KIF4A knockdown suppresses ovarian cancer cell proliferation and induces apoptosis by downregulating BUB1 expression

BUB1 potentiates gastric cancer proliferation and metastasis by activating TRAF6/NF-κB/FGF18 through m6A modification

AIMS

Gastric cancer (GC) is one of the most common malignant tumors of the digestive system. High expression of the mitotic kinase BUB1 has been shown to be associated with the development of many cancers, but the role of BUB1 in GC is still unclear. The current study aimed to investigate the role of BUB1 in GC.

MATERIALS AND METHODS

BUB1 inhibitor, siRNA or BUB1 overexpression plasmid-mediated functional studies were performed in vitro and in vivo to explore the oncogenic role of BUB1 in GC. The expression of BUB1 and FGF18 in GC tumor samples was determined by IHC staining. RNA-seq, Western blot, MeRIP-qPCR and Co-IP assays were used to investigate the molecular mechanisms by which BUB1 regulates GC progression.

KEY FINDINGS

Knockdown of BUB1 significantly inhibited the proliferation and metastasis of GC cells in vitro and in vivo. Moreover, overexpression of BUB1 significantly promoted the proliferation, migration and invasion of GC cells. High expression of BUB1 and FGF18 in GC tissues predicted poor prognosis in GC patients. Mechanistically, BUB1 interacted with METTL3 and induced m6A modification of TRAF6 mRNA, further activating the NF-κB/FGF18 axis in GC cells.

SIGNIFICANCE

Our results confirmed that BUB1 acts as a positive regulator of GC cell proliferation and metastasis by activating the TRAF6/NF-κB/FGF18 pathway through METTL3-mediated m6A methylation. Targeting the BUB1/METTL3/TRAF6/NF-κB/FGF18 axis might be a novel diagnostic and therapeutic strategy in GC.

Correlation of BUB1 and BUB1B with the development and prognosis of endometrial cancer

This study aimed to evaluate the expression and clinical significance of budding uninhibited by benzimidazole 1 (BUB1) and BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B) in endometrial carcinoma (EC). BUB1 and BUBIB expressions were evaluated by bioinformatics. Protein expression, clinical features, prognosis and immune cell infiltration were explored in 20 EC tumors. siRNA was used to evaluate BUB1 and BUBIB function in EC cells. BUB1 and BUBIB were highly expressed in 26 cancers. BUB1 was associated with overall survival (OS) in eight cancers and disease-free survival in ten; BUB1B was associated with OS in nine cancers and DFS in eleven. BUB1 and BUBIB exhibited high frequencies of gene changes (mainly mutations, > 5%) in cancer. BUB1 was negatively correlated and BUB1B was positively correlated with cancer-associated fibroblasts and endothelial cell infiltration. BUB1 and BUBIB knockdown decreased migration and invasion in EC cells. High BUB1 expression correlated with tumor malignant phenotypes (P < 0.05). High BUB1 mRNA expression reduced OS (P = 0.00036) and recurrence-free survival (P = 0.0011). High BUB1B mRNA expression reduced OS (P = 0.0024). BUB1/BUB1B correlated with activated CD8 + T and CD4 + T cell infiltration. BUB1 and BUBIB are highly expressed and correlated with clinicopathological characteristics in EC. BUB1 and BUBIB are potential prognosis markers and immunotherapy targets.

WZ-3146 acts as a novel small molecule inhibitor of KIF4A to inhibit glioma progression by inducing apoptosis

BACKGROUND

Glioma is considered the most common primary malignant tumor of the central nervous system. Although traditional treatments have not achieved satisfactory outcomes, recently, targeted therapies for glioma have shown promising efficacy. However, due to the single-target nature of targeted therapy, traditional targeted therapies are ineffective; thus, novel therapeutic targets are urgently needed.

METHODS

The gene expression data for glioma patients were derived from the GEO (GSE4290, GSE50161), TCGA and CGGA databases. Next, the upregulated genes obtained from the above databases were cross-analyzed, finally, 10 overlapping genes (BIRC5, FOXM1, EZH2, CDK1, KIF11, KIF4A, NDC80, PBK, RRM2, and TOP2A) were ultimately screened and only KIF4A expression has the strongest correlation with clinical characteristics in glioma patients. Futher, the TCGA and CGGA database were utilized to explore the correlation of KIF4A expression with glioma prognosis. Then, qRT-PCR and Western blot was used to detect the KIF4A mRNA and protein expression level in glioma cells, respectively. And WZ-3146, the small molecule inhibitor targeting KIF4A, were screened by Cmap analysis. Subsequently, the effect of KIF4A knockdown or WZ-3146 treatment on glioma was measured by the MTT, EdU, Colony formation assay and Transwell assay. Ultimately, GSEA enrichment analysis was performed to find that the apoptotic pathway could be regulated by KIF4A in glioma, in addition, the effect of WZ-3146 on glioma apoptosis was detected by flow cytometry and Western blot.

