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An Integrative Approach for Mapping Differentially Expressed Genes and Network Components Using Novel Parameters to Elucidate Key Regulatory Genes in Colorectal Cancer.

Sehgal M, Gupta R, Moussa A, Singh TR - PLoS ONE (2015)

Bottom Line: The microarray analyses facilitated differential expression of 631 significant genes employed in the progression of disease and supplied interesting associated up and down regulated genes like jun, fos and mapk1.These novel parameters can assist in scrutinizing candidate markers for diseases having known biological pathways.Further, investigating and targeting these proposed genes for experimental validations, instead being spellbound by the complicated pathway will certainly endow valuable insight in a well-timed systematic understanding of CRC.

View Article: PubMed Central - PubMed

Affiliation: Department of Biotechnology and Bioinformatics, Jaypee University of Information Technology (JUIT), Waknaghat, Solan, H.P. 173234, India.

ABSTRACT
For examining the intricate biological processes concerned with colorectal cancer (CRC), a systems biology approach integrating several biological components and other influencing factors is essential to understand. We performed a comprehensive system level analysis for CRC which assisted in unravelling crucial network components and many regulatory elements through a coordinated view. Using this integrative approach, the perceptive of complexity hidden in a biological phenomenon is extensively simplified. The microarray analyses facilitated differential expression of 631 significant genes employed in the progression of disease and supplied interesting associated up and down regulated genes like jun, fos and mapk1. The transcriptional regulation of these genes was deliberated widely by examining transcription factors such as hnf4, nr2f1, znf219 and dr1 which directly influence the expression. Further, interactions of these genes/proteins were evaluated and crucial network motifs were detected to associate with the pathophysiology of CRC. The available standard statistical parameters such as z-score, p-value and significance profile were explored for the identification of key signatures from CRC pathway whereas a few novel parameters representing over-represented structures were also designed in the study. The applied approach revealed 5 key genes i.e. kras, araf, pik3r5, ralgds and akt3 via our novel designed parameters illustrating high statistical significance. These novel parameters can assist in scrutinizing candidate markers for diseases having known biological pathways. Further, investigating and targeting these proposed genes for experimental validations, instead being spellbound by the complicated pathway will certainly endow valuable insight in a well-timed systematic understanding of CRC.

No MeSH data available.


Related in: MedlinePlus

Pre-processing and normalization of DNA microarray data.2a shows the distribution of microarray files before normalization and 2b explains the uniform distribution obtained after implementing normalization i.e. removal of noise from data.
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pone.0133901.g002: Pre-processing and normalization of DNA microarray data.2a shows the distribution of microarray files before normalization and 2b explains the uniform distribution obtained after implementing normalization i.e. removal of noise from data.

Mentions: In this study, a comprehensive analysis for differentially expressed genes, TFs, interacting proteins, putative network motifs and their implications in diverse pathways related to CRC has been extensively carried out. Selected CRC dataset for DNA microarray was considered for the process of normalization for removal of errors and noise from the dataset as depicted in Fig 2. The figure illustrates the box plot for all four Affymetrix chips before and after normalization using quantile normalization and clearly demonstrates the impact of normalization step by rectifying the signal of genes across all chips.


An Integrative Approach for Mapping Differentially Expressed Genes and Network Components Using Novel Parameters to Elucidate Key Regulatory Genes in Colorectal Cancer.

Sehgal M, Gupta R, Moussa A, Singh TR - PLoS ONE (2015)

Pre-processing and normalization of DNA microarray data.2a shows the distribution of microarray files before normalization and 2b explains the uniform distribution obtained after implementing normalization i.e. removal of noise from data.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4519280&req=5

pone.0133901.g002: Pre-processing and normalization of DNA microarray data.2a shows the distribution of microarray files before normalization and 2b explains the uniform distribution obtained after implementing normalization i.e. removal of noise from data.
Mentions: In this study, a comprehensive analysis for differentially expressed genes, TFs, interacting proteins, putative network motifs and their implications in diverse pathways related to CRC has been extensively carried out. Selected CRC dataset for DNA microarray was considered for the process of normalization for removal of errors and noise from the dataset as depicted in Fig 2. The figure illustrates the box plot for all four Affymetrix chips before and after normalization using quantile normalization and clearly demonstrates the impact of normalization step by rectifying the signal of genes across all chips.

Bottom Line: The microarray analyses facilitated differential expression of 631 significant genes employed in the progression of disease and supplied interesting associated up and down regulated genes like jun, fos and mapk1.These novel parameters can assist in scrutinizing candidate markers for diseases having known biological pathways.Further, investigating and targeting these proposed genes for experimental validations, instead being spellbound by the complicated pathway will certainly endow valuable insight in a well-timed systematic understanding of CRC.

View Article: PubMed Central - PubMed

Affiliation: Department of Biotechnology and Bioinformatics, Jaypee University of Information Technology (JUIT), Waknaghat, Solan, H.P. 173234, India.

ABSTRACT
For examining the intricate biological processes concerned with colorectal cancer (CRC), a systems biology approach integrating several biological components and other influencing factors is essential to understand. We performed a comprehensive system level analysis for CRC which assisted in unravelling crucial network components and many regulatory elements through a coordinated view. Using this integrative approach, the perceptive of complexity hidden in a biological phenomenon is extensively simplified. The microarray analyses facilitated differential expression of 631 significant genes employed in the progression of disease and supplied interesting associated up and down regulated genes like jun, fos and mapk1. The transcriptional regulation of these genes was deliberated widely by examining transcription factors such as hnf4, nr2f1, znf219 and dr1 which directly influence the expression. Further, interactions of these genes/proteins were evaluated and crucial network motifs were detected to associate with the pathophysiology of CRC. The available standard statistical parameters such as z-score, p-value and significance profile were explored for the identification of key signatures from CRC pathway whereas a few novel parameters representing over-represented structures were also designed in the study. The applied approach revealed 5 key genes i.e. kras, araf, pik3r5, ralgds and akt3 via our novel designed parameters illustrating high statistical significance. These novel parameters can assist in scrutinizing candidate markers for diseases having known biological pathways. Further, investigating and targeting these proposed genes for experimental validations, instead being spellbound by the complicated pathway will certainly endow valuable insight in a well-timed systematic understanding of CRC.

No MeSH data available.


Related in: MedlinePlus