Dataset Information

Development and Validation of a Three-gene Prognostic Signature for Patients with Hepatocellular Carcinoma.

ABSTRACT: Hepatocellular carcinoma (HCC) is the leading cause of cancer-related death worldwide, because recurrence often occurs in most HCC patients undergoing hepatectomy. It is necessary to identify patients with high risk for recurrence and adopt effective therapies. An obstacle to monitor patients at high risk for poor prognosis has been the lack of useful predictive biomarkers. Fortunately, recent progress in system biology allows to screen the biomarkers for HCC prognosis in a high-throughput manner. In this study, we performed systematic Kaplan-Meier survival analysis of the whole mRNA transcriptomics based on the Cancer Genome Atlas project (TCGA) and developed a three-gene prognostic signature composing of three genes UPB1, SOCS2 and RTN3. The model was validated in two independent microarray data sets retrieved from Gene Expression Omnibus (GEO) and the expression pattern of these three predictive genes in HCC was confirmed by western blot and immunohistochemistry with our HCC samples. In conclusion, our results showed that this three-gene signature has prognostic value for HCC patients.

SUBMITTER: Li B

PROVIDER: S-EPMC5514049 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Development and Validation of a Three-gene Prognostic Signature for Patients with Hepatocellular Carcinoma.

Li Binghua B Feng Wendu W Luo Ouyang O Xu Tiancheng T Cao Yajuan Y Wu Hongyan H Yu Decai D Ding Yitao Y

Scientific reports 20170717 1

Hepatocellular carcinoma (HCC) is the leading cause of cancer-related death worldwide, because recurrence often occurs in most HCC patients undergoing hepatectomy. It is necessary to identify patients with high risk for recurrence and adopt effective therapies. An obstacle to monitor patients at high risk for poor prognosis has been the lack of useful predictive biomarkers. Fortunately, recent progress in system biology allows to screen the biomarkers for HCC prognosis in a high-throughput manne ...[more]

PMID: 28717245

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Project description:Hepatocellular carcinoma (HCC), a common type of primary liver cancer, is one of the most aggressive malignant tumors worldwide. Although overall survival (OS) rates for HCC has significantly improved in recent years, however, the exact predictive value of microRNA (miRNA) for the prognosis of HCC has not yet been recognized. Here, we aimed to identify potential prognostic miRNAs involved in HCC by bioinformatics analysis and validated expression levels through quantitative polymerase chain reaction (qPCR) and GEO database. The RNA expression profiles and corresponding clinical information of HCC were available from The Cancer Genome Atlas (TCGA) datasets. Differentially expression and standardization analysis of miRNAs, Kaplan-Meier curve and time dependent ROC curve were performed by using R tools. Differentially expressed miRNAs (DEmiRNAs) and clinical parameters involved in the OS of HCC were confirmed by Cox regression models. And functional enrichment analysis was used to establish functions of the targeted genes of DEmiRNAs. A total of 300 DEmiRNAs were significantly related with HCC, of which 40 were down-regulated and 260 were up-regulated. A total of 344 patients with DEmiRNAs, status, overall survival (OS) time were randomized into training group (172) and test group (172). Multivariate Cox regression analyses revealed that 3 miRNA (hsa-miR-139-3p, hsa-miR-760, hsa-miR-7-5p) had independent prognostic significance for the OS of HCC in both training and test group. Moreover, according to Kaplan Meier analysis, the OS of HCC patients with high-risk score was shorter in validation and entire series. The time dependent ROC curve demonstrated high accuracy of the signature for OS. Besides, target genes of three miRNAs were analyzed by functional enrichment analysis and 20 genes associated with OS were verified by using Kaplan-Meier method. Compared with normal and benign group, the relative expression level of hsa-miR-139-3p was significantly decreased, while hsa-miR-7-5p and hsa-miR-760 were distinctly increased in the plasma of HCC patients. The same results were observed in the independent cohort. Collectively, our research suggested that three-miRNA signature could serve as an independent prognostic indicator for HCC patients.

Project description:Background: An accumulating body of evidence suggests that long non-coding RNAs (lncRNAs) can serve as potential cancer prognostic factors. However, the utility of lncRNA combinations in estimating overall survival (OS) for hepatocellular carcinoma (HCC) remains to be elucidated. This study aimed to construct a powerful lncRNA signature related to the OS for HCC to enhance prognostic accuracy. Methods: The expression patterns of lncRNAs and related clinical data of 371 HCC patients were obtained based on The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs (DElncRNAs) were acquired by comparing tumors with adjacent normal samples. lncRNAs displaying significant association with OS were screened through univariate Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) algorithm. All cases were classified into the validation or training group at the ratio of 3:7 to validate the constructed lncRNA signature. Data from the Gene Expression Omnibus (GEO) were used for external validation. We conducted real-time polymerase chain reaction (PCR) and assays for Transwell invasion, migration, CCK-8, and colony formation to determine the biological roles of lncRNA. Gene set enrichment analysis (GSEA) of the lncRNA model risk score was also conducted. Results: We identified 1292 DElncRNAs, among which 172 were significant in univariate Cox regression analysis. In the training group (n = 263), LASSO regression analysis confirmed 11 DElncRNAs including AC010547.1, AC010280.2, AC015712.7, GACAT3 (gastric cancer associated transcript 3), AC079466.1, AC089983.1, AC051618.1, AL121721.1, LINC01747, LINC01517, and AC008750.3. The prognostic risk score was calculated, and the constructed risk model showed significant correlation with HCC OS (log-rank P-value of 8.489e-9, hazard ratio of 3.648, 95% confidence interval: 2.238-5.945). The area under the curve (AUC) for this lncRNA model was up to 0.846. This risk model was confirmed in the validation group (n = 108), the entire cohort, and the external GEO dataset (n = 203). GACAT3 was highly expressed in HCC tissues and cell lines. Based on online databases, GACAT3 expression independently affects both OS and disease-free survival in HCC patients. Silencing GACAT3 in vitro significantly suppressed HCC cell proliferation, invasion, and migration. Moreover, pathways related to the lncRNA model risk score were confirmed by GSEA. Conclusion: The lncRNA signature established in this study can be used to predict HCC prognosis, which could provide novel clinical evidence to guide targeted HCC treatment.

Dataset Information

Development and Validation of a Three-gene Prognostic Signature for Patients with Hepatocellular Carcinoma.

Publications

Development and Validation of a Three-gene Prognostic Signature for Patients with Hepatocellular Carcinoma.

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