scholarly journals Evaluation of the role of kras gene in colon cancer pathway using string and Cytoscape software

2020 ◽  
Vol 7 (6) ◽  
pp. 3835-3842
Author(s):  
Sandya Menon Prabhakaran Menon ◽  
Asita Elengoe
Keyword(s):  
Colon Cancer ◽  
Biological Networks ◽  
Screening Program ◽  
Interaction Network ◽  
Protein Network ◽  
Vegf Receptor ◽  
Protein Activity ◽  
Kras Gene ◽  
Network Interaction ◽  
Common Cancer

Introduction: cancer is one of the top three most commonly occurring cancer worldwide with more than 1.8 million cases in 2018. In Malaysia, cancer is the most common cancer in males and the second most common cancer in females. Albeit being the second most common form of cancer in Malaysia, there is a lack of informal or structured national cancer screening program in Malaysia and it remains a low priority in healthcare planning and expenditure. The risk of developing colon cancer is greatly influenced by factors such as lifestyle habits, genetic inheritance, diet, weight, and exercise. KRAS, the most frequently mutated oncogene in cancer, occurs in about 50 percent of cancers. This study maps the KRAS gene involved in colon cancer pathway using applications such as STRING version 11.0 and version 3.7.0 to provide a clear visualization of all the related and involved proteins and genes that interact with KRAS gene in the pathway. Methods: Using KRAS as a seed, a protein-protein interaction network was constructed with 3391 interactions which were retrieved from the STRING version 11.0 database. The protein network interaction was further grouped into 6 clusters using the MCODE application. Molecular function and biological processes of the genes involved in the KRAS protein network were determined using Biological Networks Gene Ontology (BiNGO). Results: According to the resulting protein-protein network interaction map, it revealed that KRAS mechanism and co-expressed genes interconnected with protein or enzyme binding, receptor signaling protein activity and vascular endothelial growth factor (VEGF) receptor 2 binding. Conclusion: Understanding these protein-protein interactions provide insight into cellular activities and thus aid in the understanding of the cause of disease.  

Computational Analysis of miRNA and their Gene Targets Significantly Involved in Colorectal Cancer Progression

MicroRNA ◽  
2018 ◽  
Vol 8 (1) ◽  
pp. 68-75 ◽  
Author(s):  
Jeyalakshmi Kandhavelu ◽  
Kumar Subramanian ◽  
Amber Khan ◽  
Aadilah Omar ◽  
Paul Ruff ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Colon Cancer ◽  
Cancer Progression ◽  
Target Prediction ◽  
Computational Prediction ◽  
Initial Step ◽  
Kegg Pathways ◽  
Candidate Mirnas ◽  
Common Cancer ◽  
Colon Cancer Progression

Background:Globally, colorectal cancer (CRC) is the third most common cancer in women and the fourth most common cancer in men. Dysregulation of small non-coding miRNAs have been correlated with colon cancer progression. Since there are increasing reports of candidate miRNAs as potential biomarkers for CRC, this makes it important to explore common miRNA biomarkers for colon cancer. As computational prediction of miRNA targets is a critical initial step in identifying miRNA: mRNA target interactions for validation, we aim here to construct a potential miRNA network and its gene targets for colon cancer from previously reported candidate miRNAs, inclusive of 10 up- and 9 down-regulated miRNAs from tissues; and 10 circulatory miRNAs. </P><P> Methods: The gene targets were predicted using DIANA-microT-CDS and TarBaseV7.0 databases. Each miRNA and its targets were analyzed further for colon cancer hotspot genes, whereupon DAVID analysis and mirPath were used for KEGG pathway analysis.Results:We have predicted 874 and 157 gene targets for tissue and serum specific miRNA candidates, respectively. The enrichment of miRNA revealed that particularly hsa-miR-424-5p, hsa-miR-96-5p, hsa-miR-1290, hsa-miR-224, hsa-miR-133a and has-miR-363-3p present possible targets for colon cancer hallmark genes, including BRAF, KRAS, EGFR, APC, amongst others. DAVID analysis of miRNA and associated gene targets revealed the KEGG pathways most related to cancer and colon cancer. Similar results were observed in mirPath analysis. A new insight gained in the colon cancer network pathway was the association of hsa-mir-133a and hsa-mir-96-5p with the PI3K-AKT signaling pathway. In the present study, target prediction shows that while hsa-mir-424-5p has an association with mostly 10 colon cancer hallmark genes, only their associations with MAP2 and CCND1 have been experimentally validated.These miRNAs and their targets require further evaluation for a better understanding of their associations, ultimately with the potential to develop novel therapeutic targets.

