Dataset Information

Development of a Bile Acid-Related Gene Signature for Predicting Survival in Patients with Hepatocellular Carcinoma.

ABSTRACT:

Background

Hepatocellular carcinoma (HCC) is one of the most common diseases that threaten millions of lives annually. Evidence supports that bile acid (BA) affects HCC through inflammation, DNA damage, or other mechanisms.

Methods

A total of 127 BA-associated genes were analyzed in HCC tumor and nontumor samples using The Cancer Genome Atlas data. Genes correlated to the prognosis of patients with HCC were identified using univariate and multivariate Cox regression analyses. Furthermore, a prediction model with identified genes was constructed to evaluate the risk of patients with HCC for prognosis.

Results

Out of 26 genes with differential expressions between the HCC and nontumor samples, 19 and 7 genes showed upregulated and downregulated expressions, respectively. Three genes, NPC1, ABCC1, and SLC51B, were extrapolated to construct a prediction model for the prognosis of patients with HCC.

Conclusion

The three-gene prediction model was more reliable than the pathological staging characters of the tumor for the prognosis and survival of patients with HCC. In addition, the upregulated genes facilitating the transport of BAs are associated with poor prognosis of patients with HCC, and genes of de novo synthesis of BAs benefit patients with HCC.

SUBMITTER: Wang G

PROVIDER: S-EPMC9113879 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

Development of a Bile Acid-Related Gene Signature for Predicting Survival in Patients with Hepatocellular Carcinoma.

Wang Gang G Guan Jun J Yang Qin Q Wu Fengtian F Shao Junwei J Zhou Qihui Q Guo Zixuan Z Ren Yanli Y Zhu Haihong H Chen Zhi Z

Computational and mathematical methods in medicine 20220510

<h4>Background</h4>Hepatocellular carcinoma (HCC) is one of the most common diseases that threaten millions of lives annually. Evidence supports that bile acid (BA) affects HCC through inflammation, DNA damage, or other mechanisms.<h4>Methods</h4>A total of 127 BA-associated genes were analyzed in HCC tumor and nontumor samples using The Cancer Genome Atlas data. Genes correlated to the prognosis of patients with HCC were identified using univariate and multivariate Cox regression analyses. Furt ...[more]

PMID: 35592684

Similar Datasets

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.

Dataset Information

Development of a Bile Acid-Related Gene Signature for Predicting Survival in Patients with Hepatocellular Carcinoma.

Background

Methods

Results

Conclusion

Publications

Development of a Bile Acid-Related Gene Signature for Predicting Survival in Patients with Hepatocellular Carcinoma.

Similar Datasets

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