scholarly journals Drug-disease interactions of differentially expressed genes in COVID-19 liver samples: an in-silico analysis

2021 ◽  
Vol 62 (4) ◽  
pp. 316-324
Author(s):  
Susan Omar Rasool ◽  
Ata Mirzaei Nahr ◽  
Sania Eskandari ◽  
Milad Hosseinzadeh ◽  
Soheila Asoudeh Moghanloo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cytochrome P450 ◽  
Differentially Expressed Genes ◽  
In Silico ◽  
In Silico Analysis ◽  
Gene Interaction ◽  
Differentially Expressed ◽  
Alcoholic Fatty Liver ◽  
Interaction Database ◽  
Cytochrome P450 Genes

While COVID-19 liver injuries have been reported in various studies, concerns are raised about disease-drug reactions in COVID-19 patients. In this study, we examined the hypothesis of gene-disease interactions in an in-silico model of gene expression to seek changes in cytochrome P450 genes. The Gene Expression Omnibus dataset of the liver autopsy in deceased COVID-19 patients (GSE150316) was used in this study. Non-alcoholic fatty liver biopsies were used as the control (GSE167523). Besides, gene expression analysis was performed using the DESeq/EdgeR method. The GO databases were used, and the paths were set at p<0.05. The drug-gene interaction database (DGIdb) was searched for interactions. According to the results, 5,147 genes were downregulated, and 5,122 genes were upregulated in SARS-CoV-2 compared to healthy livers. Compared to the cytochromes, 34 cytochromes were downregulated, while 4 cytochromes were upregulated among the detected differentially expressed genes (DEG). The drug-gene interaction database (DGIdb) provided a list of medications with potential interactions with COVID-19 as well as metacetamol, phenethyl isocyanate, amodiaquine, spironolactone, amiloride, acenocoumarol, clopidogrel, phenprocoumon, trimipramine, phenazepam, etc. Besides, dietary compounds of isoflavones, valerian, and coumarin, as well as caffeine metabolism were shown to have possible interactions with COVID-19 disease. Our study showed that expression levels of cytochrome P450 genes could get altered following COVID-19. In addition, a drug-disease interaction list is recommended to be used for evaluations in clinical considerations in further studies.

scholarly journals Identification of Diagnostic Markers for Breast Cancer Based on Differential Gene Expression and Pathway Network

2022 ◽  
Vol 9 ◽  
Author(s):  
Shumei Zhang ◽  
Haoran Jiang ◽  
Bo Gao ◽  
Wen Yang ◽  
Guohua Wang
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Gene Interaction ◽  
Diagnostic Markers ◽  
Differentially Expressed ◽  
Breast Cancer Patients ◽  
Gene Interaction Network

Background: Breast cancer is the second largest cancer in the world, the incidence of breast cancer continues to rise worldwide, and women’s health is seriously threatened. Therefore, it is very important to explore the characteristic changes of breast cancer from the gene level, including the screening of differentially expressed genes and the identification of diagnostic markers.Methods: The gene expression profiles of breast cancer were obtained from the TCGA database. The edgeR R software package was used to screen the differentially expressed genes between breast cancer patients and normal samples. The function and pathway enrichment analysis of these genes revealed significant enrichment of functions and pathways. Next, download these pathways from KEGG website, extract the gene interaction relations, construct the KEGG pathway gene interaction network. The potential diagnostic markers of breast cancer were obtained by combining the differentially expressed genes with the key genes in the network. Finally, these markers were used to construct the diagnostic prediction model of breast cancer, and the predictive ability of the model and the diagnostic ability of the markers were verified by internal and external data.Results: 1060 differentially expressed genes were identified between breast cancer patients and normal controls. Enrichment analysis revealed 28 significantly enriched pathways (p &lt; 0.05). They were downloaded from KEGG website, and the gene interaction relations were extracted to construct the gene interaction network of KEGG pathway, which contained 1277 nodes and 7345 edges. The key nodes with a degree greater than 30 were extracted from the network, containing 154 genes. These 154 key genes shared 23 genes with differentially expressed genes, which serve as potential diagnostic markers for breast cancer. The 23 genes were used as features to construct the SVM classification model, and the model had good predictive ability in both the training dataset and the validation dataset (AUC = 0.960 and 0.907, respectively).Conclusion: This study showed that the difference of gene expression level is important for the diagnosis of breast cancer, and identified 23 breast cancer diagnostic markers, which provides valuable information for clinical diagnosis and basic treatment experiments.

