Identification of potential molecular mechanisms of radiation pneumonitis development in non-small-cell lung cancer treatment by data mining

Introduction: Radiation pneumonitis (RP) is the most significant dose-limiting toxicity in patients receiving thoracic radiotherapy. The underlying mechanisms of RP are still inconclusive. Our objective was to determine the genes and molecular pathways associated with RP using computational tools and publicly available data. Methods: RP-associated genes were determined by text mining, and the intersection of the two gene sets was selected for Gene Ontology analysis using the GeneCodis program. Protein-protein interaction network analysis was performed using STRINGdb to identify the final genes. Results: Our analysis identified 256 genes related to RP with text mining. The enriched biological process annotations resulted in 47 sets of annotations containing a total of 156 unique genes. KEGG analysis of the enriched pathways identified 24 pathways containing a total of 41 unique genes. The protein-protein interaction analysis yielded 23 genes (mostly the PI3K family). Conclusion: Gene discovery using in silico text mining and pathway analysis tools can facilitate the identification of the underlying mechanisms of RP.

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Computational Drug Discovery in Patients with Endometriosis-Induced Infertility via Text Mining and Biomedical Databases

10.21203/rs.3.rs-744890/v1 ◽

2021 ◽

Author(s):

Yingying Lin ◽

Zhiwei Chen ◽

Jianping Huang ◽

Jiaoning Fang ◽

Yuanjie Qi ◽

...

Keyword(s):

Text Mining ◽

Protein Interaction ◽

Interaction Analysis ◽

Interaction Network ◽

Enrichment Analysis ◽

Gene Interaction ◽

Topical Administration ◽

Reproductive Age ◽

Physical And Mental Health ◽

Protein Protein Interaction

Abstract Background: Endometriosis (EMT) is the most common benign gynecological disease among women of reproductive age, causing infertility and seriously affects women's physical and mental health. However, the current treatment was not always effective. This study was designed to use publicly available data to identify drugs targeting the relevant gene with EMT-induced-infertility using computational tools.Methods: EMT and infertility genes were determined by text mining, and the GeneCodis program was used to analyzed gene ontology of the intersection of the two gene sets. A string database was used to analyze the protein-protein interaction network. The Drug-Gene Interaction database is queried for the rich gene set belonging to the identified pathways to find drug candidates that can be used in EMT-induced infertility.Results: Our analysis identified 550 genes common to both the EMT and infertility by text mining. Gene enrichment analysis and protein-protein interaction analysis found 39 genes potentially targetable by a total of 49 drugs that could be formulated for application, which have not been used in EMT-induced infertility. Conclusions: The findings from the present analysis can facilitate the Identification of existing drugs that have the potential of topical administration to improve EMT-induced infertility and present tremendous opportunities to study novel targets pharmacology using in silico text mining and pathway analysis tools.

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Identifying Novel Candidate Genes Related to Apoptosis from a Protein-Protein Interaction Network

Computational and Mathematical Methods in Medicine ◽

10.1155/2015/715639 ◽

2015 ◽

Vol 2015 ◽

pp. 1-11 ◽

Cited By ~ 4

Author(s):

Baoman Wang ◽

Fei Yuan ◽

Xiangyin Kong ◽

Lan-Dian Hu ◽

Yu-Dong Cai

Keyword(s):

Cell Death ◽

Candidate Genes ◽

Protein Interaction ◽

Permutation Test ◽

Interaction Network ◽

Weighted Graph ◽

Computational Method ◽

Protein Protein Interaction ◽

Underlying Mechanisms ◽

Multicellular Organisms

Apoptosis is the process of programmed cell death (PCD) that occurs in multicellular organisms. This process of normal cell death is required to maintain the balance of homeostasis. In addition, some diseases, such as obesity, cancer, and neurodegenerative diseases, can be cured through apoptosis, which produces few side effects. An effective comprehension of the mechanisms underlying apoptosis will be helpful to prevent and treat some diseases. The identification of genes related to apoptosis is essential to uncover its underlying mechanisms. In this study, a computational method was proposed to identify novel candidate genes related to apoptosis. First, protein-protein interaction information was used to construct a weighted graph. Second, a shortest path algorithm was applied to the graph to search for new candidate genes. Finally, the obtained genes were filtered by a permutation test. As a result, 26 genes were obtained, and we discuss their likelihood of being novel apoptosis-related genes by collecting evidence from published literature.

