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A Mouse Brain-based Multi-omics Integrative Approach Reveals Potential Blood Biomarkers for Ischemic Stroke.

ABSTRACT: Stroke remains a leading cause of death and disability worldwide. Despite continuous advances, the identification of key molecular signatures in the hyper-acute phase of ischemic stroke is still a primary interest for translational research on stroke diagnosis, prognosis, and treatment. Data integration from high-throughput -omics techniques has become crucial to unraveling key interactions among different molecular elements in complex biological contexts, such as ischemic stroke. Thus, we used advanced data integration methods for a multi-level joint analysis of transcriptomics and proteomics data sets obtained from mouse brains at 2 h after cerebral ischemia. By modeling net-like correlation structures, we identified an integrated network of genes and proteins that are differentially expressed at a very early stage after stroke. We validated 10 of these deregulated elements in acute stroke, and changes in their expression pattern over time after cerebral ischemia were described. Of these, CLDN20, GADD45G, RGS2, BAG5, and CTNND2 were next evaluated as blood biomarkers of cerebral ischemia in mice and human blood samples, which were obtained from stroke patients and patients presenting stroke-mimicking conditions. Our findings indicate that CTNND2 levels in blood might potentially be useful for distinguishing ischemic strokes from stroke-mimicking conditions in the hyper-acute phase of the disease. Furthermore, circulating GADD45G content within the first 6 h after stroke could also play a key role in predicting poor outcomes in stroke patients. For the first time, we have used an integrative biostatistical approach to elucidate key molecules in the initial stages of stroke pathophysiology and highlight new notable molecules that might be further considered as blood biomarkers of ischemic stroke.

SUBMITTER: Simats A

PROVIDER: S-EPMC7710142 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Stroke remains a major leading cause of death and disability worldwide. Despite continuous advances, the identification of key molecular signatures of ischemic stroke within the hyper-acute phase of the disease is still of primary interest for a real translational research on stroke diagnosis, prognosis and treatment. High-throughput - omics technologies are enabling large-scale studies on stroke pathology at different molecular levels. Data integration resulting from these -omics approaches is becoming crucial to unravel the interactions among all different molecular elements and highly contribute to interpret all findings in a complex biological context. Here, we have used advanced data integration methods for multi-level joint analysis of transcriptomics and proteomics datasets depicted from the mouse brain 2h after cerebral ischemia. By modeling network-like correlation structures, we identified a set of differentially expressed genes and proteins by ischemia with a relevant association in stroke pathology. The ischemia-induced deregulation of 10 of these inter-correlated elements was successfully verified in a new cohort of ischemic mice, and changes in their expression pattern were also evaluated at a later time-point after cerebral ischemia. Of those, CLDN20, GADD45G, RGS2, BAG5 and CTNND2 were highlighted and evaluated as potential blood biomarkers of cerebral ischemia in blood samples from ischemic and sham-control mice and from ischemic strokes and other patients presenting stroke-mimicking conditions. Our findings indicated that CTNND2 and GADD45G levels in blood within the first hours after ischemic stroke might be potentially useful to discriminate ischemic strokes from mimics and to predict patients’ poor outcome after stroke, respectively. In summary, we have here used for the first time an integrative approach to elucidate by means of biostatistical tools key elements of the initial stages of the stroke pathophysiology, highlighting new outstanding proteins that might be further considered as blood biomarkers of ischemic stroke.

Project description:Recent technological advances and international efforts, such as The Cancer Genome Atlas (TCGA), have made available several pan-cancer datasets encompassing multiple omics layers with detailed clinical information in large collection of samples. The need has thus arisen for the development of computational methods aimed at improving cancer subtyping and biomarker identification from multi-modal data. Here we apply the Integrative Network Fusion (INF) pipeline, which combines multiple omics layers exploiting Similarity Network Fusion (SNF) within a machine learning predictive framework. INF includes a feature ranking scheme (rSNF) on SNF-integrated features, used by a classifier over juxtaposed multi-omics features (juXT). In particular, we show instances of INF implementing Random Forest (RF) and linear Support Vector Machine (LSVM) as the classifier, and two baseline RF and LSVM models are also trained on juXT. A compact RF model, called rSNFi, trained on the intersection of top-ranked biomarkers from the two approaches juXT and rSNF is finally derived. All the classifiers are run in a 10x5-fold cross-validation schema to warrant reproducibility, following the guidelines for an unbiased Data Analysis Plan by the US FDA-led initiatives MAQC/SEQC. INF is demonstrated on four classification tasks on three multi-modal TCGA oncogenomics datasets. Gene expression, protein expression and copy number variants are used to predict estrogen receptor status (BRCA-ER, N = 381) and breast invasive carcinoma subtypes (BRCA-subtypes, N = 305), while gene expression, miRNA expression and methylation data is used as predictor layers for acute myeloid leukemia and renal clear cell carcinoma survival (AML-OS, N = 157; KIRC-OS, N = 181). In test, INF achieved similar Matthews Correlation Coefficient (MCC) values and 97% to 83% smaller feature sizes (FS), compared with juXT for BRCA-ER (MCC: 0.83 vs. 0.80; FS: 56 vs. 1801) and BRCA-subtypes (0.84 vs. 0.80; 302 vs. 1801), improving KIRC-OS performance (0.38 vs. 0.31; 111 vs. 2319). INF predictions are generally more accurate in test than one-dimensional omics models, with smaller signatures too, where transcriptomics consistently play the leading role. Overall, the INF framework effectively integrates multiple data levels in oncogenomics classification tasks, improving over the performance of single layers alone and naive juxtaposition, and provides compact signature sizes.

