Dataset Information

Identification of a novel DNA repair-related prognostic signature predicting survival of patients with hepatocellular carcinoma.

ABSTRACT: Purpose:Hepatocellular carcinoma (HCC) is the sixth most lethal neoplasm worldwide. Traditional biomarkers often exploit the relationship between a certain gene and cancer progression, but they cannot predict patient survival or prognosis accurately. We aim to construct a new DNA repair-related gene signature that combines several genes to improve prognosis prediction in HCC. Methods:We selected an HCC mRNA sequencing (mRNA-seq) dataset (n=365) from The Cancer Genome Atlas (TCGA), and gene set enrichment analysis (GSEA) was used to explore bioinformatics information and further screen genes. We then built a gene signature based on the Cox proportional hazards regression model. Results:GSEA revealed that the hallmark DNA repair gene set was significantly upregulated in the tumor phenotype. A set of seven genes, namely, ADA, FEN1, POLR2G, SAC3D1, SEC61A1, SF3A3, and UPF3B, were significantly a ssociated with overall survival (OS) and used to form a gene signature. The signature risk score was calculated and used to divide patients into high- and low-risk groups. The high-risk group showed worse prognosis (log-rank test p<0.0001). Univariate and multivariate Cox regression analysis showed that the prognostic performance of this risk score signature was robust in different subgroups based on clinicopathological features, with p-values <0.05 (HR=2.38, 95% CI (confidence interval) =1.355-4.184), indicating that it can serve as an independent prognostic indicator. Conclusion:We developed and identified a seven-gene signature related to the DNA repair process that can predict survival in HCC. It can be used as an effective classification tool and to guide clinical treatment.

SUBMITTER: Li N

PROVIDER: S-EPMC6689532 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Identification of a novel DNA repair-related prognostic signature predicting survival of patients with hepatocellular carcinoma.

Li Na N Zhao Lan L Guo Chunyan C Liu Chang C Liu Yongyu Y

Cancer management and research 20190806

<h4>Purpose</h4>Hepatocellular carcinoma (HCC) is the sixth most lethal neoplasm worldwide. Traditional biomarkers often exploit the relationship between a certain gene and cancer progression, but they cannot predict patient survival or prognosis accurately. We aim to construct a new DNA repair-related gene signature that combines several genes to improve prognosis prediction in HCC.<h4>Methods</h4>We selected an HCC mRNA sequencing (mRNA-seq) dataset (n=365) from The Cancer Genome Atlas (TCGA), ...[more]

PMID: 31496805

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Project description:Background: Liver progenitor cells (LPCs) play significant roles in the development and progression of hepatocellular carcinoma (HCC). However, no studies on the value of LPC-related genes for evaluating HCC prognosis exist. We developed a gene signature of LPC-related genes for prognostication in HCC. Methods: To identify LPC-related genes, we analyzed mRNA expression arrays from a dataset (GSE57812 & GSE 37071) containing LPCs, mature hepatocytes, and embryonic stem cell samples. HCC RNA-Seq data from The Cancer Genome Atlas (TCGA) were used to explore the differentially expressed genes (DEGs) related to prognosis through DEG analysis and univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed to construct the LPC-related gene prognostic model in the TCGA training dataset. This model was validated in the TCGA testing set and an external dataset (International Cancer Genome Consortium [ICGC] dataset). Finally, we investigated the relationship between this prognostic model with tumor-node-metastasis stage, tumor grade, and vascular invasion of HCC. Results: Overall, 1770 genes were identified as LPC-related genes, of which 92 genes were identified as DEGs in HCC tissues compared with normal tissues. Furthermore, we randomly assigned patients from the TCGA dataset to the training and testing cohorts. Twenty-six DEGs correlated with overall survival (OS) in the univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed in the TCGA training set, and a 3-gene signature was constructed to stratify patients into 2 risk groups: high-risk and low-risk. Patients in the high-risk group had significantly lower OS than those in the low-risk group. Receiver operating characteristic curve analysis confirmed the signature's predictive capacity. Moreover, the risk score was confirmed to be an independent predictor for patients with HCC. Conclusion: We demonstrated that the LPC-related gene signature can be used for prognostication in HCC. Thus, targeting LPCs may serve as a therapeutic alternative for HCC.