scholarly journals In-silico analysis of differentially expressed genes and their regulating microRNA involved in lymph node metastasis in invasive breast carcinoma

2020 ◽  
Author(s):  
Anupama Modi ◽  
Purvi Purohit ◽  
Ashita Gadwal ◽  
Shweta Ukey ◽  
Dipayan Roy ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Candidate Genes ◽  
Differentially Expressed Genes ◽  
In Silico ◽  
Nodal Metastasis ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Node Metastasis

AbstractIntroductionAxillary nodal metastasis is related to poor prognosis in breast cancer (BC). The metastatic progression in BC is related to molecular signatures. The currently popular methods to evaluate nodal status may give false negatives or give rise to secondary complications. In this study, key candidate genes in BC lymph node metastasis have been identified from publicly available microarray datasets and their roles in BC have been explored through survival analysis and target prediction.MethodsGene Expression Omnibus datasets have been analyzed for differentially expressed genes (DEGs) in lymph node-positive BC patients compared to nodal-negative and healthy tissues. The functional enrichment analysis was done in database for annotation, visualization and integrated discovery (DAVID). Protein-protein interaction (PPI) network was constructed in Search Tool for the Retrieval of Interacting Genes and proteins (STRING) and visualized on Cytoscape. The candidate hub genes were identified and their expression analyzed for overall survival (OS) in Gene Expression Profiling Interactive Analysis (GEPIA). The target miRNA and transcription factors were analyzed through miRNet.ResultsA total of 102 overlapping DEGs were found. Gene Ontology revealed eleven, seventeen, and three significant terms for cellular component, biological process, and molecular function respectively. Six candidate genes, DSC3, KRT5, KRT6B, KRT17, KRT81, and SERPINB5 were significantly associated with nodal metastasis and OS in BC patients. A total of 83 targeting miRNA were identified through miRNet and hsa-miR-155-5p was found to be the most significant miRNA which was targeting five out of six hub genes.ConclusionIn-silico survival and expression analyses revealed six candidate genes and 83 miRNAs, which may be potential diagnostic markers and therapeutic targets in BC patients and miR-155-5p shows promise as it targeted five important hub genes related to lymph-node metastasis.

scholarly journals A Comparative Meta-Analysis and in silico Analysis of Differentially Expressed Genes and Proteins in Canine and Human Bladder Cancer

2020 ◽  
Vol 7 ◽  
Author(s):  
Victoria Vitti Gambim ◽  
Renee Laufer-Amorim ◽  
Ricardo Henrique Fonseca Alves ◽  
Valeria Grieco ◽  
Carlos Eduardo Fonseca-Alves
Keyword(s):  
Bladder Cancer ◽  
Differentially Expressed Genes ◽  
In Silico ◽  
Meta Analysis ◽  
In Silico Analysis ◽  
Differentially Expressed ◽  
Human Bladder Cancer ◽  
Human Bladder ◽  
Silico Analysis

scholarly journals Gene Network Analysis in Amygdala following Taste Aversion Learning in Rats

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Siva K. Panguluri ◽  
Nobuyuki Kuwabara ◽  
Nigel Cooper ◽  
Srinivas M. Tipparaju ◽  
Kevin B. Sneed ◽  
...  
Keyword(s):  
Gene Expression ◽  
Taste Aversion ◽  
Differentially Expressed Genes ◽  
Conditioned Taste Aversion ◽  
In Silico ◽  
Regulation Of Gene Expression ◽  
Memory Formation ◽  
Nervous System Development ◽  
Differentially Expressed ◽  
Taste Aversion Learning

