HnRNPR/CCNB1/CENPF Axis Contributes to Gastric Cancer Proliferation and Metastasis

Chen, Er‐Bao; Qin, Xuan; Peng, Ke; Li, Qian; Tang, Cheng; Cheng, Xi; Wei, Yimin; Yu, Shan; Gan, Lu; Liu, Tianshu

doi:10.2139/ssrn.3391171

Cited by 26 publications

(31 citation statements)

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“…The same phenomenon occurs in the stem cells of human multiple myeloma ( Huang et al, 2009 ; Zhou et al, 2014 ). CENPF is associated with GC proliferation and tumor metastasis ( Chen et al, 2019 ) and may be an important regulator of prostate cancer ( Shahid et al, 2018 ). Yang et al linked the upregulation of KIF14 in GC to poor prognosis, suggesting that it could play a critical function in GC pathogenesis ( Yang et al, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

Prognostic Prediction Using a Stemness Index-Related Signature in a Cohort of Gastric Cancer

Chen

Zhang

Jiang

et al. 2020

Front. Mol. Biosci.

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Background With characteristic self-renewal and multipotent differentiation, cancer stem cells (CSCs) have a crucial influence on the metastasis, relapse and drug resistance of gastric cancer (GC). However, the genes that participates in the stemness of GC stem cells have not been identified. Methods The mRNA expression-based stemness index (mRNAsi) was analyzed with differential expressions in GC. The weighted gene co-expression network analysis (WGCNA) was utilized to build a co-expression network targeting differentially expressed genes (DEG) and discover mRNAsi-related modules and genes. We assessed the association between the key genes at both the transcription and protein level. Gene Expression Omnibus (GEO) database was used to validate the expression levels of the key genes. The risk model was established according to the least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Furthermore, we determined the prognostic value of the model by employing Kaplan-Meier (KM) plus multivariate Cox analysis. Results GC tissues exhibited a substantially higher mRNAsi relative to the healthy non-tumor tissues. Based on WGCNA, 17 key genes (ARHGAP11A, BUB1, BUB1B, C1orf112, CENPF, KIF14, KIF15, KIF18B, KIF4A, NCAPH, PLK4, RACGAP1, RAD54L, SGO2, TPX2, TTK, and XRCC2) were identified. These key genes were clearly overexpressed in GC and validated in the GEO database. The protein-protein interaction (PPI) network as assessed by STRING indicated that the key genes were tightly connected. After LASSO analysis, a nine-gene risk model (BUB1B, NCAPH, KIF15, RAD54L, KIF18B, KIF4A, TTK, SGO2, C1orf112) was constructed. The overall survival in the high-risk group was relatively poor. The area under curve (AUC) of risk score was higher compared to that of clinicopathological characteristics. According to the multivariate Cox analysis, the nine-gene risk model was a predictor of disease outcomes in GC patients (HR, 7.606; 95% CI, 3.037–19.051; P < 0.001). We constructed a prognostic nomogram with well−fitted calibration curve based on risk score and clinical data. Conclusion The 17 mRNAsi-related key genes identified in this study could be potential treatment targets in GC treatment, considering that they can inhibit the stemness properties. The nine-gene risk model can be employed to predict the disease outcomes of the patients.

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Section: Discussionmentioning

confidence: 99%

Prognostic Prediction Using a Stemness Index-Related Signature in a Cohort of Gastric Cancer

Chen

Zhang

Jiang

et al. 2020

Front. Mol. Biosci.

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“…BioMed Research International composition of the heterogeneous ribonucleoprotein-CCNB1/CENPF axis [50]. The overexpression of CCNB1, CD44, and CXCL8 in this study significantly improved the quality of life of GC patients.…”

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confidence: 55%

“…Overexpression of CCNB1 contributes to the proliferation of colorectal cancer cells [ 47 ], promotes the proliferation, migration, and invasion of bladder cancer [ 48 ], and also leads to poor prognosis of hepatocellular carcinoma [ 49 ]. CCNB1 can promote the proliferation and metastasis of GC by participating in the composition of the heterogeneous ribonucleoprotein-CCNB1/CENPF axis [ 50 ]. The overexpression of CCNB1, CD44, and CXCL8 in this study significantly improved the quality of life of GC patients.…”

Section: Discussionmentioning

confidence: 99%

Bioinformatics Analysis of Key Genes and circRNA-miRNA-mRNA Regulatory Network in Gastric Cancer

