scholarly journals Multi-Data Aspects of Protein Similarity with a Learning Technique to Identify Drug-Disease Associations

2021 ◽  
Vol 11 (7) ◽  
pp. 2914
Author(s):  
Satanat Kitsiranuwat ◽  
Apichat Suratanee ◽  
Kitiporn Plaimas
Keyword(s):  
High Performance ◽  
Drug Repositioning ◽  
Area Under The Curve ◽  
Interaction Network ◽  
Biological Information ◽  
P Value ◽  
Protein Protein Interaction ◽  
Proteomic Data ◽  
Disease Associations ◽  
Functional Pathway

Drug repositioning has been proposed to develop drugs for diseases. However, the similarity in a single aspect may not be sufficient to reveal hidden information. Therefore, we established protein–protein similarity vectors (PPSVs) based on potential similarities in various types of biological information associated with proteins, including their network topology, proteomic data, functional analysis, and druggable property. Based on the proposed PPSVs, a separate drug–disease matrix was constructed for individual to prevent characteristics from being obscured between diseases. The classification technique was employed for prediction. The results showed that more than half of the tested disease models exhibited high performance, with overall F1 scores of more than 80%. Furthermore, comparing all diseases using traditional methods in one run, we obtained an (area under the curve) AUC of 98.9%. All candidate drugs were then tested in clinical trials (p-value < 2.2 × 10−16) and were known drugs based on their functions (p-value < 0.05). An analysis revealed that, in the functional aspect, the confidence value of an interaction in the protein–protein interaction network and the functional pathway score were the best descriptors for prediction. Based on the learning processes of PPSVs with an isolated disease, the classifier exhibited high performance in predicting and identifying new potential drugs for that disease.

scholarly journals DDA: A Novel Network-Based Scoring Method to Identify Disease-Disease Associations

2015 ◽  
Vol 9 ◽  
pp. BBI.S35237 ◽  
Author(s):  
Apichat Suratanee ◽  
Kitiporn Plaimas
Keyword(s):  
Large Scale ◽  
Association Studies ◽  
Area Under The Curve ◽  
Interaction Network ◽  
Disease Diagnosis ◽  
Scoring Method ◽  
Protein Protein Interaction ◽  
Disease Associations ◽  
Statistical Relationships ◽  
Complex Relationships

Categorizing human diseases provides higher efficiency and accuracy for disease diagnosis, prognosis, and treatment. Disease-disease association (DDA) is a precious information that indicates the large-scale structure of complex relationships of diseases. However, the number of known and reliable associations is very small. Therefore, identification of DDAs is a challenging task in systems biology and medicine. Here, we developed a novel network-based scoring algorithm called DDA to identify the relationships between diseases in a large-scale study. Our method is developed based on a random walk prioritization in a protein-protein interaction network. This approach considers not only whether two diseases directly share associated genes but also the statistical relationships between two different diseases using known disease-related genes. Predicted associations were validated by known DDAs from a database and literature supports. The method yielded a good performance with an area under the curve of 71% and outperformed other standard association indices. Furthermore, novel DDAs and relationships among diseases from the clusters analysis were reported. This method is efficient to identify disease-disease relationships on an interaction network and can also be generalized to other association studies to further enhance knowledge in medical studies.

scholarly journals Evaluation of SYK Gene as a Prognostic Biomarker and Suggested Aromatic Potential Phytochemicals to Halt the Colorectal Cancer

2021 ◽  
Author(s):  
Partha Biswas ◽  
Dipta Dey ◽  
Atikur Rahman ◽  
Md. Aminul Islam ◽  
Tasmina Ferdous Susmi ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Molecular Docking ◽  
Drug Repositioning ◽  
Treatment Strategies ◽  
Interaction Network ◽  
Genetic Alterations ◽  
Gene Encoding ◽  
Protein Protein Interaction ◽  
Normal Tissues ◽  
Potential Biomarker