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The molecular mechanisms associated with PIN7, a protein-protein interaction network of seven pleiotropic proteins

10.7287/peerj.preprints.27547v1 ◽

2019 ◽

Author(s):

Jarmila Nahálková

Keyword(s):

Signaling Pathway ◽

Protein Interaction ◽

Protein Interaction Network ◽

Molecular Mechanisms ◽

Interaction Network ◽

Protein Protein Interaction ◽

P53 Signaling ◽

Age Related ◽

P53 Signaling Pathway ◽

Protein Protein Interaction Network

The protein-protein interaction network of seven pleiotropic proteins (PIN7) contains proteins with multiple functions in the aging and age-related diseases (TPPII, CDK2, MYBBP1A, p53, SIRT6, SIRT7, and BSG). At the present work, the pathway enrichment, the gene function prediction and the protein node prioritization analysis were applied for the examination of main molecular mechanisms driving PIN7 and the extended network. Seven proteins of PIN7 were used as an input for the analysis by GeneMania, a Cytoscape application, which constructs the protein interaction network. The software also extends it using the interactions retrieved from databases of experimental and predicted protein-protein and genetic interactions. The analysis identified the p53 signaling pathway as the most dominant mediator of PIN7. The extended PIN7 was also analyzed by Cytohubba application, which showed that the top-ranked protein nodes belong to the group of histone acetyltransferases and histone deacetylases. These enzymes are involved in the reverse epigenetic regulation mechanisms linked to the regulation of PTK2, NFκB, and p53 signaling interaction subnetworks of the extended PIN7. The analysis emphasized the role of PTK2 signaling, which functions upstream of the p53 signaling pathway and its interaction network includes all members of the sirtuin family. Further, the analysis suggested the involvement of molecular mechanisms related to metastatic cancer (prostate cancer, small cell lung cancer), hemostasis, the regulation of the thyroid hormones and the cell cycle G1/S checkpoint. The additional data-mining analysis showed that the small protein interaction network MYBBP1A-p53-TPPII-SIRT6-CD147 controls Warburg effect and MYBBP1A-p53-TPPII-SIRT7-BSG influences mTOR signaling and autophagy. Further investigations of the detail mechanisms of these interaction networks would be beneficial for the development of novel treatments for aging and age-related diseases.

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Integrating protein networks and machine learning for disease stratification in the Hereditary Spastic Paraplegias

10.1101/2021.01.14.425874 ◽

2021 ◽

Author(s):

Nikoleta Vavouraki ◽

James E. Tomkins ◽

Eleanna Kara ◽

Henry Houlden ◽

John Hardy ◽

...

Keyword(s):

Machine Learning ◽

Protein Interaction ◽

Protein Interaction Network ◽

Molecular Mechanisms ◽

Topological Analysis ◽

Interaction Network ◽

Protein Protein Interaction ◽

Hereditary Spastic Paraplegias ◽

Core Proteins ◽

Protein Protein Interaction Network

AbstractThe Hereditary Spastic Paraplegias are a group of neurodegenerative diseases characterized by spasticity and weakness in the lower body. Despite the identification of causative mutations in over 70 genes, the molecular aetiology remains unclear. Due to the combination of genetic diversity and variable clinical presentation, the Hereditary Spastic Paraplegias are a strong candidate for protein-protein interaction network analysis as a tool to understand disease mechanism(s) and to aid functional stratification of phenotypes. In this study, experimentally validated human protein-protein interactions were used to create a protein-protein interaction network based on the causative Hereditary Spastic Paraplegia genes. Network evaluation as a combination of both topological analysis and functional annotation led to the identification of core proteins in putative shared biological processes such as intracellular transport and vesicle trafficking. The application of machine learning techniques suggested a functional dichotomy linked with distinct sets of clinical presentations, suggesting there is scope to further classify conditions currently described under the same umbrella term of Hereditary Spastic Paraplegias based on specific molecular mechanisms of disease.

