Construction of a 13‐microRNA‐based signature and prognostic nomogram for predicting overall survival in patients with hepatocellular carcinoma

Zheng, Zhihua; Wen, Yi; Nie, Kechao; Tang, Shiyun; Xu, Chen; Lan, Shaoyang; Pan, Jinglin; Jiang, Kailin; Jiang, Xiaotao; Liu, Peng; Yan, Yanhua; Liu, Fengbin; Liu, Yufeng; Li, Peiwu

doi:10.1111/hepr.13538

Cited by 9 publications

(10 citation statements)

References 43 publications

(74 reference statements)

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“…The model incorporating too many variables was not easy to use, which limited its clinical application. Besides, the robustness of predictive models was not given enough attention in the research of Zheng et al (41). An external validation in different population was needed to test the reliability of predictive models.…”

Section: Discussionmentioning

confidence: 99%

Systematic Construction and Validation of an RNA-Binding Protein-Associated Model for Prognosis Prediction in Hepatocellular Carcinoma

et al. 2021

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BackgroundEvidence from prevailing studies show that hepatocellular carcinoma (HCC) is among the top cancers with high mortality globally. Gene regulation at post-transcriptional level orchestrated by RNA-binding proteins (RBPs) is an important mechanism that modifies various biological behaviors of HCC. Currently, it is not fully understood how RBPs affects the prognosis of HCC. In this study, we aimed to construct and validate an RBP-related model to predict the prognosis of HCC patients.MethodsDifferently expressed RBPs were identified in HCC patients based on the GSE54236 dataset from the Gene Expression Omnibus (GEO) database. Integrative bioinformatics analyses were performed to select hub genes. Gene expression patterns were validated in The Cancer Genome Atlas (TCGA) database, after which univariate and multivariate Cox regression analyses, as well as Kaplan-Meier analysis were performed to develop a prognostic model. Then, the performance of the prognostic model was assessed using receiver operating characteristic (ROC) curves and clinicopathological correlation analysis. Moreover, data from the International Cancer Genome Consortium (ICGC) database were used for external validation. Finally, a nomogram combining clinicopathological parameters and prognostic model was established for the individual prediction of survival probability.ResultsThe prognostic risk model was finally constructed based on two RBPs (BOP1 and EZH2), facilitating risk-stratification of HCC patients. Survival was markedly higher in the low-risk group relative to the high-risk group. Moreover, higher risk score was associated with advanced pathological grade and late clinical stage. Besides, the risk score was found to be an independent prognosis factor based on multivariate analysis. Nomogram including the risk score and clinical stage proved to perform better in predicting patient prognosis.ConclusionsThe RBP-related prognostic model established in this study may function as a prognostic indicator for HCC, which could provide evidence for clinical decision making.

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Section: Discussionmentioning

confidence: 99%

Systematic Construction and Validation of an RNA-Binding Protein-Associated Model for Prognosis Prediction in Hepatocellular Carcinoma

et al. 2021

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“…The risk score of each patient relied on the miRNA expression level and the regression coefficient obtained from the LASSO regression analysis. The risk score was calculated as follows: Risk score (miRNAbased classifier) = sum of coefficients × expression level of miRNAs, refer to previous description [34]. According to the median risk score value, 67 patients with HNSCC were divided into high-risk and low-risk groups.…”

Section: Construction Of Risk Score Formulamentioning

confidence: 99%

“…The C-index values were calculated to measure the predictive performance of the risk signature. All analyses were performed to compare the consistency between the primary and internal validated cohorts, refer to previous description [34].…”

Section: Assessment Of Mirna Signaturementioning

confidence: 99%

“…Three online databases, including TargetScan, miRTar-Base, and miRDB, were used to predict the potential target genes of prognostic miRNAs. The overlapping miRNA target genes predicted by these three databases were undergo to GO analysis and KEGG pathway enrichment analysis using DAVID (http://david.ncifcrf.gov/ ) refer to previous description [34]. P < 0.05 was considered the cut-off value.…”

Section: Functional Enrichment Analysismentioning

confidence: 99%

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Construction of an 11-microRNA-based signature and a prognostic nomogram to predict the overall survival of head and neck squamous cell carcinoma patients

