Five Core Genes Related to the Progression and Prognosis of Hepatocellular Carcinoma Identified by Analysis of a Coexpression Network

Kong, Junjie; Wang, Tao; Zhang, Zifei; Shen, Shu; Wang, Wentao

doi:10.1089/dna.2019.4932

Cited by 18 publications

(18 citation statements)

References 48 publications

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“…Therefore, we tried to establish a prognosis/diagnosis-associated mRNA-miRNA-lncRNA ceRNA triple sub-network in hepatocellular carcinoma. By performing survival (OS and RFS) analysis using TCGA hepatocellular carcinoma data, nine genes including three novel genes (CELSR3, GPSM2 and CHEK1) and six "known" genes [CLEC3B (26), DNASE1L3 (27), PTTG1 (28,29), KIF2C (30), XPO5 (31,32) and UBE2S (33,34)] were identified as prognosisassociated genes in hepatocellular carcinoma. The prognostic values of the three novel genes in hepatocellular carcinoma have not been investigated so far.…”

Section: Discussionmentioning

confidence: 99%

“…By intersecting OS-associated genes and RFSassociated genes, 9 genes (CLEC3B, DNASE1L3, PTTG1, CELSR3, GPSM2, KIF2C, XPO5, UBE2S, and CHEK1) were defined as candidate genes, which were commonly appeared in OS-associated gene set and RFS-associated gene set ( Figure 1A). After reviewing the published literatures and previous studies, we found that 6 of 9 genes [CLEC3B (26), DNASE1L3 (27), PTTG1 (28,29), KIF2C (30), XPO5 (31,32), and UBE2S (33,34)] have been demonstrated to act as promising prognostic biomarkers for hepatocellular carcinoma. The rest three genes (CELSR3, GPSM2, and CHEK1) have not been studied for their prognostic values in hepatocellular carcinoma so far.…”

Section: Celsr3 Gpsm2 and Chek1 Were Identified As Three Novel Progmentioning

confidence: 98%

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A Key mRNA-miRNA-lncRNA Competing Endogenous RNA Triple Sub-network Linked to Diagnosis and Prognosis of Hepatocellular Carcinoma

Zhang

Lou

2020

Front. Oncol.

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Growing evidence has illustrated critical roles of competing endogenous RNA (ceRNA) regulatory network in human cancers including hepatocellular carcinoma. In this study, we aimed to find promising diagnostic and prognostic biomarkers for patients with hepatocellular carcinoma. Three novel unfavorable prognosis-associated genes (CELSR3, GPSM2, and CHEK1) was first identified. We also demonstrated that these genes were significantly upregulated in hepatocellular carcinoma cell lines and tissues. Next, 154 potential miRNAs of CELSR3, GPSM2, and CHEK1 were predicted. CHEK1-hsa-mir-195-5p/hsa-mir-497-5p and GPSM2-hsa-mir-122-5p axes were defined as two key pathways in carcinogenesis of hepatocellular carcinoma by combination of in silico analysis and experimental validation. Subsequently, lncRNAs binding to hsa-mir-195-5p, hsa-mir-497-5p, and hsa-mir-122-5p were predicted via starBase and miRNet databases. After performing expression analysis and survival analysis for these predicted lncRNAs, we showed that nine lncRNAs (SNHG1,

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Section: Discussionmentioning

confidence: 99%

Section: Celsr3 Gpsm2 and Chek1 Were Identified As Three Novel Progmentioning

confidence: 98%

A Key mRNA-miRNA-lncRNA Competing Endogenous RNA Triple Sub-network Linked to Diagnosis and Prognosis of Hepatocellular Carcinoma

Zhang

Lou

2020

Front. Oncol.

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“…In addition, results of a phase III prospective randomized study showed that TOP2A is associated with the histological grade of liver cancer, microvascular invasion, early onset of malignant tumors (≤40 years), and chemotherapy resistance ( Wong et al, 2009 ). Replication factor C subunit 4 (RFC4) has recently been identified as a hub gene affecting prognosis of patients with HCC ( Kong et al, 2019 ). The knockdown of endogenous RFC4 suppresses HCC cell growth and enhances the chemosensitivity of HepG2 cells ( Arai et al, 2009 ).…”

Section: Discussionmentioning

confidence: 99%

Identification of Hub Genes Associated With Hepatocellular Carcinoma Using Robust Rank Aggregation Combined With Weighted Gene Co-expression Network Analysis

Song

Ding

et al. 2020

Front. Genet.

