Proteome-based identification and validation of NXPE3 in childhood acute lymphoblastic leukaemia
Ontology highlight
ABSTRACT:
INSTRUMENT(S): impact II
ORGANISM(S): Homo Sapiens (ncbitaxon:9606)
SUBMITTER: 
Ahmad Syed
PROVIDER: MSV000100202 | MassIVE | Fri Dec 12 12:21:00 GMT 2025
SECONDARY ACCESSION(S): PXD071925
REPOSITORIES: MassIVE
Similar Datasets
Project description:Background: How prenatal smoke exposure affects DNA methylation leading to atopic disorders remains to be addressed. Epigenetic biomarkers informative of prenatal smoke exposure and atopic disorders are wanting. Since most children suffering from atopic dermatitis (AD) continue to develop asthma later in life, we explored whether prenatal smoke exposure e induces DNA methylation and searched for predictive epigenetic biomarkers for smoke related atopic disorders. Methods: Methylation differences associated with smoke exposure were screened by Illumina methylation panel for children from the Taiwan birth panel study cohort initially. Information about development of atopic dermatitis (AD) and risk factors were collected. Cord blood cotinine levels were measured to represent prenatal smoke exposure. CpG loci that demonstrated a statistically significant difference in methylation were validated by methylation-dependent fragment separation (MDFS). Differential methylation in three genes (TSLP, GSTT1, and CYB5R3) was identified through the screen and their functions were investigated. Results: Among these, only thymic stromal lymphopoietin (TSLP) gene displayed significant difference in promoter methylation percentage after being validated by MDFS (p=0.029). TSLP gene was further investigated in a larger sample of 92 children from the cohort. Methylation status of the TSLP 5′-CpG island (CGI) was found to be significantly associated with prenatal smoke exposure (OR=3.59, 95%CI=1.49-8.64; cotinine level 0.10 ng/ml, sensitivity= 77%; specificity = 61%) and with AD (OR=4.77, 95%CI=1.47-15.53). The degree of TSLP 5′CGI methylation inversely correlated with TSLP protein expression levels (per unit: β=－6.69 ng/ml; 95% CIs, －12.80~－0.59; p=0.032). Conclusions: The effect of prenatal tobacco smoke exposure on the risk for AD may be mediated through DNA methylation. Cord blood methylated TSLP 5′CGI may be a potential epigenetic biomarker for environmentally-related atopic disorders. The buffy coat and plasma samples were separated and stored at −80°C. DNA (100 ng-500 ng) was extracted from cord white blood cells. Microarrays have been performed to investigate fourteen samples, which were classified as two groups according to cotinine exposure dosage (7 versus 7 : high exposure verses low exposure).
2013-12-31 | E-GEOD-35222 | biostudies-arrayexpress
Project description:PAPER 1:"Identification of novel subgroups of high-risk pediatric precursor B acute lymphoblastic leukemia (B-ALL) by unsupervised microarray analysis: clinical correlates and therapeutic implications. A Children's Oncology Group (COG) study." ABSTRACT We examined gene expression profiles of pre-treatment specimens from 207 patients from the COG P9906 study to identify signatures of children with high risk B-precursor acute lymphoblastic leukemia (ALL) and to determine whether the resulting clusters are associated with either specific clinical features or treatment response characteristics. Four unsupervised clustering methods were utilized to classify patients into similar groups. The different clustering algorithms showed significant overlap in cluster membership. Two clusters contained all cases with either t(1;19)(q23;p13) translocations or MLL rearrangements. The other six clusters were novel and had no recurring chromosomal abnormalities or distinctive clinical features. Members of two of these novel clusters had significant survival differences when compared to the overall 4-year relapse-free survival (RFS) of 61%. These included clusters of patients with either significantly better (94.7%) or worse (21.0%) RFS at 4 years. Children of Hispanic/Latino ethnicity were disproportionately present in the poor outcome cluster. The poor outcome cluster represents a novel biologically distinctive subset of B-precursor ALL that may occur at least as frequently as BCR/ABL. Further molecular characterization of this cluster may lead to the discovery of genomic abnormalities that can be targeted to improve the currently dismal outcome for children with this gene signature. The Sample data have also been used in another study: PAPER 2: "Gene expression classifiers for minimal residual disease and relapse free survival improve outcome prediction and risk classification in children with high risk acute lymphoblastic leukemia. A Children's Oncology Group study". ABSTRACT Background. Nearly 25% of children with B-precursor ALL present with "high-risk" disease (HR-ALL) that is resistant to current therapies. Gene expression profiling may yield molecular classifiers for outcome prediction that can be used to improve risk classification and therapeutic targeting. Methods. Expression profiles were obtained in pre-treatment leukemic samples from 207 uniformly treated children with HR-ALL. Relapse free survival (RFS) was 61% at 4 years and flow cytometric measures of minimal residual disease (MRD) at the end of induction (day 29) were predictive of outcome (P<0.001). Molecular classifiers predictive of RFS and MRD were developed using extensive cross-validation procedures. Results. A 38 gene molecular risk classifier predictive of RFS (MRC-RFS) distinguished two groups in HR-ALL with different relapse risks: low (4 yr RFS: 81%, n=109) vs. high (4 yr RFS: 50%, n=98) (P<0.0001). In multivariate analysis, the best predictor combined MRC-RFS and day 29 flow MRD data, classifying children into low (87% RFS), intermediate (62% RFS), or high risk (29% RFS) groups (P<0.0001). A 