Conditioned taste aversion (CTA) is an adaptive behavior that benefits survival of animals including humans and also serves as a powerful model to study the neural mechanisms of learning. Memory formation is a necessary component of CTA learning and involves neural processing and regulation of gene expression in the amygdala. Many studies have been focused on the identification of intracellular signaling cascades involved in CTA, but not late responsive genes underlying the long-lasting behavioral plasticity. In this study, we explored in silico experiments to identify persistent changes in gene expression associated with CTA in rats. We used oligonucleotide microarrays to identify 248 genes in the amygdala regulated by CTA. Pathway Studio and IPA software analyses showed that the differentially expressed genes in the amygdala fall in diverse functional categories such as behavior, psychological disorders, nervous system development and function, and cell-to-cell signaling. Conditioned taste aversion is a complex behavioral trait which involves association of visceral and taste inputs, consolidation of taste and visceral information, memory formation, retrieval of stored information, and extinction phase. In silico analysis of differentially expressed genes is therefore necessary to manipulate specific phase/stage of CTA to understand the molecular insight.

scholarly journals Differential gene expression analysis of ankylosing spondylitis shows deregulation of the HLA-DRB, HLA-DQB, ITM2A, and CTLA4 genes

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Rowan AlEjielat ◽  
Anas Khaleel ◽  
Amneh H. Tarkhan
Keyword(s):  
Gene Expression ◽  
Ankylosing Spondylitis ◽  
Differentially Expressed Genes ◽  
Gene Network ◽  
Disease Diagnosis ◽  
Receptor Activation ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Inflammatory Disorder ◽  
Data Set

Abstract Background Ankylosing spondylitis (AS) is a rare inflammatory disorder affecting the spinal joints. Although we know some of the genetic factors that are associated with the disease, the molecular basis of this illness has not yet been fully elucidated, and the genes involved in AS pathogenesis have not been entirely identified. The current study aimed at constructing a gene network that may serve as an AS gene signature and biomarker, both of which will help in disease diagnosis and the identification of therapeutic targets. Previously published gene expression profiles of 16 AS patients and 16 gender- and age-matched controls that were profiled on the Illumina HumanHT-12 V3.0 Expression BeadChip platform were mined. Patients were Portuguese, 21 to 64 years old, were diagnosed based on the modified New York criteria, and had Bath Ankylosing Spondylitis Disease Activity Index scores > 4 and Bath Ankylosing Spondylitis Functional Index scores > 4. All patients were receiving only NSAIDs and/or sulphasalazine. Functional enrichment and pathway analysis were performed to create an interaction network of differentially expressed genes. Results ITM2A, ICOS, VSIG10L, CD59, TRAC, and CTLA-4 were among the significantly differentially expressed genes in AS, but the most significantly downregulated genes were the HLA-DRB6, HLA-DRB5, HLA-DRB4, HLA-DRB3, HLA-DRB1, HLA-DQB1, ITM2A, and CTLA-4 genes. The genes in this study were mostly associated with the regulation of the immune system processes, parts of cell membrane, and signaling related to T cell receptor and antigen receptor, in addition to some overlaps related to the IL2 STAT signaling, as well as the androgen response. The most significantly over-represented pathways in the data set were associated with the “RUNX1 and FOXP3 which control the development of regulatory T lymphocytes (Tregs)” and the “GABA receptor activation” pathways. Conclusions Comprehensive gene analysis of differentially expressed genes in AS reveals a significant gene network that is involved in a multitude of important immune and inflammatory pathways. These pathways and networks might serve as biomarkers for AS and can potentially help in diagnosing the disease and identifying future targets for treatment.

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