Dataset Information

MiR-378a-3p as a putative biomarker for hepatocellular carcinoma diagnosis and prognosis: Computational screening with experimental validation.

ABSTRACT:

Background

Hepatocellular carcinoma (HCC) is a malignant disease with high morbidity and mortality, and the molecular mechanism for the genesis and progression is complex and heterogeneous. Biomarker discovery is crucial for the personalized and precision treatment of HCC. The accumulation of reported microRNA biomarkers makes it possible to combine computational identification with experimental validation to accelerate the discovery of novel biomarker.

Results

In the present work, we applied a rational computer-aided biomarker discovery model to screen for the HCC diagnosis biomarker. Two HCC-associated networks were constructed based on the microRNA and mRNA expression profiles, and the potential microRNA biomarkers were identified based on their unique regulatory and influential power in the network. These putative biomarkers were then experimentally validated. One prominent example among these identified biomarkers is MiR-378a-3p: It was shown to independently regulate several important transcription factors such as PLAGL2 and ?-catenin, affecting the ?-catenin signaling. Such mechanism may indicate a potential tumor suppressor role of MiR-378a-3p and the impact of its abnormal expression on the cell growth and invasion of HCC.

Conclusions

A bioinformatics model with network topological and functional characterization was successfully applied to the identification of HCC biomarkers. The predicted microRNA biomarkers were than validated with experiments using human HCC cell lines, model animal, and clinical specimens. The results confirmed the prediction by our proposed model that miR-378a-3p was a putative biomarker for diagnosis and prognosis of HCC.

SUBMITTER: Qian F

PROVIDER: S-EPMC7882078 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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MiR-378a-3p as a putative biomarker for hepatocellular carcinoma diagnosis and prognosis: Computational screening with experimental validation.

Qian Fuliang F Wang Jinghan J Wang Ying Y Gao Qian Q Yan Wenying W Lin Yuxin Y Shen Li L Xie Yufeng Y Jiang Xiaoqing X Shen Bairong B

Clinical and translational medicine 20210201 2

<h4>Background</h4>Hepatocellular carcinoma (HCC) is a malignant disease with high morbidity and mortality, and the molecular mechanism for the genesis and progression is complex and heterogeneous. Biomarker discovery is crucial for the personalized and precision treatment of HCC. The accumulation of reported microRNA biomarkers makes it possible to combine computational identification with experimental validation to accelerate the discovery of novel biomarker.<h4>Results</h4>In the present work ...[more]

PMID: 33634974

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Project description:Introduction: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths worldwide due to poor survival outcome. Thus, there is an urgent need to identify effective biomarkers for early diagnosis and prognosis prediction. Methods: A total of 389 differentially expressed genes (DEGs) between HCC samples and normal were selected based on the Robust Rank Aggregation (RRA) method. We combined DEGs expression and clinical traits to construct a gene co-expression network through WGCNA. Forty hub genes were selected from the key module. Among them, YWHAB, PPAT, NOL10 were eventually identified as prognostic biomarkers using multivariate Cox regression model. Biomarkers expression pattern was investigated by informatic analysis and verified by RNA-seq of 32 patients with HCC. DiseaseMeth 2.0, MEXPRESS, and Tumor Immune Estimation Resource (TIMER) were used to assess the methylation and immune status of biomarkers. GSVA, CCK8, colony formation assay, Edu imaging kit, wound-healing assay, and xenograft tumor model were utilized to investigate the effects of biomarkers on proliferation, metastasis of HCC cells in vitro, and in vivo. The Kaplan-Meier (KM) plotter and ROC curves were used to validate the prognostic and diagnostic value of biomarker expression. Results: All the selected biomarkers were upregulated in HCC samples and higher expression levels were associated with advanced tumor stages and T grades. The regulation of YWHAB, PPAT, NOL10 promoter methylation varied in tumors, and precancerous normal tissues. Immune infiltration analysis suggested that the abnormal regulations of these biomarkers were likely attributed to B cells and dendritic cells. GSVA for these biomarkers showed their great contributions to proliferation of HCC. Specific inhibition of their expression had strong effects on tumorigenesis in vitro and in vivo. ROC and KM curves confirmed their usefulness of diagnosis and prognosis of HCC. Conclusions: These findings identified YWHAB, PPAT, and NOL10 as novel biomarkers and validated their diagnostic and prognostic value for HCC.

Project description:Background Hepatocellular carcinoma (HCC) is the most common type of liver cancer with a high mortality rate. However, spliceosomal genes are still lacking in the diagnosis and prognosis of HCC. Methods Identification of differentially expressed genes (DEGs) was performed using the limma package in R software. Modules highly related to HCC were obtained by weighted gene co-expression network analysis (WGCNA), and the module genes were analyzed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. The biomarker for diagnosing HCC was determined by receiver operating characteristic (ROC) curve analysis, and the effect of the biomarker in the diagnosis of HCC was evaluated by performing five-fold cross-validation with logistic regression. HCC specimens from preoperatively treated patients were tested for biomarker by real-time quantitative polymerase chain reaction (RT-qPCR). Kaplan-Meier analysis was used to assess the relationship between biomarker and patient survival. The role of biomarker was evaluated using ESTIMATE analysis in the tumor microenvironment. Results In this study, 389 DEGs were screened out from three Gene Expression Omnibus (GEO) datasets. We also found that the turquoise module of 123 genes from The Cancer Genome Atlas (TCGA) data was the key module with the highest correlation with HCC traits. Then, 123 genes were analyzed using the KEGG enrichment pathway, and eight genes were found to be most significantly related to the spliceosome pathway. We selected 8 genes and 389 DEGs shared genes, and finally got the only gene, heterogeneous nuclear ribonucleoprotein (hnRNPU). The high expression of hnRNPU was associated with poor prognosis of HCC, and hnRNPU was a biomarker for diagnosing HCC. In the tissues of patients with excellent HCC treatment hnRNPU messenger RNA (mRNA) was lower than in the tissues of patients with poor HCC treatment. High expression of hnRNPU was significantly increased in HCC patients with low stromal (P<0.05), low immune (P<0.05), and low estimation scores (P<0.05), and with high tumor purity (P<0.05) and high malignant progression (P<0.05) of the HCC. Conclusions The hnRNPU gene identified in this study may become a new biomarker for the diagnosis and prognosis of HCC.

Dataset Information

MiR-378a-3p as a putative biomarker for hepatocellular carcinoma diagnosis and prognosis: Computational screening with experimental validation.

Background

Results

Conclusions

Publications

MiR-378a-3p as a putative biomarker for hepatocellular carcinoma diagnosis and prognosis: Computational screening with experimental validation.

Similar Datasets

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