Tian

Yang

Zhang

et al. 2020

BioMed Research International

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Gastric cancer (GC) is one of the most common malignancies in the world, with morbidity and mortality ranking second among all cancers. Accumulating evidences indicate that circular RNAs (circRNAs) are closely correlated with tumorigenesis. However, the mechanisms of circRNAs still remain unclear. This study is aimed at determining hub genes and circRNAs and analyzing their potential biological functions in GC. Expression profiles of mRNAs and circRNAs were downloaded from the Gene Expression Omnibus (GEO) data sets of GC and paracancer tissues. Differentially expressed genes (DEGs) and differentially expressed circRNAs (DE-circRNAs) were identified. The target miRNAs of DE-circRNAs and the bidirectional interaction between target miRNAs and DEGs were predicted. Functional analysis was performed, and the protein-protein interaction (PPI) network and the circRNA-miRNA-mRNA network were established. A total of 456 DEGs and 2 DE-circRNAs were identified with 3 mRNA expression profiles and 2 circRNA expression profiles. GO analysis indicated that DEGs were mainly enriched in extracellular matrix and cell adhesion, and KEGG confirmed that DEGs were mainly associated with focal adhesion, the PI3K-Akt signaling pathway, extracellular matrix- (ECM)- receptor interaction, and gastric acid secretion. 15 hub DEGs (BGN, COL1A1, COL1A2, FBN1, FN1, SPARC, SPP1, TIMP1, UBE2C, CCNB1, CD44, CXCL8, COL3A1, COL5A2, and THBS1) were identified from the PPI network. Furthermore, the survival analysis indicate that GC patients with a high expression of the following 9 hub DEGs, namely, BGN, COL1A1, COL1A2, FBN1, FN1, SPARC, SPP1, TIMP1, and UBE2C, had significantly worse overall survival. The circRNA-miRNA-mRNA network was constructed based on 1 circRNA, 15 miRNAs, and 45 DEGs. In addition, the 45 DEGs included 5 hub DEGs. These results suggested that hub DEGs and circRNAs could be implicated in the pathogenesis and development of GC. Our findings provide novel evidence on the circRNA-miRNA-mRNA network and lay the foundation for future research of circRNAs in GC.

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“…CENPF is a member of the mitochondrial family and regulates the proliferation of various tumor cells [28]. Chen et al [29] showed that CENPF was overexpressed in GC and promoted its proliferation and metastasis. TTK is also known as a mitotic kinase [30], which is highly expressed in many types of tumors and promotes the proliferation of cancer cells [31][32].…”

Section: Discussionmentioning

confidence: 99%

Identification of Key Genes for Controlling Cancer Stem Cell Characteristics in Gastric Cancer

Huang

Zhao

Ning

et al. 2020

Preprint

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Background Self-renewal of gastric cancer stem cells (GCSCs) is considered to be the underlying cause of the metastasis, drug resistance and recurrence of gastric cancer (GC). The purpose of this study was to characterize the expression of stem cell-related genes in GC. Methods The RNA sequencing results and clinical data for gastric adenoma and adenocarcinoma samples were obtained from the Cancer Genome Atlas (TCGA) database, and the results of the GC mRNA expression-based stemness index (mRNAsi) were analyzed. Weighted gene coexpression network analysis (WGCNA) was then used to find modules of interest and their key genes. Survival analysis of key genes was performed using the online tool Kaplan-Meier Plotter, and the online database Oncomine was used to assess the expression of key genes in GC. Results mRNAsi was significantly upregulated in GC tissues compared to normal gastric tissues (p < 0.0001). A total of 16 modules were obtained from the gene coexpression network; the brown module was most positively correlated with mRNAsi. Sixteen key genes (BUB1, BUB1B, NCAPH, KIF14, RACGAP1, RAD54L, TPX2, KIF15, KIF18B, CENPF, TTK, KIF4A, SGOL2, PLK4, XRCC2 and C1orf112) were identified in the brown module. Survival analysis and Oncomine analysis revealed that the prognosis of patients with GC and the expression of three genes (RAD54L, TPX2 and XRCC2) were consistently related. Conclusion Sixteen key genes play an important role in the maintenance of GCSCs. RAD54L, TPX2 and XRCC2 are the most likely therapeutic targets for inhibiting the stemness characteristics of GC cells.

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HnRNPR/CCNB1/CENPF Axis Contributes to Gastric Cancer Proliferation and Metastasis

Cited by 26 publications

References 0 publications

Prognostic Prediction Using a Stemness Index-Related Signature in a Cohort of Gastric Cancer

Prognostic Prediction Using a Stemness Index-Related Signature in a Cohort of Gastric Cancer

Bioinformatics Analysis of Key Genes and circRNA-miRNA-mRNA Regulatory Network in Gastric Cancer

Identification of Key Genes for Controlling Cancer Stem Cell Characteristics in Gastric Cancer

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