ArticleJournal of translational medicine2021
MAPK14 over-expression is a transcriptomic feature of polycythemia vera and correlates with adverse clinical outcomes.
Article in Journal of translational medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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7 authors at 1 institution in 1 country.
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Abstract
backgroundThe transcriptomic signature has not been fully elucidated in PV, as well as mRNA markers for clinical variables (thrombosis, leukemic transformation, survival, etc.). We attempted to reveal and validate crucial co-expression modules and marker mRNAs correlating with polycythemia vera (PV) by weighted gene co-expression network analysis (WGCNA). MATERIAL AND
methodsThe GSE57793/26014/61629 datasets were downloaded from Gene Expression Omnibus (GEO) database and integrated into one fused dataset. By R software and 'WGCNA' package, the PV-specific co-expression module was identified, the pathway enrichment profile of which was obtained by over-representation analysis (ORA). Protein-protein interaction (PPI) network and hub gene analysis identified MAPK14 as our target gene. Then the distribution of MAPK14 expression in different disease/mutation types, were depicted based on external independent datasets. Genome-scale correlation analysis revealed the association of MAPK14 and JAK/STAT family genes. Then gene set enrichment analysis (GSEA) was performed to detect the activated and suppressed pathways associating with MAPK14 expression. Moreover, GSE47018 dataset was utilized to compare clinical variables (thrombosis, leukemic transformation, survival, etc.) between MAPK14-high and MAPK14-low groups.
resultsAn integrated dataset including 177 samples (83 PV, 35 ET, 17 PMF and 42 normal donors) were inputted into WGCNA. The 'tan' module was identified as the PV-specific module (R
conclusionMAPK14 over-expression was identified as a transcriptomic feature of PV, which was also related to inferior clinical outcomes. The results provided novel insights for biomarkers and therapeutic targets for PV.
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