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.

Background: Survival analysis is a cornerstone of medical research, enabling the assessment of clinical outcomes for disease progression and treatment efficiency. Despite its central importance, no commonly used spreadsheet software can handle survival analysis and there is no web server available for its computation. Objective: Here, we introduce a web-based tool capable of performing univariate and multivariate Cox proportional hazards survival analysis using data generated by genomic, transcriptomic, proteomic, or metabolomic studies. Methods: We implemented different methods to establish cut-off values for the trichotomization or dichotomization of continuous data. The false discovery rate is computed to correct for multiple hypothesis testing. A multivariate analysis option enables comparing omics data with clinical variables. Results: We established a registration-free web-based survival analysis tool capable of performing univariate and multivariate survival analysis using any custom-generated data. Conclusions: This tool fills a gap and will be an invaluable contribution to basic medical and clinical research.

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.

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.

Genes showing higher expression in either tumor or metastatic tissues can help in better understanding tumor formation and can serve as biomarkers of progression or as potential therapy targets. Our goal was to establish an integrated database using available transcriptome-level datasets and to create a web platform which enables the mining of this database by comparing normal, tumor and metastatic data across all genes in real time. We utilized data generated by either gene arrays from the Gene Expression Omnibus of the National Center for Biotechnology Information (NCBI-GEO) or RNA-seq from The Cancer Genome Atlas (TCGA), Therapeutically Applicable Research to Generate Effective Treatments (TARGET), and The Genotype-Tissue Expression (GTEx) repositories. The altered expression within different platforms was analyzed separately. Statistical significance was computed using Mann–Whitney or Kruskal–Wallis tests. False Discovery Rate (FDR) was computed using the Benjamini–Hochberg method. The entire database contains 56,938 samples, including 33,520 samples from 3180 gene chip-based studies (453 metastatic, 29,376 tumorous and 3691 normal samples), 11,010 samples from TCGA (394 metastatic, 9886 tumorous and 730 normal), 1193 samples from TARGET (1 metastatic, 1180 tumorous and 12 normal) and 11,215 normal samples from GTEx. The most consistently upregulated genes across multiple tumor types were TOP2A (FC = 7.8), SPP1 (FC = 7.0) and CENPA (FC = 6.03), and the most consistently downregulated gene was ADH1B (FC = 0.15). Validation of differential expression using equally sized training and test sets confirmed the reliability of the database in breast, colon, and lung cancer at an FDR below 10%. The online analysis platform enables unrestricted mining of the database and is accessible at TNMplot.com.