Identification of Nephrogenic Therapeutic Biomarkers of Wilms Tumor Using Machine Learning.
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ABSTRACT:
SUBMITTER: Liu H
PROVIDER: S-EPMC8371641 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Wilms tumor (WT) is the most frequently diagnosed malignant renal tumor in children. With current treatments, ~90% of children diagnosed with WT survive and generally present with tumors characterized by favorable histology (FHWT), whereas prognosis is poor for the remaining 10% of cases where the tumors are characterized by cellular diffuse anaplasia (DAWT). Relatively few studies have investigated microRNA-related epigenetic regulation and its relationship with altered gene expression in WT. Here, we aim to identify microRNAs differentially expressed in WT and describe their expression in terms of cellular anaplasia, metastasis, and association with the main genetic alterations in WT to identify potential prognostic biomarkers. Expression profiling using TaqMan low-density array was performed in a discovery cohort consisting of four DAWT and eight FHWT samples. Relative quantification resulted in the identification of 109 (48.7%) microRNAs differentially expressed in both WT types. Of these, miR-10a-5p, miR-29a-3p, miR-181a-5p, miR-200b-3p, and miR-218-5p were selected and tested by RT-qPCR on a validation cohort of 53 patient samples. MiR-29a and miR-218 showed significant differences in FHWT with low (P = 0.0018) and high (P = 0.0131) expression, respectively. To discriminate between miRNA expression FHWTs and healthy controls, the receiver operating characteristic (ROC) curves were obtained; miR-29a AUC was 0.7843. Furthermore, low expression levels of miR-29a and miR-200b (P = 0.0027 and P = 0.0248) were observed in metastatic tumors. ROC curves for miR-29a discriminated metastatic patients (AUC = 0.8529) and miR-200b (AUC = 0.7757). To confirm the differences between cases with poor prognosis, we performed in situ hybridization for three microRNAs in five DAWT and 17 FHWT samples, and only significant differences between adjacent tissues and FHWT tumors were found for miR-181a, miR-200b, and miR-218, in both total pixels and nuclear analyses. Analysis of copy number variation in genes showed that the most prevalent alterations were WTX (47%), IGF2 (21%), 1q (36%) gain, 1p36 (16%), and WTX deletion/1q duplicate (26%). The five microRNAs evaluated are involved in the Hippo signaling pathway and participate in Wilms tumor development through their effects on differentiation, proliferation, angiogenesis, and metastasis.
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Project description:Screening agrochemicals and pharmaceuticals for potential liver toxicity is required for regulatory approval and is an expensive and time-consuming process. The identification and utilization of early exposure gene signatures and robust predictive models in regulatory toxicity testing has the potential to reduce time and costs substantially. In this study, comparative supervised machine learning approaches were applied to the rat liver TG-GATEs dataset to develop feature selection and predictive testing. We identified ten gene biomarkers using three different feature selection methods that predicted liver necrosis with high specificity and selectivity in an independent validation dataset from the Microarray Quality Control (MAQC)-II study. Nine of the ten genes that were selected with the supervised methods are involved in metabolism and detoxification (Car3, Crat, Cyp39a1, Dcd, Lbp, Scly, Slc23a1, and Tkfc) and transcriptional regulation (Ablim3). Several of these genes are also implicated in liver carcinogenesis, including Crat, Car3 and Slc23a1. Our biomarker gene signature provides high statistical accuracy and a manageable number of genes to study as indicators to potentially accelerate toxicity testing based on their ability to induce liver necrosis and, eventually, liver cancer.