Background: Colorectal cancer is considered the third most fetal among all type of cancer. Spleen tyrosine kinase (SYK) is a non-receptor type tyrosine-protein that plays crucial role in signaling mediated via immune receptor. We adopted an onco-informatics analysis to evaluate the SYK expression and prognostic value of SYK in colorectal cancer, and identification of potential phytochemicals which may inhibit overexpression of SYK protein as well as minimized colorectal cancer. Materials &amp; Methods: Differential expression of SYK gene was analyzed using the several transcriptomic databases including Oncomine, UALCAN, GENT2 and GEPIA2. The server, cBioPortal was used to analyze mutation and copy number alterations whereas GENT2, GEPIA, OncoLnc and PrognoScan were employed to examine the survival rate. A protein-protein interaction network of SYK and co-expressed genes of SYK was conducted via GeneMANIA. Considering SYK gene encoding protein as drug target, selected phytochemicals were assessed by molecular docking using PyRx 0.8 packages. YASARA molecular dynamics simulators were applied for the post validation of the molecular docking data. Results: We have observed significant overexpression of mRNA expression levels of SYK gene colorectal adenocarcinoma (COAD) samples compared with normal tissues. Significant methylation level and various genetic alterations are assembled in SYK gene which can lead to the development of colorectal cancer. As a result, lower level of SYK expression was related to the more chances of patients&rsquo; survival by which all the outcomes from the multiple bioinformatics platforms and web resources have demonstrated the significant evidences that the SYK kinsase can possess as a potential biomarker for the treatment of colorectal cancer. Here, aromatic phytochemicals namely, Kaempferol and Glabridin targeting SYK showed more stability compared to controls and may be useful for the treatment of colorectal cancer. Conclusion: Our study showed dysregulated expression of SYK in colorectal cancer and potentiality to act as a biomarker for the prognosis of CRC. Moreover, we have shown phytochemicals (Kaempferol and Glabridin) target SYK as potential treatment strategies and drug repositioning potentiality in colorectal cancer.

scholarly journals Ten genes are considered as potential biomarkers for the diagnosis of dermatomyositis

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0260511
Author(s):  
Lu Xiao ◽  
Wei Xiao ◽  
Shudian Lin
Keyword(s):  
Characteristic Curve ◽  
Area Under The Curve ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Type I ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Healthy Control ◽  
Potential Biomarkers

Objective This study aimed to identify the biomarkers and mechanisms for dermatomyositis (DM) progression at the transcriptome level through a combination of microarray and bioinformatic analyses. Method Microarray datasets for skeletal muscle of DM and healthy control (HC) were downloaded from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were identified by using GEO2R. Enrichment analyses were performed to understand the functions and enriched pathways of DEGs. A protein–protein interaction network was constructed to identify hub genes. The top 10 hub genes were validated by other GEO datasets. The diagnostic accuracy of the top 10 hub genes for DM was evaluated using the area under the curve of the receiver operating characteristic curve. Result A total of 63 DEGs were identified between 10 DM samples and 9 HC samples. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis indicated that DEGs are mostly enriched in response to virus, defense response to virus, and type I interferon signaling pathway. 10 hub genes and 3 gene cluster modules were identified by Cytoscape. The identified hub genes were verified by GSE1551 and GSE11971 datasets and proven to be potential biomarkers for the diagnosis of DM. Conclusion Our work identified 10 valuable genes as potential biomarkers for the diagnosis of DM and explored the potential underlying molecular mechanism of the disease.

scholarly journals Proteomic Analysis of Rhesus Macaque Brain Explants Infected With Borrelia Burgdorferi Identifies Host GAP-43 As A Potential Factor Associated With Lyme Neuroborreliosis

2020 ◽  
Author(s):  
Lianbao Li ◽  
Lisha Luo ◽  
Taigui Chen ◽  
Wenjing Cao ◽  
Xin Xu ◽  
...  
Keyword(s):  
Borrelia Burgdorferi ◽  
Bioinformatics Analysis ◽  
Interaction Network ◽  
Lyme Neuroborreliosis ◽  
P Value ◽  
Protein Protein Interaction ◽  
Potential Marker ◽  
Potential Factor ◽  
Phosphate Buffered Saline ◽  
Protein Protein Interaction Network

