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Background: Pleiotropy refers the genetic mechanism that a gene affects multiple phenotypes, like different types of cancer.In the past, more efforts have been dedicated to illustrate the phenotypic relationship between cancers and their shared genes.The aim of this study is to identify functional modules with pleiotropic effects on multiple cancers based on microarray data.Methods: Raw gene expression datasets involving affected cancer tissues and corresponding unaffected tissues are obtained from Gene Expression Omnibus (GEO).After Robust Multi-Array Analysis preprocessing, both moderated t-statistic and log2-fold change (logFC) are applied to filter out genes with adjusted p-value > 0.01 or logFC
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