scholarly journals An Effective Statistical Integrative Algorithm (Aeiapp) for Protein Prediction

2019 ◽  
Vol 8 (11) ◽  
pp. 132-137 ◽  
Keyword(s):  
Protein Interaction ◽  
Biological Networks ◽  
Protein Interaction Network ◽  
Protein Identification ◽  
Research Work ◽  
Interaction Network ◽  
Integrative Approach ◽  
Target Proteins ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

The task of predicting target proteins for new drug discovery is typically difficult. Target proteins are biologically most important to control a keen functional process. The recent research of experimental and computational -based approaches has been widely used to predict target proteins using biological networks analysis techniques. Perhaps with available methods and statistical algorithm needs to be modified and should be clearer to tag the main target. Meanwhile identifying wrong protein leads to unwanted molecular interaction and pharmacological activity. In this research work, a novel method to identify essential target proteins using integrative graph coloring algorithm has been proposed. The proposed integrative approach helps to extract essential proteins in protein-protein interaction network (PPI) by analyzing neighborhood of the active target protein. Experimental results reviewed based on protein-protein interaction network for homosapiens showed that AEIAPP based approach shows an improvement in the essential protein identification by assuming the source protein as biologically proven protein. The AEIAPP statistical model has been compared with other state of art approaches on human PPI for various diseases to produce good accurate outcome in faster manner with little memory consumption.

scholarly journals Network Protein Interaction in the Link between Stroke and Periodontitis Interplay: A Pilot Bioinformatic Analysis

2021 ◽  
Author(s):  
Yago Leira ◽  
Paulo Mascarenhas ◽  
Juan Blanco ◽  
Tomás Sobrino ◽  
José João Mendes ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Exploratory Study ◽  
Plaque Rupture ◽  
Bioinformatic Analysis ◽  
Protein Network ◽  
Biological Mechanism ◽  
Ppi Networks ◽  
Network Interaction ◽  
Atheroma Plaque ◽  
New Protein

The clinical interaction between stroke and periodontitis has been consistently studied and confirmed. Hence, forecasting potentially new protein interactions in this association using bioinformatic strategies presents potential interest. In this exploratory study, we conducted a protein-protein network interaction (PPI) search with documented encoded proteins for both stroke and periodontitis. Genes of interest were collected via GWAS database. The STRING database was used to predict the PPI networks, first in a sensitivity purpose (confidence cut-off of 0.7), and then with a highest confidence cut-off (0.9). Genes over-representation was inspected in the final network. As a result, we foresee a prospective protein network of interaction between stroke and periodontitis. Inflammation, pro-coagulant/pro-thrombotic state and ultimately atheroma plaque rupture is the main biological mechanism derived from the network. These pilot results may pave the way to future molecular and therapeutic studies to further comprehend the mechanisms between these two conditions.

scholarly journals Community Detection in Large-Scale Bipartite Biological Networks

2021 ◽  
Vol 12 ◽  
Author(s):  
Genís Calderer ◽  
Marieke L. Kuijjer
Keyword(s):  
Biological Networks ◽  
Large Scale ◽  
Interaction Network ◽  
Gene Interaction ◽  
Community Structures ◽  
Gene Interaction Network ◽  
Structure Detection ◽  
Wide Range ◽  
Disease Associations ◽  
Or Gene

