I think "validation" might take on several meanings in the context of your question.

You can use ROC curves to assess how well your selected biomarkers perform, either on their own or combined, using the data you have. If you want to "validate" your biomarkers, you might be meaning that you want to measure the biomarkers in an independent cohort (separate from the one you already have) and get the same results.

In any case, you want to know how to generate a ROC curve.

GraphPad Prism will allow you to create a ROC curve and perform AUC analysis, and this is the way I have always done it. I am assuming other statistical packages such as SPSS will do it too. R is also a (free) alternative, and here is an r-bloggers.com post about how to use some of the features of R to do this: https://www.r-bloggers.com/illustrated-guide-to-roc-and-auc/

Thanks for the guidance Sophie. I further want to know whether it is essential to do this for a biomarker identification process (rather than doing this in the lab and this is not a classification process) or not. Is it ok to use FDR corrected p-value instead of this? There is a function available in R for this.

Per Sophie's first point (I think), remember that you can't validate the biomarkers' performance in the same samples that you used to identify their significance. Doing so is "self confirmatory" and will give you nothing. Either do ROC analysis in new samples or re-identify significant markers in a subset of your samples and test them on the rest. 

Thanks Anders. I can work on the 2nd solution you given, but have to redo it for many experiments. Is it accurate to continue with FDR correction?

Do you mean FDR corrected p-values to filter your biomarker list? Yes, I think that is a good start as long as you use only the training set to calculate them. Feature selection is a whole area in itself that you may what to look into to optimize your biomarker list for whatever model you want to put it into.