Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1224285 | ENA |
REPOSITORIES: ENA
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Project description:Histologic diagnosis of sellar masses can be challenging, particularly in rare neoplasms and tumors without definitive biomarkers. DNA methylation has recently emerged as a useful diagnostic tool. To illustrate the clinical utility of machine-learning-based DNA methylation classifiers, we report a rare case of primary sellar esthesioneuroblastoma diagnosed by DNA methylation classificiation but histologically mimicking a nonfunctioning pituitary adenoma.
2022-10-04 | GSE211634 | GEO
Project description:Precision medicine's potential to transform complex autoimmune-disease treatment is often challenged by limited data availability and inadequate sample size when compared to the number of molecular features found in high-throughput multi-omics datasets. Addressing this issue, the novel framework PRoBeNet (Predictive Response Biomarkers using Network medicine) was developed. ProBeNet operates under the hypothesis that the therapeutic effect of a drug propagates through a protein–protein interaction network to reverse disease states. ProBeNet prioritizes biomarkers by considering (1) therapy-targeted proteins, (2) disease-specific molecular signatures, and (3) an underlying network of interactions among cellular components (the human interactome). With ProBeNet, biomarkers were discovered predicting patient responses to both an established autoimmune therapy (infliximab) and an investigational compound (a MAPK3/1 inhibitor). Predictive power of ProBeNet biomarkers was validated with retrospective gene-expression data from ulcerative-colitis and rheumatoid-arthritis patients and prospective data from ulcerative-colitis and Crohn’s disease patient-derived tissues. Machine-learning models using ProBeNet biomarkers significantly outperformed models using either all genes or randomly selected genes, especially when data were limited (fewer than 20 samples). These results illustrate the value of ProBeNet for reducing features and for constructing robust machine-learning models when limited data are available. ProBeNet may be used to develop companion and complementary diagnostic assays for complex autoimmune-disease therapies, which may help stratify suitable patient subgroups in clinical trials, approve new drugs, and improve patient outcomes.
2024-03-13 | GSE261205 | GEO
Project description:This study used a trans-omics approach—combining genome-wide SNP analysis and metabolomics—to distinguish coronary artery disease (CAD) patients from high-risk and healthy individuals. It identified declining plasma phospholipids as potential biomarkers, linked key SNPs and genes (notably LPCAT1) to lipid changes, and developed a machine-learning model that accurately predicts CAD (AUC = 0.917). The results highlight the role of phospholipid metabolism and genetic variation in CAD progression.
2025-07-28 | GSE303414 | GEO
Project description:Background: Inflammation-driven fibrosis represents a common pathological endpoint in both heart failure (HF) and chronic kidney disease (CKD), which together affect over 1 billion people worldwide. Understanding the shared molecular mechanisms by which inflammation contributes to the pathogenesis of HF and CKD is crucial for enabling early diagnosis and guiding the development of broad-spectrum therapeutic strategies. Methods: Utilizing multi-omics technologies and machine learning algorithms, we performed an integrative analysis of HF and chronic kidney disease CKD samples to uncover shared mechanisms underlying inflammation-induced fibrosis. Furthermore, key regulators identified through bioinformatic analysis were experimentally validated using primary cell co-culture assays, gene knockout approaches, and bulk RNA sequencing. Results: Single-nucleus RNA sequencing (snRNA-seq) revealed concurrent upregulation of IL-1β and Pleckstrin Homology-Like Domain Family A Member 1 (PHLDA1) in both cardiac M1 macrophages and injured proximal tubular epithelial (PTE) cells. PHLDA1 promotes IL-1β expression and knockout of PHLDA1 suppressed NF-κB signaling and renal fibrosis. Administration of IL-1β induced PHLDA1 expression in cardiac fibroblasts and renal PDGFRβ⁺ cells, suggesting a positive feedback loop that contributes to fibrosis. Conclusions: In this study, we identified PHLDA1 as a key driver of fibrosis in both the heart and kidney, acting through IL-1β mediated intercellular crosstalk. These findings highlight PHLDA1 as a promising therapeutic target for mitigating fibrosis in cardio-renal syndrome.
