Additional file 3: Figure S1. of Identification of common oncogenic and early developmental pathways in the ovarian carcinomas controlling by distinct prognostically significant microRNA subsets
Creators: name: Kuznetsov, Vladimir
name: Zhiqun Tang
name: Ivshina, Anna
Publisher: name: Figshare
Year: 2017
Subjects: subject: Cell Biology
subject: Genetics
subject: FOS: Biological sciences
subject: FOS: Biological sciences
subject: Molecular Biology
subject: Immunology
subject: FOS: Clinical medicine
subject: FOS: Clinical medicine
subject: 69999 Biological Sciences not elsewhere classified
subject: 19999 Mathematical Sciences not elsewhere classified
subject: FOS: Mathematics
subject: FOS: Mathematics
subject: Developmental Biology
subject: Cancer
subject: Hematology
subject: 110309 Infectious Diseases
subject: FOS: Health sciences
subject: FOS: Health sciences
subject: Computational Biology

description: Three data-driven patient grouping methods. A: DDSS-1D method with a single cut-off value of a single prognostic variable (miRNA-222). It is an example of patient separation into relatively low- and high- risk subgroups; the cut-off value of miRNA-222 expression levels is defined at a minimum of the Wald statistics log (P-value) (left panel) for two K-M functions (right panel). This cut-off value separates patients into statistically significant survival subgroups. High expression level of the miRNA-222 (at cut-off value &gt;5.56) corresponding to the relatively poor prognosis of the patient subgroup (red K-M curve; right panel). B: The DDSS-1D method uses two cut-off values within dynamic range of a single prognostic variable (miRNA-148b expression). The method uses 2 similar strongest minima of the log (P-value) function (left panel) separating patients into three statistically significant prognostic subgroups (right panel). C: A schema of the DDSS-2D method of patient’s grouping, using one cut-off value for each predictive variable in its domain. The method provides ‘the most significant/optimal’ patient’s grouping (at the smallest Wald statistics P-value) for the paired variables (miRNA pairs). The cut-off value for each of the miRNA is optimized via selection of the most significant/optimal variant of patient’s grouping. Seven possible grouping models of the paired data within the 2D domain can be indicated. D: The expression levels of the miRNA pair (let-7a and mir-130a) which separates the HG-SOC patients into two subgroups with grouping design 2. Figure S2. Cross validation analysis of the data-driven survival stratification system. Venn diagram analysis of the miRNA from three prognostic models: DDSS-SWV (SWVg, used 84 miRNAs, input data from DDSS-1D; Additional file 2: Table S2), DDSS-1D_10CV (DDSS-1D with ten-fold cross validation robustness, 25 miRNAs; Additional file 2: Table S3) and DDSS-2D (DDSS-2D, used top 52 miRNAs, having at least 50 synergistic miRNA pairs; Additional file 2: Table S4). The subset, which was common across these miRNA sets includes 19 miRNAs. This miRNA subset was used by SWVg to construct the19-miRNA prognostic signature. Figure S3. Correlation between two prognostic signatures: 19-miRNA prognostic classifier and 21-miRNA prognostic classifier. Figure S4. Significant canonical pathways using the algorithm of transcription regulation that was generated from the 19-miRNA and 31-miRNA prognostic signatures. Pathway data was generated using MetaCore, GeneGo, Inc. The detailed legend of the symbols can be found at https://portal.genego.com/legends/network_legend.html . A: Gene interconnection subnetwork putatively regulated by the miRNAs included into the 19-miRNA prognostic signature. B: Gene interconnection subnetwork formed by the miRNAs included into the 31-miRNA prognostic signature. Figure S5. Typical skewed frequency distribution of the number of miRNA:mRNA links. Data for the neurotrophin signaling pathway are presented. (PDF 606 kb)