microarray gene
Recently Published Documents


TOTAL DOCUMENTS

791
(FIVE YEARS 95)

H-INDEX

58
(FIVE YEARS 5)

Life ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1128
Author(s):  
Patrizia Virdis ◽  
Irene Marchesi ◽  
Francesco Paolo Fiorentino ◽  
Rossana Migheli ◽  
Luca Sanna ◽  
...  

(1) Tomentosin is the most representative sesquiterpene lactone extracted by I. viscosa. Recently, it has gained particular attention in therapeutic oncologic fields due to its anti-tumor properties. (2) In this study, the potential anticancer features of tomentosin were evaluated on human Burkitt’s lymphoma (BL) cell line, treated with increasing tomentosin concentration for cytotoxicity screening. (3) Our data showed that both cell cycle arrest and cell apoptosis induction are responsible of the antiproliferative effects of tomentosin and may end in the inhibition of BL cell viability. Moreover, a microarray gene expression profile was performed to assess differentially expressed genes contributing to tomentosin activity. Seventy-five genes deregulated by tomentosin have been identified. Downregulated genes are enriched in immune-system pathways, and PI3K/AKT and JAK/STAT pathways which favor proliferation and growth processes. Importantly, different deregulated genes identified in tomentosin-treated BL cells are prevalent in molecular pathways known to lead to cellular death, specifically by apoptosis. Tomentosin-treatment in BL cells induces the downregulation of antiapoptotic genes such as BCL2A1 and CDKN1A and upregulation of the proapoptotic PMAIP1 gene. (4) Overall, our results suggest that tomentosin could be taken into consideration as a potential natural product with limited toxicity and relevant anti-tumoral activity in the therapeutic options available to BL patients.


2021 ◽  
Author(s):  
Neetu Tyagi ◽  
Dinesh Gupta

Abstract Background Autoimmune diseases develop when a person’s immune system starts developing immune response against its own healthy cells, tissues, or any other cell constituents. Rheumatoid Arthritis (RA) and Systemic Lupus Erythromatosus (SLE) are the two most common systemic inflammatory autoimmune diseases, sharing various clinical as well as pathological signatures. Although multiple studies have been conducted to date, very little is known about molecular pathogenesis and overlapping molecular signatures of the two diseases. Motivated to explore the common molecular disease features, we conducted a meta-analysis of the publicly available microarray gene expression datasets of RA and SLE. Methods Common and unique gene signatures of RA and SLE were identified based on analysis of microarray gene-expression datasets. Hub genes were identified by performing network analysis of protein-protein interaction (PPI) networks of the identified genes. Gene ontology functional enrichment and integrative pathway analysis was also performed to understand the underlying molecular mechanisms in the diseases. Results Intriguingly, out of the identified signature genes, 9 are upregulated and 24 are downregulated. Many of the common gene signatures identified in this study provide clues to the shared pathological mechanisms of RA and SLE. Amongst the identified signatures, MMP8, NFIL3, B4GALT5, HIST1H1C, NMT2, PTGDS and DUSP14, are the robust gene signatures shared by all the RA and SLE datasets. Functional analysis revealed that the common signatures are involved in the pathways such as mTOR signaling pathways, virus infection-related pathways, bone remodeling, activation of matrix metalloproteinase pathway, immune and inflammatory response-related pathways. Conclusions The common gene signatures and related pathways identified in this study substantiate the shared pathological mechanism involved in both diseases. Furthermore, our analysis of multi-cohort and multiple microarray datasets allow discovery of novel leads for clinical diagnosis and potential novel drug targets.


PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0251800
Author(s):  
Dominik Schaack ◽  
Markus A. Weigand ◽  
Florian Uhle

We investigate the feasibility of molecular-level sample classification of sepsis using microarray gene expression data merged by in silico meta-analysis. Publicly available data series were extracted from NCBI Gene Expression Omnibus and EMBL-EBI ArrayExpress to create a comprehensive meta-analysis microarray expression set (meta-expression set). Measurements had to be obtained via microarray-technique from whole blood samples of adult or pediatric patients with sepsis diagnosed based on international consensus definition immediately after admission to the intensive care unit. We aggregate trauma patients, systemic inflammatory response syndrome (SIRS) patients, and healthy controls in a non-septic entity. Differential expression (DE) analysis is compared with machine-learning-based solutions like decision tree (DT), random forest (RF), support vector machine (SVM), and deep-learning neural networks (DNNs). We evaluated classifier training and discrimination performance in 100 independent iterations. To test diagnostic resilience, we gradually degraded expression data in multiple levels. Clustering of expression values based on DE genes results in partial identification of sepsis samples. In contrast, RF, SVM, and DNN provide excellent diagnostic performance measured in terms of accuracy and area under the curve (>0.96 and >0.99, respectively). We prove DNNs as the most resilient methodology, virtually unaffected by targeted removal of DE genes. By surpassing most other published solutions, the presented approach substantially augments current diagnostic capability in intensive care medicine.


2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Nathaly M. Sweeney ◽  
Shareef A. Nahas ◽  
Sh. Chowdhury ◽  
Sergey Batalov ◽  
Michelle Clark ◽  
...  

AbstractCongenital heart disease (CHD) is the most common congenital anomaly and a major cause of infant morbidity and mortality. While morbidity and mortality are highest in infants with underlying genetic conditions, molecular diagnoses are ascertained in only ~20% of cases using widely adopted genetic tests. Furthermore, cost of care for children and adults with CHD has increased dramatically. Rapid whole genome sequencing (rWGS) of newborns in intensive care units with suspected genetic diseases has been associated with increased rate of diagnosis and a net reduction in cost of care. In this study, we explored whether the clinical utility of rWGS extends to critically ill infants with structural CHD through a retrospective review of rWGS study data obtained from inpatient infants < 1 year with structural CHD at a regional children’s hospital. rWGS diagnosed genetic disease in 46% of the enrolled infants. Moreover, genetic disease was identified five times more frequently with rWGS than microarray ± gene panel testing in 21 of these infants (rWGS diagnosed 43% versus 10% with microarray ± gene panels, p = 0.02). Molecular diagnoses ranged from syndromes affecting multiple organ systems to disorders limited to the cardiovascular system. The average daily hospital spending was lower in the time period post blood collection for rWGS compared to prior (p = 0.003) and further decreased after rWGS results (p = 0.000). The cost was not prohibitive to rWGS implementation in the care of this cohort of infants. rWGS provided timely actionable information that impacted care and there was evidence of decreased hospital spending around rWGS implementation.


Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 437
Author(s):  
Titin Siswantining ◽  
Noval Saputra ◽  
Devvi Sarwinda ◽  
Herley Shaori Al-Ash

Clustering is a mathematical approach that allows one to find a group of data with similar attributes. This approach is also often used in the field of computer science to group a large amounts of data. Triclustering analysis is an analysis technique on 3D data (observation—attribute—context). Triclustering analysis can group observations on several attributes and contexts simultaneously. Triclustering analysis has been frequently applied to analyze microarray gene expression data. We proposed the δ-Trimax method to perform triclustering analysis on microarray gene expression data. The δ-Trimax method aims to find a tricluster that has a mean square residual smaller than δ and a maximum volume. Tricluster is obtained by deleting nodes from 3D data using multiple node deletion and single node deletion algorithms. The tricluster candidates that have been obtained are checked again by adding some previously deleted nodes using the node addition algorithm. In this research, the program improvement of the δ-Trimax method was carried out and also the calculation of the resulting tricluster evaluation result. The δ-Trimax method is implemented in two microarray gene expression data. The first implementation was carried out on gene expression data from the differentiation process of human-induced pluripotent stem cells (HiPSCs) from patients with heart disease, resulting in the best simulation when δ=0.0068, λ=1.2, and obtained five tricluster, which are considered as characteristics of heart disease. The second implementation was implemented on HIV-1 data, best simulation when δ=0.0046, λ=1.25 and produced three genes as biomarkers, with the gene names AGFG1, EGR1 and HLA-C. This gene group can be used by medical experts in providing further treatment.


Sign in / Sign up

Export Citation Format

Share Document