21 gene molecular classifier predictive of MRD could effectively substitute for day 29 flow MRD, yielding a combined classifier that similarly distinguished three risk groups at pre-treatment (low: 82% RFS; intermediate: 63% RFS; and high risk: 45% RFS) (P<0.0001). This combined molecular classifier was further validated on an independent cohort of 84 children with HR-ALL (P = 0.006). Conclusions. Molecular classifiers predictive of RFS and MRD can be used to distinguish distinct prognostic groups within HR-ALL, significantly improving risk classification schemes and the ability to prospectively identify children at diagnosis who will respond to or fail current treatment regimens. NOTE: Due to Children's Oncology Group (COG) restrictions, outcome and MRD data cannot be provided as part of the covariate data for this dataset at the present time. If you would like to arrange individual access to this data, please contact COG or the PI of this study, Dr. Cheryl Willman, at the University of New Mexico Cancer Center (cwillman@unm.edu) to arrange a collaboration.
2009-06-25 | GSE11877 | GEO
Project description:PAPER 1:"Identification of novel subgroups of high-risk pediatric precursor B acute lymphoblastic leukemia (B-ALL) by unsupervised microarray analysis: clinical correlates and therapeutic implications. A Children's Oncology Group (COG) study." ABSTRACT We examined gene expression profiles of pre-treatment specimens from 207 patients from the COG P9906 study to identify signatures of children with high risk B-precursor acute lymphoblastic leukemia (ALL) and to determine whether the resulting clusters are associated with either specific clinical features or treatment response characteristics. Four unsupervised clustering methods were utilized to classify patients into similar groups. The different clustering algorithms showed significant overlap in cluster membership. Two clusters contained all cases with either t(1;19)(q23;p13) translocations or MLL rearrangements. The other six clusters were novel and had no recurring chromosomal abnormalities or distinctive clinical features. Members of two of these novel clusters had significant survival differences when compared to the overall 4-year relapse-free survival (RFS) of 61%. These included clusters of patients with either significantly better (94.7%) or worse (21.0%) RFS at 4 years. Children of Hispanic/Latino ethnicity were disproportionately present in the poor outcome cluster. The poor outcome cluster represents a novel biologically distinctive subset of B-precursor ALL that may occur at least as frequently as BCR/ABL. Further molecular characterization of this cluster may lead to the discovery of genomic abnormalities that can be targeted to improve the currently dismal outcome for children with this gene signature. The Sample data have also been used in another study: PAPER 2: "Gene expression classifiers for minimal residual disease and relapse free survival improve outcome prediction and risk classification in children with high risk acute lymphoblastic leukemia. A Children's Oncology Group study". ABSTRACT Background. Nearly 25% of children with B-precursor ALL present with "high-risk" disease (HR-ALL) that is resistant to current therapies. Gene expression profiling may yield molecular classifiers for outcome prediction that can be used to improve risk classification and therapeutic targeting. Methods. Expression profiles were obtained in pre-treatment leukemic samples from 207 uniformly treated children with HR-ALL. Relapse free survival (RFS) was 61% at 4 years and flow cytometric measures of minimal residual disease (MRD) at the end of induction (day 29) were predictive of outcome (P<0.001). Molecular classifiers predictive of RFS and MRD were developed using extensive cross-validation procedures. Results. A 38 gene molecular risk classifier predictive of RFS (MRC-RFS) distinguished two groups in HR-ALL with different relapse risks: low (4 yr RFS: 81%, n=109) vs. high (4 yr RFS: 50%, n=98) (P<0.0001). In multivariate analysis, the best predictor combined MRC-RFS and day 29 flow MRD data, classifying children into low (87% RFS), intermediate (62% RFS), or high risk (29% RFS) groups (P<0.0001). A 21 gene molecular classifier predictive of MRD could effectively substitute for day 29 flow MRD, yielding a combined classifier that similarly distinguished three risk groups at pre-treatment (low: 82% RFS; intermediate: 63% RFS; and high risk: 45% RFS) (P<0.0001). This combined molecular classifier was further validated on an independent cohort of 84 children with HR-ALL (P = 0.006). Conclusions. Molecular classifiers predictive of RFS and MRD can be used to distinguish distinct prognostic groups within HR-ALL, significantly improving risk classification schemes and the ability to prospectively identify children at diagnosis who will respond to or fail current treatment regimens. NOTE: Due to Children's Oncology Group (COG) restrictions, outcome and MRD data cannot be provided as part of the covariate data for this dataset at the present time. If you would like to arrange individual access to this data, please contact COG or the PI of this study, Dr. Cheryl Willman, at the University of New Mexico Cancer Center (cwillman@unm.edu) to arrange a collaboration. Unsupervised clustering and supervised risk classification analyses of 207 diagnostic samples and associated clinical covariate data. See the Summary for greater details. The data were analyzed using Microarray Suite version 5.0 (MAS 5.0) in the Affymetrix Gene Chip Operating Software Version 1.4. Probe masking was used (see 9906_TT207_Affymetrix_probe_mask.msk, linked below as a supplementary file). Otherwise all Affymetrix default parameter settings were used. Global scaling as the normalization method, with the default target intensity of 500, was used.