Abstract Background: Lyme neuroborreliosis (LNB) is one of the most dangerous manifestations of Lyme disease, but the pathogenesis and inflammatory mechanisms are not fully understood.Methods: Cultured explants from the frontal cortex of rhesus monkey brain (n=3) were treated with live Borrelia burgdorferi (Bb) or phosphate-buffered saline (PBS) for 6, 12, and 24 h. Total protein was collected for sequencing and bioinformatics analysis. Changes in protein expression in the explants over time following Bb infection were screened.Results: We identified 1237 differentially expressed proteins (DEPs; fold change ≥1.5 or ≤0.67, P-value ≤0.05). One of these, growth-associated protein 43 (GAP-43), was highly expressed at all time points in the explants. The results of the protein-protein interaction network analysis of DEPs suggested that GAP-43 plays a role in the neuroinflammation associated with LNB. In HMC3 cells incubated with live Bb or PBS for 6, 12, and 24 h, real-time PCR and western blot analyses confirmed the upregulation of GAP-43 mRNA and protein, respectively.Conclusions: Elevated GAP-43 expression is a potential marker for LNB that may be useful for diagnosis or treatment.

Combining Sequence Entropy and Subgraph Topology for Complex Prediction in Protein Protein Interaction (PPI) Network

2019 ◽  
Vol 14 (6) ◽  
pp. 516-523 ◽  
Author(s):  
Aisha Sikandar ◽  
Waqas Anwar ◽  
Misba Sikandar
Keyword(s):  
Protein Complexes ◽  
Interaction Network ◽  
Amino Acid Sequences ◽  
Biological Information ◽  
Biological Study ◽  
Model Complexes ◽  
Sequence Entropy ◽  
Protein Protein Interaction ◽  
Topological Features ◽  
Logistic Model Tree

Background: Complex prediction from interaction network of proteins has become a challenging task. Most of the computational approaches focus on topological structures of protein complexes and fewer of them consider important biological information contained within amino acid sequences. Objective: To capture the essence of information contained within protein sequences we have computed sequence entropy and length. Proteins interact with each other and form different sub graph topologies. Methods: We integrate biological features with sub graph topological features and model complexes by using a Logistic Model Tree. Results: The experimental results demonstrated that our method out performs other four state-ofart computational methods in terms of the number of detecting known protein complexes correctly. Conclusion: In addition, our framework provides insights into future biological study and might be helpful in predicting other types of sub graph topologies.

scholarly journals Aberrantly methylated-differentially genes and pathways among Iranian patients with colorectal cancer

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Mahla Ghorbani ◽  
Marjan Azghandi ◽  
Mohammad Amin Kerachian
Keyword(s):  
Colorectal Cancer ◽  
Candidate Genes ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
P Value ◽  
Gene Methylation ◽  
Protein Protein Interaction ◽  
Kegg Pathways ◽  
Differentially Methylated Genes ◽  
Methylation Profiling

Abstract Background Methylation plays an important role in colorectal cancer (CRC) pathogenesis. The goal of this study was to identify aberrantly differentially methylated genes (DMGs) and pathways through bioinformatics analysis among Iranian CRC patients using Methylation Next Generation Sequencing. Methods This study has integrated results of SureSelectXT Methyl-Seq Target with the potential key candidate genes and pathways in CRC. Six CRC and six samples of normal colon were integrated and deeply analyzed. In addition to this gene methylation profiling, several other gene methylation profiling datasets were obtained from Gene Expression Omnibus (GEO) and TCGA datasets. DMGs were sorted and candidate genes and enrichment pathways were analyzed. DMGs-associated protein–protein interaction network (PPI) was constructed based on the STRING online database. Results Totally, 320 genes were detected as common genes between our patients and selected GEO and TCGA datasets from the Agilent SureSelect analysis with selecting criteria of p-value < 0.05 and FC ≥ 1.5. DMGs were identified from hyper-DMGs PPI network complex and 10 KEGG pathways were identified. The most important modules were extracted from MCODE, as most of the corresponding genes were involved in cellular process and protein binding. Conclusions Hub genes including WNT2, SFRP2, ZNF726 and BMP2 were suggested as potentially diagnostic and therapeutic targets for CRC.

