Weighted gene co‑expression network analysis for identifying hub genes in association with prognosis in Wilms tumor

Cancer predisposition signaling in Beckwith-Wiedemann Syndrome drives Wilms tumor development

BACKGROUND

Wilms tumor (WT) exhibits structural and epigenetic changes at chromosome 11p15, which also cause Beckwith-Wiedemann Syndrome (BWS). Children diagnosed with BWS have increased risk for WT. The aim of this study is to identify the molecular signaling signatures in BWS driving these tumors.

METHODS

We performed whole exome sequencing, methylation array analysis, and gene expression analysis on BWS-WT samples. Our data were compared to publicly available nonBWS data. We categorized WT from BWS and nonBWS patients by assessment of 11p15 methylation status and defined 5 groups- control kidney, BWS-nontumor kidney, BWS-WT, normal-11p15 nonBWS-WT, altered-11p15 nonBWS-WT.

RESULTS

BWS-WT samples showed single nucleotide variants in BCORL1, ASXL1, ATM and AXL but absence of recurrent gene mutations associated with sporadic WT. We defined a narrow methylation range stratifying nonBWS-WT samples. BWS-WT and altered-11p15 nonBWS-WT showed enrichment of common and unique molecular signatures based on global differential methylation and gene expression analysis. CTNNB1 overexpression and broad range of interactions were seen in the BWS-WT interactome study.

CONCLUSION

While WT predisposition in BWS is well-established, as are 11p15 alterations in nonBWS-WT, this study focused on stratifying tumor genomics by 11p15 status. Further investigation of our findings may identify novel therapeutic targets in WT oncogenesis.

The potential key genes and pathways associated with Wilms tumor in quest of proper candidates for diagnostic and therapeutic purposes

To designate the probable most important differentially expressed genes and genetic pathways in Wilms tumor and assess their expression and diagnostic potential by RT-PCR and statistical analysis. Systematic review of the literature and various bioinformatics analysis was carried out to gather and narrow down data. The expression of end-resulting genes was compared in Wilms tumor and normal tissue samples using RT-PCR. Statistical tests reported the diagnostic accuracy of genes and their correlation with clinicopathological features. Four genes including CDH1, NCAM1, EGF, and IGF2 were designated. The panel combining them has 100% sensitivity and specificity in differentiating tumors from normal tissue. Eight pathways, most involved in cell-cell and cell-basal matrix junction interactions, were found to be associated with disease pathogenesis. The suggested genes should undergo further evaluation to be validated as diagnostic biomarkers. Further research on the eight proposed pathways is recommended.

Implications of cell division cycle associated 4 on the Wilm's tumor cells viability via AKT/mTOR signaling pathway

Objective

The aim of present report was to elucidate the effect of cell division cycle associated 4 (CDCA4) on the proliferation and apoptosis of Wilm’s tumor cells, and to further evaluate its underlying mechanism.

Methods

The expression profiles of CDCA4 and clinical information of Wilm’s tumor patients were obtained from public Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database portal. Real-time qPCR and western blot analyses were utilized to determine the expression levels of CDCA4. Gain- and loss-of-function of CDCA4 assays were conducted with transfection technology to investigate the biological role of CDCA4 in Wilm’s tumor cells. Cell counting kit 8 and flow cytometer assays were employed to examine the effect of CDCA4 on the cells proliferation and apoptosis. Protein expression levels of indicated markers in each group of Wilm’s tumor cells were measured by western blot.

Results

The transcriptional expression of CDCA4 was drastically upregulated in Wilm’s tumor tissues according to the public TARGET database and in Wilm’s tumor cells. The cells viability was remarkably reduced whereas the cells apoptosis was increased in CDCA4-knockdown group compared with negative control group. However, CDCA4-overexpression group promoted the cells proliferation and suppressed the cells apoptosis. Furthermore, the protein expression levels of p-AKT, p-mTOR, and Cyclin D1 were significantly reduced after depletion of CDCA4, whereas overexpression of CDCA4 dramatically elevated these markers’ expression levels.

Conclusions

CDCA4 is highly expressed in Wilm’s tumor and promoted the proliferation whereas inhibited the apoptosis of Wilm’s tumor cells through activating the AKT/mTOR signaling pathway.

