Downregulation of WDR20 due to loss of 14q is involved in the malignant transformation of clear cell renal cell carcinoma

IFI16 promotes the progression of clear cell renal cell carcinoma through the IL6/PI3K/AKT axis

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is a common disease in the urinary system, with a high incidence and poor prognosis in advanced stages. Although γ-interferon-inducible protein 16 (IFI16) has been reported to play a role in various tumors, its involvement in ccRCC remains poorly documented, and the molecular mechanisms are not yet clear.

METHODS

We conducted bioinformatics analysis to study the expression of IFI16 in ccRCC using public databases. Additionally, we analyzed and validated clinical specimens that we collected. Subsequently, we explored the impact of IFI16 on ccRCC cell proliferation, migration, and invasion through in vitro and in vivo experiments. Furthermore, we predicted downstream molecules and pathways using transcriptome analysis and confirmed them through follow-up experimental validation.

RESULTS

IFI16 was significantly upregulated in ccRCC tissue and correlated with poor patient prognosis. In vitro, IFI16 promoted ccRCC cell proliferation, migration, and invasion, while in vivo, it facilitated subcutaneous tumor growth and the formation of lung metastatic foci. Knocking down IFI16 suppressed its oncogenic function. At the molecular level, IFI16 promoted the transcription and translation of IL6, subsequently activating the PI3K/AKT signaling pathway and inducing epithelial-mesenchymal transition (EMT).

CONCLUSION

IFI16 induced EMT through the IL6/PI3K/AKT axis, promoting the progression of ccRCC.

SERPINE1 and its co-expressed genes are associated with the progression of clear cell renal cell carcinoma

BACKGROUND

Clear cell renal cell carcinoma(ccRCC) is a frequently occurring malignant tumor of the urinary system. Despite extensive research, the regulatory mechanisms underlying the pathogenesis and progression of ccRCC remain largely unknown.

METHODS

We downloaded 5 ccRCC expression profiles from the Gene Expression Omnibus (GEO) database and obtained the list of differentially expressed genes (DEGs). Using String and Cytoscape tools, we determined the hub genes of ccRCC, and then analyzed their relationship with ccRCC patient survival. Ultimately, we identified SERPINE1 as a prognostic factor in ccRCC. Meanwhile, we confirmed the role of SERPINE1 in 786-O cells by cell transfection and in vitro experiments.

RESULTS

Our analysis yielded a total of 258 differentially expressed genes, comprising 105 down-regulated genes and 153 up-regulated genes. Survival analysis of SERPINE1 expression in The Cancer Genome Atlas (TCGA) confirmed its association with the increase of tumor grade, lymph node metastasis, and tumor stage, as well as with shorter survival. Furthermore, we found that SERPINE1 expression levels were associated with CD8 + T cells, CD4 + T cells, B cells, macrophages, neutrophils, and dendritic cells. Cell experiments showed that knockdown SERPINE1 expression could inhibit the proliferation, migration and invasion of ccRCC cells. Among the co-expressed genes with the highest correlation, ITGA5, SLC2A3, SLC2A14, SHC1, CEBPB, and ADA were overexpressed and associated with shorter overall survival (OS) in ccRCC.

CONCLUSIONS

In this study, we identified hub genes that are strongly related to ccRCC, and highlights the potential utility of overexpressed SERPINE1 and its co-expressed genes could be used as prognostic and diagnostic biomarkers in ccRCC.

PBX1, EMCN and ERG are associated with the sub-clusters and the prognosis of VHL mutant clear cell renal cell carcinoma

The molecular heterogeneity of primary clear cell renal cell carcinoma (ccRCC) has been reported. However, the classifications of Von Hippel-Lindau (VHL) mutant ccRCC are unclear. Here, VHL mutant ccRCC from The Cancer Genome Atlas and E-MTAB-1980 datasets were divided into two sub-clusters through non-negative matrix factorization algorithm. Most VHL mutant ccRCC patients in sub-cluster2 were with pathological T1 stage and VHL mutant ccRCC patients in sub-cluster1 were with decreased overall survival. DNA replication and homologous recombination scores were higher, while, WNT signaling pathway and regulation of autophagy scores were lower in sub-cluster1 VHL mutant ccRCC. Moreover, PBX1 transcriptional scores and mRNA expressions were lower in sub-cluster1 VHL mutant ccRCC patients and were associated with the overall survival of VHL mutant ccRCC. Furthermore, PBX1 associated genes EMCN and ERG were down-regulated in sub-cluster1 VHL mutant ccRCC and overall survival was decreased in EMCN or ERG lowly expressed VHL mutant ccRCC patients. Also, PBX1 and EMCN were down-regulated in ccRCC tissues, compared with normal kidney tissues. At last, we constructed risk models based on PBX1, EMCN and EGR expression features. With the increase of the risk score, the number of death of VHL mutant ccRCC patients was increased.

