Dataset Information

Identification of a six-gene signature predicting overall survival for hepatocellular carcinoma.

ABSTRACT:

Background

Hepatocellular 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 methods

Level 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.

Results

A 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.

Conclusions

Our 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.

SUBMITTER: Liu GM

PROVIDER: S-EPMC6528264 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Publications

Identification of a six-gene signature predicting overall survival for hepatocellular carcinoma.

Liu Gao-Min GM Zeng Hua-Dong HD Zhang Cai-Yun CY Xu Ji-Wei JW

Cancer cell international 20190521

<h4>Background</h4>Hepatocellular 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.<h4>Materials and methods</h4>Level 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 ( ...[more]

PMID: 31139015

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Project description:BackgroundLung cancer is the leading cause of cancer-related deaths worldwide. Lung adenocarcinoma (LUAD) is one of the main subtypes of lung cancer. Hundreds of metabolic genes are altered consistently in LUAD; however, their prognostic role remains to be explored. This study aimed to establish a molecular signature that can predict the prognosis in patients with LUAD based on metabolic gene expression.MethodsThe transcriptome expression profiles and corresponding clinical information of LUAD were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases. The differentially expressed genes (DEGs) between LUAD and paired non-tumor samples were identified by the Wilcoxon rank sum test. Univariate Cox regression analysis and the lasso Cox regression model were used to construct the best-prognosis molecular signature. A nomogram was established comprising the prognostic model for predicting overall survival. To validate the prognostic ability of the molecular signature and the nomogram, the Kaplan-Meier survival analysis, Cox proportional hazards model, and receiver operating characteristic analysis were used.ResultsThe six-gene molecular signature (PFKP, PKM, TPI1, LDHA, PTGES, and TYMS) from the DEGs was constructed to predict the prognosis. The molecular signature demonstrated a robust independent prognostic ability in the training and validation sets. The nomogram including the prognostic model had a greater predictive accuracy than previous systems. Furthermore, a gene set enrichment analysis revealed several significantly enriched metabolic pathways, which suggests a correlation of the molecular signature with metabolic systems and may help explain the underlying mechanisms.ConclusionsOur study identified a novel six-gene metabolic signature for LUAD prognosis prediction. The molecular signature could reflect the dysregulated metabolic microenvironment, provide potential biomarkers for predicting prognosis, and indicate potential novel metabolic molecular-targeted therapies.

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:Background: Nowadays, clinical treatment outcomes of patients with hepatocellular carcinoma (HCC) have been improved. However, due to the complexity of the molecular mechanisms, the recurrence rate and mortality in HCC inpatients are still at a high level. Therefore, there is an urgent need in screening biomarkers of HCC to show therapeutic effects and improve the prognosis. Methods: In this study, we aim to establish a gene signature that can predict the prognosis of HCC patients by downloading and analyzing RNA sequencing data and clinical information from three independent public databases. Firstly, we applied the limma R package to analyze biomarkers by the genetic data and clinical information downloaded from the Gene Expression Omnibus database (GEO), and then used the least absolute shrinkage and selection operator (LASSO) Cox regression and survival analysis to establish a gene signature and a prediction model by data from the Cancer Genome Atlas (TCGA). Besides, messenger RNA (mRNA) and protein expressions of the six-gene signature were explored using Oncomine, Human Protein Atlas (HPA) and the International Cancer Genome Consortium (ICGC). Results: A total of 8,306 differentially expressed genes (DEGs) were obtained between HCC (n = 115) and normal tissues (n = 52). Top 5,000 significant genes were selected and subjected to the weighted correlation network analysis (WGCNA), which constructed nine gene co-expression modules that assign these genes to different modules by cluster dendrogram trees. By analyzing the most significant module (red module), six genes (SQSTM1, AHSA1, VNN2, SMG5, SRXN1, and GLS) were screened by univariate, LASSO, and multivariate Cox regression analysis. By a survival analysis with the HCC data in TCGA, we established a nomogram based on the six-gene signature and multiple clinicopathological features. The six-gene signature was then validated as an independent prognostic factor in independent HCC cohort from ICGC. Receiver operating characteristic (ROC) curve analysis confirmed the predictive capacity of the six-gene signature and nomogram. Besides, overexpression of the six genes at the mRNA and protein levels was validated using Oncomine and HPA, respectively. Conclusion: The predictive six-gene signature and nomograms established in this study can assist clinicians in selecting personalized treatment for patients with HCC.

Dataset Information

Identification of a six-gene signature predicting overall survival for hepatocellular carcinoma.

Background

Materials and methods

Results

Conclusions

Publications

Identification of a six-gene signature predicting overall survival for hepatocellular carcinoma.

Similar Datasets

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