Inhibition of Ganglioside Synthesis Suppressed Liver Cancer Cell Proliferation through Targeting Kinetochore Metaphase Signaling

Su, Ting; Qin, Xian‐Yang; Dohmae, Naoshi; Wei, Feifei; Furutani, Yutaka; Kojima, Soichi; Yu, Wenkui

doi:10.3390/metabo11030167

Cited by 14 publications

(10 citation statements)

References 77 publications

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“…Furthermore, LPL was mainly enriched in chromosome segregation. A paradoxical response to DNA damage occurs in HCC, which leads to errors in chromosome segregation [ 42 ]. Error in mitosis occurs in metabolic disorders and causes numerical and structural chromosome aberrations during cell division [ 43 ].…”

Section: Discussionmentioning

confidence: 99%

Identification of metabolic biomarkers associated with nonalcoholic fatty liver disease

Jiang,

Hu,

Zhang

et al. 2023

Lipids Health Dis

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Background Nonalcoholic fatty liver disease (NAFLD) is the most common liver disease. Metabolism-related genes significantly influence the onset and progression of the disease. Hence, it is necessary to screen metabolism-related biomarkers for the diagnosis and treatment of NAFLD patients. Methods GSE48452, GSE63067, and GSE89632 datasets including nonalcoholic steatohepatitis (NASH) and healthy controls (HC) analyzed in this study were retrieved from the Gene Expression Omnibus (GEO) database. First, differentially expressed genes (DEGs) between NASH and HC samples were obtained. Next, metabolism-related DEGs (MR-DEGs) were identified by overlapping DEGs and metabolism-related genes (MRG). Further, a protein–protein interaction (PPI) network was developed to show the interaction among MR-DEGs. Subsequently, the “Least absolute shrinkage and selection operator regression” and “Random Forest” algorithms were used to screen metabolism-related genes (MRGs) in patients with NAFLD. Next, immune cell infiltration and gene set enrichment analyses (GSEA) were performed on these metabolism-related genes. Finally, the expression of metabolism-related gene was determined at the transcription level. Results First, 129 DEGs related to NAFLD development were identified among patients with nonalcoholic steatohepatitis (NASH) and healthy control. Next, 18 MR-DEGs were identified using the Venn diagram. Subsequently, four genes, including AMDHD1, FMO1, LPL, and P4HA1, were identified using machine learning algorithms. Moreover, a regulatory network consisting of four genes, 25 microRNAs (miRNAs), and 41 transcription factors (TFs) was constructed. Finally, a significant increase in FMO1 and LPL expression levels and a decrease in AMDHD1 and P4HA1 expression levels were observed in patients in the NASH group compared to the HC group. Conclusion Metabolism-related genes associated with NAFLD were identified, containing AMDHD1, FMO1, LPL, and P4HA1, which provide insights into diagnosing and treating patients with NAFLD.

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Section: Discussionmentioning

confidence: 99%

Identification of metabolic biomarkers associated with nonalcoholic fatty liver disease

Jiang,

Hu,

Zhang

et al. 2023

Lipids Health Dis

View full text Add to dashboard Cite

show abstract

“…Furthermore, LPL was mainly enriched in chromosome segregation. A paradoxical response to DNA damage occurs in HCC, which leads to errors in chromosome segregation [35]. Error in mitosis occurs in metabolic disorders and causes numerical and structural chromosome aberrations during cell division [36].…”

Section: Discussionmentioning

confidence: 99%

Based on bioinformatics analysis of nonalcoholic fatty liver disease associated with metabolic biomarkers