Shuttling of cellular proteins between the plasma membrane and nucleus (Review)

Recently accumulated evidence has indicated that the nucleomembrane shuttling of cellular proteins is common, which provides new insight into the subcellular translocation and biological functions of proteins synthesized in the cytoplasm. The present study aimed to clarify the trafficking of proteins between the plasma membrane and nucleus. These proteins primarily consist of transmembrane receptors, membrane adaptor proteins, adhesive proteins, signal proteins and nuclear proteins, which contribute to proliferation, apoptosis, chemoresistance, adhesion, migration and gene expression. The proteins frequently undergo cross-talk, such as the interaction of transmembrane proteins with signal proteins. The transmembrane proteins undergo endocytosis, infusion into organelles or proteolysis into soluble forms for import into the nucleus, while nuclear proteins interact with membrane proteins or act as receptors. The nucleocytosolic translocation involves export or import through nuclear membrane pores by importin or exportin. Nuclear proteins generally interact with other transcription factors, and then binding to the promoter for gene expression, while membrane proteins are responsible for signal initiation by binding to other membrane and/or adaptor proteins. Protein translocation occurs in a cell-specific manner and is closely linked to cellular biological events. The present review aimed to improve understanding of cytosolic protein shuttling between the plasma membrane and nucleus and the associated signaling pathways.

An 8-gene DNA methylation signature predicts the recurrence risk of cervical cancer

Objective

This study examined the predictive utility of DNA methylation for cervical cancer recurrence.

Methods

DNA methylation and RNA expression data for patients with cervical cancer were downloaded from The Cancer Genome Atlas. Differentially methylated genes (DMGs) and differentially expressed genes were screened and extracted via correlation analysis. A support vector machine (SVM)-based recurrence prediction model was established using the selected DMGs. Cox regression analysis and receiver operating characteristic curve analysis were used for self-evaluation. The Gene Expression Omnibus (GEO) database was applied for external validation. Functional enrichment was determined using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses.

Results

An eight-gene DNA methylation signature identified patients with a high risk of recurrence (area under the curve = 0.833). The SVM score was an independent risk factor for recurrence (hazard ratio [HR] = 0.418; 95% confidence interval [CI] = 0.26–0.67). The independent GEO database analysis further supported the result.

Conclusion

An eight-gene DNA methylation signature predictive of cervical cancer recurrence was identified in this study, and this signature may help identify patients at high risk of recurrence and improve clinical treatment.

Development of a four-gene prognostic model for clear cell renal cell carcinoma based on transcriptome analysis

This study aimed to develop a prognostic model for clear cell renal cell carcinoma (ccRCC) based on transcriptome analysis. We screened Gene Expression Omnibus (GEO) database and the Cancer Genome Atlas (TCGA) database for gene expression data and clinical characteristics of ccRCC. After differentially expression analysis, we identified 533 key genes of the development of ccRCC. Next, a weighted gene co-expression network analysis (WGCNA) was executed to investigate the association between differentially expressed genes and clinical characteristics. Then, based on protein-protein interaction (PPI) network, least absolute shrinkage and selection operator (LASSO) regression and Cox regression, a four-gene (COL4A5, ABCB1, NR3C2 and PLG) prognostic model has been constructed in TCGA training cohort. Finally, we examined the predictive power of this model with survival analysis and receiver operating characteristic (ROC) curve both in training cohort and in validation cohorts. And we found this model was significantly correlated with infiltrating immune cells. The four-gene prognosis model could be a potential prediction tool with high accuracy and reliability for ccRCC patients.

Cross-Disorder Analysis of De Novo Variants Increases the Power of Prioritising Candidate Genes

De novo variants (DNVs) are critical to the treatment of neurodevelopmental disorders (NDDs). However, effectively identifying candidate genes in small cohorts is challenging in most NDDs because of high genetic heterogeneity. We hypothesised that integrating DNVs from multiple NDDs with genetic similarity can significantly increase the possibility of prioritising the candidate gene. We catalogued 66,186 coding DNVs in 50,028 individuals with nine types of NDDs in cohorts with sizes spanning from 118 to 31,260 from Gene4Denovo database to validate this hypothesis. Interestingly, we found that integrated DNVs can effectively increase the number of prioritised candidate genes for each disorder. We identified 654 candidate genes including 481 shared candidate genes carrying putative functional variants in at least two disorders. Notably, 13.51% (65/481) of shared candidate genes were prioritised only via integrated analysis including 44.62% (29/65) genes validated in recent large cohort studies. Moreover, we estimated that more novel candidate genes will be prioritised with the increase in cohort size, in particular for some disorders with high putative functional DNVs per individual. In conclusion, integrated DNVs may increase the power of prioritising candidate genes, which is important for NDDs with small cohort size.

