scholarly journals Bioinformatics analysis of aggressive behavior of breast cancer via an integrated gene regulatory network

2014 ◽  
Vol 10 (4) ◽  
pp. 1013 ◽  
Author(s):  
Gang Yu ◽  
Mingguang Jia ◽  
Yujun Gao ◽  
Xingwang Yang ◽  
Zhaodong Li ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Aggressive Behavior ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Bioinformatics Analysis ◽  
Gene Regulatory

An Improved Integrative Random Forest for Gene Regulatory Network Inference for Breast Cancer

2018 ◽  
Vol 24 (10) ◽  
pp. 7566-7571
Author(s):  
Suntharaamurthi Chandran ◽  
Kohbalan Moorthy ◽  
Mohd Arfian Ismail ◽  
Mohd Zamri Osman ◽  
Mohd Azwan Mohamad Hamza ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Random Forest ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Network Inference ◽  
Gene Regulatory Network Inference ◽  
Gene Regulatory

scholarly journals Identification of candidate biomarkers and pathways associated with type 1 diabetes mellitus using bioinformatics analysis

2021 ◽  
Author(s):  
Basavaraj Mallikarjunayya Vastrad ◽  
Chanabasayya Mallikarjunayya Vastrad
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Bioinformatics Analysis ◽  
Enrichment Analysis ◽  
Ppi Network ◽  
Hub Genes ◽  
Hub Gene ◽  
Network Modules ◽  
Gene Regulatory

Type 1 diabetes mellitus (T1DM) is a metabolic disorder for which the underlying molecular mechanisms remain largely unclear. This investigation aimed to elucidate essential candidate genes and pathways in T1DM by integrated bioinformatics analysis. In this study, differentially expressed genes (DEGs) were analyzed using DESeq2 of R package from GSE162689 of the Gene Expression Omnibus (GEO). Gene ontology (GO) enrichment analysis, REACTOME pathway enrichment analysis, and construction and analysis of protein-protein interaction (PPI) network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network, and validation of hub genes were then performed. A total of 952 DEGs (477 up regulated and 475 down regulated genes) were identified in T1DM. GO and REACTOME enrichment result results showed that DEGs mainly enriched in multicellular organism development, detection of stimulus, diseases of signal transduction by growth factor receptors and second messengers, and olfactory signaling pathway. The top hub genes such as MYC, EGFR, LNX1, YBX1, HSP90AA1, ESR1, FN1, TK1, ANLN and SMAD9 were screened out as the critical genes among the DEGs from the PPI network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network. Receiver operating characteristic curve (ROC) analysis and RT-PCR confirmed that these genes were significantly associated with T1DM. In conclusion, the identified DEGs, particularly the hub genes, strengthen the understanding of the advancement and progression of T1DM, and certain genes might be used as candidate target molecules to diagnose, monitor and treat T1DM.

scholarly journals Screening key genes and signaling pathways in COVID-19 infection and its associated complications by integrated bioinformatics analysis

2021 ◽  
Author(s):  
Basavaraj Mallikarjunayya Vastrad ◽  
Chanabasayya Mallikarjunayya Vastrad
Keyword(s):  
Immune System ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Molecular Mechanisms ◽  
Bioinformatics Analysis ◽  
Pathway Enrichment Analysis ◽  
Hub Genes ◽  
Hub Gene ◽  
Gene Regulatory ◽  
Key Genes

Severe acute respiratory syndrome corona virus 2 (SARS-CoV-2)/ coronavirus disease 2019 (COVID-19) infection is the leading cause of respiratory tract infection associated mortality worldwide. The aim of the current investigation was to identify the differentially expressed genes (DEGs) and enriched pathways in COVID-19 infection and its associated complications by bioinformatics analysis, and to provide potential targets for diagnosis and treatment. Valid next-generation sequencing (NGS) data of 93 COVID 19 samples and 100 non COVID 19 samples (GSE156063) were obtained from the Gene Expression Omnibus database. Gene ontology (GO) and REACTOME pathway enrichment analysis was conducted to identify the biological role of DEGs. In addition, a protein-protein interaction network, modules, miRNA-hub gene regulatory network, TF-hub gene regulatory network and receiver operating characteristic curve (ROC) analysis were used to identify the key genes. A total of 738 DEGs were identified, including 415 up regulated genes and 323 down regulated genes. Most of the DEGs were significantly enriched in immune system process, cell communication, immune system and signaling by NTRK1 (TRKA). Through PPI, modules, miRNA-hub gene regulatory network, TF-hub gene regulatory network analysis, ESR1, UBD, FYN, STAT1, ISG15, EGR1, ARRB2, UBE2D1, PRKDC and FOS were selected as hub genes, which were expressed in COVID-19 samples relative to those in non COVID-19 samples, respectively. Among them, ESR1, UBD, FYN, STAT1, ISG15, EGR1, ARRB2, UBE2D1, PRKDC and FOS were suggested to be diagonstic factors for COVID-19. The findings from this bioinformatics analysis study identified molecular mechanisms and the key hub genes that might contribute to COVID-19 infection and its associated complications.

