A novel microRNA signature predicts vascular invasion in hepatocellular carcinoma

Han, Bing; Yang, Zheng; Wang, Le; Wang, Haixu; Jiaxin, DU; Ye, Fanglei; Sun, Tongwen; Zhang, Lianfeng

doi:10.1002/jcp.28690

Cited by 18 publications

(14 citation statements)

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“…Therefore, there is an urgent need to find genes that associated with the pathogenesis and progression in LIHC and to search biomarkers for diagnosis and prognosis. Many studies have reported that LIHC patients have certain dysregulated expression of mRNAs [2], miRNAs [39], and lncRNAs [40]. However, there are few studies about the crosstalk between mRNAs, miRNAs, and lncRNAs.…”

Section: Discussionmentioning

confidence: 99%

Construction of liver hepatocellular carcinoma-specific lncRNA-miRNA-mRNA network based on bioinformatics analysis

et al. 2021

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Liver hepatocellular carcinoma (LIHC) is one of the major causes of cancer-related death worldwide with increasing incidences, however there are very few studies about the underlying mechanisms and pathways in the development of LIHC. We obtained LIHC samples from The Cancer Genome Atlas (TCGA) to screen differentially expressed mRNAs, lncRNAs, miRNAs and driver mutations. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, Gene ontology enrichment analyses and protein–protein interaction (PPI) network were performed. Moreover, we constructed a competing endogenous lncRNAs-miRNAs-mRNAs network. Finally, cox proportional hazards regression analysis was used to identify important prognostic differentially expressed genes. Total of 1284 mRNAs, 123 lncRNAs, 47 miRNAs were identified within different tissues of LIHC patients. GO analysis indicated that upregulated and downregulated differentially expressed mRNAs (DEmRNAs) were mainly associated with cell division, DNA replication, mitotic sister chromatid segregation and complement activation respectively. Meanwhile, KEGG terms revealed that upregulated and downregulated DEmRNAs were primarily involved in DNA replication, Metabolic pathways, cell cycle and Metabolic pathways, chemical carcinogenesis, retinol metabolism pathway respectively. Among the DERNAs, 542 lncRNAs-miRNAs-mRNAs pairs were predicted to construct a ceRNA regulatory network including 35 DElncRNAs, 26 DEmiRNAs and 112 DEmRNAs. In the Kaplan‐Meier analysis, total of 43 mRNAs, 14 lncRNAs and 3 miRNAs were screened out to be significantly correlated with overall survival of LIHC. The mutation signatures were analyzed and its correlation with immune infiltrates were evaluated using the TIMER in LIHC. Among the mutation genes, TTN mutation is often associated with poor immune infiltration and a worse prognosis in LIHC. This work conducted a novel lncRNAs-miRNAs-mRNAs network and mutation signatures for finding potential molecular mechanisms underlying the development of LIHC. The biomarkers also can be used for predicting prognosis of LIHC.

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Section: Discussionmentioning

confidence: 99%

Construction of liver hepatocellular carcinoma-specific lncRNA-miRNA-mRNA network based on bioinformatics analysis

et al. 2021

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“…lncRNA based prognostic indexes have been proved to be useful to predict survival, metastasis, and recurrence of tumor patients [31][32][33]. And established by different methods, mRNA [34], lncRNA [35,36], miRNA [37], or mixed molecule type signature [38] had shown the prognostic value of HCC. For instance, using six prediction machine-learning algorithms, Yuan and colleagues established a metastasis-related signature comprised of five mRNAs and one lncRNA, which presented the well prognostic value of HCC [38].…”

Section: Discussionmentioning

confidence: 99%

The Prediction of Survival in Hepatocellular Carcinoma Based on A Four Long Non-coding RNAs Expression Signature