Bioinformatics analysis and verification of gene targets for renal clear cell carcinoma

BACKGROUND

It is estimated that there are 338,000 new renal-cell carcinoma releases every year in the world. Renal cell carcinoma (RCC) is a heterogeneous tumor, of which more than 70% is clear cell renal cell carcinoma (ccRCC). It is estimated that about 30% of new renal-cell carcinoma patients have metastases at the time of diagnosis. However, the pathogenesis of renal clear cell carcinoma has not been elucidated. Therefore, it is necessary to further study the pathogenesis of ccRCC.

METHODS

Two expression profiling datasets (GSE68417, GSE71963) were downloaded from the GEO database. Differentially expressed genes (DEGs) between ccRCC and normal tissue samples were identified by GEO2R. Functional enrichment analysis was made by the DAVID tool. Protein-protein interaction (PPI) network was constructed. The hub genes were excavated. The clustering analysis of expression level of hub genes was performed by UCSC (University of California Santa Cruz) Xena database. The hub gene on overall survival rate (OS) in patients with ccRCC was performed by Kaplan-Meier Plotter. Finally, we used the ccRCC renal tissue samples to verify the hub genes.

RESULTS

1182 common DEGs between the two datasets were identified. The results of GO and KEGG analysis revealed that variations in were predominantly enriched in intracellular signaling cascade, oxidation reduction, intrinsic to membrane, integral to membrane, nucleoside binding, purine nucleoside binding, pathways in cancer, focal adhesion, cell adhesion molecules. 10 hub genes ITGAX, CD86, LY86, TLR2, TYROBP, FCGR2A, FCGR2B, PTPRC, ITGB2, ITGAM were identified. FCGR2B and TYROBP were negatively correlated with the overall survival rate in patients with ccRCC (P < 0.05). RT-qPCR analysis showed that the relative expression levels of CD86, FCGR2A, FCGR2B, TYROBP, LY86, and TLR2 were significantly higher in ccRCC samples, compared with the adjacent renal tissue groups.

CONCLUSIONS

In summary, bioinformatics technology could be a useful tool to predict the progression of ccRCC. In addition, there are DEGs between ccRCC tumor tissue and normal renal tissue, and these DEGs might be considered as biomarkers for ccRCC.

miR-224, miR-147b and miR-31 associated with lymph node metastasis and prognosis for lung adenocarcinoma by regulating PRPF4B, WDR82 or NR3C2

Background

The present study is to screen lymph node metastasis-related microRNAs (miRNAs) in lung adenocarcinoma (LUAD) and uncover their underlying mechanisms.

Methods

The miRNA microarray dataset was collected from the Gene Expression Omnibus database under accession number GSE64859. The differentially expressed miRNAs (DEMs) were identified using a t-test. Target genes of DEMs were predicted through the miRWalk2.0 database. The function of these target genes was annotated with the clusterProfiler and the Database for Annotation, Visualization and Integrated Discovery (DAVID) tools. Protein-protein interaction network was established using the STRING database to extract hub target genes. The expressions and associations with survival and lymph node metastasis of miRNAs and target genes were validated by analysis of The Cancer Genome Atlas (TCGA) dataset.

Results

Eight DEMs were identified between lymph node metastasis and non-metastasis samples of GSE64859 dataset. miRNA-target gene pairs were predicted between six DEMs and 251 target genes (i.e. hsa-miR-224-PRPF4B, hsa-miR-147b-WDR82 and hsa-miR-31-NR3C2). The clusterProfiler analysis showed WDR82 was involved in the mRNA surveillance pathway, while the GO enrichment analysis using the DAVID database indicated PRPF4B participated in the protein phosphorylation and NR3C2 was related with the transcription, DNA-templated. WDR82 and PRPF4B may be hub genes because they could interact with others. Two DEMs (miR-31-5p and miR-31-3p) and 45 target genes (including PRPF4B and NR3C2) were significantly associated with overall survival. The expressions of miR-224 and miR-147b were validated to be upregulated, while WDR82, PRPF4B and NR3C2 were downregulated in lymph node metastasis samples of TCGA datasets compared with non-metastasis samples. Also, there were significantly negative expression correlations between miR-147b and WDR82, between miR-224 and PRPF4B, as well as between miR-31 and NR3C2 in LUAD samples.