Project description:ObjectivesCervical Squamous Cell Carcinoma (CESC) is one of the most fatal female malignancies, and the underlying molecular mechanisms governing this disease have not been fully explored. In this research, we planned to conduct the analysis of Gene Expression Omnibus (GEO) cervical squamous cell carcinoma microarray datasets by a detailed in silico approach and to explore some novel biomarkers of CESC.MethodsThe top commonly differentially expressed genes (DEGs) from the GSE138080 and GSE113942 datasets were analyzed by Limma package-based GEO2R tool. The protein-protein interaction (PPI) network of the DEGs was drawn through Search Tool for the Retrieval of Interacting Genes (STRING), and top 6 hub genes were obtained from Cytoscape. Expression analysis and validation of hub genes expression in CESC samples and cell lines were done using UALCAN, OncoDB, GENT2, and HPA. Additionally, cBioPortal, Gene set enrichment analysis (GSEA) tool, Kaplan-Meier (KM) plotter, ShinyGO, and DGIdb databases were also used to check some important values of hub genes in CESC.ResultsOut of 79 DEGs, the minichromosome maintenance complex component 4 (MCM4), nucleolar and spindle-associated protein 1 (NUSAP1), cell division cycle associated 5 (CDCA5), cell division cycle 45 (CDC45), denticleless E3 ubiquitin protein ligase homolog (DTL), and chromatin licensing and DNA replication factor 1 (CDT1) genes were regarded as hub genes in CESC. Further analysis revealed that the expressions of all these hub genes were significantly elevated in CESC cell lines and samples of diverse clinical attributes. In this study, we also documented some important correlations between hub genes and some other diverse measures, including DNA methylation, genetic alterations, and Overall Survival (OS). Last, we also identify hub genes associated ceRNA network and 31 important chemotherapeutic drugs.ConclusionThrough detailed in silico methodology, we identified 6 hub genes, including MCM4, NUSAP1, CDCA5, CDC45, DTL, and CDT1, which are likely to be associated with CESC development and diagnosis.

Project description:BackgroundNeuronal apoptosis, which is the primary pathological transform of cerebral injury following ischemic stroke (IS), is considered to be induced by endoplasmic reticulum stress (ERS) by numerous reports. However, ERS biomarkers in IS have not been fully identified yet. Consequently, the present study is aimed at exploring potential blood biomarkers by investigating the molecular mechanisms of ERS promoting neuronal apoptosis following IS development.MethodsA comprehensive analysis was performed with two free-accessible whole-blood datasets (GSE16561 and GSE37587) from the Gene Expression Omnibus database. Genetic information from 107 IS and 24 healthy controls was employed to analyze the differentially expressed genes (DEGs). Genes related to ERS (ERS-DEGs) were identified from the analysis. Enrichment analyses were performed to explore the biofunction and correlated signal pathways of ERS-DEGs. Protein-protein interaction (PPI) network and immune correlation analyses were performed to identify the hub genes along with their correspondent expressions and functions, all of which contributed to incremental diagnostic values.ResultsA total of 60 IS-related DEGs were identified, of which 27 genes were confirmed as ERS-DEGs. GO and KEGG enrichment analysis corroborated that upregulated ERS-DEGs were principally enriched in pathways related to immunity, including neutrophil activation and Th17 cell differentiation. Moreover, the GSEA and GSVA indicated that T cell-related signal pathways were the most considerably immune pathways for ERS-DEG enrichment. A total of 10 hub genes were filtered out via the PPI network analysis. Immune correlation analysis confirmed that the expression of hub genes is associated with immune cell infiltration.ConclusionsBy integrating and analyzing the two gene expression data profiles, it can be inferred that ERS may be involved in the development of neuronal apoptosis following IS via immune homeostasis. The identified hub genes, which are associated with immune cell infiltration, may serve as potential biomarkers for relative diagnosis and therapy.

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A Mouse Brain-based Multi-omics Integrative Approach Reveals Potential Blood Biomarkers for Ischemic Stroke.

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