Additional file 4: of Identification of common oncogenic and early developmental pathways in the ovarian carcinomas controlling by distinct prognostically significant microRNA subsets

Software. R codes. (PDF 194 kb

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

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 >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

Additional file 1: of Identification of common oncogenic and early developmental pathways in the ovarian carcinomas controlling by distinct prognostically significant microRNA subsets

Methods. Additional Methods. (DOCX 39 kb

Additional file 2: Table S1. of Identification of common oncogenic and early developmental pathways in the ovarian carcinomas controlling by distinct prognostically significant microRNA subsets

The 13-miRNA subset from K-means. Table S2. The 100 survival significant miRNAs and results of the DDSS-1D, DDSS-2D and SWVg analyses. TCGA dataset was analysed. The 10 survival significant miRNAs belonging to the 13 miRNA subset selected by K-means analysis are highlighted. Table S3. The 25-miR subset selected by DDSS-1D with ten-fold cross validation. Table S4. The frequency of miRs in DDSS-2D miR pairs. The miRNAs which was reported in 13 miRNAs selected by K-means analysis were highlighted in yellow cell. Table S5. 28 miRNAs which display the patterns with three groups in DDSS-1D. Table S6. Analysis of 19-miRNA prognostic signature. Significant clusters generated from DAVID functional annotation tools. Each DAVID annotation cluster represents one biological theme by grouping similar annotation terms according to the common genes shared by them. Table S7. Association analysis of clinical indicators with groups separated by 19 miRNAs from SWV based on DDSS-1D. Table S8. Seventeen survival-significant miRNAs in Shih et al signature [5]. Table S9. Wald P-values of 4 significant miRNA in TCGA dataset, supported by independent dataset [5]. Table S10. Three miRNA survival prediction signatures of HG-SOC. Table S11. Comparison of the 19-miRNA survival prediction signature with other miRNA-based survival prediction signatures of HG-SOC. Table S12. Concensus subset of survival significant miRNAs. Table S13.  DDSS-1D-based  selection of the 31 miRNAs  and SWVg analysis. Table S14.  Interactions between miRNAs and predicted direct  target  mRNAs found  in 36 mRNA HG-EOC prognostic classifier. Table S15. Significant signaling pathways (by DIANA-mirPath v.2 software) targeting by the miRNA subsets belonging to five miRNA signatures. The common pathways shared by miRNA signatures from K-means, DDSS-D1, DDSS-D2 and SWVg analyses are marked in boldface. Table S16. Initial data lists  for for miRPath-7.3 analysis,  the number target genes and the target identification methods. Table S17. Common signaling pathways. Table S18. Neurotrophin signalling pathway: Updated lists of the miRNAs refereeing to the 19-miRNA (19_mir_N), 21-miRNA (21_mir_N) and 31miRNA (31_mir_N) prognostic signatures and their characteristics. Table S19. Neurotrophin signaling pathway data (miRNAs, mRNA). Table S20. Number of miRNAs interacting with a target mRNA associated with gene encoding neurophilin signaling pathway. Table S21. Tarbase Experimentally Supported Interactions for miRNAs. (XLS 400 kb