ArticleBioscience reports2021
Identification of a thirteen-gene signature predicting overall survival for hepatocellular carcinoma.
Article in Bioscience reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Inhibiting SSBP1 enhances ferroptosis and improves the effectiveness of sorafenib treatment for liver cancer.International journal of oncology · 2025Article
- Identification of necroptosis-related gene signatures for predicting the prognosis of ovarian cancer.Scientific reports · 2024Article
- A Transcriptomic Biomarker for Predicting the Response to TACE Correlates with the Tumor Microenvironment and Radiomics Features in Hepatocellular Carcinoma.Journal of hepatocellular carcinoma · 2024Article
- Construction of a novel signature based on immune-related lncRNA to identify high and low risk pancreatic adenocarcinoma patients.BMC gastroenterology · 2023Article
- Development and validation of a novel N6-methyladenosine (m6A)-related multi- long non-coding RNA (lncRNA) prognostic signature in pancreatic adenocarcinoma.Bioengineered · 2021Article
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Authors and funding
5 authors.
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Abstract
backgroundHepatocellular carcinoma (HCC) is a malignant tumor of the digestive system characterized by mortality rate and poor prognosis. To indicate the prognosis of HCC patients, lots of genes have been screened as prognostic indicators. However, the predictive efficiency of single gene is not enough. Therefore, it is essential to identify a risk-score model based on gene signature to elevate predictive efficiency.
methodsLasso regression analysis followed by univariate Cox regression was employed to establish a risk-score model for HCC prognosis prediction based on The Cancer Genome Atlas (TCGA) dataset and Gene Expression Omnibus (GEO) dataset GSE14520. R package 'clusterProfiler' was used to conduct function and pathway enrichment analysis. The infiltration level of various immune and stromal cells in the tumor microenvironment (TME) were evaluated by single-sample GSEA (ssGSEA) of R package 'GSVA'.
resultsThis prognostic model is an independent prognostic factor for predicting the prognosis of HCC patients and can be more effective by combining with clinical data through the construction of nomogram model. Further analysis showed patients in high-risk group possess more complex TME and immune cell composition.
conclusionsTaken together, our research suggests the thirteen-gene signature to possess potential prognostic value for HCC patients and provide new information for immunological research and treatment in HCC.
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