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Identification of key candidate genes and molecular pathways in white fat browning: an anti-obesity drug discovery based on computational biology

Human Genomics ◽

10.1186/s40246-019-0239-x ◽

2019 ◽

Vol 13 (1) ◽

Cited By ~ 3

Author(s):

Yuyan Pan ◽

Jiaqi Liu ◽

Fazhi Qi

Keyword(s):

Adipose Tissue ◽

Drug Discovery ◽

Text Mining ◽

Protein Interaction ◽

Metabolic Diseases ◽

Interaction Analysis ◽

Brown Fat ◽

Molecular Pathways ◽

Protein Protein Interaction ◽

White Fat

Abstract Background Obesity—with its increased risk of obesity-associated metabolic diseases—has become one of the greatest public health epidemics of the twenty-first century in affluent countries. To date, there are no ideal drugs for treating obesity. Studies have shown that activation of brown adipose tissue (BAT) can promote energy consumption and inhibit obesity, which makes browning of white adipose tissue (WAT) a potential therapeutic target for obesity. Our objective was to identify genes and molecular pathways associated with WAT and the activation of BAT to WAT browning, by using publicly available data and computational tools; this knowledge might help in targeting relevant signaling pathways for treating obesity and other related metabolic diseases. Results In this study, we used text mining to find out genes related to brown fat and white fat browning. Combined with biological process and pathway analysis in GeneCodis and protein-protein interaction analysis by using STRING and Cytoscape, a list of high priority target genes was developed. The Human Protein Atlas was used to analyze protein expression. Candidate drugs were derived on the basis of the drug-gene interaction analysis of the final genes. Our study identified 18 genes representing 6 different pathways, targetable by a total of 33 drugs as possible drug treatments. The final list included 18 peroxisome proliferator-activated receptor gamma (PPAR-γ) agonists, 4 beta 3 adrenoceptor (β3-AR) agonists, 1 insulin sensitizer, 3 insulins, 6 lipase clearing factor stimulants and other drugs. Conclusions Drug discovery using in silico text mining, pathway, and protein-protein interaction analysis tools may be a method of exploring drugs targeting the activation of brown fat or white fat browning, which provides a basis for the development of novel targeted therapies as potential treatments for obesity and related metabolic diseases.

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Integrative analysis of ferroptosis-related genes in ulcerative colitis

Journal of International Medical Research ◽

10.1177/03000605211042975 ◽

2021 ◽

Vol 49 (9) ◽

pp. 030006052110429

Author(s):

De-jun Cui ◽

Chen Chen ◽

Wen-qiang Yuan ◽

Yun-han Yang ◽

Lu Han

Keyword(s):

Ulcerative Colitis ◽

Protein Interaction ◽

Peripheral Blood ◽

Interaction Analysis ◽

Animal Experiments ◽

Interaction Network ◽

Enrichment Analysis ◽

Hub Genes ◽

Hub Gene ◽

Protein Protein Interaction

Objective The aim of this study was to identify and validate ferroptosis-related markers in ulcerative colitis (UC) to explore new directions for UC diagnosis and treatment. Methods We screened UC chips and ferroptosis-related genes from the Gene Expression Omnibus (GEO), FerrDb, and GeneCards databases. The differentially expressed genes (DEGs) and ferroptosis-related DEGs between the UC group and normal controls were analyzed using bioinformatics methods. Enrichment analysis, protein–protein interaction analysis, and hub genes were screened. Peripheral blood chip and animal experiments were used to validate the ferroptosis-related hub genes. Finally, hub gene–transcription factor, hub gene–microRNA (miRNA), and hub gene–drug interaction networks were constructed. Results Overall, 26 ferroptosis-related DEGs were identified that were significantly enriched in energy pathways and metabolism. We identified ten ferroptosis-related hub genes from the protein–protein interaction network: IL6, PTGS2, HIF1A, CD44, MUC1, CAV1, NOS2, CXCL2, SCD, and ACSL4. In the peripheral blood chip GSE94648, CD44 and MUC1 were upregulated, which was consistent with the expression trend in GSE75214. Animal experiments showed that CD44 expression was significantly increased in the colon. Conclusions Our findings indicate that CD44 and MUC1 may be ferroptosis-related markers in UC.

