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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

Identification of differential expression.Significance analysis of microarrays (SAM) and volcano plot were generated for detecting the differentially expressed genes in the early colorectal cancer dataset. In SAM, 631 significant genes were identified for their over or under expression in the diseased state whereas the volcano plot evidently elucidates the differentially expressed genes with red spots having signal log ratio (SLR)>2 or SLR<2.
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pone.0133901.g003: Identification of differential expression.Significance analysis of microarrays (SAM) and volcano plot were generated for detecting the differentially expressed genes in the early colorectal cancer dataset. In SAM, 631 significant genes were identified for their over or under expression in the diseased state whereas the volcano plot evidently elucidates the differentially expressed genes with red spots having signal log ratio (SLR)>2 or SLR<2.

Mentions: The microarray dataset was examined for the identification of specific patterns or markers that may differentiate normal vs. diseased state for signifying the susceptibility and facilitate early diagnosis of CRC. After preliminary pre-processing and manual inspection based on the proportional analysis, final set subjected to SAM composed of only the robust candidates (see S2 Table). SAM revealed a total of 631 genes (Fig 3A) from the microarray dataset which were differentially expressed among the tested conditions since data points lie aside the diagonal line in a substantial manner. The volcano plot between control and the diseased state for CRC clearly elucidated the difference between genes that were differentially expressed in the two groups as shown in Fig 3B. Here, the spots represented in black are the genes showing normal expression whereas the red ones with signal log ratio (SLR)>2 are over expressed and those with SLR<-2 are under expressed genes in the diseased state. Moreover, SOM significant clusters are depicted in S1 Fig and PCA (well described in S2 and S3 Figs) revealed the projections for 3 different conditions, i.e. over-expressed genes, under-expressed genes and genes showing normal expression.


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)

Identification of differential expression.Significance analysis of microarrays (SAM) and volcano plot were generated for detecting the differentially expressed genes in the early colorectal cancer dataset. In SAM, 631 significant genes were identified for their over or under expression in the diseased state whereas the volcano plot evidently elucidates the differentially expressed genes with red spots having signal log ratio (SLR)>2 or SLR<2.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0133901.g003: Identification of differential expression.Significance analysis of microarrays (SAM) and volcano plot were generated for detecting the differentially expressed genes in the early colorectal cancer dataset. In SAM, 631 significant genes were identified for their over or under expression in the diseased state whereas the volcano plot evidently elucidates the differentially expressed genes with red spots having signal log ratio (SLR)>2 or SLR<2.
Mentions: The microarray dataset was examined for the identification of specific patterns or markers that may differentiate normal vs. diseased state for signifying the susceptibility and facilitate early diagnosis of CRC. After preliminary pre-processing and manual inspection based on the proportional analysis, final set subjected to SAM composed of only the robust candidates (see S2 Table). SAM revealed a total of 631 genes (Fig 3A) from the microarray dataset which were differentially expressed among the tested conditions since data points lie aside the diagonal line in a substantial manner. The volcano plot between control and the diseased state for CRC clearly elucidated the difference between genes that were differentially expressed in the two groups as shown in Fig 3B. Here, the spots represented in black are the genes showing normal expression whereas the red ones with signal log ratio (SLR)>2 are over expressed and those with SLR<-2 are under expressed genes in the diseased state. Moreover, SOM significant clusters are depicted in S1 Fig and PCA (well described in S2 and S3 Figs) revealed the projections for 3 different conditions, i.e. over-expressed genes, under-expressed genes and genes showing normal expression.

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