Identification of the Resveratrol Potential Targets in the Treatment of Osteoarthritis

Objectives. Osteoarthritis (OA) is a chronic joint degenerative disease and has become an important health problem for the elderly. However, there is still a lack of effective drugs for the treatment of OA. Our research combines bioinformatics and experimental strategies to determine the target of resveratrol for OA treatment. Methods. First, the differentially expressed genes (DEGs) of OA joint tissues were obtained from the related microarray gene expression data. Second, resveratrol, a natural polyphenol compound, was used to screen the drug treatment target genes. Third, the drug-disease network was established, and the resveratrol target genes for OA treatment were obtained and verified through experimental verification. Results. A total of 300 differentially expressed genes with 246 upregulated and 54 downregulated were found in OA joint tissues, and 310 resveratrol potential target genes were obtained. Finally, six genes, namely, CXCL1, HIF1A, IL-6, MMP3, NOX4, and PTGS2, were selected to validate the treatment effects of the resveratrol. The results showed that all six genes in human OA chondrocytes were significantly increased. In addition, in these chondrocytes, CXCL1, HIF1A, IL-6, MMP3, NOX4, and PTGS2 were reduced considerably, but HIF1A was significantly increased after resveratrol treatment. Conclusions. Our data indicates that CXCL1, HIF1A, IL-6, MMP3, NOX4, and PTGS2 are all targets of resveratrol therapy. Our findings may provide valuable information for the mechanism and therapeutic of OA.

MACHINE LEARNING TECHNIQUE TO PREDICT THE MODEL FOR NON-SMALL CELL LUNG CANCER-BINARY LOGISTIC REGRESSION MODEL APPROACH

Lung cancer is one of the common types and deadly cancer in both men and women.This lung cancer accounts for high mortality and morbidity throughout the world.Detection of lung cancer has been made through surgery,chemotherapy, biopsy and microarray studies.Gene expression plays an important role in molecular fluctuations and disease prophecy of a disease.The aim of the study is to design a statistical model and to find the genes influencing the cause of lung cancer. Microarray gene expression data was collected from Gene Expression Omnibus datasets (GEO-DATASET)-an open source database.The dataset contains a total of 161 samples which has 89 lung cancer samples and 72 normal samples. From this the upregulated and influenced genes were identified and determined by using logfc from the GPL file.Wide use of statistical models leads to exploring machine learning methods to find a better model. These study methods implement the performance of regression analysis using multilayer perceptron. By using the regression analysis method,the overall accuracy is found to be 91.3%.By this,the gene expression data analysis reveals that the regression analysis is one of the best models to show the accuracy in implementation of genes influencing the NSCLC.

Cross-Tissue Transcriptomic Analysis Leveraging Machine Learning Approaches Identifies New Biomarkers for Rheumatoid Arthritis

There is an urgent need to identify biomarkers for diagnosis and disease activity monitoring in rheumatoid arthritis (RA). We leveraged publicly available microarray gene expression data in the NCBI GEO database for whole blood (N=1,885) and synovial (N=284) tissues from RA patients and healthy controls. We developed a robust machine learning feature selection pipeline with validation on five independent datasets culminating in 13 genes: TNFAIP6, S100A8, TNFSF10, DRAM1, LY96, QPCT, KYNU, ENTPD1, CLIC1, ATP6V0E1, HSP90AB1, NCL and CIRBP which define the RA score and demonstrate its clinical utility: the score tracks the disease activity DAS28 (p = 7e-9), distinguishes osteoarthritis (OA) from RA (OR 0.57, p = 8e-10) and polyJIA from healthy controls (OR 1.15, p = 2e-4) and monitors treatment effect in RA (p = 2e-4). Finally, the immunoblotting analysis of six proteins on an independent cohort confirmed two proteins, TNFAIP6/TSG6 and HSP90AB1/HSP90.

Comparison of machine-learning methodologies for accurate diagnosis of sepsis using microarray gene expression data

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.

Identification of potential genomic biomarkers for Parkinson’s disease using data pooling of gene expression microarrays

Aim: In this study, we aimed to identify potential diagnostic biomarkers Parkinson’s disease (PD) by exploring microarray gene expression data of PD patients. Materials & methods: Differentially expressed genes associated with PD were screened from the GSE99039 dataset using weighted gene co-expression network analysis, followed by gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses, gene–gene interaction network analysis and receiver operator characteristics analysis. Results: We identified two PD-associated modules, in which genes from the chemokine signaling pathway were primarily enriched. In particular, CS, PRKCD, RHOG and VAMP2 directly interacted with known PD-associated genes and showed higher expression in the PD samples, and may thus be potential biomarkers in PD diagnosis. Conclusion: A DFG-analysis identified a four-gene panel ( CS, PRKCD, RHOG, VAMP2) as a potential diagnostic predictor to diagnose PD.

A data-driven approach for the discovery of biomarkers associated with thyroid eye disease

Background Thyroid eye disease (TED) is the most common autoimmune disease and usually occurs in patients with hyperthyroidism. In this disease, eye-related tissue, such as eye muscles, eyelids, tear glands, etc., become inflated, which causes the eyes and eyelids to become red, swollen, and uncomfortable. The pathophysiology of this disease is still poorly known. Aim This study aims to discover potential biomarkers and regulatory pathways of TED which will not only help to diagnose the disease and understand orbital involvement in thyroid dysfunction but also provide an insight for better therapeutics. Methods We applied a data-driven approach by combining gene biomarkers both from published literature and computationally predicted from microarray gene expression data. Further, the DAVID tool is used for Gene Ontology-based enrichment analysis. Results We obtained a total of 22 gene biomarkers, including 18 semi-automatically curated from the literature and 4 predicted using data-driven approaches, involved in the pathogenesis of TED that can be used as potential information for therapeutic targets. Further, we constructed a regulatory pathway of TED biomarkers comprises of 310 connected components, and 1134 interactions using four prominent interaction databases. Conclusion This constructed pathway can be further utilized for disease dynamics and simulation studies.

Reanalysis of Non-Small-Cell Lung Cancer Microarray Gene Expression Data

Cancer is one of the leading causes of death in many countries, and this continues to be the case because of the lack of sufficient treatment. One of the most common types is non-small-cell lung cancer (NSCLC). The increasingly large and diverse public datasets about NSCLC constitute a rich source of data on which several analyses can be performed so as to find candidate oncogenic drivers or therapeutic targets. The aim of this study is to reanalyze an existing NSCLC NCBI GEO Dataset (accession = GSE19804) in order to see if novel involved genes can be found. For this, we used microarray technology for preprocessing and, based on random forest, support vector machine and C5.0 decision tree models, made a comparison of the 10 most important genes recorded. This study was realized with R-Studio 4.0.2 and Bioconductor 3.11. In conclusion, the EFNA4 gene and other genes, namely KANK3, GRK5, CLIC5, SH3GL3, ACACB, LIN7A, JCAD, and NEDD1, are thought to be potential genes that may play a role in NSCLC and it is recommended that researchers working in the wet laboratory should focus on these genes.