Background PDZ-binding kinase (PBK) encodes a serine/threonine protein kinase related to the dual specific mitogen-activated protein kinase kinase (MAPKK) family. There is evidence that overexpression of this gene is associated with tumorigenesis. However, the role of PBK in hepatocellular carcinoma (HCC) remains unclear. Therefore, we evaluated the prognostic role of PBK and its correlation with immune infiltrates in hepatocellular carcinoma. Methods The expression of PBK in pan-cancers was studied by Onconmine and TIMER. The expression of PBK in HCC patients and its relationship with clinicopathological characteristics were analyzed using The Gene Expression Profiling Interactive Analysis (GEPIA), The human protein atlas database (HPA), The Cancer Genome Atlas (TCGA), and Gene Expression Omnibus (GEO) databases. Receiver operating characteristic (ROC) curve was used to determine the diagnostic value of PBK in HCC patients. The relationship between PBK and prognosis of HCC was performed by GEPIA and Kaplan Meier plotter web tool. The correlations between the clinical characteristics and overall survival were analyzed by Univariate Cox regression and Multivariate Cox hazards regression to identify possible prognostic factors for HCC patients. LinkedOmics was applied to investigate co-expression associated with PBK and to analyze Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The network map of PBK and related genes is constructed by GeneMANIA. Finally, TIMER and TISIDB were used to analyze the correlations between PBK and tumor-infiltrating immune cells. Results Multiple database analysis shows that PBK was highly expressed in many types of tumors, including hepatocellular carcinoma, and was significantly related to tumor stage (P=0.0089), age (P=0.0131), and race (P=0.0024) of HCC patients. The receiver operating characteristic (ROC) curve analysis showed that PBK had high diagnostic potential to HCC in GSE76427 (AUC=0.8799), GSE121248 (AUC=0.9224), GSE62232 (AUC=0.9975), and GSE84402 (AUC=0.9541). Multivariate Cox hazards regression showed that high expression of PBK may be an independent risk factor for overall survival in HCC patients (HR = 1.566, 95% CI=1.062–2.311, P= 0.024). The Protein–protein interaction network showed that PBK significantly interacted with LRRC47, ARAF, LGALS9B, TTK, DLG1, and other essential genes. Furthermore, enrichment analysis showed that PBK and co-expressed genes participated in many biological processes, cell composition, molecular functions, and pathways in HCC. Finally, the immune infiltration analysis by TIMER and TISIDB indicated that a significant tightly correlation between PBK and macrophages, neutrophils, as well as chemokines and receptors. Conclusions High expression of PBK is significantly correlated with poor survival and immune infiltrates in hepatocellular carcinoma. Our study suggests that PBK can be used as a biomarker of poor prognosis and potential immune therapy target in hepatocellular carcinoma.
There is limited and discordant evidence on the role of nicotine in diabetic vascular disease. Exacerbated endothelial cell dysregulation in smokers with diabetes is associated with the disrupted adipose function. Adipokines possess vascular protective, anti-inflammatory, and anti-diabetic properties. However, whether and how nicotine primes and aggravates diabetic vascular disorders remain uncertain. In this study, we evaluated the alteration of adiponectin (APN) level in high-fat diet (HFD) mice with nicotine (NIC) administration. The vascular pathophysiological response was evaluated with vascular ring assay. Confocal and co-immunoprecipitation analysis were applied to identify the signal interaction and transduction. These results indicated that the circulating APN level in nicotine-administrated diabetic Apolipoprotein E-deficient (ApoE−/−) mice was elevated in advance of 2 weeks of diabetic ApoE−/− mice. NIC and NIC addition in HFD groups (NIC + HFD) reduced the vascular relaxation and signaling response to APN at 6 weeks. Mechanistically, APN receptor 1 (AdipoR1) level was decreased in NIC and further significantly reduced in NIC + HFD group at 6 weeks, while elevated suppressor of cytokine signaling 3 (SOCS3) expression was induced by NIC and further augmented in NIC + HFD group. Additionally, nicotine provoked SOCS3, degraded AdipoR1, and attenuated APN-activated ERK1/2 in the presence of high glucose and high lipid (HG/HL) in human umbilical vein endothelial cells (HUVECs). MG132 (proteasome inhibitor) administration manifested that AdipoR1 was ubiquitinated, while inhibited SOCS3 rescued the reduced AdipoR1. In summary, this study demonstrated for the first time that nicotine primed vascular APN resistance via SOCS3-mediated degradation of ubiquitinated AdipoR1, accelerating diabetic endothelial dysfunction. This discovery provides a potential therapeutic target for preventing nicotine-accelerated diabetic vascular dysfunction.
With continually improving treatment strategies and patient care, the overall mortality of cardiovascular disease (CVD) has been significantly reduced. However, this success is a double-edged sword, as many patients who survive cardiovascular complications will progress towards a chronic disorder over time. A family of adiponectin paralogs designated as C1q complement/tumor necrosis factor (TNF)-associated proteins (CTRPs) has been found to play a role in the development of CVD. CTRPs, which are comprised of 15 members, CTRP1 to CTRP15, are secreted from different organs/tissues and exhibit diverse functions, have attracted increasing attention because of their roles in maintaining inner homeostasis by regulating metabolism, inflammation, and immune surveillance. In particular, studies indicate that CTRPs participate in the progression of CVD, influencing its prognosis. This review aims to improve understanding of the role of CTRPs in the cardiovascular system by analyzing current knowledge. In particular, we examine the association of CTRPs with endothelial cell dysfunction, inflammation, and diabetes, which are the basis for development of CVD. Additionally, the recently emerged novel coronavirus (COVID-19), officially known as severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2), has been found to trigger severe cardiovascular injury in some patients, and evidence indicates that the mortality of COVID-19 is much higher in patients with CVD than without CVD. Understanding the relationship of CTRPs and the SARS-CoV-2-related damage to the cardiovascular system, as well as the potential mechanisms, will achieve a profound insight into a therapeutic strategy to effectively control CVD and reduce the mortality rate.