RESULTS

In the present study, we confirmed that KIF4A is abnormally overexpressed in glioma. In addition, KIF4A overexpression is a key indicator of glioma prognosis; moreover, suppressing KIF4A expression can inhibit glioma progression. We also discovered that WZ-3146, a small molecule inhibitor of KIF4A, can induce apoptosis in glioma cells and exhibit antiglioma effects.

CONCLUSION

In conclusion, these observations demonstrated that targeting KIF4A can inhibit glioma progression. With further research, WZ-3146, a small molecule inhibitor of KIF4A, could be combined with other molecular targeted drugs to cooperatively inhibit glioma progression.

Expression of the checkpoint kinase BUB1 is a predictor of response to cancer therapies

The identification of clinically-relevant biomarkers is of upmost importance for the management of cancer, from diagnosis to treatment choices. We performed a pan-cancer analysis of the mitotic checkpoint budding uninhibited by benzimidazole 1 gene BUB1, in the attempt to ascertain its diagnostic and prognostic values, specifically in the context of drug response. BUB1 was found to be overexpressed in the majority of cancers, and particularly elevated in clinically aggressive molecular subtypes. Its expression was correlated with clinico-phenotypic features, notably tumour staging, size, invasion, hypoxia, and stemness. In terms of prognostic value, the expression of BUB1 bore differential clinical outcomes depending on the treatment administered in TCGA cancer cohorts, suggesting sensitivity or resistance, depending on the expression levels. We also integrated in vitro drug sensitivity data from public projects based on correlation between drug efficacy and BUB1 expression to produce a list of candidate compounds with differential responses according to BUB1 levels. Gene Ontology enrichment analyses revealed that BUB1 overexpression in cancer is associated with biological processes related to mitosis and chromosome segregation machinery, reflecting the mechanisms of action of drugs with a differential effect based on BUB1 expression.

Inhibition of the serine/threonine kinase BUB1 reverses taxane resistance in prostate cancer

Men with incurable castration resistant prostate cancer (CRPC) are typically treated with taxanes; however, drug resistance rapidly develops. We previously identified a clinically relevant seven gene network in aggressive CRPC, which includes the spindle assembly checkpoint (SAC) kinase BUB1. Since SAC is deregulated in taxane resistant PC, we evaluated BUB1 and found that it was over-expressed in advanced PC patient datasets and taxane resistant PC cells. Treatment with a specific BUB1 kinase inhibitor re-sensitized resistant CRPC cells, including cells expressing constitutively active androgen receptor (AR) variants, to clinically used taxanes. Consistent with a role of AR variants in taxane resistance, ectopically expressed AR-V7 increased BUB1 levels and reduced sensitivity to taxanes. This work shows that disruption of BUB1 kinase activity reverted resistance to taxanes, which is essential to advancing BUB1 as a potential therapeutic target for intractable chemotherapy resistant CRPC including AR variant driven CRPC, which lacks durable treatment options.

Comprehensive analysis of BUBs gene family in lung adenocarcinoma with immunological analysis

Lung adenocarcinoma (LUAD) is one of the most commonly malignant tumors, and major challenges remain in the treatment of LUAD. Budding uninhibited by benzimidazole 1/3 (BUB1/3) play significant roles in the process of spindle-assembly checkpoint (SAC) during mitosis. However, their roles in LUAD have not been established. Here, we performed an immunological analysis of BUB1/3 in LUAD using a comprehensive bioinformatics approach, quantitative real-time-PCR and Western blotting technique. Our results indicated that the expression levels of BUB1 and BUB3 in LUAD samples were higher than the expression levels in the control groups and were associated with some clinicopathologic parameters in patients with LUAD. BUB1/3 and their related genes were enriched in cell immune, and the immune infiltration analysis revealed that the BUB1/3 expression profile was significantly correlated with characteristics of immune cell infiltration. Survival analysis showed that the disease-free survival and overall survival of patients with LUAD decreased with an increase in the BUB1/3 expression levels. The mRNA and protein expression levels of BUB1 and BUB3 in each of the LUAD cell lines were upregulated to varying degrees. BUB1 and BUB3 are the potential immunological therapeutic intervention targets for patients with LUAD.

