Expression of the cytokinesis regulator PRC1 results in p53-pathway activation in A549 cells but does not directly regulate gene expression in the nucleus

Hanselmann, Steffen; Gertzmann, Dörthe; Shin, Woo Jin; Ade, Carsten P.; Gaubatz, Stefan

doi:10.1080/15384101.2022.2122258

Cited by 3 publications

(2 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In another study, the number of cancer cells blocked at the G2/M phase was higher after the knockdown of PRC1 in colon cancer, which reduced the invasive ability of colon cancer 47 . Previous studies on tumors report cytoplasmic division failure as a significant risk factor for tumor development and progression, and PRC1 overexpression may lead to impaired cytoplasmic division and chromosomal instability, which could be one of the oncogenic mechanisms of PRC1 48 . In addition, PRC1 overexpression reportedly activates the Wnt/β-catenin signaling pathway to promote the progression of hepatocellular carcinoma and lung adenocarcinoma 49 50 .…”

Section: Discussionmentioning

confidence: 99%

Identification of Key Genes and Related Drugs of Adrenocortical Carcinoma by Integrated Bioinformatics Analysis

Wei,

Zeng,

et al. 2023

Horm Metab Res

View full text Add to dashboard Cite

Adrenocortical carcinoma (ACC) is a malignant carcinoma with an extremely poor prognosis, and its pathogenesis remains to be understood to date, necessitating further investigation. This study aims to discover biomarkers and potential therapeutic agents for ACC through bioinformatics, enhancing clinical diagnosis and treatment strategies. Differentially expressed genes (DEGs) between ACC and normal adrenal cortex were screened out from the GSE19750 and GSE90713 datasets available in the GEO database. An online Venn diagram tool was utilized to identify the common DEGs between the two datasets. The identified DEGs were subjected to functional assessment, pathway enrichment, and identification of hub genes by performing the protein-protein interaction (PPI), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The differences in the expressions of hub genes between ACC and normal adrenal cortex were validated at the GEPIA2 website, and the association of these genes with the overall patient survival was also assessed. Finally, on the QuartataWeb website, drugs related to the identified hub genes were determined. A total of 114 DEGs, 10 hub genes, and 69 known drugs that could interact with these genes were identified. The GO and KEGG analyses revealed a close association of the identified DEGs with cellular signal transduction. The 10 hub genes identified were overexpressed in ACC, in addition to being significantly associated with adverse prognosis in ACC. Three genes and the associated known drugs were identified as potential targets for ACC treatment.

show abstract

Section: Discussionmentioning

confidence: 99%

Identification of Key Genes and Related Drugs of Adrenocortical Carcinoma by Integrated Bioinformatics Analysis

Wei,

Zeng,

et al. 2023

Horm Metab Res

View full text Add to dashboard Cite

show abstract

“…Targeting PRC1 expression or function may result in therapeutic vulnerabilities in cancer treatment [40]. Bioinformatics analysis has revealed that PRC1 may be a prognostic biomarker for predicting OS in LUAD.…”

Section: Construction Of the Ppi Network And Identification Of Key Genesmentioning

confidence: 99%

Bioinformatics-based screening and analysis of the key genes involved in the influence of antiangiogenesis on myeloid-derived suppressor cells and their effects on the immune microenvironment

Zhao,

Wen

et al. 2024

Med Oncol

View full text Add to dashboard Cite

Background: This study aimed to screen differentially expressed genes (DEGs) involved in the influence of antiangiogenic therapy on myeloid-derived suppressor cell (MDSC) infiltration and investigate their mechanisms of action.Methods: Data on DEGs after the action of antiangiogenic drugs in a pan-cancer context were obtained from the Gene Expression Omnibus (GEO) database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the clusterProfiler package in R software.Single-sample gene set enrichment analysis was performed using the gene set variation analysis package to evaluate the levels of immune cells and the activity of immune-related pathways. The relationships of DEGs with the infiltration levels of MDSCs and specific immune cell subpopulations were investigated via gene module analysis. The top 10 key genes were subsequently obtained from PPI network analysis using the cytoHubba plugin of the Cytoscape platform. Results: When the DEGs of the four datasets were intersected, a DEG in the intersection of three datasets and 12 DEGs in the intersection of two datasets were upregulated, and 28 DEGs in the intersection of two datasets were downregulated. GO and KEGG pathway enrichment analyses revealed that the DEGs were associated with multiple important signaling pathways closely related to tumor onset and development, including cell differentiation, cell proliferation, the cell cycle, and immune responses. Most downregulated genes in lung adenocarcinoma (LUAD) were positively correlated with MDSC expression. Only MGP was negatively correlated; the correlation between CACNG6 and MDSC expression was statistically insignificant. In lung squamous cell carcinoma (LUSC), the relationships of PMEPA1, PCDH7 , NEURL1B, and CACNG6 with MDSC expression were statistically insignificant; MGP was negatively correlated with MDSC expression. The top 10 key genes with the highest degree scores obtained using the cytoHubba plugin of Cytoscape were AURKB, RRM2, BUB1, NUSAP1, PRC1, TOP2A, NCAPH, CENPA, KIF2C, and CCNA2. Most of these genes were upregulated in LUAD and associated with immune cell infiltration and prognosis in tumors. An analysis of the relationships between DEGs and infiltration by other specific immune cells revealed the presence of consistent patterns in the downregulated genes, which exhibited positive correlations with the levels of Th2 cells, γδ T cells, and CD56dim NK cells, and negative correlations with other infiltrating immune cells. Conclusions: Antiangiogenic therapy may regulate MDSC infiltration through multiple important signaling pathways closely associated with tumor onset and development, such as cell differentiation, cell proliferation, the cell cycle, and immune responses. Antiangiogenic drugs may exert effects by affecting various types of infiltrating cells associated with immune suppression.

