miRpower: a web-tool to validate survival-associated miRNAs utilizing expression data from 2178 breast cancer patients

PURPOSE: The proper validation of prognostic biomarkers is an important clinical issue in breast cancer research. MicroRNAs (miRNAs) have emerged as a new class of promising breast cancer biomarkers. In the present work, we developed an integrated online bioinformatic tool to validate the prognostic relevance of miRNAs in breast cancer. METHODS: A database was set up by searching the GEO, EGA, TCGA, and PubMed repositories to identify datasets with published miRNA expression and clinical data. Kaplan-Meier survival analysis was performed to validate the prognostic value of a set of 41 previously published survival-associated miRNAs. RESULTS: All together 2178 samples from four independent datasets were integrated into the system including the expression of 1052 distinct human miRNAs. In addition, the web-tool allows for the selection of patients, which can be filtered by receptors status, lymph node involvement, histological grade, and treatments. The complete analysis tool can be accessed online at: www.kmplot.com/mirpower . We used this tool to analyze a large number of deregulated miRNAs associated with breast cancer features and outcome, and confirmed the prognostic value of 26 miRNAs. A significant correlation in three out of four datasets was validated only for miR-29c and miR-101. CONCLUSIONS: In summary, we established an integrated platform capable to mine all available miRNA data to perform a survival analysis for the identification and validation of prognostic miRNA markers in breast cancer

Online Survival Analysis Software to Assess the Prognostic Value of Biomarkers Using Transcriptomic Data in Non-Small-Cell Lung Cancer

In the last decade, optimized treatment for non-small cell lung cancer had lead to improved prognosis, but the overall survival is still very short. To further understand the molecular basis of the disease we have to identify biomarkers related to survival. Here we present the development of an online tool suitable for the real-time meta-analysis of published lung cancer microarray datasets to identify biomarkers related to survival. We searched the caBIG, GEO and TCGA repositories to identify samples with published gene expression data and survival information. Univariate and multivariate Cox regression analysis, Kaplan-Meier survival plot with hazard ratio and logrank P value are calculated and plotted in R. The complete analysis tool can be accessed online at: www.kmplot.com/lung. All together 1,715 samples of ten independent datasets were integrated into the system. As a demonstration, we used the tool to validate 21 previously published survival associated biomarkers. Of these, survival was best predicted by CDK1 (p<1E-16), CD24 (p<1E-16) and CADM1 (p = 7E-12) in adenocarcinomas and by CCNE1 (p = 2.3E-09) and VEGF (p = 3.3E-10) in all NSCLC patients. Additional genes significantly correlated to survival include RAD51, CDKN2A, OPN, EZH2, ANXA3, ADAM28 and ERCC1. In summary, we established an integrated database and an online tool capable of uni- and multivariate analysis for in silico validation of new biomarker candidates in non-small cell lung cancer

Survival characteristics of the patients included in the database including histology of adenocarcinoma (adeno), squamous cell carcinoma (SCC) and large cell carcinoma (large), gender, stage (only with overall survival) and smoking history.

<p>Survival characteristics of the patients included in the database including histology of adenocarcinoma (adeno), squamous cell carcinoma (SCC) and large cell carcinoma (large), gender, stage (only with overall survival) and smoking history.</p

Performance of previously published biomarker candidates associated with survival in non-small-cell lung cancer.

<p>Performance of previously published biomarker candidates associated with survival in non-small-cell lung cancer.</p

Validation of 29 previously published NSCLC biomarkers.

<p>Meta-analysis of these genes and signatures in the respective sample cohort yielded CCNE1, CDC2 and CADM1 as the best performing individual genes <b>(A–C)</b> and the signature of Yamauchi et al. <b>(D)</b>. A funnel plot depicting the hazard ratios (with confidence intervals) versus sample number for CDC2 and VEGF shows more reliable estimation with larger database sizes <b>(E–F)</b>.</p

RecurrenceOnline: an online analysis tool to determine breast cancer recurrence and hormone receptor status using microarray data

In the last decades, several gene expression-based predictors of clinical behavior were developed for breast cancer. A common feature of these is the use of multiple genes to predict hormone receptor status and the probability of tumor recurrence, survival or response to chemotherapy. We developed an online analysis tool to compute ER and HER2 status, Oncotype DX 21-gene recurrence score and an independent recurrence risk classification using gene expression data obtained by interrogation of Affymetrix microarray profiles. We implemented rigorous quality control algorithms to promptly exclude any biases related to sample processing, hybridization and scanning. After uploading the raw microarray data, the system performs the complete evaluation automatically and provides a report summarizing the results. The system is accessible online at http://www.recurrenceonline.com . We validated the system using data from 2,472 publicly available microarrays. The validation of the prediction of the 21-gene recurrence score was significant in lymph node negative patients (Cox-Mantel, P = 5.6E-16, HR = 0.4, CI = 0.32-0.5). A correct classification was obtained for 88.5% of ER- and 90.5% of ER + tumors (n = 1,894). The prediction of recurrence risk in all patients by using the mean of the independent six strongest genes (P < 1E-16, HR = 2.9, CI = 2.5-3.3), of the four strongest genes in lymph node negative ER positive patients (P < 1E-16, HR = 2.8, CI = 2.2-3.5) and of the three genes in lymph node positive patients (P = 3.2E-9, HR = 2.5, CI = 1.8-3.4) was highly significant. In summary, we integrated available knowledge in one platform to validate currently used predictors and to provide a global tool for the online determination of different prognostic parameters simultaneously using genome-wide microarrays

Implementing an online tool for genome-wide validation of survival-associated biomarkers in ovarian-cancer using microarray data from 1287 patients

The validation of prognostic biomarkers in large independent patient cohorts is a major bottleneck in ovarian cancer research. We implemented an online tool to assess the prognostic value of the expression levels of all microarray-quantified genes in ovarian cancer patients. First, a database was set up using gene expression data and survival information of 1287 ovarian cancer patients downloaded from Gene Expression Omnibus and The Cancer Genome Atlas (Affymetrix HG-U133A, HG-U133A 2.0, and HG-U133 Plus 2.0 microarrays). After quality control and normalization, only probes present on all three Affymetrix platforms were retained (n=22,277). To analyze the prognostic value of the selected gene, we divided the patients into two groups according to various quantile expressions of the gene. These groups were then compared using progression-free survival (n=1090) or overall survival (n=1287). A Kaplan-Meier survival plot was generated and significance was computed. The tool can be accessed online at www.kmplot.com/ovar. We used this integrative data analysis tool to validate the prognostic power of 37 biomarkers identified in the literature. Of these, CA125 (MUC16; P=3.7×10(-5), hazard ratio (HR)=1.4), CDKN1B (P=5.4×10(-5), HR=1.4), KLK6 (P=0.002, HR=0.79), IFNG (P=0.004, HR=0.81), P16 (P=0.02, HR=0.66), and BIRC5 (P=0.00017, HR=0.75) were associated with survival. The combination of several probe sets can further increase prediction efficiency. In summary, we developed a global online biomarker validation platform that mines all available microarray data to assess the prognostic power of 22,277 genes in 1287 ovarian cancer patients. We specifically used this tool to evaluate the effect of 37 previously published biomarkers on ovarian cancer prognosis