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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.

Cancer hallmark genes are responsible for the most essential phenotypic characteristics of malignant transformation and progression. In this study, our aim was to estimate the prognostic effect of the established cancer hallmark genes in multiple distinct cancer types. RNA-seq HTSeq counts and survival data from 26 different tumor types were acquired from the TCGA repository. DESeq was used for normalization. Correlations between gene expression and survival were computed using the Cox proportional hazards regression and by plotting Kaplan–Meier survival plots. The false discovery rate was calculated to correct for multiple hypothesis testing. Signatures based on genes involved in genome instability and invasion reached significance in most individual cancer types. Thyroid and glioblastoma were independent of hallmark genes (61 and 54 genes significant, respectively), while renal clear cell cancer and low grade gliomas harbored the most prognostic changes (403 and 419 genes significant, respectively). The eight genes with the highest significance included BRCA1 (genome instability, HR 4.26, p < 1E−16), RUNX1 (sustaining proliferative signaling, HR 2.96, p = 3.1E−10) and SERPINE1 (inducing angiogenesis, HR 3.36, p = 1.5E−12) in low grade glioma, CDK1 (cell death resistance, HR = 5.67, p = 2.1E−10) in kidney papillary carcinoma, E2F1 (tumor suppressor, HR 0.38, p = 2.4E−05) and EREG (enabling replicative immortality, HR 3.23, p = 2.1E−07) in cervical cancer, FBP1 (deregulation of cellular energetics, HR 0.45, p = 2.8E−07) in kidney renal clear cell carcinoma and MYC (invasion and metastasis, HR 1.81, p = 5.8E−05) in bladder cancer. We observed unexpected heterogeneity and tissue specificity when correlating cancer hallmark genes and survival. These results will help to prioritize future targeted therapy development in different types of solid tumors.

Multiple studies suggested using different miRNAs as biomarkers for prognosis of hepatocellular carcinoma (HCC). We aimed to assemble a miRNA expression database from independent datasets to enable an independent validation of previously published prognostic biomarkers of HCC. A miRNA expression database was established by searching the TCGA (RNA-seq) and GEO (microarray) repositories to identify miRNA datasets with available expression and clinical data. A PubMed search was performed to identify prognostic miRNAs for HCC. We performed a uni- and multivariate Cox regression analysis to validate the prognostic significance of these miRNAs. The Limma R package was applied to compare the expression of miRNAs between tumor and normal tissues. We uncovered 214 publications containing 223 miRNAs identified as potential prognostic biomarkers for HCC. In the survival analysis, the expression levels of 55 and 84 miRNAs were significantly correlated with overall survival in RNA-seq and gene chip datasets, respectively. The most significant miRNAs were hsa-miR-149, hsa-miR-139, and hsa-miR-3677 in the RNA-seq and hsa-miR-146b-3p, hsa-miR-584, and hsa-miR-31 in the microarray dataset. Of the 223 miRNAs studied, the expression was significantly altered in 102 miRNAs in tumors compared to normal liver tissues. In summary, we set up an integrated miRNA expression database and validated prognostic miRNAs in HCC.

Gene or protein expression data are usually represented by metric or at least ordinal variables. In order to translate a continuous variable into a clinical decision, it is necessary to determine a cutoff point and to stratify patients into two groups each requiring a different kind of treatment. Currently, there is no standard method or standard software for biomarker cutoff determination. Therefore, we developed Cutoff Finder, a bundle of optimization and visualization methods for cutoff determination that is accessible online. While one of the methods for cutoff optimization is based solely on the distribution of the marker under investigation, other methods optimize the correlation of the dichotomization with respect to an outcome or survival variable. We illustrate the functionality of Cutoff Finder by the analysis of the gene expression of estrogen receptor (ER) and progesterone receptor (PgR) in breast cancer tissues. This distribution of these important markers is analyzed and correlated with immunohistologically determined ER status and distant metastasis free survival. Cutoff Finder is expected to fill a relevant gap in the available biometric software repertoire and will enable faster optimization of new diagnostic biomarkers. The tool can be accessed at http://molpath.charite.de/cutoff.

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 (nZ22 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 (nZ1090) or overall survival (nZ1287). 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; PZ3.7!10 K5

IntroductionMultiple gene expression based prognostic biomarkers have been repeatedly identified in gastric carcinoma. However, without confirmation in an independent validation study, their clinical utility is limited. Our goal was to establish a robust database enabling the swift validation of previous and future gastric cancer survival biomarker candidates.ResultsThe entire database incorporates 1,065 gastric carcinoma samples, gene expression data. Out of 29 established markers, higher expression of BECN1 (HR = 0.68, p = 1.5E-05), CASP3 (HR = 0.5, p = 6E-14), COX2 (HR = 0.72, p = 0.0013), CTGF (HR = 0.72, p = 0.00051), CTNNB1 (HR = 0.47, p = 4.3E-15), MET (HR = 0.63, p = 1.3E-05), and SIRT1 (HR = 0.64, p = 2.2E-07) correlated to longer OS. Higher expression of BIRC5 (HR = 1.45, p = 1E-04), CNTN1 (HR = 1.44, p = 3.5E- 05), EGFR (HR = 1.86, p = 8.5E-11), ERCC1 (HR = 1.36, p = 0.0012), HER2 (HR = 1.41, p = 0.00011), MMP2 (HR = 1.78, p = 2.6E-09), PFKB4 (HR = 1.56, p = 3.2E-07), SPHK1 (HR = 1.61, p = 3.1E-06), SP1 (HR = 1.45, p = 1.6E-05), TIMP1 (HR = 1.92, p = 2.2E- 10) and VEGF (HR = 1.53, p = 5.7E-06) were predictive for poor OS.MATERIALS AND METHODSWe integrated samples of three major cancer research centers (Berlin, Bethesda and Melbourne datasets) and publicly available datasets with available follow-up data to form a single integrated database. Subsequently, we performed a literature search for prognostic markers in gastric carcinomas (PubMed, 2012–2015) and re-validated their findings predicting first progression (FP) and overall survival (OS) using uni- and multivariate Cox proportional hazards regression analysis.ConclusionsThe major advantage of our analysis is that we evaluated all genes in the same set of patients thereby making direct comparison of the markers feasible. The best performing genes include BIRC5, CASP3, CTNNB1, TIMP-1, MMP-2, SIRT, and VEGF.

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.