Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Peripheral mRNA expression of first-episode drug-naïve patients with schizophrenia

ABSTRACT: Schizophrenia is a complex mental disorder. Accurate diagnosis and classification of schizophrenia has always been a major challenge in clinic due to the lack of biomarkers. Therefore, identifying molecular biomarkers, particularly in the peripheral blood, is of great significance. This study aimed to identify immune-related molecular biomarkers of schizophrenia in peripheral blood. Eighty-four Peripheral blood leukocytes of first-episode drug-naïve (FEDN) patients with schizophrenia and 97 healthy controls were collected and examined using high-throughput RNA-sequencing. Differentially-expressed genes (DEGs) were analysed. Weighted correlation network analysis (WGCNA) was employed to identify schizophrenia-associated module genes. The CIBERSORT algorithm was adopted to analyse immune cell proportions. Then, machine-learning algorithms including random forest, LASSO, and SVM-RFE were employed to screen immune-related predictive genes of schizophrenia. The RNA-seq analyses revealed 734 DEGs. Further machine-learning-based bioinformatic analyses screened out three immune-related predictive genes of schizophrenia (FOSB, NUP43, and H3C1), all of which were correlated with neutrophils and natural killer cells resting.

ORGANISM(S): Homo sapiens

PROVIDER: GSE243841 | GEO | 2025/02/28

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Shared Molecules

Only show the datasets with similarity scores above: 0.5

Threshold

0.5

Similar Datasets

Plasma exosomes lncRNA-miRNA-mRNA network construction and its diagnostic efficacy identification in first-episode schizophrenia

Project description:This study examined the miRNA expression level in exosomal derived from the plasma of first episode schizophrenia (FOS) patients and Healthy controls (HC), and explored the the potential of exosomes as biomarkers for schizophrenia. This study examined the lncRNA expression level in exosomal derived from the plasma of first episode schizophrenia (FOS) patients and Healthy controls (HC), and explored the the potential of exosomes as biomarkers for schizophrenia. This study examined the mRNA expression level in exosomal derived from the plasma of first episode schizophrenia (FOS) patients and Healthy controls (HC), and explored the the potential of exosomes as biomarkers for schizophrenia.

2023-08-30 | GSE228881 | GEO

Machine learning analysis to identify endometrial transcriptomic biomarkers predictive of pregnancy success following artificial insemination in dairy cows

Project description:The objective of this research was to identify a set of genes whose transcript abundance is predictive of a cow’s ability to become pregnant following artificial insemination (AI). Endometrial epithelial cells from the uterine body were collected for RNA sequencing using the cytobrush method from 193 first-service Holstein cows at estrus prior to AI (day 0). A group of 253 first-service cows not used for cytobrush collection were controls. There was no effect of cytobrush collection on pregnancy outcomes at day 30 or 70, or on pregnancy loss between day 30 and 70. There were 2 upregulated and 214 downregulated genes (FDR < 0.05, absolute fold change > 2-fold) for cows pregnant at day 30 versus those that were not pregnant. Functional terms overrepresented in the downregulated genes included those related to immune and inflammatory responses. Machine learning for fertility biomarkers with the R package BORUTA resulted in identification of 57 biomarkers that predicted pregnancy outcome at day 30 with an average accuracy of 77%. Thus, machine learning can identify predictive biomarkers of pregnancy in endometrium with high accuracy. Moreover, sampling of endometrial epithelium using the cytobrush can help understand functional characteristics of the endometrium at AI without compromising cow fertility.

2024-04-10 | GSE248266 | GEO

RNA and surface epitope sequencing of single cells involved in spondyloarthritis

Project description:Early diagnosis of psoriatic arthritis (PSA) and differentiation of this disease from similar but distinct arthritides, such as ankylosing spondylitis (AS), is important for successful therapeutic intervention but currently remains challenging due, in part, to the scarcity of non-invasive biomarkers. In this study, we perform single cell profiling of transcriptome and cell surface protein expression on the peripheral blood immunocyte populations of individuals with PSA, individuals with AS, individuals with cutaneous psoriasis (PSO) alone, and healthy individuals to identify gene and protein features differentially expressed between these groups across 30 immune cell types and to further evaluate the diagnostic potential of these features using a machine learning classification approach.

2022-03-07 | GSE194315 | GEO

Gene Expression Profiles Predict Postpartum-onset Depression Patients with Treatment-Resistant Depression Patients with Treatment-Resistant Depression

Project description:To identify early predictive biomarkers for postpartum depression using peripheral blood gene expression profiles

2013-03-29 | GSE45603 | GEO

Project description:Immune system aging contributes significantly to declining health with age. It is unknown to what extent human and mice immune systems go through similar age-related changes and whether there are conserved biomarkers of aging. We characterized age-related changes in the immune system of long (C57BL/6J) and short-lived (NZO/HILtJ) strains and compared them with blood-driven human aging signatures. Peripheral blood lymphocytes (PBL), spleen cells and spleen-driven naive and memory CD8+ T cells were profiled using flow cytometry, RNA-seq and ATAC-seq from young (3 months) and old (18 months) mice. Pro-inflammatory myeloid genes were activated with age across strains and tissues, echoing human 'inflammaging' signatures. ATAC-seq footprinting analyses uncovered increased binding of pro-inflammatory transcription factors with age (e.g., AP1 complex, NFKB signaling pathway). Machine learning models identified inflammation, cytotoxic, and naive T cell derived genes to be strong signatures of immunosenescence. These data are publicly shared as a resource for immune aging (https://immune-aging.jax.org/mice).