Networks are useful tools to represent and analyze interactions on a large, or genome-wide scale and have therefore been widely used in biology. Many biological networks—such as those that represent regulatory interactions, drug-gene, or gene-disease associations—are of a bipartite nature, meaning they consist of two different types of nodes, with connections only forming between the different node sets. Analysis of such networks requires methodologies that are specifically designed to handle their bipartite nature. Community structure detection is a method used to identify clusters of nodes in a network. This approach is especially helpful in large-scale biological network analysis, as it can find structure in networks that often resemble a “hairball” of interactions in visualizations. Often, the communities identified in biological networks are enriched for specific biological processes and thus allow one to assign drugs, regulatory molecules, or diseases to such processes. In addition, comparison of community structures between different biological conditions can help to identify how network rewiring may lead to tissue development or disease, for example. In this mini review, we give a theoretical basis of different methods that can be applied to detect communities in bipartite biological networks. We introduce and discuss different scores that can be used to assess the quality of these community structures. We then apply a wide range of methods to a drug-gene interaction network to highlight the strengths and weaknesses of these methods in their application to large-scale, bipartite biological networks.

A Transfer Learning Approach and Selective Integration of Multiple Types of Assays for Biological Network Inference

2013 ◽  
pp. 188-202
Author(s):  
Tsuyoshi Kato ◽  
Kinya Okada ◽  
Hisashi Kashima ◽  
Masashi Sugiyama
Keyword(s):  
Metabolic Network ◽  
Transfer Learning ◽  
Protein Interaction ◽  
Biological Networks ◽  
Protein Interaction Network ◽  
Network Inference ◽  
Interaction Network ◽  
Statistical Test ◽  
Learning Approach ◽  
Selective Integration

The authors’ algorithm was favorably examined on two kinds of biological networks: a metabolic network and a protein interaction network. A statistical test confirmed that the weight that our algorithm assigned to each assay was meaningful.

scholarly journals Oxaliplatin–Biomimetic Magnetic Nanoparticle Assemblies for Colon Cancer-Targeted Chemotherapy: An In Vitro Study

Pharmaceutics ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 395 ◽  
Author(s):  
Ylenia Jabalera ◽  
Beatriz Garcia-Pinel ◽  
Raul Ortiz ◽  
Guillermo Iglesias ◽  
Laura Cabeza ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Electrostatic Interactions ◽  
Magnetic Nanoparticle ◽  
In Vitro Study ◽  
Targeted Chemotherapy ◽  
Proof Of Concept ◽  
Therapeutic Approaches ◽  
Ph Values ◽  
Common Cancer

Conventional chemotherapy against colorectal cancer (CRC), the third most common cancer in the world, includes oxaliplatin (Oxa) which induces serious unwanted side effects that limit the efficiency of treatment. Therefore, alternative therapeutic approaches are urgently required. In this work, biomimetic magnetic nanoparticles (BMNPs) mediated by MamC were coupled to Oxa to evaluate the potential of the Oxa–BMNP nanoassembly for directed local delivery of the drug as a proof of concept for the future development of targeted chemotherapy against CRC. Electrostatic interactions between Oxa and BMNPs trigger the formation of the nanoassembly and keep it stable at physiological pH. When the BMNPs become neutral at acidic pH values, the Oxa is released, and such a release is greatly potentiated by hyperthermia. The coupling of the drug with the BMNPs improves its toxicity to even higher levels than the soluble drug, probably because of the fast internalization of the nanoassembly by tumor cells through endocytosis. In addition, the BMNPs are cytocompatible and non-hemolytic, providing positive feedback as a proof of concept for the nanoassembly. Our study clearly demonstrates the applicability of Oxa–BMNP in colon cancer and offers a promising nanoassembly for targeted chemotherapy against this type of tumor.

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