A combined analysis of data from a series of literature studies can lead to more reliable results than that based on a single study. A common problem in performing combined analyses of literature microarray gene expression data is that the original raw data are not always available and not always easy to combine in one analysis. We propose an approach that does not require analyzing original raw data, but instead takes literature gene sets derived from (supplementary) tables as input and uses gene co-occurrence in these sets for mapping a co-regulation network. An algorithm for this method was applied to a collection of literature-derived gene sets related to embryonic stem cell (ESC) differentiation. In the resulting network, genes involved in similar biological processes or expressed at similar time points during differentiation were found to cluster together. Using this information, we identified 43 genes not previously associated with cardiac ESC differentiation for which we were able to assign a putative novel biological function. For 6 of these genes (Apobec2, Cth, Ptges, Rrad, Zfp57, and 2410146L05Rik), literature data on mouse knockout phenotypes support their putative function. Three other genes (Rcor2, Zfp503, and Hspb3) are part of major pathways within the network and therefore likely mechanistically relevant candidate genes. We anticipate that these 43 genes can help to improve the understanding of the molecular events underlying ESC differentiation. Moreover, the approach introduced here can be more widely applied to identify possible novel gene functions in biological processes.