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Project description:Breast cancer is one of the major causes of death in women worldwide. It is a diverse illness with substantial intersubject heterogeneity, even among individuals with the same type of tumor, and customized therapy has become increasingly important in this sector. Because of the clinical and physical variability of different kinds of breast cancers, multiple staging and classification systems have been developed. As a result, these tumors exhibit a wide range of gene expression and prognostic indicators. To date, no comprehensive investigation of model training procedures on information from numerous cell line screenings has been conducted together with radiation data. We used human breast cancer cell lines and drug sensitivity information from Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) databases to scan for potential drugs using cell line data. The results are further validated through three machine learning approaches: Elastic Net, LASSO, and Ridge. Next, we selected top-ranked biomarkers based on their role in breast cancer and tested them further for their resistance to radiation using the data from the Cleveland database. We have identified six drugs named Palbociclib, Panobinostat, PD-0325901, PLX4720, Selumetinib, and Tanespimycin that significantly perform on breast cancer cell lines. Also, five biomarkers named TNFSF15, DCAF6, KDM6A, PHETA2, and IFNGR1 are sensitive to all six shortlisted drugs and show sensitivity to the radiations. The proposed biomarkers and drug sensitivity analysis are helpful in translational cancer studies and provide valuable insights for clinical trial design.
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Project description: Not available
| S-EPMC10681392 | biostudies-literature
Project description:BackgroundWilms 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.MethodsWeighted gene coexpression network analysis is used for the detection of clinically important biomarkers associated with WT.ResultsIn 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.ConclusionIn 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.
| S-EPMC7992941 | biostudies-literature
Project description:Genome wide DNA methylation profiling of normal kidney (n=36), nephrogenic rest (n=22) and Wilms tumour (n=37) was performed using the Illumina 450k array. Two papers were composed after analysis of this data (1) describes comparative analysis of 22 matched normal kidney-Wilms tumour pairs which identified biomarker differentially methylated regions (DMRs) that could be detected in patient blood; (2) describes comparative analysis of 20 matched trios which identified changes in methylation associated with progression from the precursor lesion towards tumourigenesis.
2014-07-08 | GSE59157 | GEO
Project description:The RNA polymerase II (Pol II) core promoter is the strategic site of convergence of the signals that lead to the initiation of DNA transcription, but the downstream core promoter in humans has been difficult to understand. Here we analyse the human Pol II core promoter and use machine learning to generate predictive models for the downstream core promoter region (DPR) and the TATA box. We developed a method termed HARPE (high-throughput analysis of randomized promoter elements) to create hundreds of thousands of DPR (or TATA box) variants, each with known transcriptional strength. We then analysed the HARPE data by support vector regression (SVR) to provide comprehensive models for the sequence motifs, and found that the SVR-based approach is more effective than a consensus-based method for predicting transcriptional activity. These results show that the DPR is a functionally important core promoter element that is widely used in human promoters. Notably, there appears to be a duality between the DPR and the TATA box, as many promoters contain one or the other element. More broadly, these findings show that functional DNA motifs can be identified by machine learning analysis of a comprehensive set of sequence variants.
2020-06-04 | GSE139635 | GEO
Project description:Women with uncomplicated urinary tract infection (UTI) symptoms are commonly treated with empirical antibiotics, resulting in overuse of antibiotics, which promotes antimicrobial resistance. Available diagnostic tools are either not cost-effective or diagnostically sub-optimal. Here, we identified clinical and urinary immunological predictors for UTI diagnosis. We explored 17 clinical and 42 immunological potential predictors for bacterial culture among women with uncomplicated UTI symptoms using random forest or support vector machine coupled with recursive feature elimination. Urine cloudiness was the best performing clinical predictor to rule out (negative likelihood ratio [LR-]?=?0.4) and rule in (LR+?=?2.6) UTI. Using a more discriminatory scale to assess cloudiness (turbidity) increased the accuracy of UTI prediction further (LR+?=?4.4). Urinary levels of MMP9, NGAL, CXCL8 and IL-1? together had a higher LR+ (6.1) and similar LR- (0.4), compared to cloudiness. Varying the bacterial count thresholds for urine culture positivity did not alter best clinical predictor selection, but did affect the number of immunological predictors required for reaching an optimal prediction. We conclude that urine cloudiness is particularly helpful in ruling out negative UTI cases. The identified urinary biomarkers could be used to develop a point of care test for UTI but require further validation.
| S-EPMC6928162 | biostudies-literature
Project description:Genome wide DNA methylation profiling of normal kidney (n=36), nephrogenic rest (n=22) and Wilms tumour (n=37) was performed using the Illumina 450k array. Two papers were composed after analysis of this data (1) describes comparative analysis of 22 matched normal kidney-Wilms tumour pairs which identified biomarker differentially methylated regions (DMRs) that could be detected in patient blood; (2) describes comparative analysis of 20 matched trios which identified changes in methylation associated with progression from the precursor lesion towards tumourigenesis. Bisulfte converted DNA from 95 samples of normal kidney, nephrogenic rest and Wilms tumour was hybridised to Illumina HumanMethylation450 bead chips.