Identification of CDC20 as a Novel Biomarker in Diagnosis and Treatment of Wilms Tumor

Objective: Wilms tumor (WT) is a common malignant solid tumor in children. Many tumor biomarkers have been reported; however, there are poorly targetable molecular mechanisms which have been defined in WT. This study aimed to identify the oncogene in WT and explore the potential mechanisms. Methods: Differentially expressed genes (DEGs) in three independent RNA-seq datasets were downloaded from The Cancer Genome Atlas data portal and the Gene Expression Omnibus database (GSE66405 and GSE73209). The common DEGs were then subjected to Gene Ontology enrichment analysis, protein-protein interaction (PPI) network analysis, and gene set enrichment analysis. The protein expression levels of the hub gene were analyzed by immunohistochemical analysis and Western blotting in a 60 WT sample. The univariate Kaplan-Meier analysis for overall survival was performed, and the log-rank test was utilized. A small interfering RNA targeting cell division cycle 20 (CDC20) was transfected into G401 and SK-NEP-1 cell lines. The Cell Counting Kit-8 assay and wound healing assay were used to observe the changes in cell proliferation and migration after transfection. Flow cytometry was used to detect the effect on the cell cycle. Western blot was conducted to study the changes of related functional proteins. Results: We commonly identified 44 upregulation and 272 downregulation differentially expressed genes in three independent RNA-seq datasets. Gene and pathway enrichment analyses of the regulatory networks involving hub genes suggested that cell cycle changes are crucial in WT. The top 15 highly connected genes were found by PPI network analysis. Furthermore, we demonstrated that one candidate biomarker, CDC20, for the diagnosis of WT was detected, and its high expression predicted poor prognosis of WT patients. Moreover, the area under the curve value obtained by receiver operating characteristic curve analysis from paired WT samples was 0.9181. Finally, we found that the suppression of CDC20 inhibited proliferation and migration and resulted in G2/M phase arrest in WT cells. The mechanism may be involved in increasing the protein level of securin, cyclin B1, and cyclin A Conclusion: Our results suggest that CDC20 could serve as a candidate diagnostic and prognostic biomarker for WT, and suppression of CDC20 may be a potential approach for the prevention and treatment of WT.

Identification of the potential novel biomarkers as susceptibility gene for Wilms tumor

Background

Wilms tumor (WT) is the most common malignant renal tumor in children. The aim of this study was to identify potential susceptibility gene of WT for better prognosis.

Methods

Weighted gene coexpression network analysis is used for the detection of clinically important biomarkers associated with WT.

Results

In the study, 59 tissue samples from National Cancer Institute were pretreated for constructing gene co-expression network, while 224 samples also downloaded from National Cancer Institute were used for hub gene validation and module preservation analysis. Three modules were found to be highly correlated with WT, and 44 top hub genes were identified in these key modules eventually. In addition, both the module preservation analysis and gene validation showed ideal results based on other dataset with 224 samples. Meanwhile, Functional enrichment analysis showed that genes in module were enriched to sister chromatid cohesion, cell cycle, oocyte meiosis.

Conclusion

In summary, we established a gene co-expression network to identify 44 hub genes are closely to recurrence and staging of WT, and 6 of these hub genes was closely related to the poor prognosis of patients. Our findings revealed that those hub genes may be used as potential susceptibility gene for clinical diagnosis and prognosis of this tumor.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08034-w.

Network-Based Analysis Reveals Association of FOXE1 Gene Polymorphisms in Thyroid Cancer Patients; A Case-Control Study in Southeast of Iran

Thyroid cancer (TC) is the mainly frequent endocrine cancer by different incidence rate in worldwide. However, early prediction of  this cancer is still challenging due to the unclear pathogenicity. In this study with the aid of systems biology approach, performed a holistic study on GSE65144 dataset containing anaplastic thyroid carcinoma tissues. Co-expression network analysis by WGCNA suggested that highly preserved turquoise module with 1,480 genes was significantly correlated to TC. Most of the top 54 hub-genes of this module are functionality correlated to thyroid hormone generation (GO:0006590). Of these 54 hub-genes, FOXE1 has been reported previously to contain mutation asosiated to TC and chosen for experimental validation step. To this end, we conducted a case-control study including 81 TC patients and 165 controls individuals to evaluate the effects of FOXE1 functional polymorphisms (rs1867277) on the development of TC in Sistan and Balouchestan province of Iran. The polymorphisms of FOXE1 gene (rs1867277) assessed by tetra-ARMS PCR technique. Homozygous (GG) and (AA) variant of rs1867277 polymorphism were detected in 26 (32.1%) and 15 (18.5 %) of TC patients, and 66 (40.0%), and 15 (9.1%) in controls, respectively (p-value= 0.03, OR= 2.53). The A allele frequency was 70 (43.2%) in TC patients and 114 (34.5%) in controls (p-value= 0.06, OR= 1.44). Overall, our results suggested that FOXE1 gene could be used as a prognostic marker in TC and also provides information related to FOXE1 functional polymorphisms (rs1867277) in Sistan and Balouchestan province of Iran.
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Transcriptomic determinants of the response of ST-111 Pseudomonas aeruginosa AG1 to ciprofloxacin identified by a top-down systems biology approach