Conclusions

The present study identified several crucial miRNA-mRNA interaction pairs, which may provide novel explanations for the lymph node metastasis and poor prognosis for LUAD patients.

Integrated bioinformatics analysis for the identification of potential key genes affecting the pathogenesis of clear cell renal cell carcinoma

Clear cell renal cell carcinoma (CCRCC) is a typical type of RCC with the worst prognosis among the common epithelial neoplasms of the kidney. However, its molecular pathogenesis remains unknown. Therefore, the aim of the present study was to screen for effective and potential pathogenic biomarkers of CCRCC. The gene expression profile of the GSE16441, GSE36895, GSE40435, GSE46699, GSE66270 and GSE71963 datasets were downloaded from the Gene Expression Omnibus database. First, the limma package in R language was used to identify differentially expressed genes (DEGs) in each dataset. The robust and strong DEGs were explored using the robust rank aggregation method. A total of 980 markedly robust DEGs were identified (429 upregulated and 551 downregulated). According to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, these DEGs exhibited an obvious enrichment in various cancer-related biological pathways and functions. The Search Tool for the Retrieval of Interacting Genes/Proteins database was used for the construction of a protein-protein interaction (PPI) network, the Cytoscape MCODE plug-in for module analysis and the cytoHubba plug-in to identify hub genes from the aforementioned DEGs. A total of four key modules were identified in the PPI network. A total of six hub genes, including C-X-C motif chemokine ligand 12, bradykinin receptor B2, adenylate cyclase 7, calcium sensing receptor (CASR), kininogen 1 and lysophosphatidic acid receptor 5, were identified. The DEG results of the hub genes were verified using The Cancer Genome Atlas database, and CASR was found to be significantly associated with the prognosis of patients with CCRCC. In conclusion, the present study provided new insight and potential biomarkers for the diagnosis and prognosis of CCRCC.

A BAG's life: Every connection matters in cancer

The members of the BCL-2 associated athanogene (BAG) family participate in the regulation of a variety of interrelated physiological processes, such as autophagy, apoptosis, and protein homeostasis. Under normal circumstances, the six BAG members described in mammals (BAG1-6) principally assist the 70 kDa heat-shock protein (HSP70) in protein folding; however, their role as oncogenes is becoming increasingly evident. Deregulation of the BAG multigene family has been associated with cell transformation, tumor recurrence, and drug resistance. In addition to BAG overexpression, BAG members are also involved in many oncogenic protein-protein interactions (PPIs). As such, either the inhibition of overloading BAGs or of specific BAG-client protein interactions could have paramount therapeutic value. In this review, we will examine the role of each BAG family member in different malignancies, focusing on their modular structure, which enables interaction with a variety of proteins to exert their pro-tumorigenic role. Lastly, critical remarks on the unmet needs for proposing effective BAG inhibitors will be pointed out.

MicroRNA-654-5p suppresses ovarian cancer development impacting on MYC, WNT and AKT pathways

Ovarian cancer is the most lethal gynecological malignancy due to the silent nature on its early onset and the rapid acquisition of drug resistance. Histologically heterogeneous, it includes several subtypes with different mutational landscapes, hampering the development of effective targeted therapies. Non-coding RNAs are emerging as potential new therapeutic targets in cancer. To search for a microRNA signature related to ovarian carcinomas and study its potential as effective targeted therapy, we examined the expression of 768 miRNA in a large collection of tumor samples and found miR-654-5p to be infraexpressed in ovarian serous carcinomas, the most common and aggressive type. Restoration of miR-654-5p levels reduced tumor cell viability in vitro and in vivo and impaired sphere formation capacity and viability of ovarian cancer patient-derived ascitic cells ex vivo. CDCP1 and PLAGL2 oncogenes were found to be the most relevant direct miR-654-5p targets and both genes convey in a molecular signature associated with key cancer pathways relevant to ovarian tumorigenesis, such as MYC, WNT and AKT pathways. Together, we unveiled the tumor suppressor function of miR-654-5p, suggesting that its restoration or co-targeting of CDCP1 and PLAGL2 may be an effective therapeutic approach for ovarian cancer.

Multi-omic tumor data reveal diversity of molecular mechanisms that correlate with survival

Outcomes for cancer patients vary greatly even within the same tumor type, and characterization of molecular subtypes of cancer holds important promise for improving prognosis and personalized treatment. This promise has motivated recent efforts to produce large amounts of multidimensional genomic (multi-omic) data, but current algorithms still face challenges in the integrated analysis of such data. Here we present Cancer Integration via Multikernel Learning (CIMLR), a new cancer subtyping method that integrates multi-omic data to reveal molecular subtypes of cancer. We apply CIMLR to multi-omic data from 36 cancer types and show significant improvements in both computational efficiency and ability to extract biologically meaningful cancer subtypes. The discovered subtypes exhibit significant differences in patient survival for 27 of 36 cancer types. Our analysis reveals integrated patterns of gene expression, methylation, point mutations, and copy number changes in multiple cancers and highlights patterns specifically associated with poor patient outcomes.