Jiang

Zhang

et al. 2023

Preprint

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Background: Nonalcoholic fatty liver disease (NAFLD) is a metabolic syndrome of the liver. Metabolism-related genes significantly influence the onset and progression of the disease. Hence, it is necessary to screen metabolism-related biomarkers for diagnosing and treating patients with NAFLD. Methods: First, the transcriptomic data of patients with NAFLD were screened to identify differentially expressed genes (DEGs). Next, Venn Diagram was used to identify metabolism-related DEGs (MR-DEGs). Further, a protein-protein interaction network was developed to show the interaction between MR-DEGs. Subsequently, the “Least absolute shrinkage and selection operator regression” and “Random Forest” algorithms were used to screen metabolism-related genes in patients with NAFLD. Next, immune cell infiltration and gene set enrichment analyses were performed on these metabolism-related genes. Finally, metabolism-related gene expression was determined at the transcription level. Results: First, 129 DEGs related to NAFLD development were identified among patients with nonalcoholic steatohepatitis (NASH) and healthy control. Next, 18 MR-DEGs were identified using the Venn diagram. Subsequently, four genes, including AMDHD1, FMO1, LPL, and P4HA1,were identified using machine learning. Moreover, a regulatory network consisting of four genes, 25 miRNAs, and 41 transcription factors was constructed. Finally, a significant increase in FMO1 and LPLexpression levels and a decrease in AMDHD1 and P4HA1 expression levels were observed in patients in the NASH group compared to the healthy control group. Conclusion: Metabolism-related genes associated with NAFLD, such as AMDHD1, FMO1, LPL, and P4HA1, were identified which provide insights into diagnosing and treating patients with NAFLD.

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“…KIF14 is a potential oncogene, promotes gastric cancer progression and metastasis ( Yang et al, 2019 ). AURKB and TTK participate in chromosomes segregation during mitosis via regulating kinetochore metaphase signaling ( Su et al, 2021 ). Gene functional enrichment analysis showed ARHGAP11A and its interacting proteins involved in numerous processes including lymphocyte activation, cell division, cell killing, immune effector process regulating, and myeloid leukocyte differentiation.…”

Section: Discussionmentioning

confidence: 99%

ARHGAP11A Is a Prognostic Biomarker and Correlated With Immune Infiltrates in Gastric Cancer

Fan

et al. 2021

Front. Mol. Biosci.

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Background: ARHGAP11A, belongs to RhoGAPs family, is vital for cell motility. However, the role of ARHGAP11A in gastric cancer is obscure.Methods: The expression level of ARHGAP11A was analyzed by Oncomine database. The correlation of ARHGAP11A expression with immune infiltrates and associated gene markers was clarified by Tumor IMmune Estimation Resource and Gene Expression Profiling Interactive Analysis database. The correlation between ARHGAP11A expression and the patient prognosis was identified by Kaplan-Meier plotter and PrognoScan. Genetic changes of ARHGAP11A were analyzed by cBioPortal. The protein-protein interaction network and gene functional enrichment analysis were constructed and performed by GeneMANIA and Metascape.Results: We found that the expression levels of ARHGAP11A were elevated in various cancers including gastric cancer when compared with normal tissues. High expression of ARHGAP11A was significantly correlated with a better prognosis in gastric cancer. We revealed that the expression of ARHGAP11A was negatively associated with infiltration levels of CD8+ T cells, CD4+ T cells, macrophages and dendritic cells. In addition, ARHGAP11A expression was significantly correlated with gene markers of these immune cells. Lastly, gene functional enrichment analysis indicated that ARHGAP11A involved in regulating lymphocyte activation, cell division, cell killing, myeloid leukocyte differentiation and leukocyte apoptosis.Conclusion: Our findings demonstrated that ARHGAP11A was a valuable prognostic biomarker in gastric cancer. Further work is needed to validate its role and underlying mechanisms in regulating immune infiltrates.

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Inhibition of Ganglioside Synthesis Suppressed Liver Cancer Cell Proliferation through Targeting Kinetochore Metaphase Signaling

Cited by 14 publications

References 77 publications

Identification of metabolic biomarkers associated with nonalcoholic fatty liver disease

Identification of metabolic biomarkers associated with nonalcoholic fatty liver disease

Based on bioinformatics analysis of nonalcoholic fatty liver disease associated with metabolic biomarkers

ARHGAP11A Is a Prognostic Biomarker and Correlated With Immune Infiltrates in Gastric Cancer

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