2009-06-25 | E-GEOD-11877 | biostudies-arrayexpress
Project description:We identified germline single nucleotide polymorphisms (SNPs) associated with childhood acute lymphoblastic leukemia (ALL) and its subtypes. Using the Affymetrix 500K Mapping array and publicly available genotypes, we identified 18 SNPs whose allele frequency differed (P<1x10-5) between a pediatric ALL population (n=317) and non-ALL controls (n=17,958). Six of these SNPs differed (P≤0.05) in allele frequency among four ALL subtypes. Two SNPs in ARID5B not only differed between ALL and non-ALL groups (rs10821936, P=1.4x10-15, odds ratio[OR]=1.91; rs10994982, P=5.7x10-9, OR=1.62) but also distinguished B-hyperdiploid ALL from other subtypes (rs10821936, P=1.62 x10-5, OR=2.17; rs10994982, P=0.003, OR 1.72). These ARID5B SNPs also distinguished B-hyperdiploid ALL from other subtypes in an independent validation cohort (n=124 children with ALL) (P=0.003 and P=0.0008, OR 2.45 and 2.86, respectively) and were associated with methotrexate accumulation and gene expression pattern in leukemic lymphoblasts. We conclude that germline genomic variations affect susceptibility to and characteristics of specific ALL subtypes. We used microarrays to detail the global program of gene expression in primary leukemic blasts cells from children diagnosed with acute lymphoblastic leukemia.
2009-08-16 | E-GEOD-17195 | biostudies-arrayexpress
Project description:ALL is the most common form of childhood cancer with >80% cured with contemporary treatment protocols. Accurate risk stratification in childhood ALL is essential to avoid under- and over-treatment. Currently, we use presenting clinical, biological features, and minimal residual disease (MRD) quantitation to risk stratify patients. Although whole genome gene expression profiling (GEP) can accurately classify patients with ALL into various WHO 2008 defined subgroups, its value in predicting relapse remained to be defined. We hypothesized that global time-series GEPs of bone marrow (BM) samples at diagnosis and specific points during initial remission-induction therapy can measure the success of cytoreduction and be used for relapse prediction. We generated time-series GEPs from 210 children with de novo ALL at diagnosis, and Day 8 of remission-induction therapy. We computed the time-series changes from diagnosis to follow-up time point of remission-induction, termed Effective Response Metric (ERM), that measures both the magnitude and direction of time-series change in multi-dimensional gene space towards the normal centroid, and we compared its ability to predict relapse against contemporary risk assignment methods including NCI criteria, cytogenetics and MRD.
2016-05-26 | GSE67684 | GEO
Project description:The cure rate for childhood ALL has improved considerably in part because therapy is routinely tailored to the predicted risk of relapse. Various clinical and laboratory variables are used in current risk-stratification schemes, but many children who fail therapy lack adverse prognostic factors at initial diagnosis. Using gene expression analysis, we have identified genes and pathways in a NCI high-risk childhood B-precursor ALL cohort at diagnosis that may play a role in early blast regression as correlated with the Day 7 marrow status. We have also identified a 47-probeset signature (representing 41 unique genes) that was predictive of long term outcome in our dataset as well as three large independent datasets of childhood ALL treated on different protocols. Keywords: ordered, case-control
2008-02-15 | GSE7440 | GEO
Project description:We identified germline single nucleotide polymorphisms (SNPs) associated with childhood acute lymphoblastic leukemia (ALL) and its subtypes. Using the Affymetrix 500K Mapping array and publicly available genotypes, we identified 18 SNPs whose allele frequency differed (P<1x10-5) between a pediatric ALL population (n=317) and non-ALL controls (n=17,958). Six of these SNPs differed (P≤0.05) in allele frequency among four ALL subtypes. Two SNPs in ARID5B not only differed between ALL and non-ALL groups (rs10821936, P=1.4x10-15, odds ratio[OR]=1.91; rs10994982, P=5.7x10-9, OR=1.62) but also distinguished B-hyperdiploid ALL from other subtypes (rs10821936, P=1.62 x10-5, OR=2.17; rs10994982, P=0.003, OR 1.72). These ARID5B SNPs also distinguished B-hyperdiploid ALL from other subtypes in an independent validation cohort (n=124 children with ALL) (P=0.003 and P=0.0008, OR 2.45 and 2.86, respectively) and were associated with methotrexate accumulation and gene expression pattern in leukemic lymphoblasts. We conclude that germline genomic variations affect susceptibility to and characteristics of specific ALL subtypes.