Integration of Transcriptome and Epigenome to Identify and Develop Prognostic Markers for Ovarian Cancer

DNA methylation is a widely researched epigenetic modification. It is associated with the occurrence and development of cancer and has helped evaluate patients' prognoses. However, most existing DNA methylation prognosis models have not simultaneously considered the changes of the downstream transcriptome. Methods. The RNA-Sequencing data and DNA methylation omics data of ovarian cancer patients were downloaded from The Cancer Genome Atlas (TCGA) database. The Consensus Cluster Plus algorithm was used to construct the methylated molecular subtypes of the ovary. Lasso regression was employed to build a multi-gene signature. An independent data set was applied to verify the prognostic value of the signature. The Gene Set Variation Analysis (GSVA) was used to carry out the enrichment analysis of the pathways linked to the gene signature. The IMvigor 210 cohort was used to explore the predictive efficacy of the gene signature for immunotherapy response. Results. We distinguished ovarian cancer samples into two subtypes with different prognosis, based on the omics data of DNA methylation. Differentially expressed genes and enrichment analysis among subtypes indicated that DNA methylation was related to fatty acid metabolism and the extracellular matrix (ECM)-receptor. Furthermore, we constructed an 8-gene signature, which proved to be efficient and stable in predicting prognostics in ovarian cancer patients with different data sets and distinctive pathological characteristics. Finally, the 8-gene signature could predict patients' responses to immunotherapy. The polymerase chain reaction experiment was further used to verify the expression of 8 genes. Conclusion. We analyzed the prognostic value of the related genes of methylation in ovarian cancer. The 8-gene signature predicted the prognosis and immunotherapy response of ovarian cancer patients well and is expected to be valuable in clinical application.

Identification and Validation of the Diagnostic Characteristic Genes of Ovarian Cancer by Bioinformatics and Machine Learning

Background: Ovarian cancer (OC) has a high mortality rate and poses a severe threat to women’s health. However, abnormal gene expression underlying the tumorigenesis of OC has not been fully understood. This study aims to identify diagnostic characteristic genes involved in OC by bioinformatics and machine learning.

Methods: We utilized five datasets retrieved from the Gene Expression Omnibus (GEO) database, The Cancer Genome Atlas (TCGA) database, and the Genotype-Tissue Expression (GTEx) Project database. GSE12470 and GSE18520 were combined as the training set, and GSE27651 was used as the validation set A. Also, we combined the TCGA database and GTEx database as validation set B. First, in the training set, differentially expressed genes (DEGs) between OC and non-ovarian cancer tissues (nOC) were identified. Next, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Disease Ontology (DO) enrichment analysis, and Gene Set Enrichment Analysis (GSEA) were performed for functional enrichment analysis of these DEGs. Then, two machine learning algorithms, Least Absolute Shrinkage and Selector Operation (LASSO) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE), were used to get the diagnostic genes. Subsequently, the obtained diagnostic-related DEGs were validated in the validation sets. Then, we used the computational approach (CIBERSORT) to analyze the association between immune cell infiltration and DEGs. Finally, we analyzed the prognostic role of several genes on the KM-plotter website and used the human protein atlas (HPA) online database to analyze the expression of these genes at the protein level.

Results: 590 DEGs were identified, including 276 upregulated and 314 downregulated DEGs.The Enrichment analysis results indicated the DEGs were mainly involved in the nuclear division, cell cycle, and IL−17 signaling pathway. Besides, DEGs were also closely related to immune cell infiltration. Finally, we found that BUB1, FOLR1, and PSAT1 have prognostic roles and the protein-level expression of these six genes SFPR1, PSAT1, PDE8B, INAVA and TMEM139 in OC tissue and nOC tissue was consistent with our analysis.

Conclusions: We screened nine diagnostic characteristic genes of OC, including SFRP1, PSAT1, BUB1B, FOLR1, ABCB1, PDE8B, INAVA, BUB1, TMEM139. Combining these genes may be useful for OC diagnosis and evaluating immune cell infiltration.