Pseudomonas aeruginosa is an opportunistic pathogen that thrives in diverse environments and causes a variety of human infections. Pseudomonas aeruginosa AG1 (PaeAG1) is a high-risk sequence type 111 (ST-111) strain isolated from a Costa Rican hospital in 2010. PaeAG1 has both blaVIM-2 and blaIMP-18 genes encoding for metallo-β-lactamases, and it is resistant to β-lactams (including carbapenems), aminoglycosides, and fluoroquinolones. Ciprofloxacin (CIP) is an antibiotic commonly used to treat P. aeruginosa infections, and it is known to produce DNA damage, triggering a complex molecular response. In order to evaluate the effects of a sub-inhibitory CIP concentration on PaeAG1, growth curves using increasing CIP concentrations were compared. We then measured gene expression using RNA-Seq at three time points (0, 2.5 and 5 h) after CIP exposure to identify the transcriptomic determinants of the response (i.e. hub genes, gene clusters and enriched pathways). Changes in expression were determined using differential expression analysis and network analysis using a top–down systems biology approach. A hybrid model using database-based and co-expression analysis approaches was implemented to predict gene–gene interactions. We observed a reduction of the growth curve rate as the sub-inhibitory CIP concentrations were increased. In the transcriptomic analysis, we detected that over time CIP treatment resulted in the differential expression of 518 genes, showing a complex impact at the molecular level. The transcriptomic determinants were 14 hub genes, multiple gene clusters at different levels (associated to hub genes or as co-expression modules) and 15 enriched pathways. Down-regulation of genes implicated in several metabolism pathways, virulence elements and ribosomal activity was observed. In contrast, amino acid catabolism, RpoS factor, proteases, and phenazines genes were up-regulated. Remarkably, > 80 resident-phage genes were up-regulated after CIP treatment, which was validated at phenomic level using a phage plaque assay. Thus, reduction of the growth curve rate and increasing phage induction was evidenced as the CIP concentrations were increased. In summary, transcriptomic and network analyses, as well as the growth curves and phage plaque assays provide evidence that PaeAG1 presents a complex, concentration-dependent response to sub-inhibitory CIP exposure, showing pleiotropic effects at the systems level. Manipulation of these determinants, such as phage genes, could be used to gain more insights about the regulation of responses in PaeAG1 as well as the identification of possible therapeutic targets. To our knowledge, this is the first report of the transcriptomic analysis of CIP response in a ST-111 high-risk P. aeruginosa strain, in particular using a top-down systems biology approach.

Multi-Omics Integration Reveals a Competitive Endogenous RNAs Network for the Identification of Progression Biomarkers and the Stratification of Patients Diagnosed With Nephroblastoma

Specific types of nephroblastoma (Wilms' tumor, WT) are known to associate with poor overall survival. Emerging experimental evidence has demonstrated that competitive endogenous RNA (ceRNA) networks have important roles in regulating cancer occurrence, but the roles of ceRNAs in regulating the WT progression and the patient outcomes remain unclear. Using the multi-omics data of 132 WT patients collected from TARGET database, an integration analysis pipeline was performed to construct a highly reliable ceRNA network. As results, a total of 147 nodes (116 mRNAs, 15 miRNAs, and 16 lncRNAs) were identified and used to explore the underlying mechanism for WT progression. WGCNA analysis further identified several prognostic molecules, including hsa-mir-93, LINC00087 and RP5-1086K13, that significantly associated with the overall survival rate. And, enrichment analysis verified the participation of these molecules in tumor-related pathways, such as those controlling autophagy and cadherin-mediated adhesion. Importantly, the WT patients were classified into three categories according to the ceRNA network, which significantly correlated with the overall survival. In conclusion, the ceRNA network could be a promising tool to further validate the prognostic biomarkers and categories of patients diagnosed with WT.

RNA-Sequencing, Connectivity Mapping, and Molecular Docking to Investigate Ligand-Protein Binding for Potential Drug Candidates for the Treatment of Wilms Tumor

Background

Wilms tumor, or nephroblastoma, is a malignant pediatric embryonal renal tumor that has a poor prognosis. This study aimed to use bioinformatics data, RNA-sequencing, connectivity mapping, molecular docking, and ligand-protein binding to identify potential targets for drug therapy in Wilms tumor.

Material/Methods

Wilms tumor and non-tumor samples were obtained from high throughput gene expression databases, and differentially expressed genes (DEGs) were analyzed using the voom method in the limma package. The overlapping DEGs were obtained from the intersecting drug target genes using the Connectivity Map (CMap) database, and systemsDock was used for molecular docking. Gene databases were searched for gene expression profiles for complementary analysis, analysis of clinical significance, and prognosis analysis to refine the study.