BackgroundPDZ-binding kinase (PBK) encodes a serine/threonine protein kinase related to the dual specific mitogen-activated protein kinase kinase (MAPKK) family. There is evidence that overexpression of this gene is associated with tumorigenesis. However, the role of PBK in hepatocellular carcinoma (HCC) remain unclear. Therefore, we evaluated the prognostic role of PBK and its correlation with immune infiltrates in Hepatocellular Carcinoma.MethodsThe expression of PBK in pan-cancers was studied by Onconmine and TIMER. The expression of PBK in HCC patients and its relationship with clinicopathological characteristics were analyzed using The Gene Expression Profiling Interactive Analysis (GEPIA), The human protein atlas database (HPA), The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Receiver operating characteristic (ROC) curve was used to determine the diagnostic value of PBK in HCC patients. The relationship between PBK and prognosis of HCC was performed by GEPIA and Kaplan Meier plotter web tool. The correlations between the clinical characteristics and overall survival were analyzed by Univariate Cox regression and Multivariate Cox hazards regression to identify possible prognostic factors for HCC patients. LinkedOmics was applied to investigate co-expression associated with PBK and to analyze Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The network map of PBK and related genes is constructed by GeneMANIA. Finally, TIMER and TISIDB were used to analyze the correlations between PBK and tumor-infiltrating immune cells.ResultsPBK was highly expressed in many types of tumors, including hepatocellular carcinoma, and was significantly related to tumor stage (P=0.0089), age (P=0.0131) and race (P=0.0024) of HCC patients. The receiver operating characteristic (ROC) curve analysis from 4 GEO databases showed that PBK is a predictive marker for HCC. Moreover, high expression of PBK correlated with poor prognosis. Multivariate Cox hazards regression showed that high expression of PBK may be an independent risk factor for overall survival in HCC patients (HR = 1.566, 95% CI=1.062-2.311, P= 0.024). The Protein–protein interaction network showed that PBK significantly interacted with LRRC47, ARAF, LGALS9B, TTK, MELK and other essential genes. Furthermore, enrichment analysis showed that PBK and co-expressed genes participated in many biological processes, cell composition, molecular functions and pathways in HCC. Finally, the immune infiltration analysis by TIMER and TISIDB indicated that PBK expression tightly correlated with the infiltration of tumor-infiltrating lymphocytes (TILs), chemokines, and receptors.ConclusionsHigh expression of PBK is significantly correlated with poor survival and immune infiltrates in hepatocellular carcinoma. Our study suggests that PBK can be used as a biomarker of poor prognosis and potential immune therapy target in hepatocellular carcinoma.
Objectives There is a paradox of nicotine and diabetes in patients with cardiomyopathy. Although great progress has been made in understanding the pathogenesis, it is urgent to clarify the in‐depth mechanism and identify new therapeutic targets for preventing diabetic cardiomyopathy (DCM) in smoker. Methods Human gene expression profiles in DCM (GSE4745 and GSE99203) corrected by SVA algorithm were analyzed and then “limma” packet gene screening was performed. By constructing the related co‐expression WGCNA network of DCM, combined with the analysis of Metascape pathway, the correlation of module and genes with DCM was determined. Furthermore, the Drug bank and STRING databases were used to screen nicotine targets and expand the gene network. The hub gene was identified by crossing the targets with DCM, which was obtained by WGCNA analysis. r‐packet “clusterprofiler” was used for GO analysis and KEGG pathway analysis. Then, the network of hub genes affecting DCM and the interaction diagram of core target related pathways that influence DCM is constructed by using the Cytoscape software. The relationship between hub genes was analyzed by using “corrplot”, “vioplot” packages and cibersort algorithm, and the potential mechanism of hub genes affecting DCM progress was further explored. To explore the relationship between the targets in the process of DCM, ROC curve of diagnostic efficacy was drawn with “pROC” package. Finally, a miRNA‐mRNA regulatory network including hub genes was further constructed by Targetscan. The regulatory network of transcription factors of hub genes was constructed from TRRUST database. Results 3 gene modules were detected by WGCNA analysis, blue (202), grey (31) and turquoise (397), among which turquoise module had the highest correlation with DCM (cor =0.87, P = (3e −10)). The genes rich in monocarboxylic acid and sulfur compound metabolic processes in response to extracellular stimulus were identified. The top 13 nicotine‐related are targeted. The intersection of 113 target genes and turquoise module gene were screened out. There are the two hub genes involved Iron (III) ion response and retinoic acid biosynthetic process, involving Tryptophan Metabolism and Ovarian steroidogenesis pathways. The AUC values of the two hub genes are Cyp1a1(AUC: 0.769 (0.580 ‐ 0.958)) and Chrnb4(AUC: 0.707 (0.511 ‐ 0.902)) and both genes are strongly correlated with immune cell response. A total of 40 miRNA‐mRNA pairs, including 35 miRNAs and 2 mRNAs, were predicted by Targetscan. Three Cyp1a1 transcription factors were predicted by TRRUST database. Conclusion We demonstrated that two genes, Chrnb4 and Ccyp1a11, are the potential genes involved in nicotine modulating DCM, and these two genes may regulate DCM’s immune response through miRNA‐mRNA mode. It provides new potential targets for preventing DCM in smoking population.
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