2014-07-08 | E-GEOD-59157 | biostudies-arrayexpress
Project description:Purpose: Bulk transcriptomics analysis of Wilms tumor SIX2+CITED1+ cells to compare and identify unique nephron progenitor transcriptome profiling (RNA-seq) signature between unfavorable and favorable Wilms Tumors and against human fetal kidney Methods: Wilms tumor and human fetal kidney samples were collected and transported on ice at 4°C in RPMI-1640 and a single cell suspensions were prepared following mechanical and enzymatic dissociation as previously publication. Enzymatic dissociation was performed with 125 U/ml collagenase I in RPMI-1640 at 37 °C for 35 min. The digested cells were then passed through a 100-μm cell strainer and a 40-μm cell strainer with washes of 1x PBS. The cell suspension was than centrifuged at 1500 rpm for 5 min and erythrocytes were eliminated using a red blood cell lysis kit. WT SIX2+CITED1+ cells were isolated using SIX2-Cy5 and CITED1-Cy3 Smartflare RNA probes following manufacturer’s instructions and previous publication. Briefly, cells were incubated over night at 37 °C with both RNA probes diluted at 1:20 in PBS and 25ul/ml in RPMI-1640 supplemented with 5% FBS, and 0.2% antimicrobial agent Primocin. After 16-18 h, cells were dissociation using TrypLE 1x for 5 min, cells were centrifuged at 1500 rpm for 5 min and prepared for FACS. Freshly isolated SIX2+CITED1+ cells from a 16 WGA hFK, freshly isolated SIX2+CITED1+ cells from WT8 (favorable stage II Wilms’ Tumor), freshly digested xenograft derived from cultured SIX2+CITED1+ cells (Passage 6) isolated from WT8, freshly digested xenograft generated from freshly isolated SIX2+CITED1+ cells from WT8, and total cell population of WT8. Approximately 3,000 cells were captured on a 10x Chromium device using a 10X V2 Single Cell 3′ Solution kit. All protocols were performed following manufacturer's instructions. Final library concentrations were determined using a Qubit high Sensitivity DNA assay Kit. Final sequencing libraries were analyzed on a Sequencing: HiSeq 4000, 2x150bp PE, and ~625M total reads/lane (QuickBiology) to determine the library size; Approximately, 300 million reads per sample were sequenced Results: Transcriptomics analysis of Wilms Tumor SIX2+CITED1+ cells in comparison to human fetal kidneys confirmed the nephrogenic signature of hFK-SIX2+CITED1+ and WT-SIX2+CITED1+ cells but highlighted differences in expression of pluripotency and self renewal-related genes.Trajectory inference analysis generated from sc-RNAseq data of SIX2+CITED1+ cells and cells dissociated from total WT (from where the SIX2+CITED1+ cells were isolated), suggests that SIX2+CITED1+ cells are the root cells that give rise to all the tumor cell types. Conclusion: Our study represents the first single cell transcriptomic characterization of Wilms Tumor cancer stem cells (SIX2+CITED1+) against human fetal kidney SIX2+CITED1+ cells and against the total tumor and xenografts from cultured and fresh isolated SIX2+CITED1+ WT cells. Identifying that WT SIX2+CITED1+ cells arise early in nephrogenesis and have an expression pattern favoring proliferation over differentiation that overlaps considerably with the expression pattern of both the tumor of origin and with the expression pattern of WT they generate after grafting.
2023-04-30 | GSE175698 | GEO