Abstract
Background: Hepatocellular 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.Methods: Lasso 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’.Results: This 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.Conclusions: Taken 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.
Highlights
Hepatocellular carcinoma (HCC) is a common malignant tumor of human digestive system
Kaplan–Meier curve and receiver operating characteristic (ROC) analysis were performed to estimate the efficiency of the thirteen-gene signature in the internal training cohort (Figure 2A), internal testing cohort (Figure 2B) and external testing cohort (Figure 2C)
Our results showed that SLC29A3 and PPAT were high-expressed and EMCN was low-expressed in HCC tissues compared with adjacent normal tissues (Supplementary Figure S3A–C)
Summary
Hepatocellular carcinoma (HCC) is a common malignant tumor of human digestive system. Zheng et al established a four-gene signature based on SPINK1, TXNRD1, LCAT and PZP to predict the OS of HCC patients in The Cancer Genome Atlas (TCGA) cohort [3]. Little research has been done to study causes of different risk between patients, such as the composition of tumor microenvironment (TME). It is essential to identify a risk-score model based on gene signature to elevate predictive efficiency. Methods: Lasso 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. Further analysis showed patients in high-risk group possess more complex TME and immune cell composition. Conclusions: Taken 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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