Bioinformatic identification of key genes and analysis of prognostic values in clear cell renal cell carcinoma

The present study aimed to identify new key genes as potential biomarkers for the diagnosis, prognosis or targeted therapy of clear cell renal cell carcinoma (ccRCC). Three expression profiles (GSE36895, GSE46699 and GSE71963) were collected from Gene Expression Omnibus. GEO2R was used to identify differentially expressed genes (DEGs) in ccRCC tissues and normal samples. The Database for Annotation, Visualization and Integrated Discovery was utilized for functional and pathway enrichment analysis. STRING v10.5 and Molecular Complex Detection were used for protein-protein interaction (PPI) network construction and module analysis, respectively. Regulation network analyses were performed with the WebGestal tool. UALCAN web-portal was used for expression validation and survival analysis of hub genes in ccRCC patients from The Cancer Genome Atlas (TCGA). A total of 65 up- and 164 downregulated genes were identified as DEGs. DEGs were enriched with functional terms and pathways compactly related to ccRCC pathogenesis. Seventeen hub genes and one significant module were filtered out and selected from the PPI network. The differential expression of hub genes was verified in TCGA patients. Kaplan-Meier plot showed that high mRNA expression of enolase 2 (ENO2) was associated with short overall survival in ccRCC patients (P=0.023). High mRNA expression of cyclin D1 (CCND1) (P<0.001), fms related tyrosine kinase 1 (FLT1) (P=0.004), plasminogen (PLG) (P<0.001) and von Willebrand factor (VWF) (P=0.008) appeared to serve as favorable factors in survival. These findings indicate that the DEGs may be key genes in ccRCC pathogenesis and five genes, including ENO2, CCND1, PLT1, PLG and VWF, may serve as potential prognostic biomarkers in ccRCC.

Characterization of WDR20: A new regulator of the ERAD machinery

ERAD is an important process of protein quality control that eliminates misfolded or unassembled proteins from ER. Before undergoing proteasome degradation, the misfolded proteins are dislocated from ER membrane into cytosol, which requires the AAA ATPase p97/VCP and its cofactor, the NPL4-UFD1 dimer. Here, we performed a CRISPR-based screen and identify many candidates for ERAD regulation. We further confirmed four proteins, FBOX2, TRIM6, UFL1 and WDR20, are novel regulators for ERAD. Then the molecular mechanism for WDR20 in ERAD is further characterized. Depletion of WDR20 inhibits the degradation of TCRα, a typical ERAD substrate, while WDR20 overexpression reduces TCRα protein level. WDR20 associates with TCRα and central regulators of the ERAD system, p97, GP78 and HRD1. A portion of WDR20 localizes to the ER-containing microsomal membrane. WDR20 expression increases TCRα ubiquitination, and HRD1 E3 ligase is essential for the process. WDR20 seems to serve as an adaptor protein to mediate the interaction between p97 and TCRα. Our study provides novel candidates and reveals an unexpected role of WDR20 in ERAD regulation.

Downregulation of WDR20 due to loss of 14q is involved in the malignant transformation of clear cell renal cell carcinoma

Previously, we reported that genomic loss of 14q occurs more frequently in high-grade than in low-grade clear cell renal cell carcinomas (ccRCCs), and has a significant impact on the levels of expression of genes located in this region, suggesting that such genes may be involved in the malignant transformation of ccRCCs. Here, we found that six of the genes located in the minimal common region of 14q loss were significantly downregulated in high-grade ccRCCs due to copy number loss. Using a dataset from The Cancer Genome Atlas Research Network, we found that downregulation of one of these six genes, WDR20, was significantly associated with poorer outcome in patients with ccRCC, suggesting that WDR20 downregulation may be involved in the malignant transformation of ccRCCs. In functional assays, exogenous WDR20 significantly inhibited the growth of RCC cell lines and induced apoptosis. Interestingly, the phosphorylation levels of ERK and protein kinase B/AKT, which reportedly contribute to the malignant phenotype of RCC cells, were clearly reduced by exogenous expression of WDR20. Thus, our data suggest that downregulation of WDR20 due to 14q loss may be involved in the malignant transformation of ccRCCs, in part through activation of the ERK and protein kinase B/AKT pathways.