Project description:BackgroundHepatocellular carcinoma (HCC) remains a major challenge for public health worldwide. Considering the great heterogeneity of HCC, more accurate prognostic models are urgently needed. To identify a robust prognostic gene signature, we conduct this study.Materials and methodsLevel 3 mRNA expression profiles and clinicopathological data were obtained in The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC). GSE14520 dataset from the gene expression omnibus (GEO) database was downloaded to further validate the results in TCGA. Differentially expressed mRNAs between HCC and normal tissue were investigated. Univariate Cox regression analysis and lasso Cox regression model were performed to identify and construct the prognostic gene signature. Time-dependent receiver operating characteristic (ROC), Kaplan-Meier curve, multivariate Cox regression analysis, nomogram, and decision curve analysis (DCA) were used to assess the prognostic capacity of the six-gene signature. The prognostic value of the gene signature was further validated in independent GSE14520 cohort. Gene Set Enrichment Analyses (GSEA) was performed to further understand the underlying molecular mechanisms. The performance of the prognostic signature in differentiating between normal liver tissues and HCC were also investigated.ResultsA novel six-gene signature (including CSE1L, CSTB, MTHFR, DAGLA, MMP10, and GYS2) was established for HCC prognosis prediction. The ROC curve showed good performance in survival prediction in both the TCGA HCC cohort and the GSE14520 validation cohort. The six-gene signature could stratify patients into a high- and low-risk group which had significantly different survival. Cox regression analysis showed that the six-gene signature could independently predict OS. Nomogram including the six-gene signature was established and shown some clinical net benefit. Furthermore, GSEA revealed several significantly enriched oncological signatures and various metabolic process, which might help explain the underlying molecular mechanisms. Besides, the prognostic signature showed a strong ability for differentiating HCC from normal tissues.ConclusionsOur study established a novel six-gene signature and nomogram to predict overall survival of HCC, which may help in clinical decision making for individual treatment.

Project description:The heterogeneity of hepatocellular carcinoma (HCC) poses a challenge for accurate prognosis prediction. DNA damage repair genes (DDRGs) have an impact on a wide range of malignancies. However, the relevance of these genes in HCC prognosis has received little attention. In this study, we aimed to develop a prognostic signature to identify novel therapy options for HCC. We acquired mRNA expression profiles and clinical data for HCC patients from The Cancer Genome Atlas (TCGA) database. A polygenic prognostic model for HCC was constructed using selection operator Cox analysis and least absolute shrinkage. The model was validated using International Cancer Genome Consortium (ICGC) data. Overall survival (OS) between the high-risk and low-risk groups was compared using Kaplan‒Meier analysis. Independent predictors of OS were identified through both univariate and multivariate Cox analyses. To determine immune cell infiltration scores and activity in immune-related pathways, a single-sample gene set enrichment analysis was performed. The protein and mRNA expression levels of the prognostic genes between HCC and normal liver tissues were also examined by immunohistochemistry (IHC), immunofluorescence (IF) and quantitative real-time PCR (qRT-PCR). A novel ten-gene signature (CHD1L, HDAC1, KPNA2, MUTYH, PPP2R5B, NEIL3, POLR2L, RAD54B, RUVBL1 and SPP1) was established for HCC prognosis prediction. Patients in the high-risk group had worse OS than those in the low-risk group. Receiver operating characteristic curve analysis confirmed the predictive ability of this prognostic gene signature. Multivariate Cox analysis showed that the risk score was an independent predictor of OS. Functional analysis revealed a strong association with cell cycle and antigen binding pathways, and the risk score was highly correlated with tumor grade, tumor stage, and types of immune infiltrate. High expression levels of the prognostic genes were significantly correlated with increased sensitivity of cancer cells to antitumor drugs. IHC, IF and qRT-PCR all indicated that the prognostic genes were highly expressed in HCC relative to normal liver tissue, consistent with the results of bioinformatics analysis. Ten DDRGs were utilized to create a new signature for identifying the immunological state of HCC and predicting prognosis. In addition, blocking these genes could represent a promising treatment.

Dataset Information

Identification of a novel DNA repair-related prognostic signature predicting survival of patients with hepatocellular carcinoma.

Publications

Identification of a novel DNA repair-related prognostic signature predicting survival of patients with hepatocellular carcinoma.

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