Results

From 177 cases of Wilms tumor, there were 648 upregulated genes and 342 down-regulated genes. Gene Ontology (GO) enrichment analysis showed that the identified DEGs that affected the cell cycle. After obtaining 21 candidate drugs, there were seven overlapping genes with 75 drug target genes and DEGs. Molecular docking results showed that relatively high scores were obtained when retinoic acid and the cyclin-dependent kinase inhibitor, alsterpaullone, were docked to the overlapping genes. There were significant standardized mean differences for three overlapping genes, CDK2, MAP4K4, and CRABP2. However, four upregulated overlapping genes, CDK2, MAP4K4, CRABP2, and SIRT1 had no prognostic significance.

Conclusions

RNA-sequencing, connectivity mapping, and molecular docking to investigate ligand-protein binding identified retinoic acid and alsterpaullone as potential drug candidates for the treatment of Wilms tumor.

Depicting the genetic architecture of pediatric cancers through an integrative gene network approach

The genetic etiology of childhood cancers still remains largely unknown. It is therefore essential to develop novel strategies to unravel the spectrum of pediatric cancer genes. Statistical network modeling techniques have emerged as powerful methodologies for enabling the inference of gene-disease relationship and have been performed on adult but not pediatric cancers. We performed a deep multi-layer understanding of pan-cancer transcriptome data selected from the Treehouse Childhood Cancer Initiative through a co-expression network analysis. We identified six modules strongly associated with pediatric tumor histotypes that were functionally linked to developmental processes. Topological analyses highlighted that pediatric cancer predisposition genes and potential therapeutic targets were central regulators of cancer-histotype specific modules. A module was related to multiple pediatric malignancies with functions involved in DNA repair and cell cycle regulation. This canonical oncogenic module gathered most of the childhood cancer predisposition genes and clinically actionable genes. In pediatric acute leukemias, the driver genes were co-expressed in a module related to epigenetic and post-transcriptional processes, suggesting a critical role of these pathways in the progression of hematologic malignancies. This integrative pan-cancer study provides a thorough characterization of pediatric tumor-associated modules and paves the way for investigating novel candidate genes involved in childhood tumorigenesis.

Identification of Key Modules, Hub Genes, and Noncoding RNAs in Chronic Rhinosinusitis with Nasal Polyps by Weighted Gene Coexpression Network Analysis

Chronic rhinosinusitis with nasal polyps (CRSwNP) is a chronic inflammatory disease with relatively easy recurrence. However, the precise molecular mechanisms of this disease are poorly known. Based on gene sequencing data obtained from the Gene Expression Omnibus (GEO) database, we constructed coexpression networks by weighted gene coexpression network analysis (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed by the Database for Annotation, Visualization, and Integrated Discovery (DAVID). The core gene of pathogenesis, CRSwNP, was screened by protein-protein interaction data (PPI) from the HPRD database. Unsupervised clustering was applied to screen hub genes related to the phenotype of CRSwNP. Blue and turquoise modules were found to be most significantly related to the pathogenicity of CRSwNP. Functional enrichment analysis showed that cell proliferation in the blue modules, the apoptotic process in the turquoise module, and the cancer pathway in both modules were mostly significantly correlated with the development of CRSwNP. The noncoding RNAs (long noncoding RNA and microRNA) and the top 10 core genes in each module were found to be associated with the pathogenesis of CRSwNP. A total of nine hub genes were identified to be related to the CRSwNP phenotype. By qRT-PCR analysis, AKT1, CDH1, PIK3R1, CBL, LRP1, MALAT1, and XIST were proven to be associated with the pathogenesis of CRSwNP. AGR2, FAM3D, PIP, DSE, and TMC were identified to be related to the CRSwNP phenotype. Further exploration of these genes will reveal more important information about the mechanisms of CRSwNP.

Statistical genomics in rare cancer

Rare cancers make of more than 20% of cancer cases. Due to the rare nature, less research has been conducted on rare cancers resulting in worse outcomes for patients with rare cancers compared to common cancers. The ability to study rare cancers is impaired by the ability to collect a large enough set of patients to complete an adequately powered genomic study. In this manuscript we outline analytical approaches and public genomic datasets that have been used in genomic studies of rare cancers. These statistical analysis approaches and study designs include: gene set / pathway analyses, pedigree and consortium studies, meta-analysis or horizontal integration, and integration of multiple types of genomic information or vertical integration. We also discuss some of the publicly available resources that can be leveraged in rare cancer genomic studies.