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.
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.
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.
Project description:This study evaluated and compared three sample preparation strategies for proteomic analysis of equine cerebrospinal fluid (CSF): native in-solution digestion, the ProteoMiner™ Small-Capacity Enrichment Kit (Bio-Rad®), and the PreOmics Enrich-iST™ Kit (PreOmics®). CSF is in direct contact with the central nervous system (CNS), and its protein content reflects both physiological and pathological states, making it an ideal fluid for biomarker discovery in neurological diseases. However, its analysis is hindered by the wide dynamic range of protein concentrations and the predominance of highly abundant proteins (HAPs), such as albumin, which can obscure the detection of diagnostically relevant low-abundance proteins (LAPs). To address these limitations, enrichment kits such as ProteoMiner and PreOmics have been employed to reduce HAPs and enhance LAP detection prior to mass spectrometry. While ProteoMiner uses a combinatorial hexapeptide ligand library to compress the dynamic range by saturating binding sites of abundant proteins, PreOmics relies on paramagnetic beads with hydrophobic and charge-based retention to selectively bind peptides and improve sample purity. This is the first study to apply the PreOmics Enrich-iST kit to equine CSF. Label-free LC-MS/MS was used to analyse the samples. A total of 849 unique proteins were identified across all methods, with the PreOmics kit identifying the highest number of proteins and achieving the most consistent depletion of HAPs. emPAI scores were used to quantify protein abundance, revealing that albumin levels decreased by over 60% with PreOmics but slightly increased with ProteoMiner. Pathway enrichment analysis using PANTHER showed that the PreOmics kit enabled detection of a broader and more statistically significant array of biological pathways, while ProteoMiner selectively enriched for HAPs with known roles in neurodegenerative disorders, such as Dickkopf WNT signalling pathway inhibitor 3 (DKK3) and clusterin (CLU). These findings highlight that although both kits are capable of enriching LAPs, they differ in their enrichment patterns and efficiency. PreOmics provides better overall proteome coverage and greater statistical power in pathway analysis, whereas ProteoMiner may be more effective for targeted studies of specific neuropathology-related proteins. This study provides valuable insights into the applicability and complementarity of these enrichment methods for biomarker discovery in equine neurological disease.
Project description:Diluted urine for biomarker discovery.
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GNPS link for IIN paper:
https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=a5480529261b4a13bb867f2edad1dcbe