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Blocking and randomization to improve molecular biomarker discovery

ABSTRACT: Blocking and randomization to improve molecular biomarker discovery

PROVIDER: PRJNA429509 | ENA |

REPOSITORIES: ENA

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Blocking and randomization to improve molecular biomarker discovery

Project description:This SuperSeries is composed of the SubSeries listed below.

2018-01-10 | GSE109059 | GEO

Blocking and randomization to improve molecular biomarker discovery.

Project description:Randomization and blocking have the potential to prevent the negative impacts of nonbiologic effects on molecular biomarker discovery. Their use in practice, however, has been scarce. To demonstrate the logistic feasibility and scientific benefits of randomization and blocking, we conducted a microRNA study of endometrial tumors (n = 96) and ovarian tumors (n = 96) using a blocked randomization design to control for nonbiologic effects; we profiled the same set of tumors for a second time using no blocking or randomization. We assessed empirical evidence of differential expression in the two studies. We performed simulations through virtual rehybridizations to further evaluate the effects of blocking and randomization. There was moderate and asymmetric differential expression (351/3,523, 10%) between endometrial and ovarian tumors in the randomized dataset. Nonbiologic effects were observed in the nonrandomized dataset, and 1,934 markers (55%) were called differentially expressed. Among them, 185 were deemed differentially expressed (185/351, 53%) and 1,749 not differentially expressed (1,749/3,172, 55%) in the randomized dataset. In simulations, when randomization was applied to all samples at once or within batches of samples balanced in tumor groups, blocking improved the true-positive rate from 0.95 to 0.97 and the false-positive rate from 0.02 to 0.002; when sample batches were unbalanced, randomization was associated with the true-positive rate (0.92) and the false-positive rate (0.10) regardless of blocking. Normalization improved the detection of true-positive markers but still retained sizeable false-positive markers. Randomization and blocking should be used in practice to more fully reap the benefits of genomics technologies.

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Discovery of diagnostic biomarker and molecular mechanisms of placenta accreta spectrum

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A method for increasing the robustness of stable feature selection for biomarker discovery in molecular medicine developed using serum small extracellular vesicle associated miRNAs and the Barrett’s oesophagus disease spectrum

Project description:The biomarker development field within molecular medicine remains limited by the methods that are available for building predictive models. We developed an efficient method for conservatively estimating confidence intervals for the cross validation derived prediction errors of biomarker models. This new method was investigated for its ability to improve the capacity of our previously developed method, StaVarSel, for selecting stable biomarkers. Compared with the standard cross validation method StaVarSel markedly improved the estimated generalisable predictive capacity of serum miRNA biomarkers for the detection of disease states that are at increased risk of progressing to oesophageal adenocarcinoma. The incorporation of our new method for conservatively estimating confidence intervals into StaVarSel resulted in the selection of less complex models with increased stability and improved or similar predictive capacities. The methods developed in this study have the potential to improve progress from biomarker discovery to biomarker driven translational research.

2023-05-12 | GSE227710 | GEO

Biomarker discovery in paired mid luteal endometrial biopsies

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Biomarker discovery in paired mid luteal endometrial biopsies

Project description:Cell state determination is apparent within the endometrium during the mid-luteal window of implantation. Using previously identified biomarkers of cell states (SCARA5 and DIO2) we performed bulk RNA sequencing on 6 pairs of endometrial biopsies characterised by either normal biomarker expression in both biopsies, or abnormal biomarker expression in one biopsy (low SCARA5 and high DIO2) and normal expression in a second biopsy. We demonstrate that “abnormal cycles” are associated with a distinct gene expression profile when compared to “normal cycles” and have utilised this dataset for biomarker discovery.

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GNPS - IIN - Urine biomarker discovery

Project description:Diluted urine for biomarker discovery. Processed in MZmine 2.37.corr17.7_kei_merge2 GNPS link for IIN paper: https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=a5480529261b4a13bb867f2edad1dcbe

2019-07-29 | MSV000084148 | GNPS

Hepatocellular Carcinoma (HCC) biomarker discovery cohort for small RNA profiling

Project description:Hepatocellular Carcinoma (HCC) biomarker discovery cohort for small RNA profiling

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Dystrophia Myotonica Biomarker Discovery Initiative

Project description:As part of the Dystrophia Myotonica Biomarker Discovery Initiative (DMBDI) a dataset was obtained from 35 participants, including 31 Myotonic Dystrophy type 1 (DM1) cases and four unaffected controls. All DM1 cases in this research were heterozygous for the abnormally expanded CTG repeat. The mode of the length of the DM1 CTG expansion (Modal Allele Length, MAL) was determined by small-pool PCR of blood DNA for 35/36 patients. For this work we did not attempt to measure the repeat length from muscle, due to a very high degree of repeat instability in muscle cells, and associated difficulties in its experimental measurement. One patient refused blood donation. For each of the 35 blood-donating patients mRNA expression profiling of blood was performed using Affymetrix GeneChip™ Human Exon 1.0 ST microarray. For 28 of 36 patients a successful quadriceps muscle biopsy was obtained. The muscle tissue was mRNA profiled using the same type of microarray. In total, a complete set of samples (blood and muscle) was obtained for 27 of 36 patients; samples were given a disease staging score based on muscle impairment rating. mRNA profiling was carried out by the GeneLogic service lab (on a fee-for-service basis) using standard Affymetrix hybridisation protocol.

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