2009-08-16 | GSE17195 | GEO
Project description:Background Hypertrophic cardiomyopathy (HCM) is defined clinically by pathological left ventricular hypertrophy (LVH). We have previously developed a plasma proteomics biomarker panel that correlates with clinical markers of disease severity and sudden cardiac death (SCD) risk in adult patients with HCM. The aim of this study was to validate the adult biomarkers and perform new discovery proteomics in childhood-onset HCM. Methods Fifty-nine protein biomarkers were identified from an exploratory plasma proteomics screen in children with HCM and augmented into our existing multiplexed targeted liquid chromatography-tandem/mass spectrometry-based assay. The association of these biomarkers with clinical phenotypes and outcomes was prospectively tested in plasma collected from 148 children with HCM and 50 healthy controls. Machine learning techniques were used to develop novel paediatric plasma proteomic biomarker panels.Results Four previously validated adult HCM markers (Aldolase Fructose-Bisphosphate A, Complement C3a, Talin-1 and Thrombospondin 1) and three new markers (Glycogen Phosphorylase B, Lipoprotein A and Profilin 1) were elevated in paediatric HCM. Using supervised machine learning applied to training (n=137) and validation cohorts (n=61), this 7-biomarker panel differentiated HCM from healthy controls with an area under the curve of 1.0 in the training dataset (sensitivity 100% [95% confidence interval: 95–100]; specificity 100% [96–100]) and 0.82 in the validation dataset (sensitivity 75% [59–86]; specificity 88% [75–94]). Reduced circulating levels of 4 other peptides (Apolipoprotein L1-, Complement 5b-, Immunoglobulin Heavy Constant Epsilon- and Serum Amyloid A4-peptide) found in children with high SCD risk provided complete separation from the low and intermediate risk groups, and predicted mortality and adverse cardiovascular outcomes (hazard ratio 1.53 [1.2 –2.0], p = 0.001). ConclusionIn children, a 7-biomarker proteomics panel can distinguish HCM from controls with high sensitivity and specificity, and a second 4-biomarker panel identifies those at high risk of adverse outcomes, including sudden cardiac death.
2024-07-03 | PXD045304 | Pride
Project description:There are hundreds of risk genes for autism spectrum disorder (ASD), but signaling networks at the protein level remain unexplored. We used neuron-specific proximity-labeling proteomics (BioID) to identify protein-protein interaction (PPI) networks for 41 ASD-risk genes. Neuron-specific PPI networks included synaptic transmission proteins, which are disrupted by de novo missense variants. The PPI network map revealed convergent pathways including mitochondrial/metabolic processes, Wnt signaling, ion channel activity and MAPK signaling. CRISPR knockout validations revealed an association between mitochondrial activity and ASD-risk genes. The PPI network showed an enrichment of 112 additional ASD-risk genes and differentially expressed genes from post-mortem ASD patients. Clustering of risk genes based on PPI networks identified gene groups corresponding to clinical behavior score severity. Our data reveal that cell type-specific PPI networks can identify previously unknown individual and convergent ASD signaling networks, provide a method to assess patient variants, and reveal biological insight into disease mechanisms and sub-cohorts of ASD.
2022-09-24 | GSE213899 | GEO
Project description:In the past few decades, the prevalence of overweight and obesity has sharply increased in children and adolescents. Childhood obesity life are associated with increased risk of cardiovascular disease (CVD), diabetes mellitus, metabolic syndrome, sleep disturbances and certain cancers in adulthood. Childhood obesity has become a serious global public health challenge. Long noncoding RNAs (lncRNAs) have an important role in adipose tissue function and energy metabolism homeostasis, and abnormalities may lead to obesity. We used microarrays to detail the differential expression profile of lncRNAs and mRNAs in obese children compared with non-obese children.
2018-06-14 | GSE104815 | GEO