scholarly journals The Distinct Gene Regulatory Network of Myoglobin in Prostate and Breast Cancer

PLoS ONE ◽  
2015 ◽  
Vol 10 (11) ◽  
pp. e0142662 ◽  
Author(s):  
Anne Bicker ◽  
Alexandra M. Brahmer ◽  
Sebastian Meller ◽  
Glen Kristiansen ◽  
Thomas A. Gorr ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Distinct Gene ◽  
Gene Regulatory

scholarly journals Identification of Key Pathways and Genes in Dementia via Integrated Bioinformatics Analysis

2021 ◽  
Author(s):  
Basavaraj Mallikarjunayya Vastrad ◽  
Chanabasayya Mallikarjunayya Vastrad
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Bioinformatics Analysis ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Hub Genes ◽  
Hub Gene ◽  
Network Modules ◽  
Gene Regulatory ◽  
Key Pathways

To provide a better understanding of dementia at the molecular level, this study aimed to identify the genes and key pathways associated with dementia by using integrated bioinformatics analysis. Based on the expression profiling by high throughput sequencing dataset GSE153960 derived from the Gene Expression Omnibus (GEO), the differentially expressed genes (DEGs) between patients with dementia and healthy controls were identified. With DEGs, we performed a series of functional enrichment analyses. Then, a protein protein interaction (PPI) network, modules, miRNA hub gene regulatory network and TF hub gene regulatory network was constructed, analyzed and visualized, with which the hub genes miRNAs and TFs nodes were screened out. Finally, validation of hub genes was performed by using receiver operating characteristic curve (ROC) analysis and RT PCR. A total of 948 DEGs were screened out, among which 475 genes were up regulated; while 473 were down regulated. Functional enrichment analyses indicated that DEGs were mainly involved in defense response, ion transport, neutrophil degranulation and neuronal system. The hub genes (CDK1, TOP2A, MAD2L1, RSL24D1, CDKN1A, NOTCH3, MYB, PWP2, WNT7B and HSPA12B) were identified from PPI network, modules, miRNA hub gene regulatory network and TF hub gene regulatory network. We identified a series of key genes along with the pathways that were most closely related with dementia initiation and progression. Our results provide a more detailed molecular mechanism for the advancement of dementia, shedding light on the potential biomarkers and therapeutic targets.

Abstract IA01: Gene regulatory network approaches to interpreting breast cancer GWAS results

2014 ◽  
Author(s):  
Bruce A. J. Ponder ◽  
Kerstin Meyer ◽  
Michael Fletcher ◽  
Florian Markowetz ◽  
Ines de Santiago ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Gene Regulatory ◽  
Network Approaches

Application of a Hidden Markov Model in Identifying Target Genes in Breast Cancer

2020 ◽  
pp. 523-532
Author(s):  
Parisa Torkaman
Keyword(s):  
Breast Cancer ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Target Genes ◽  
Hidden Markov ◽  
Transition Probability ◽  
Number Of Patients ◽  
Gene Regulatory

Breast cancer is one of the most common malignant cancers among women with increasing number of patients. Gene regulatory network and identifying target genes for cancer treatment, and reducing breast cancer death rates is of great importance medically. This study aims to model gene regulatory network of breast cancer using hidden Markov model which greatly aids doctors in early diagnosis and faster treatment of breast cancer using identification of target genes. In this study, gene expressions of $206$ patients diagnosed with four subtypes of breast cancer including, Basal, Her2, LumA, LumB, were obtained from the Cancer Genome Atlas (TCGA). $8$ genes with the verified interaction among them were investigated by hidden Markov model of gene regulatory network and target genes. with the results of transition probability matrix, FADD, TNFRSF10B, CASP8 are the target genes in the mentioned cancer subtypes so that genes that their transmit probabilities are more than an initial value of $0.125$ are regulatory genes and transmit matrix identifies the probability of the mentioned cancers regarding gene expression level.

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