Investigating　how　genes　jointly　affect　complex　human　diseases　is　important,　yet　challenging.　The　network　approach　(e.g.,　weighted　gene　co-expression　network　analysis　(WGCNA))　is　a　powerful　tool.　However,　genomic　data　usually　contain　substantial　batch　effects,　which　could　mask　true　genomic　signals.　Paired　design　is　a　powerful　tool　that　can　reduce　batch　effects.　However,　it　is　currently　unclear　how　to　appropriately　apply　WGCNA　to　genomic　data　from　paired　design.　In　this　paper,　we　modified　the　current　WGCNA　pipeline　to　analyse　high-throughput　genomic　data　from　paired　design.　We　illustrated　the　modified　WGCNA　pipeline　by　analysing　the　miRNA　dataset　provided　by　Shiah　etal.　(2014),　which　contains　forty　oral　squamous　cell　carcinoma　(OSCC)　specimens　and　their　matched　non-tumourous　epithelial　counterparts.　OSCC　is　the　sixth　most　common　cancer　worldwide.　The　modified　WGCNA　pipeline　identified　two　sets　of　novel　miRNAs　associated　with　OSCC,　in　addition　to　the　existing　miRNAs　reported　by　Shiah　etal.　(2014).　Thus,　this　work　will　be　of　great　interest　to　readers　of　various　scientific　disciplines,　in　particular,　genetic　and　genomic　scientists　as　well　as　medical　scientists　working　on　cancer.