Yang

Zhou

et al. 2020

J. Cancer

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Prognostic stratification in hepatocellular carcinoma (HCC) patients is still challenging. Long non-coding RNAs (lncRNAs) have been proven to play a crucial role in tumorigenesis and progression of cancers. The aim of this study is to develop a useful prognostic index based on lncRNA signature to identify patients at high risk of disease progression. We obtained lncRNA expression profiles from three publicly available datasets from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). By the risk scoring method, we built an individualized four-lncRNA signature (HCCLnc-4) to predict survival of HCC patients in the discovery set (ROC curve, AUC: 0.83, 95% CI: 0.65-1.00, P < 0.05, Kaplan-Meier analysis and log-rank test, P < 0.01). Similar prognostic value of HCCLnc-4 has been further verified in two other independent sets. Stratified analysis and multivariate Cox regression analysis suggested the independence of HCCLnc-4 for prediction of HCC patient survival from traditional clinicopathological factors. Area under curve (AUC) analysis suggested that HCCLnc-4 could compete sufficiently with, or might be even better than classical pathological staging systems to predict HCC patient prognosis in the same data sets. Functional analysis and network analysis suggested the potential implication of lncRNA biomarkers. Our study developed and validated the lncRNA prognostic index of HCC patients, warranting further clinical evaluation and preventive interventions.

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“…A number of miRNAs have been suggested as possible diagnostic and prognostic biomarkers and promising therapeutic targets (6). Additionally, several miRNA signatures have been constructed for prognostic estimation in HCC patients (7)(8)(9)(10)(11)(12). However, the common drawbacks of these studies are that they lack validation, ongoing enrollment patients, discovery of novel miRNAs, and uniformity in processing and analyzing the data.…”

Section: Introductionmentioning

confidence: 99%

“…Wang et al identified a miRNA profile in hepatitis B virus-induced HCC (14), while Fu et al performed an analysis specifically for resectable HCC(11). Following this, Liao et al(10) andBai et al (12) identified potential miRNA biomarkers for prognostic prediction in HCC, while Lin et al(7) and Han et al(9) proposed miRNA signatures for vascular invasion to predict survival in HCC. However, the abovementioned studies only used conventional Cox regression rather than LASSO regression for model construction and thus lacked further validation.…”

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confidence: 99%

Bioinformatic analysis and in vitro validation of a five-microRNA signature as a prognostic biomarker of hepatocellular carcinoma

Kong

Huang

et al. 2020

Ann Transl Med

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Background: Existing research has identified correlations between numerous microRNAs (miRNAs) and the prognosis of hepatocellular carcinoma (HCC). However, the role of a combination of miRNAs in predicting HCC survival requires further elucidation. Methods: miRNA expression profiles and clinical data from HCC patients were downloaded from The Cancer Genome Atlas (TCGA). Differentially expressed (DE) miRNAs in tumor versus normal samples were identified. All HCC patients were randomly assigned to a training cohort or a validation cohort at a ratio of 1 to 1. A least absolute shrinkage and selection operator (LASSO) Cox regression model was subsequently employed to establish the miRNA signature. The constructed miRNA signature was then developed and validated. Results: In total, 127 DE miRNAs were detected between HCC and paracancerous tissue using HCC RNA sequencing (RNA-Seq) data extracted from TCGA database. LASSO Cox regression generated a five-miRNA signature consisting of has-mir-105-2, has-mir-9-3, has-mir-137, has-mir-548f-1, and has-mir-561 in the training cohort. This risk model was significantly related to survival (P=5.682e-6). Log-rank tests and multivariate Cox regression analyses revealed the five-miRNA signature as an independent prognostic indicator [HR =3.285, 95% confidence interval (CI): 1.737-6.213], with the area under curve (AUC) of the miRNA signature being 0.728. The effects of the miRNA signature were further confirmed in the validation cohort and in the OncomiR Cancer Database and Gene Expression Omnibus (GEO) dataset. Functional enrichment analysis revealed the potential effects of the five-miRNA signature in tumor-related biological pathways and processes. Cell Counting Kit-8, Transwell, and wound healing assays, were used to evaluate the role of has-mir-137 in HCC cell proliferation and migration in vitro.Conclusions: We established a novel five-miRNA signature which reliably predicted prognosis in HCC patients and which could be used to assist in both strategic counseling and personalized management in HCC.

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A novel microRNA signature predicts vascular invasion in hepatocellular carcinoma

Cited by 18 publications

References 50 publications

Construction of liver hepatocellular carcinoma-specific lncRNA-miRNA-mRNA network based on bioinformatics analysis

Construction of liver hepatocellular carcinoma-specific lncRNA-miRNA-mRNA network based on bioinformatics analysis

The Prediction of Survival in Hepatocellular Carcinoma Based on A Four Long Non-coding RNAs Expression Signature

Bioinformatic analysis and in vitro validation of a five-microRNA signature as a prognostic biomarker of hepatocellular carcinoma

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