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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

Identified network motifs from colorectal cancer pathway.Some 4 and 5 node sub-graphs have been symbolized with gene names and their interactions if any. If the given interaction in the pathway was found to be missing, it is depicted as unknown (black coloured arrow).
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pone.0133901.g005: Identified network motifs from colorectal cancer pathway.Some 4 and 5 node sub-graphs have been symbolized with gene names and their interactions if any. If the given interaction in the pathway was found to be missing, it is depicted as unknown (black coloured arrow).

Mentions: After acquiring the differential expression pattern, we intended to identify chief sub-networks configured by these genes; facilitating annotation of intricate biological network implicated in CRC. Based on the rationale, detection of crucial network motifs and network patterns was made; providing essential clues concerning the hierarchical decomposition of CRC network. Here the patterns being referred are small connected sub-networks occurring in significantly higher frequencies in a network than would be expected for a given random network. These patterns or motifs are considerably overrepresented and characterize certain essential functional aspects associated with CRC related pathways and its progression. Several motifs ranging from 4–8 sub-graph nodes were generated and annotated for the CRC pathway which is available as supplementary data (available at: http://www.bioinfoindia.org/CRCData), and a few have been depicted in Fig 5. The applied bottom-up approach is clearly demonstrated in Fig 6 starting from 4-node sub-graphs and then proceeding one by one till 8-node sub-graphs were generated; all the interacting genes were annotated along with their functional relationships.


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)

Identified network motifs from colorectal cancer pathway.Some 4 and 5 node sub-graphs have been symbolized with gene names and their interactions if any. If the given interaction in the pathway was found to be missing, it is depicted as unknown (black coloured arrow).
© Copyright Policy
Related In: Results  -  Collection

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

pone.0133901.g005: Identified network motifs from colorectal cancer pathway.Some 4 and 5 node sub-graphs have been symbolized with gene names and their interactions if any. If the given interaction in the pathway was found to be missing, it is depicted as unknown (black coloured arrow).
Mentions: After acquiring the differential expression pattern, we intended to identify chief sub-networks configured by these genes; facilitating annotation of intricate biological network implicated in CRC. Based on the rationale, detection of crucial network motifs and network patterns was made; providing essential clues concerning the hierarchical decomposition of CRC network. Here the patterns being referred are small connected sub-networks occurring in significantly higher frequencies in a network than would be expected for a given random network. These patterns or motifs are considerably overrepresented and characterize certain essential functional aspects associated with CRC related pathways and its progression. Several motifs ranging from 4–8 sub-graph nodes were generated and annotated for the CRC pathway which is available as supplementary data (available at: http://www.bioinfoindia.org/CRCData), and a few have been depicted in Fig 5. The applied bottom-up approach is clearly demonstrated in Fig 6 starting from 4-node sub-graphs and then proceeding one by one till 8-node sub-graphs were generated; all the interacting genes were annotated along with their functional relationships.

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