2021-12-01 | GSE159795 | GEO

2021-12-01 | GSE159794 | GEO

Coupling of response biomarkers between tumour and peripheral blood in patients undergoing chemoimmunotherapy II

Project description:Platinum-based chemotherapy in combination with anti-PD-L1 antibodies has shown promising results in mesothelioma. However, the immunological mechanisms underlying its efficacy are not well understood and there are no predictive biomarkers to guide treatment decisions. Here, we combine time-course RNA sequencing of peripheral blood mononuclear cells with pre-treatment tumour transcriptome data from the 54-patient single arm phase II DREAM study. Single cell RNA-seq and TCR-seq reveal CD8+ T effector memory (TEM) cells with stem-like properties are more abundant in peripheral blood of responders and this population expands upon treatment. These peripheral blood changes are linked to the transcriptional state of the tumour microenvironment. Combining information from both compartments, rather than individually, is most predictive of response. Our study highlights the complex, but predictive interactions between the tumour and immune cells in peripheral blood during the response to chemoimmunotherapy.

2024-10-31 | GSE253173 | GEO

Coupling of response biomarkers between tumour and peripheral blood in patients undergoing chemoimmunotherapy

2024-10-31 | GSE252990 | GEO

RNA sequencing of peripheral blood in amyotrophic lateral sclerosis reveals distinct molecular subtypes: considerations for biomarker discovery

Project description:Aim: Amyotrophic lateral sclerosis (ALS) is a heterogeneous neurodegenerative disease with limited therapeutic options. A key factor limiting the development of effective therapeutics is the lack of disease biomarkers. We sought to assess whether biomarkers for diagnosis, prognosis or cohort stratification could be identified by RNA sequencing (RNA-seq) of ALS patient peripheral blood. Methods: Whole blood RNA-seq data were generated for 96 Australian sporadic ALS (sALS) cases and 48 healthy controls (NCBI GEO accession GSE234297). Differences in sALS-control gene expression, transcript usage and predicted leukocyte proportions were assessed, with pathway analysis used to predict the activity state of biological processes. Weighted Gene Co-expression Network Analysis (WGCNA) and machine learning algorithms were applied to search for diagnostic and prognostic gene expression patterns. Unsupervised clustering analysis was employed to determine whether sALS patient subgroups could be detected. Results: 245 differentially expressed genes were identified in sALS patients relative to controls, with enrichment of immune, metabolic and stress related pathways. sALS patients also demonstrated switches in transcript usage across a small set of genes. We established a classification model that distinguished sALS from controls with an accuracy of 78% (sensitivity: 79%, specificity: 75%) using the expression of 20 genes. Clustering analysis identified four patient subgroups with gene expression signatures and immune cell proportions reflective of distinct peripheral effects. Conclusions: Our findings suggest that peripheral blood RNA-seq can identify diagnostic biomarkers and distinguish molecular subtypes of sALS patients however, its prognostic value requires further investigation.

2023-10-12 | GSE234297 | GEO

An unbiased machine learning exploration reveals gene sets predictive of allograft tolerance after kidney transplantation

Project description:Efforts at finding potential biomarkers of tolerance after kidney transplantation have been hindered by limited sample size, as well as the complicated mechanisms underlying tolerance and the potential risk of rejection after immunosuppressant withdrawal. In this work, three different publicly available genome-wide expression data sets of peripheral blood lymphocyte (PBL) from 63 tolerant patients were used to compare 14 different machine learning models for their ability to predict spontaneous kidney graft tolerance. We found that the Best Subset Selection (BSS) regression approach was the most powerful with a sensitivity of 91.7% and a specificity of 93.8% in the test group, and a specificity of 86.1% and a sensitivity of 80% in the validation group. A feature set with five genes (HLA-DOA, TCL1A, EBF1, CD79B, and PNOC) was identified using the BSS model. EBF1 downregulation was also an independent factor predictive of graft rejection and graft loss. An AUC value of 84.4% was achieved using the two-gene signature (EBF1 and HLA-DOA) as an input to our classifier. Overall, our systematic machine learning exploration suggests novel biological targets that might affect tolerance to renal allografts, and provides clinical insights that can potentially guide patient selection for immunosuppressant withdrawal.

2021-06-04 | GSE166865 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data