Survival Online: a web-based service for the analysis of correlations between gene expression and clinical and follow-up data

Corradi, Luca; Mirisola, Valentina; Porro, Ivan; Torterolo, Livia; Fato, Marco; Romano, Paolo; Pfeffer, Ulrich

doi:10.1186/1471-2105-10-s12-s10

Cited by 4 publications

(3 citation statements)

References 18 publications

(19 reference statements)

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“…To our knowledge, few software packages provide such graphical interface to ease the survival analysis without coding. We find Survival Online (SO) tool by Corradi et al [ 12 ] a useful online portal for Cox regression and survival analysis using gene expression data. At present, dChipSurv provides similar analysis for SNP array data.…”

Section: Discussionmentioning

confidence: 99%

The dChip survival analysis module for microarray data

et al. 2011

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BackgroundGenome-wide expression signatures are emerging as potential marker for overall survival and disease recurrence risk as evidenced by recent commercialization of gene expression based biomarkers in breast cancer. Similar predictions have recently been carried out using genome-wide copy number alterations and microRNAs. Existing software packages for microarray data analysis provide functions to define expression-based survival gene signatures. However, there is no software that can perform survival analysis using SNP array data or draw survival curves interactively for expression-based sample clusters.ResultsWe have developed the survival analysis module in the dChip software that performs survival analysis across the genome for gene expression and copy number microarray data. Built on the current dChip software's microarray analysis functions such as chromosome display and clustering, the new survival functions include interactive exploring of Kaplan-Meier (K-M) plots using expression or copy number data, computing survival p-values from the log-rank test and Cox models, and using permutation to identify significant chromosome regions associated with survival.ConclusionsThe dChip survival module provides user-friendly way to perform survival analysis and visualize the results in the context of genes and cytobands. It requires no coding expertise and only minimal learning curve for thousands of existing dChip users. The implementation in Visual C++ also enables fast computation. The software and demonstration data are freely available at http://dchip-surv.chenglilab.org.

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Section: Discussionmentioning

confidence: 99%

The dChip survival analysis module for microarray data

et al. 2011

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“…This is important since several of the reasons involved in the failure of biomarkers in clinical trials are related to data analysis [3]. For the analysis of biomarkers, tools as ITTACA, KMPlot, RecurrenceOnline, bc-GeneExMiner, GOBO, and PrognoScan have been proposed [1], [4]–[9]. However, these tools have serious restrictions (Table 1), complicating and limiting the assessment of multi-gene biomarkers in cancer.…”

Section: Introductionmentioning

confidence: 99%

SurvExpress: An Online Biomarker Validation Tool and Database for Cancer Gene Expression Data Using Survival Analysis

et al. 2013

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Validation of multi-gene biomarkers for clinical outcomes is one of the most important issues for cancer prognosis. An important source of information for virtual validation is the high number of available cancer datasets. Nevertheless, assessing the prognostic performance of a gene expression signature along datasets is a difficult task for Biologists and Physicians and also time-consuming for Statisticians and Bioinformaticians. Therefore, to facilitate performance comparisons and validations of survival biomarkers for cancer outcomes, we developed SurvExpress, a cancer-wide gene expression database with clinical outcomes and a web-based tool that provides survival analysis and risk assessment of cancer datasets. The main input of SurvExpress is only the biomarker gene list. We generated a cancer database collecting more than 20,000 samples and 130 datasets with censored clinical information covering tumors over 20 tissues. We implemented a web interface to perform biomarker validation and comparisons in this database, where a multivariate survival analysis can be accomplished in about one minute. We show the utility and simplicity of SurvExpress in two biomarker applications for breast and lung cancer. Compared to other tools, SurvExpress is the largest, most versatile, and quickest free tool available. SurvExpress web can be accessed in http://bioinformatica.mty.itesm.mx/SurvExpress (a tutorial is included). The website was implemented in JSP, JavaScript, MySQL, and R.

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“…The prognostic value of the TGFβR2 gene was assessed with the Kaplan Meier plotter, a meta-analysis tool based in silico biomarker assessment, which assesses the effect of genes on survival in cancer patients ( http://www.kmplot.com/lung ) [ 22 , 23 ]. Each median was computed and the best performing threshold was used as the final cutoff in a univariate and Cox regression analysis.…”

Section: Methodsmentioning

confidence: 99%

Increased Expression of TGFβR2 Is Associated with the Clinical Outcome of Non-Small Cell Lung Cancer Patients Treated with Chemotherapy

et al. 2015

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To investigate the prognostic significance of TGFβR2 expression and chemotherapy in Chinese non-small cell lung cancer (NSCLC) patients, TGFβR2 expression NSCLC was analyzed in silico using the Oncomine database, and subsequently analyzed with quantitative RT-PCR in 308 NSCLC biopsies, 42 of which were paired with adjacent non-neoplastic tissues. Our results show that TGFβR2 expression was also increased in NSCLC biopsies relative to normal tissue samples and correlated with poor prognosis. TGFβR2 expression was also significantly correlated with other clinical parameters such as tumor differentiation, invasion of lung membrane, and chemotherapy. Moreover, overall survival (OS) and disease free survival (DFS) was increased in patients with low TGFβR2 expressing NSCLC and who had undergone chemotherapy. Thus, high expression of TGFβR2 is a significant risk factor for decreased OS and DFS in NSCLC patients. Thus, TGFβR2 is a potential prognostic tumor biomarker for chemotherapy.

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Survival Online: a web-based service for the analysis of correlations between gene expression and clinical and follow-up data

Cited by 4 publications

References 18 publications

The dChip survival analysis module for microarray data

The dChip survival analysis module for microarray data

SurvExpress: An Online Biomarker Validation Tool and Database for Cancer Gene Expression Data Using Survival Analysis

Increased Expression of TGFβR2 Is Associated with the Clinical Outcome of Non-Small Cell Lung Cancer Patients Treated with Chemotherapy

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