show abstract

Bioinformatics-Based Screening and Analysis of the Key Genes Involved in the Influence of Antiangiogenesis on Myeloid-Derived Suppressor Cells and Their Effects on the Immune Microenvironment

Zhao

Jiang

et al. 2023

Preprint

View full text Add to dashboard Cite

Background: This study aimed to screen differentially expressed genes (DEGs) involved in the influence of antiangiogenic therapy on myeloid-derived suppressor cell (MDSC) infiltration and investigate their mechanisms of action. Methods: Data on DEGs after the action of antiangiogenic drugs in a pan-cancer context were obtained from the Gene Expression Omnibus (GEO) database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the clusterProfiler package in R software. Single-sample gene set enrichment analysis was performed using the gene set variation analysis package to evaluate the levels of immune cells and the activity of immune-related pathways. The relationships of DEGs with the infiltration levels of MDSCs and specific immune cell subpopulations were investigated via gene module analysis. The top 10 key genes were subsequently obtained from PPI network analysis using the cytoHubba plugin of the Cytoscape platform. Results: When the DEGs of the four datasets were intersected, a DEG in the intersection of three datasets and 12 DEGs in the intersection of two datasets were upregulated, and 28 DEGs in the intersection of two datasets were downregulated. GO and KEGG pathway enrichment analyses revealed that the DEGs were associated with multiple important signaling pathways closely related to tumor onset and development, including cell differentiation, cell proliferation, the cell cycle, and immune responses. Most downregulated genes in lung adenocarcinoma (LUAD) were positively correlated with MDSC expression. Only MGP was negatively correlated; the correlation between CACNG6 and MDSC expression was statistically insignificant. In lung squamous cell carcinoma (LUSC), the relationships of PMEPA1, PCDH7, NEURL1B, and CACNG6 with MDSC expression were statistically insignificant; MGP was negatively correlated with MDSC expression. The top 10 key genes with the highest degree scores obtained using the cytoHubba plugin of Cytoscape were AURKB, RRM2, BUB1, NUSAP1, PRC1, TOP2A, NCAPH, CENPA, KIF2C, and CCNA2. Most of these genes were upregulated in LUAD and associated with immune cell infiltration and prognosis in tumors. An analysis of the relationships between DEGs and infiltration by other specific immune cells revealed the presence of consistent patterns in the downregulated genes, which exhibited positive correlations with the levels of Th2 cells, γδ T cells, and CD56dim NK cells, and negative correlations with other infiltrating immune cells. Conclusions: Antiangiogenic therapy may regulate MDSC infiltration through multiple important signaling pathways closely associated with tumor onset and development, such as cell differentiation, cell proliferation, the cell cycle, and immune responses. Antiangiogenic drugs may exert effects by affecting various types of infiltrating cells associated with immune suppression.

show abstract

Expression of the cytokinesis regulator PRC1 results in p53-pathway activation in A549 cells but does not directly regulate gene expression in the nucleus

Cited by 3 publications

References 45 publications

Identification of Key Genes and Related Drugs of Adrenocortical Carcinoma by Integrated Bioinformatics Analysis

Identification of Key Genes and Related Drugs of Adrenocortical Carcinoma by Integrated Bioinformatics Analysis

Bioinformatics-based screening and analysis of the key genes involved in the influence of antiangiogenesis on myeloid-derived suppressor cells and their effects on the immune microenvironment

Bioinformatics-Based Screening and Analysis of the Key Genes Involved in the Influence of Antiangiogenesis on Myeloid-Derived Suppressor Cells and Their Effects on the Immune Microenvironment

Contact Info

Product

Resources

About