Potential targets and molecular mechanism of miR-331-3p in hepatocellular carcinoma identified by weighted gene coexpression network analysis

Chi, Qingjia; Geng, Xinge; Xu, Kang; Wang, Chunli; Zhao, Han

doi:10.1042/bsr20200124

Cited by 8 publications

(8 citation statements)

References 56 publications

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“…Qin et al had demonstrated DARS2 as a hepatocarcinoma gene that could promote the progression of hepatocarcinoma cell cycle and inhibit the apoptosis of hepatocarcinoma cells [40]. EFTUD2 gene expression was upregulated in hepatocarcinoma, and had prognostic signi cance in patients with hepatocarcinoma [41]. Liu et al found the expression of FTO in esophageal squamous cell carcinoma (ESCC) to be higher than in adjacent normal tissues, and the corresponding survival curve showed the high expression of FTO to tend toward poor prognosis.…”

Section: Discussionmentioning

confidence: 99%

Comprehensive analysis of the functions and prognostic significance of RNA-binding proteins in bladder urothelial carcinoma

Guo

Shao

Jiang

et al. 2020

Preprint

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Background Alterations in RNA-binding proteins (RBPs) are reported in various cancer types; however, the role of RBPs in bladder urothelial cancer (BLCA) remains unknown. This study aimed to systematically examine the function and prognostic significance of RBPs in bladder cancer using bioinformatics analyses. Methods RNA sequencing and clinical data for BLCA were downloaded from The Cancer Genome Atlas (TCGA) database, and differentially expressed RBPs (DERBPs) between normal and cancer tissues were identified. Protein-protein interaction (PPI) network of DERBPs was established, and enrichment analysis and visualizations were performed. A total of 404 patients with BLCA from TCGA database were randomly divided into training and testing groups. A prognostic model was constructed using the data from training group, and validated in the testing group. Receiver operating characteristic (ROC) curve and survival analysis were performed to explore the prognostic value of the model. A nomogram was established to predict survival in bladder cancer patients. Finally, the verification of prognosis-related hub RBP survival analysis were performed. Results A total of 388 DERBPs were identified, including 219 upregulated and 169 downregulated RBPs. All RBPs were screened for prognostic model establishment and 9 RBPs (TRIM71, YTHDC1, DARS2, XPOT, ZNF106, FTO, IPO7, EFTUD2, and CTU1) were regarded as prognosis-related hub RBPs in BLCA. Further analysis revealed worse overall survival (OS) in the high-risk cohort compared to the model-based low-risk cohort. The area under the ROC curve was 0.752 in the training group and 0.701 in the testing group, which confirms the good prediction ability. A nomogram was established according to nine prognosis-related RBPs, which showed well predicting ability for BLCA. BLCA patients with high DARS2, XPOT, ZNF106, FTO, and IPO7 expression (on the contrary, low YTHDC1 and CTU1 expression) were correlated to poor overall survival. Conclusions The prognosis-related hub RBPs may be involved in oncogenesis, development, and metastasis of BLCA. Our results will be of great significance in revealing the pathogenesis of BLCA, and developing new therapeutic targets and prognostic molecular markers.

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Section: Discussionmentioning

confidence: 99%

Comprehensive analysis of the functions and prognostic significance of RNA-binding proteins in bladder urothelial carcinoma

Guo

Shao

Jiang

et al. 2020

Preprint

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“…Zhonglin Lv et al found that myeloid loss of EFTUD2 led to impaired activation of macrophage NF-κB and ERK, which reduced the release of pro-inflammatory cytokines and growth factors, and ultimately inhibited tumor development and progression [ 24 ]. A recent study showed that the expressions of COPZ1 and EFTUD2 were significantly higher in tumor tissues compared with normal tissues and both two genes may correlated with poor prognosis of liver cancer based on the TCGA database [ 25 ]. GAPDH is regulated by several cancer-related factors, and its overexpression is an event downstream of p53.…”

Section: Discussionmentioning

confidence: 99%

Identification of Biomarkers Related to Immune Cell Infiltration in Hepatocellular Carcinoma Using Gene Co-Expression Network

Zhou

Chen

Luo

et al. 2021

Pathol. Oncol. Res.