Huang

Liu²,

et al. 2020

BMC Genomics

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Background Head and neck squamous cell carcinoma (HNSCC) is a fatal malignancy owing to the lack of effective tools to predict overall survival (OS). MicroRNAs (miRNAs) play an important role in HNSCC occurrence, development, invasion and metastasis, significantly affecting the OS of patients. Thus, the construction of miRNA-based risk signatures and nomograms is desirable to predict the OS of patients with HNSCC. Accordingly, in the present study, miRNA sequencing data of 71 HNSCC and 13 normal samples downloaded from The Cancer Genome Atlas (TCGA) were screened to identify differentially expressed miRNAs (DEMs) between HNSCC patients and normal controls. Based on the exclusion criteria, the clinical information and miRNA sequencing data of 67 HNSCC samples were selected and used to establish a miRNA-based signature and a prognostic nomogram. Forty-three HNSCC samples were assigned to an internal validation cohort for verifying the credibility and accuracy of the primary cohort. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to explore the functions of 11 miRNA target genes. Results In total, 11 DEMs were successfully identified. An 11-miRNA risk signature and a prognostic nomogram were constructed based on the expression levels of these 11 DEMs and clinical information. The signature and nomogram were further validated by calculating the C-index, area under the curve (AUC) in receiver-operating characteristic curve analysis, and calibration curves, which revealed their promising performance. The results of the internal validation cohort shown the reliable predictive accuracy both of the miRNA-based signature and the prognostic nomogram. GO and KEGG analyses revealed that a mass of signal pathways participated in HNSCC proliferation and metastasis. Conclusion Overall, we constructed an 11-miRNA-based signature and a prognostic nomogram with excellent accuracy for predicting the OS of patients with HNSCC.

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“…Each miRNA targets hundreds of mRNAs and downregulates them post-transcriptionally by base pairing with their 3′-untranslated regions (3′-UTRs) [ 10 , 11 ]. Extensive studies have revealed that miRNAs regulate various biological and cellular processes, including proliferation, differentiation, cell behavior, and cancer development [ 12 , 13 , 14 , 15 , 16 , 17 ]. The involvement of miRNAs in fibrosis of the liver and other organs also has been reported [ 18 , 19 , 20 , 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

Inhibitory Effect of a Human MicroRNA, miR-6133-5p, on the Fibrotic Activity of Hepatic Stellate Cells in Culture

Hamada-Tsutsumi

Onishi

Matsuura

et al. 2020

IJMS

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Background: We recently identified 39 human microRNAs, which effectively suppress hepatitis B virus (HBV) replication in hepatocytes. Chronic HBV infection often results in active, hepatitis-related liver fibrosis; hence, we assessed whether any of these microRNAs have anti-fibrotic potential and predicted that miR-6133-5p may target several fibrosis-related genes. Methods: The hepatic stellate cell line LX-2 was transfected with an miR-6133-5p mimic and subsequently treated with Transforming growth factor (TGF)-β. The mRNA and protein products of the COL1A1 gene, encoding collagen, and the ACTA2 gene, an activation marker of hepatic stellate cells, were quantified. Results: The expression of COL1A1 and ACTA2 was markedly reduced in LX-2 cells treated with miR-6133-5p. Interestingly, phosphorylation of c-Jun N-terminal kinase (JNK) was also significantly decreased by miR-6133-5p treatment. The expression of several predicted target genes of miR-6133-5p, including TGFBR2 (which encodes Transforming Growth Factor Beta Receptor 2) and FGFR1 (which encodes Fibroblast Growth Factor Receptor 1), was also reduced in miR-6133-5p-treated cells. The knockdown of TGFBR2 by the corresponding small interfering RNA greatly suppressed the expression of COL1A1 and ACTA2. Treatment with the JNK inhibitor, SP600125, also suppressed COL1A1 and ACTA2 expression, indicating that TGFBR2 and JNK mediate the anti-fibrotic effect of miR-6133-5p. The downregulation of FGFR1 may result in a decrease of phosphorylated Akt, ERK (extracellular signal-regulated kinase), and JNK. Conclusion: miR-6133-5p has a strong anti-fibrotic effect, mediated by inactivation of TGFBR2, Akt, and JNK.

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Construction of a 13‐microRNA‐based signature and prognostic nomogram for predicting overall survival in patients with hepatocellular carcinoma

Cited by 9 publications

References 43 publications

Systematic Construction and Validation of an RNA-Binding Protein-Associated Model for Prognosis Prediction in Hepatocellular Carcinoma

Systematic Construction and Validation of an RNA-Binding Protein-Associated Model for Prognosis Prediction in Hepatocellular Carcinoma

Construction of an 11-microRNA-based signature and a prognostic nomogram to predict the overall survival of head and neck squamous cell carcinoma patients

Inhibitory Effect of a Human MicroRNA, miR-6133-5p, on the Fibrotic Activity of Hepatic Stellate Cells in Culture

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