2026-02-25 | GSE313814 | GEO
Project description:Despite the diversity of liquid biopsy transcriptomic repertoire, numerous studies often 30 exploit only a single RNA type signature for diagnostic biomarker potential. This frequently results 31 in insufficient sensitivity and specificity necessary to reach diagnostic utility. Combinatorial biomarker approaches may offer a more reliable diagnosis. Here we investigated the synergistic contributions of circRNA and mRNA signatures derived from blood platelets as biomarkers for lung cancer detection. We developed a comprehensive bioinformatics pipeline permitting analysis of platelet-circRNA and mRNA derived from non-cancer individuals and lung cancer patients. An optimal selected signature is then used to generate the predictive classification model using machine learning algorithm. Using an individual signature of 21 circRNA and 28 mRNA, the predictive models reached an Area Under the Curve (AUC) of 0.88 and 0.81, respectively. Importantly, combinatorial analysis including both types of RNAs resulted in an 8-target signature (6 mRNA and 2 40 circRNA) enhancing the differentiation of lung cancer from controls (AUC of 0.92). Additionally, we identified five biomarkers potentially specific for early-stage detection of lung cancer. Our proof-of-concept study presents the first multi-analyte-based approach for the analysis of platelets-derived biomarkers, providing a potential combinatorial diagnostic signature for lung cancer detection.
2023-06-23 | GSE225787 | GEO
Project description:Multi-omics data-driven personalized health profiling can provide valuable insight into metabolic perturbations in people living with HIV (PWH) under successful antiretroviral therapy (ART). We integrated transcriptomics, proteomics, and metabolomics to identify and stratify the immunometabolically compromised treated PWH (n=158). Based on clinical and lifestyle data, 44% (70/158) of PWH were at risk of immunometabolic complications. We identified six plasma biomarkers to define the at-risk phenotype using advanced machine learning and a Bayesian classifier that drives a network of proteins reasoned for monocyte immunosenescence. We identified metabolic perturbations driven by central carbon metabolic flux led to chronic monocyte activation impairing its early functional properties. Further, we discovered that the host-induced spermidine-mediated microenvironment was responsible for chronic inflammation leading to synaptic dysregulation in vitro, potentiating the risk of neuropsychiatric clinical phenotypes in the at-risk PWH. Novel intervention targeting metabolically-perturbed chronic inflammatory conditions can lead to healthier aging in the at-risk PWH.
2025-05-06 | PXD043296 | Pride
Project description:The diagnosis of primary lung adenocarcinomas with intestinal or mucinous differentiation (PAIM) remains challenging due to the overlapping histomorphological, immunohistochemical and genetic characteristics with lung metastatic colorectal cancer (lmCRC). This study aimed to explore the protein biomarkers that could distinguish between PAIM and lmCRC. To uncover differences between the two diseases, we used tandem mass tagging (TMT)-based shotgun proteomics to characterize proteomes of formalin-fixed paraffin-embedded (FFPE) tumor samples of PAIM (n = 22) and lmCRC (n = 17). Then three machine learning algorithms, namely support vector machine (SVM), random forest and the Least Absolute Shrinkage and Selection Operator (LASSO), were utilized to select protein features with diagnostic significance. These candidate proteins were further validated in an independent cohort (PAIM, n = 11; lmCRC, n = 19) by immunochemistry (IHC) to confirm their diagnostic performance. In total, 105 proteins out of 7871 proteins were significantly dysregulated between PAIM and lmCRC samples and well-separated two groups by Uniform Manifold Approximation and Projection (UMAP). The upregulated proteins in PAIM were involved in actin cytoskeleton organization, platelet degranulation, and regulation of leukocyte chemotaxis, while downregulated ones were involved in mitochondrial transmembrane transport, vasculature development, and stem cell proliferation. A set of 10 candidate proteins (high-level expression in lmCRC: CDH17, ATP1B3, GLB1, OXNAD1, LYST, FABP1; high-level expression in PAIM: NARR, MLPH, S100A14, CK7) was ultimately selected to distinguish PAIM from lmCRC by machine learning algorithms. We further confirmed using IHC that the five protein biomarkers including CDH17, CK7, MLPH, FABP1 and NARR were effective biomarkers for distinguishing PAIM from lmCRC. Our study depicts PAIM-specific proteomic characteristics and demonstrates the potential utility of new protein biomarkers for the differential diagnosis of PAIM and lmCRC. These findings may contribute to improving the diagnostic accuracy and guide appropriate treatments for these patients.
2024-06-23 | PXD050416 | Pride