Core Analysis of Differentially Expressed Genes using IPA.

<p>Core analysis using IPA was carried out using set of 760 genes that were differentially expressed in tumour samples. Important biological functions (a) pathways (b) and networks (c-e) were revealed by this analysis.</p

Gene Regulatory Network Inference diagram for Tumor and Normal Groups.

<p>A hierarchical network topology is used to visualize the degrees of interaction between transcription factor genes and target genes. (a) The inferred network for tumour group showing RUVBL1 as master regulator. (b) The inferred network for normal group showing TSHZ1as master regulator.</p

Transcription factor genes showing significant change in their effect as represented by the change in number of outbound and inbound edges in tumour and normal samples.

<p>Transcription factor genes showing significant change in their effect as represented by the change in number of outbound and inbound edges in tumour and normal samples.</p

Differentially regulated genes found to have incoherent expression levels and genomic changes.

<p>AA  =  Fold change value as calculated by AltAnalyze program.</p><p>EC  =  Fold change value as calculated by Expression Console program.</p><p>TF  =  Transcription Factor. Unknown is the TF that is not found in the driver genes.</p><p>Differentially regulated genes found to have incoherent expression levels and genomic changes.</p

Integrated Exon Level Expression Analysis of Driver Genes Explain Their Role in Colorectal Cancer

<div><p>Integrated analysis of genomic and transcriptomic level changes holds promise for a better understanding of colorectal cancer (CRC) biology. There is a pertinent need to explain the functional effect of genome level changes by integrating the information at the transcript level. Using high resolution cytogenetics array, we had earlier identified driver genes by ‘Genomic Identification of Significant Targets In Cancer (GISTIC)’ analysis of paired tumour-normal samples from colorectal cancer patients. In this study, we analyze these driver genes at three levels using exon array data – gene, exon and network. Gene level analysis revealed a small subset to experience differential expression. These results were reinforced by carrying out separate differential expression analyses (SAM and LIMMA). ATP8B1 was found to be the novel gene associated with CRC that shows changes at cytogenetic, gene and exon levels. Splice index of 29 exons corresponding to 13 genes was found to be significantly altered in tumour samples. Driver genes were used to construct regulatory networks for tumour and normal groups. There were rearrangements in transcription factor genes suggesting the presence of regulatory switching. The regulatory pattern of AHR gene was found to have the most significant alteration. Our results integrate data with focus on driver genes resulting in highly enriched novel molecules that need further studies to establish their role in CRC.</p></div

Principal Component Analysis of Exon array data from 32 patients.

<p>60 samples from 32 patients were subjected to PCA and the outliers were removed. 4 normal and 7 tumour samples were removed from the final analysis.</p

Flow diagram for Analysis Strategy.

<p>A)The entire analyses is categorized into fours stages from ‘Data Generation’ to ‘Network Analyses'. B) Analysis strategy using different programs is displayed in this diagram. There are three components of the analysis – Gene, Exon and Network handled by different programs. Gene level analyses are conducted using ‘Affymetrix, Expression/Transcriptome analysis console’ and ‘Tibco Spotfire’. Exon level analysis is carried out by ‘AltAnalyze’ and ‘Affymetrix power tools’. Network analyses employed ‘GENIE3’, ‘IPA’ and ‘Cytoscape’. ‘Nexus Copy Number’ is a program used in earlier studies to eventually generate a list of 144 driver genes.</p