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Hepatocellular carcinoma (HCC) is a common cancer with poor prognosis. Due to the lack of effective biomarkers and its complex immune microenvironment, the effects of current HCC therapies are not ideal. In this study, we used the GSE57957 microarray data from Gene Expression Omnibus database to construct a co-expression network. The weighted gene co-expression network analysis and CIBERSORT algorithm, which quantifies cellular composition of immune cells, were used to identify modules related to immune cells. Four hub genes (EFTUD2, GAPDH, NOP56, PA2G4) were identified by co-expression network and protein-protein interactions network analysis. We examined these genes in TCGA database, and found that the four hub genes were highly expressed in tumor tissues in multiple HCC groups, and the expression levels were significantly correlated with patient survival time, pathological stage and tumor progression. On the other hand, methylation analysis showed that the up-regulation of EFTUD2, GAPDH, NOP56 might be due to the hypomethylation status of their promoters. Next, we investigated the correlations between the expression levels of four hub genes and tumor immune infiltration using Tumor Immune Estimation Resource (TIMER). Gene set variation analysis suggested that the four hub genes were associated with numerous pathways that affect tumor progression or immune microenvironment. Overall, our results showed that the four hub genes were closely related to tumor prognosis, and may serve as targets for treatment and diagnosis of HCC. In addition, the associations between these genes and immune infiltration enhanced our understanding of tumor immune environment and provided new directions for the development of drugs and the monitoring of tumor immune status.

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“…Preparing the matrix of the RNA-seq of the above 105 overlapping genes at rst, the corresponding clinical data were screened according to the expression matrix. The analysis procedure was performed, as described previously [11]. First, the coexpression construction network of overlapping genes; Then, Screening gene modules; Last, identify key genes.…”

Section: Important Module and Key Genes Sorted By Wgcnamentioning

confidence: 99%

“…By integrating data from databases containing TCMSP, BATMAN-TCM, SymMap, STITCH, SEA, Swiss, and PharmMapper, the Cancer Genome Atlas (TCGA), we obtained the overlapping target genes of Tanshinone IIA against HCC. RNA-seq data of the overlapping genes and the related clinical information of Hepatocellular carcinoma (HCC) from TCGA were used for weighted gene co-expression network analysis (WGCNA), which was an analysis method to set up a gene co-expression network among the interested genes to extract the associations between gene modules and speci c clinical properties [11]. To further estimate the genes connections, the genes of the important module were analyzed for the PPI network using Cytoscape.…”

Section: Introductionmentioning

confidence: 99%

Molecular Mechanisms of TanshinoneIIA in Hepatocellular carcinomatherapy via WGCNA-based Network Pharmacology Analysis

Zhao

Guo

Chi

et al. 2021

Preprint

Self Cite

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Background: Hepatocellular carcinoma (HCC) is a worldwide malignant tumor that caused irreversible consequences. The studies of Tanshinone IIA showed that Tanshinone IIA has played a notable role in HCC treatment. However, it is still to be investigated to discover the potential targets and associating mechanism of Tanshinone IIA against HCC. Methods: To analyze the correlation between genes and specific clinical features, we applied weighted gene co-expression network analysis (WGCNA), which can help us identify the targets of Tanshinone IIA related to the clinical features of Hepatocellular carcinoma. Results: We screened out 105 overlapping genes by integrating the predicted targets of Tanshinone IIA and the gene expression profile of HCC from the Cancer Genome Atlas (TCGA) database. For WGCNA, we used the RNA-seq profile of the overlapping genes and the related clinical information of HCC from TCGA. And 23 genes related to clinical tumor grade in the important module (R2 = 0.37) were imported for Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and protein-protein interaction (PPI) analysis. Compared to the key genes in the significant module from WGCNA with the high connectivity nodes from the PPI network, we can analyze three hub genes, AURKB, KIF11, and PLK1, for further verification. We tested the binding of Tanshinone IIA to the targets of Hepatocellular carcinoma using Autodock Vina. The survival curve validated that the three hub genes represented a poor prognosis. Receiver operating characteristic (ROC) curves demonstrated that the three hub genes were effective in diagnosis. The mRNA expression of the three hub genes was upregulated in the HCC than the normal. AURKB, KIF11 and PLK1 were further upregulated in advanced tumor stage and grade. Moreover, AURKB, KIF11 and PLK1 also had higher protein expression in HCC tissues. Conclusions: In the study, WGCNA revealed grade-specific gene modules, indicating that Tanshinone IIA probably plays its therapeutical effect in the differentiation process of HCC. And the study partly interpreted the pharmacological mechanism of Tanshinone IIA against HCC.

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Potential targets and molecular mechanism of miR-331-3p in hepatocellular carcinoma identified by weighted gene coexpression network analysis

Cited by 8 publications

References 56 publications

Comprehensive analysis of the functions and prognostic significance of RNA-binding proteins in bladder urothelial carcinoma

Comprehensive analysis of the functions and prognostic significance of RNA-binding proteins in bladder urothelial carcinoma

Identification of Biomarkers Related to Immune Cell Infiltration in Hepatocellular Carcinoma Using Gene Co-Expression Network

Molecular Mechanisms of TanshinoneIIA in Hepatocellular carcinomatherapy via WGCNA-based Network Pharmacology Analysis

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