Network of cluster centres of stage-IV network.

Clustering was done using Moduland.</p

Centrality analysis of stage-II network.

<p>Centrality analysis of stage-II network.</p

Network of cluster centres of stage-III network.

<p>Clustering was done using Moduland.</p

Staging of colon cancer.

<p>The American Joint Committee on Cancer (AJCC) has staged the colorectal cancer based on the anatomical extent of the disease. Stage I: Tumor that is limited to the mucosal layer (T1) or muscularis propria (T2), without involvement of any lymph node or distant metastatic organs. Stage II: Tumor that penetrates the muscularis propria (T3) or invades nearby organs or structures (T4), without involvement of any lymph node or distant metastatic organs. Stage III: Tumor stages with lymph node metastasis but without distant metastasis. Stage IV: Any tumor stage and lymph node status with distant organ metastasis.</p

Identification of hub driver genes.

<p>Consensus novel driver genes were identified for each stage from driverDB data. Consensus central genes (‘hubs’) were identified from each stage-specific network. The overlap between these two sets of genes yields ‘hub driver’ genes for each stage.</p

Centrality analysis of stage-IV network.

<p>Centrality analysis of stage-IV network.</p

Moduland decomposition of stage-IV network.

Moduland decomposition of stage-IV network.</p

From tumor genome-sequencing data to network reconstruction.

<p>From tumor genome-sequencing data to network reconstruction.</p

GO enrichment analysis of stage-IV network.

<p>GO enrichment analysis of stage-IV network.</p

Computational Identification of Novel Stage-Specific Biomarkers in Colorectal Cancer Progression

<div><p>It is well-known that the conversion of normal colon epithelium to adenoma and then to carcinoma stems from acquired molecular changes in the genome. The genetic basis of colorectal cancer has been elucidated to a certain extent, and much remains to be known about the identity of specific cancer genes that are associated with the advancement of colorectal cancer from one stage to the next. Here in this study we attempted to identify novel cancer genes that could underlie the stage-specific progression and metastasis of colorectal cancer. We conducted a stage-based meta-analysis of the voluminous tumor genome-sequencing data and mined using multiple approaches for novel genes driving the progression to stage-II, stage-III and stage-IV colorectal cancer. The consensus of these driver genes seeded the construction of stage-specific networks, which were then analyzed for the centrality of genes, clustering of subnetworks, and enrichment of gene-ontology processes. Our study identified three novel driver genes as hubs for stage-II progression: <i>DYNC1H1</i>, <i>GRIN2A</i>, <i>GRM1</i>. Four novel driver genes were identified as hubs for stage-III progression: <i>IGF1R</i>, <i>CPS1</i>, <i>SPTA1</i>, <i>DSP</i>. Three novel driver genes were identified as hubs for stage-IV progression: <i>GSK3B</i>, <i>GGT1</i>, <i>EIF2B5</i>. We also identified several non-driver genes that appeared to underscore the progression of colorectal cancer. Our study yielded potential diagnostic biomarkers for colorectal cancer as well as novel stage-specific drug targets for rational intervention. Our methodology is extendable to the analysis of other types of cancer to fill the gaps in our knowledge.</p></div