Despite considerable efforts to identify "how" and "why" cancer cells are metabolically distinct from their normal counterparts, there are only very few studies analyzing the metabolic pattern of cancer cells. Context-specific models facilitate the unveiling of the genotype-phenotype relationship by calculating the flux states and extracting physiologically relevant information from high-quality data, thanks to the use of omics - data integration approaches. Here, we performed a benchmark to assess the influence of omics-data and integration methods on the metabolic patterns in cancers. We collected different types of omics data (transcriptome and proteome) from different sources (cell-lines and patients), as well as different platforms (RNA-Seq and microarray) and integrated them into Recon3D (the generic GEM of Humans) by four integration algorithms (GIMME, iMAT, INIT, and FASTCORE). The results of this study published in the Journal of Personalized Medicine "https://www.mdpi.com/2075-4426/11/6/496".