Validating microarray data

We recently developed an online biomarker validation tool using microarray data of 2000 breast cancer patients (Gyorffy . In this, the expression of a selected gene can be used to split patients into groups, and the proportional survival of these groups is compared to each other.

In this study, our aim was to implement an online survival analysis tool for the rapid assessment of prognosis-related genes in ovarian cancer and to test the validity of previously proposed biomarkers.

In addition, the genome-wide investigation of adequate clinical cohorts delivers unprecedented amount of potential new biomarkers (Denkert . However, most of these potential biomarkers have neither been validated in multivariate analyses nor was their discriminative power validated in large clinical cohorts. Given the large number of potential biomarkers for EOC, the immediate challenge is to validate the most robust candidates eligible for further investigation.

Even more alarmingly, many reports have questioned or rejected a correlation between a proposed biomarker and clinical outcome. Recent advances in genomic technologies together with powerful bioinformatic tools can enable us to deliver this prerequisite.

Only three microarray platforms, GPL96 (Affymetrix HG-U133A), GPL570 (Affymetrix HG-U133 Plus 2.0), and GPL571/GPL3921 (Affymetrix HG-U133A 2.0), were considered because they are frequently used and because these particular arrays have 22 277 probe sets (representing 13 435 unique genes) in common.

The use of almost identical platforms and identical probe sets is vital because different platforms for gene expression profiling measure expression of the same gene with varying accuracy, on different relative scales, and with diverse dynamic ranges (Tan . MAS5 can be applied to individual chips, making future extensions of the database uncomplicated.

Hazard ratio (HR; and 95% confidence intervals) and logrank We also implemented a set of probe-set-related options, including the option to use all probe sets available for a given gene on the microarray simultaneously and to use a combined expression of several probe sets.

Using this option, it is possible to assess the effect of the mean expression of gene combinations on survival.

he server-side scripts were developed in hypertext preprocessor (PHP), which controls the analysis requests and delivers the results.A Kaplan–Meier survival plot was generated and significance was computed. We used this integrative data analysis tool to validate the prognostic power of 37 biomarkers identified in the literature.Of these, =0.00017, HR=0.75) were associated with survival.We specifically used this tool to evaluate the effect of 37 previously published biomarkers on ovarian cancer prognosis.With a mortality of 8.4 per 100 000 women, ovarian cancer is the most common cause of death among gynecological malignancies ( with a 5-year survival rate of 10–30%.

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