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Proteinarium: Multi-Sample Protein-Protein Interaction Analysis and Visualization Tool

10.1101/589085 ◽

2019 ◽

Cited By ~ 1

Author(s):

David Armanious ◽

Jessica Schuster ◽

George F. Tollefson ◽

Anthony Agudelo ◽

Andrew T. DeWan ◽

...

Keyword(s):

High Throughput ◽

Protein Interaction ◽

Protein Interactions ◽

High Throughput Screening ◽

Interaction Analysis ◽

Interaction Network ◽

Visualization Tool ◽

Sequencing Data ◽

Protein Protein Interaction ◽

Ppi Networks

AbstractBackgroundData analysis has become crucial in the post genomic era where the accumulation of genomic information is mounting exponentially. Analyzing protein-protein interactions in the context of the interactome is a powerful approach to understanding disease phenotypes.ResultsWe describe Proteinarium, a multi-sample protein-protein interaction network analysis and visualization tool. Proteinarium can be used to analyze data for samples with dichotomous phenotypes, multiple samples from a single phenotype or a single sample. Then, by similarity clustering, the network-based relations of samples are identified and clusters of related samples are presented as a dendrogram. Each branch of the dendrogram is built based on network similarities of the samples. The protein-protein interaction networks can be analyzed and visualized on any branch of the dendrogram. Proteinarium’s input can be derived from transcriptome analysis, whole exome sequencing data or any high-throughput screening approach. Its strength lies in use of gene lists for each sample as a distinct input which are further analyzed through protein interaction analyses. Proteinarium output includes the gene lists of visualized networks and PPI interaction files where users can analyze the network(s) on other platforms such as Cytoscape. In addition, since the dendrogram is written in Newick tree format, users can visualize it in other software platforms like Dendroscope, ITOL.ConclusionsProteinarium, through the analysis and visualization of PPI networks, allows researchers to make important observations on high throughput data for a variety of research questions. Proteinarium identifies significant clusters of patients based on their shared network similarity for the disease of interest and the associated genes. Proteinarium is a command-line tool written in Java with no external dependencies and it is freely available at https://github.com/Armanious/Proteinarium.

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Exploring The Molecular Mechanisms of Classical Hodgkin Lymphoma Through Bioinformatics Analysis

10.21203/rs.3.rs-586729/v1 ◽

2021 ◽

Author(s):

Zhu Lili ◽

Zhu YuKun ◽

Zhuangzhuang Tian ◽

Yongsheng Li ◽

Liyu Cao

Keyword(s):

Hodgkin Lymphoma ◽

Protein Interaction ◽

Protein Interaction Network ◽

Molecular Mechanisms ◽

Kegg Pathway ◽

Interaction Network ◽

Enrichment Analysis ◽

Hub Genes ◽

Protein Protein Interaction ◽

Protein Protein Interaction Network

Abstract Background Classic Hodgkin lymphoma (CHL) is the most common HL in the modern society. Although the treatment of cHL has made great progress, its molecular mechanisms have yet to be deciphered. Objectives The purpose of this study is to find out the crucial potential genes and pathways associated with cHL. Methods We downloaded the cHL microarray dataset (GSE12453) from Gene Expression Omnibus (GEO) database and to identify the differentially expressed genes (DEGs) between cHL samples and normal samples through the limma package in R. Then, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were carried out. Finally, we constructed the protein-protein interaction network to screen out the hub genes using Search Tool for the Retrieval of Interacting Genes (STRING) database. Results We screened out 788 DEGs in the cHL dataset, such as BATF3, IER3, RAB13 and FCRL2. GO functional enrichment analysis indicated that the DEGs were related with regulation of lymphocyte activation, secretory granule lumen and chemokine activity. KEGG pathway analysis showed that the genes enriched in Prion disease, Complement and coagulation cascades and Parkinson disease Coronavirus disease-COVID-19 pathway. Protein-protein interaction network construction identified 10 hub genes (IL6, ITGAM, CD86, FN1, MMP9, CXCL10, CCL5, CD19, IFNG, SELL, UBB) in the network. Conclusions In the present investigation, we identified several pathways and hub genes related to the occurrence and development of cHL, which may provide an important basis for further research and novel therapeutic targets and prognostic indicators for cHL.