miR-194 as predictive biomarker of responsiveness to neoadjuvant chemoradiotherapy in patients with locally advanced rectal adenocarcinoma

2017 ◽  
Vol 71 (4) ◽  
pp. 344-350 ◽  
Author(s):  
Edoardo D’Angelo ◽  
Carlo Zanon ◽  
Francesca Sensi ◽  
Maura Digito ◽  
Massimo Rugge ◽  
...  
Keyword(s):  
Locally Advanced ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Chemical Compounds ◽  
Predictive Biomarker ◽  
P Value ◽  
Pathway Enrichment Analysis ◽  
Pathway Enrichment ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

AimsCurative surgery remains the primary form of treatment for locally advanced rectal cancer (LARC). Recent data support the use of preoperative chemoradiotherapy (pCRT) to improve the prognosis of LARC with a significant reduction of local relapse and an increase of overall survival. Unfortunately, only 20% of the patients with LARC present complete pathological response after pCRT, whereas in 20%–40%, the response is poor or absent.MethodsWe investigated the expression level of miR-194 in n=38 patients with LARC using our public microRNA (miRNA) expression dataset. miR-194 expression was further validated by real-time quantitative PCR (qRT-PCR) and in situ hybridisation (ISH). Protein–protein interaction network and pathway enrichment analysis were performed on miR-194 targets.Results and discussionUsing biopsy samples collected at diagnosis, mir-194 was significantly upregulated in patients responding to treatment (p value=0.016). The data was confirmed with qRT-PCR (p value=0.0587) and ISH (p value=0.026). Protein–protein interaction network and pathway enrichment analysis reveal a possible mechanism of susceptibility to pCRT involving Wnt pathway via its downstream mediator TRAF6. Finally, we interrogated the Comparative Toxicogenomics Database database in order to identify those chemical compounds able to mimic the biological effects of miR-194 as new possible therapeutic option in LARC treatment. The present study combining miRNA expression profiling with integrative computational biology identified miR-194 as predictive biomarker of response to pCRT. Using known and predicted drug mechanism of action, we then identified possible chemical compounds for further in vitro validation.

scholarly journals BPI and KIR6.1 as significant hub genes for vein graft restenosis

2020 ◽  
Vol 48 (11) ◽  
pp. 030006052096933
Author(s):  
Yun-peng Bai ◽  
Bo-chen Yao ◽  
Mei Wang ◽  
Xian-kun Liu ◽  
Xiao-long Zhu ◽  
...  
Keyword(s):  
Vein Graft ◽  
Area Under The Curve ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Control Group ◽  
Pathway Enrichment Analysis ◽  
Hub Genes ◽  
Protein Protein Interaction

Background Vein graft restenosis (VGR), which appears to be caused by dyslipidemia following vascular transplantation, seriously affects the prognosis and long-term quality of life of patients. Methods This study analyzed the genetic data of restenosis (VGR group) and non-stenosis (control group) vessels from patients with coronary heart disease post-vascular transplantation and identified hub genes that might be responsible for its occurrence. GSE110398 was downloaded from the Gene Expression Omnibus database. A repeatability test for the GSE110398 dataset was performed using R language. This included the identification of differentially expressed genes (DEGs), enrichment analysis via Metascape software, pathway enrichment analysis, and construction of a protein–protein interaction network and a hub gene network. Results Twenty-four DEGs were identified between VGR and control groups. The four most important hub genes ( KIR6.1, PCLP1, EDNRB, and BPI) were identified, and Pearson’s correlation coefficient showed that KIR6.1 and BPI were significantly correlated with VGR. KIR6.1 could also sensitively predict VGR (0.9 < area under the curve ≤1). Conclusion BPI and KIR6.1 were differentially expressed in vessels with and without stenosis after vascular transplantation, suggesting that these genes or their encoded proteins may be involved in the occurrence of VGR.

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