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Background Bioinformatics provides a valuable tool to explore the molecular mechanisms underlying pathogenesis of hepatocellular carcinoma (HCC). To improve prognosis of patients, identification of robust biomarkers associated with the pathogenic pathways of HCC remains an urgent research priority. Methods We employed the Robust Rank Aggregation method to integrate nine qualified HCC datasets from the Gene Expression Omnibus. A robust set of differentially expressed genes (DEGs) between tumor and normal tissue samples were screened. Weighted gene co-expression network analysis was applied to cluster DEGs and the key modules related to clinical traits identified. Based on network topology analysis, novel risk genes derived from key modules were mined and biological verification performed. The potential functions of these risk genes were further explored with the aid of miRNA–mRNA regulatory networks. Finally, the prognostic ability of these genes was assessed by constructing a clinical prediction model. Results Two key modules showed significant association with clinical traits. In combination with protein–protein interaction analysis, 29 hub genes were identified. Among these genes, 19 from one module showed a pattern of upregulation in HCC and were associated with the tumor node metastasis stage, and 10 from the other module displayed the opposite trend. Survival analyses indicated that all these genes were significantly related to patient prognosis. Based on the miRNA-mRNA regulatory network, 29 genes strongly linked to tumor activity were identified. Notably, five of the novel risk genes, ABAT, DAO, PCK2, SLC27A2, and HAO1, have rarely been reported in previous studies. Gene set enrichment analysis for each gene revealed regulatory roles in proliferation and prognosis of HCC. Least absolute shrinkage and selection operator regression analysis further validated DAO, PCK2, and HAO1 as prognostic factors in an external HCC dataset. Conclusion Analysis of multiple datasets combined with global network information presents a successful approach to uncover the complex biological mechanisms of HCC. More importantly, this novel integrated strategy facilitates identification of risk hub genes as candidate biomarkers for HCC, which could effectively guide clinical treatments.

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“…WGCNA is an innovative statistical tool used to cluster sets of genes with similar expression patterns and to analyze the correlation between genes and clinical features. WGCNA has been widely used in the analysis of cancer, such as lung ( 24 ), breast ( 25 ), colorectal ( 26 ), liver ( 27 ), thyroid ( 28 ), and HNSCC ( 29 – 31 ). However, WGCNA-related studies on laryngeal cancer metastasis have seldom been reported.…”

Section: Discussionmentioning

confidence: 99%

MYCT1 Inhibits the Adhesion and Migration of Laryngeal Cancer Cells Potentially Through Repressing Collagen VI

et al. 2021

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MYCT1, a target of c-Myc, inhibits laryngeal cancer cell migration, but the underlying mechanism remains unclear. In the study, we detected differentially expressed genes (DEGs) from laryngeal cancer cells transfected by MYCT1 using RNA-seq (GSE123275). DEGs from head and neck squamous cell carcinoma (HNSCC) were first screened by comparison of transcription data from the Gene Expression Omnibus (GSE6631) and the Cancer Genome Atlas (TCGA) datasets using weighted gene co-expression network analysis (WGCNA). GO and KEGG pathway analysis explained the functions of the DEGs. The DEGs overlapped between GSE6631and TCGA datasets were then compared with ours to find the key DEGs downstream of MYCT1 related to the adhesion and migration of laryngeal cancer cells. qRT-PCR and Western blot were applied to validate gene expression at mRNA and protein levels, respectively. Finally, the cell adhesion, migration, and wound healing assays were to check cell adhesion and migration abilities, respectively. As results, 39 overlapping genes were enriched in the GSE6631 and TCGA datasets, and most of them revealed adhesion function. Thirteen of 39 genes including COL6 members COL6A1, COL6A2, and COL6A3 were overlapped in GSE6631, TCGA, and GSE123275 datasets. Similar to our RNA-seq results, we confirmed that COL6 is a target of MYCT1 in laryngeal cancer cells. We also found that MYCT1 inhibited the adhesion and migration of laryngeal cancer cells via COL6. These indicate that COL6 is a potential target of MYCT1 and participates the adhesion and migration of laryngeal cancer cells, which provides an important clue for further study on how MYCT1 regulating COL6 in laryngeal cancer progression.

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Five Core Genes Related to the Progression and Prognosis of Hepatocellular Carcinoma Identified by Analysis of a Coexpression Network

Cited by 18 publications

References 48 publications

A Key mRNA-miRNA-lncRNA Competing Endogenous RNA Triple Sub-network Linked to Diagnosis and Prognosis of Hepatocellular Carcinoma

A Key mRNA-miRNA-lncRNA Competing Endogenous RNA Triple Sub-network Linked to Diagnosis and Prognosis of Hepatocellular Carcinoma

Identification of Hub Genes Associated With Hepatocellular Carcinoma Using Robust Rank Aggregation Combined With Weighted Gene Co-expression Network Analysis

MYCT1 Inhibits the Adhesion and Migration of Laryngeal Cancer Cells Potentially Through Repressing Collagen VI

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