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Experimental evolution of protein–protein interaction networks

Biochemical Journal ◽

10.1042/bj20130205 ◽

2013 ◽

Vol 453 (3) ◽

pp. 311-319 ◽

Cited By ~ 15

Author(s):

Betül Kaçar ◽

Eric A. Gaucher

Keyword(s):

Protein Interaction ◽

Molecular Mechanisms ◽

Protein Interaction Networks ◽

Interaction Networks ◽

Molecular Networks ◽

Protein Protein Interaction ◽

Experimental Systems ◽

Underlying Mechanisms ◽

Evolutionary Paths

The modern synthesis of evolutionary theory and genetics has enabled us to discover underlying molecular mechanisms of organismal evolution. We know that in order to maximize an organism's fitness in a particular environment, individual interactions among components of protein and nucleic acid networks need to be optimized by natural selection, or sometimes through random processes, as the organism responds to changes and/or challenges in the environment. Despite the significant role of molecular networks in determining an organism's adaptation to its environment, we still do not know how such inter- and intra-molecular interactions within networks change over time and contribute to an organism's evolvability while maintaining overall network functions. One way to address this challenge is to identify connections between molecular networks and their host organisms, to manipulate these connections, and then attempt to understand how such perturbations influence molecular dynamics of the network and thus influence evolutionary paths and organismal fitness. In the present review, we discuss how integrating evolutionary history with experimental systems that combine tools drawn from molecular evolution, synthetic biology and biochemistry allow us to identify the underlying mechanisms of organismal evolution, particularly from the perspective of protein interaction networks.

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Identification and Verification of Candidate Genes Regulating Neural Stem Cells Behavior Under Hypoxia

Cellular Physiology and Biochemistry ◽

10.1159/000489799 ◽

2018 ◽

Vol 47 (1) ◽

pp. 212-222 ◽

Cited By ~ 4

Author(s):

Zhongju Shi ◽

Zhijian Wei ◽

Jiahe Li ◽

Shiyang Yuan ◽

Bin Pan ◽

...

Keyword(s):

Gene Expression ◽

Stem Cells ◽

Neural Stem Cells ◽

Molecular Mechanisms ◽

Interaction Network ◽

Control Group ◽

Cell Therapies ◽

Protein Protein Interaction ◽

Qrt Pcr ◽

Underlying Mechanisms

Background/Aims: Neural stem cells (NSCs) reside in a hypoxic environment, and hypoxia plays an important role in their development and differentiation. This study aimed to explore the underlying mechanisms by which hypoxia affects NSC behavior. Methods: In the current study, we downloaded the gene expression dataset GSE68572 and identified the differentially expressed genes (DEGs) by analyzing high-throughput gene expression in hypoxic and normoxic NSCs. Subsequently, we analyzed these data using a combined bioinformatics approach and predicted the microRNAs (miRNAs) targeting the key gene using miRNA databases. Quantitative real-time PCR (qRT-PCR) was used to validate the expression of the top five DEGs. Results: In total, 1347 genes were identified as DEGs. We identified the predominant gene ontology categories and Kyoto Encyclopedia of Genes and Genomes pathways that were significantly over-represented in the hypoxic NSCs. A protein–protein interaction network he identification of miRNAs and their putative targets may offer new diagnostic and therapeutic strategies for liver cancer the top 10 core genes. Vascular endothelial growth factor A (VEGFA) had the highest degree and may be the key gene concerning NSC behavior under hypoxia. Further validation of the top five DEGs by qRT-PCR demonstrated that four DEGs were significantly higher and one DEG was significantly lower in the hypoxic group than in the control group. Seven miRNAs were predicted and proved to target VEGFA. Conclusion: This preliminary study can prompt the understanding of the molecular mechanisms by which hypoxia has an impact on NSC behavior and can help to optimize stem cell therapies for central nervous system injuries and diseases.

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