Dataset Information

Prognostic significance of AKR1B10 gene expression in hepatocellular carcinoma and surrounding non-tumorous liver tissue.

ABSTRACT: When assessing outcome in hepatocellular carcinoma (HCC), it is important to consider prognostic factors in background non-tumorous liver tissue as well as in the tumor, since multiple occurrence is associated with background liver status such as hepatitis. The current study aimed to elucidate molecular prognostic predictors that have an association with HCC background non-tumorous tissue. Microarray expression profiling identified aldo-keto reductase family 1, member B10 (AKR1B10) as a putative non-tumorous prognostic factor, and AKR1B10 gene expression was investigated in 158 curatively resected HCC cases by reverse transcription-quantitative polymerase chain reaction. AKR1B10 expression (AKR1B10 value/GAPDH value × 1,000) was significantly higher in tumor tissue (median, 9.2200; range, 0.0003-611.0200; n=158) than in the corresponding non-tumorous tissue (median, 0.5461; range, 0.0018-69.0300; n=158) (P<0.001). When the samples were grouped according to AKR1B10 expression in tumor tissue relative to non-tumorous tissue, tumor

SUBMITTER: Sonohara F

PROVIDER: S-EPMC5228455 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature

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Prognostic significance of AKR1B10 gene expression in hepatocellular carcinoma and surrounding non-tumorous liver tissue.

Sonohara Fuminori F Inokawa Yoshikuni Y Hishida Mitsuhiro M Kanda Mitsuro M Nishikawa Yoko Y Yamada Suguru S Fujii Tsutomu T Sugimoto Hiroyuki H Kodera Yasuhiro Y Nomoto Shuji S

Oncology letters 20161011 6

When assessing outcome in hepatocellular carcinoma (HCC), it is important to consider prognostic factors in background non-tumorous liver tissue as well as in the tumor, since multiple occurrence is associated with background liver status such as hepatitis. The current study aimed to elucidate molecular prognostic predictors that have an association with HCC background non-tumorous tissue. Microarray expression profiling identified aldo-keto reductase family 1, member B10 (AKR1B10) as a putative ...[more]

PMID: 28105190

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Project description:BackgroundHepatocellular carcinoma (HCC) ranks the sixth prevalent tumors with high mortality globally. Alternative splicing (AS) drives protein diversity, the imbalance of which might act an important factor in tumorigenesis. This study aimed to construct of AS-based prognostic signature and elucidate the role in tumor immune microenvironment (TIME) and immunotherapy in HCC.MethodsUnivariate Cox regression analysis was performed to determine the prognosis-related AS events and gene set enrichment analysis (GSEA) was employed for functional annotation, followed by the development of prognostic signatures using univariate Cox, LASSO and multivariate Cox regression. K-M survival analysis, proportional hazards model, and ROC curves were conducted to validate prognostic value. ESTIMATE R package, ssGSEA algorithm and CIBERSORT method and TIMER database exploration were performed to uncover the context of TIME in HCC. Quantitative real-time polymerase chain reaction was implemented to detect ZDHHC16 mRNA expression. Cytoscape software 3.8.0 were employed to visualize AS-splicing factors (SFs) regulatory networks.ResultsA total of 3294 AS events associated with survival of HCC patients were screened. Based on splicing subtypes, eight AS prognostic signature with robust prognostic predictive accuracy were constructed. Furthermore, quantitative prognostic nomogram was developed and exhibited robust validity in prognostic prediction. Besides, the consolidated signature was significantly correlated with TIME diversity and ICB-related genes. ZDHHC16 presented promising prospect as prognostic factor in HCC. Finally, the splicing regulatory network uncovered the potential functions of splicing factors (SFs).ConclusionHerein, exploration of AS patterns may provide novel and robust indicators (i.e., risk signature, prognostic nomogram, etc.,) for prognostic prediction of HCC. The AS-SF networks could open up new approach for investigation of potential regulatory mechanisms. And pivotal players of AS events in context of TIME and immunotherapy efficiency were revealed, contributing to clinical decision-making and personalized prognosis monitoring of HCC.

Project description:Background: Hepatocellular carcinoma (HCC) is a tumor with high morbidity and high mortality worldwide. DNA methylation, one of the most common epigenetic changes, might serve a vital regulatory role in cancer. Methods: To identify categories based on DNA methylation data, consensus clustering was employed. The risk signature was yielded by systematic bioinformatics analyses based on the remarkably methylated CpG sites of cluster 1. Kaplan-Meier analysis, variable regression analysis, and ROC curve analysis were further conducted to validate the prognosis predictive ability of risk signature. Gene set enrichment analysis (GSEA) was performed for functional annotation. To uncover the context of tumor immune microenvironment (TIME) of HCC, we employed the ssGSEA algorithm and CIBERSORT method and performed TIMER database exploration and single-cell RNA sequencing analysis. Additionally, quantitative real-time polymerase chain reaction was employed to determine the LRRC41 expression and preliminarily explore the latent role of LRRC41 in prognostic prediction. Finally, mutation data were analyzed by employing the "maftools" package to delineate the tumor mutation burden (TMB). Results: HCC samples were assigned into seven subtypes with different overall survival and methylation levels based on 5'-cytosine-phosphate-guanine-3' (CpG) sites. The risk prognostic signature including two candidate genes (LRRC41 and KIAA1429) exhibited robust prognostic predictive accuracy, which was validated in the external testing cohort. Then, the risk score was significantly correlated with the TIME and immune checkpoint blockade (ICB)-related genes. Besides, a prognostic nomogram based on the risk score and clinical stage presented powerful prognostic ability. Additionally, LRRC41 with prognostic value was corroborated to be closely associated with TIME characterization in both expression and methylation levels. Subsequently, the correlation regulatory network uncovered the potential targets of LRRC41 and KIAA1429. Finally, the methylation level of KIAA1429 was correlated with gene mutation status. Conclusion: In summary, this is the first to identify HCC samples into distinct clusters according to DNA methylation and yield the CpG-based prognostic signature and quantitative nomogram to precisely predict prognosis. And the pivotal player of DNA methylation of genes in the TIME and TMB status was explored, contributing to clinical decision-making and personalized prognosis monitoring of HCC.

Dataset Information

Prognostic significance of AKR1B10 gene expression in hepatocellular carcinoma and surrounding non-tumorous liver tissue.

Publications

Prognostic significance of AKR1B10 gene expression in hepatocellular carcinoma and surrounding non-tumorous liver tissue.

Similar Datasets

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