Univariate and multivariate analyses of overall survival and disease-free survival (Cox proportional hazards regression model) in 213 patients according to age, sex, stage and the first principal component (PC1) assigned groups calculated with the 12-gene signature.

<p>* Based on the rank order of the first principal component (PC1) of the 12 gene signature to divide samples into two groups. Significant <i>p</i> values were in bold (<i>p</i><0.05). Abbreviations: <i>HR</i>, hazard ratio; <i>CI</i>, confidence interval.</p><p>Univariate and multivariate analyses of overall survival and disease-free survival (Cox proportional hazards regression model) in 213 patients according to age, sex, stage and the first principal component (PC1) assigned groups calculated with the 12-gene signature.</p

Forest plot of the association between individual genes in the 12-gene signature and CRC survival.

<p>(A) Forest plot of the association between individual genes and OS with a fixed-effect model in datasets containing OS information (GSE17536, GSE17537, GSE39582 and GSE39084). Meta-analysis of these 12 genes in four independent datasets was conducted, and <i>HR</i>, 95% <i>CI</i> of each gene and corresponding <i>p</i> value were calculated and plotted in the forest plot. (B) Forest plot of the association between individual genes and DFS with a random-effect model in four datasets containing DFS information (GSE17536, GSE17537, GSE39582 and GSE14333). <i>CRC</i>, colorectal cancer; <i>HR</i>, hazard ratio; <i>CI</i>; confidence interval; <i>OS</i>, overall survival; <i>DFS</i>, disease-free survival.</p

Random gene sampling verified the validity of our step- gene selection procedure.

<p><b>(A)</b> Bar plot of the number of times that 12 randomly chosen genes could simultaneously discriminate four survival datasets (OS and DFS in GES17536 and GSE17537, DFS in GSE39582 and GSE14333). (B) Heatmap of 137 biopsy samples established with mRNA expression profile of the 12-gene signature. The mRNA raw data were normalized and then filtered (see “<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0137171#sec002" target="_blank">Materials and Methods</a>”). Rows represent genes, and columns represent biopsy samples. Rows, rather than columns, were reordered using UCA, whereas samples of the same type were placed together. <i>DVIG</i>, development varying immune gene; <i>UCA</i>, unsupervised clustering algorithm; <i>OS</i>, overall survival; <i>DFS</i>, disease-free survival.</p

Affymetrix microarray datasets included in this study, used to evaluate the prognostic value of our 12-gene signature.

<p>Abbreviations: <i>SD</i>, standard deviation; <i>AdjCTX</i>, whether chemotherapy was used; <i>NR</i>, not reported. Note: GSE39582 has 566 samples, including 562 samples with clear OS information and 557 samples with clear DFS information.</p><p>Affymetrix microarray datasets included in this study, used to evaluate the prognostic value of our 12-gene signature.</p

miRNA-mRNA regulatory network.

<p>Dark yellow nodes represent miRNAs. Red and sapphire nodes represent mRNAs, among which red ones are genes in the 12-gene signature. Directed solid edges represent miRNA-mRNA regulation.</p

Gene signature optimization based on Spearman correlation transition model and AUC-RF algorithm.

<p><b>(A)</b> The 665 DVIGs were projected onto a Spearman correlation transition coordinate system based on their cooperativity disorientation between the consecutive stages. Genes were colored in the same way as in the development heatmap. <b>(B)</b> The AUC-RF algorithm was used for gene signature optimization. Genes were recursively removed from an importance-ordered gene list until the largest AUC value was met. <b>(C)</b> The biggest AUC of 0.904 (95% <i>CI</i>: 0.799~1.000) was obtained when the number of genes were reduced to 12, with 81.8% sensitivity (95% <i>CI</i>: 0.636–0.955) and 89.5% specificity (95% <i>CI</i>: 0.737–1.000). <i>Dev</i>, development; <i>Prog</i>, progression; <i>TPS</i>, theoretically stable point; <i>AUC</i>, area under curve; <i>DVIG</i>, development varying immune gene; <i>CI</i>, confidence interval.</p

Schematic of the stepwise gene signature selection and evaluation workflow.

<p><i>CRC</i> colorectal cancer, <i>DVIG</i> development varying immune gene, <i>OS</i> overall survival, <i>DFS</i> disease-free survival.</p

Pearson correlation heatmaps and density curve plot of 665 DVIGs.

<p>Heatmaps of adjusted Pearson correlations for 665 DVIGs in <b>(A)</b> development, <b>(B)</b> precancerous progression and <b>(C)</b> cancer, respectively. Genes were clustered into three clusters (highlighted with different colors) by UCA. <b>(D)</b> Density plot of pairwise adjusted Pearson correlations for all three stages. The curve for the development stage is bimodal distribution, but unimodal for in progression and cancer stages. In order to render intra-immune vectors comparable, genes were reordered in the progression and cancer stage heatmaps to match the order in the development stage heatmap, to generate <b>(E)</b> reordered progression heatmap and <b>(F)</b> reordered cancer heatmap. <i>DVIG</i>, development varying immune gene; <i>UCA</i>, unsupervised clustering algorithm.</p

Kaplan–Meier survival analyses and log-rank tests of the 12-gene signature.

<p>Kaplan–Meier survival analyses and log-rank tests were conducted to evaluate the prognostic value of the 12-gene signature. (A) The performance of the 12-gene signature in OS discrimination. Datasets with OS information were GSE17536, GSE17537, GSE39582 and GSE39084. (B) The performance of the 12-gene signature in DFS discrimination. Datasets with DFS information were GSE17536, GSE17537, GSE39582 and GSE14333. <i>OS</i>, overall survival; <i>DFS</i>, disease-free survival.</p

Additional file 1: Tables S1-S4. of A network-based predictive gene-expression signature for adjuvant chemotherapy benefit in stage II colorectal cancer

indicate additional results of Cox regression analysis and genes involved in the 11-PPI-mod. Figures S1-S4. show additional information of data processing, feature selection and Kaplan-Meier analysis. (DOC 1262 kb