group had a worse prognosis and the AUC of the model in four datasets was greater than 0.64. Multivariate Cox regression analyses verified that the model was an independent prognostic indicator in predicting overall survival, and a nomogram was then built for clinical utility in predicting the prognosis of HCC. Additionally, chemotherapy drug sensitivity and immunotherapy response analyses revealed that the high-risk group exhibited a higher likelihood of benefiting from these treatments.
Conclusion
The novel stemness-related prognostic model is a promising biomarker for estimating overall survival in HCC.
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Data availability
The data used to support the findings of this study are available from the corresponding author upon request.
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Funding
National Natural Science Foundation of China [No.81973145, No. 82273735]. Key R&D Program of Jiangsu Province (Social Development) (BE2020694).
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XW, XC: methodology, data curation, formal analysis, writing—original draft preparation. MZ, GL, and DC: validation and formal analysis; JF and FY: conceptualization, writing—review and editing. All authors have read and agreed to the published version of the manuscript.
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Wang, X., Chen, X., Zhao, M. et al. Integration of scRNA-seq and bulk RNA-seq constructs a stemness-related signature for predicting prognosis and immunotherapy responses in hepatocellular carcinoma. J Cancer Res Clin Oncol 149, 13823–13839 (2023). https://doi.org/10.1007/s00432-023-05202-2
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DOI: https://doi.org/10.1007/s00432-023-05202-2