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Fig. 2 | Journal of Big Data

Fig. 2

From: Integration of transcriptomic analysis and multiple machine learning approaches identifies NAFLD progression-specific hub genes to reveal distinct genomic patterns and actionable targets

Fig. 2

Different risk subgroups with distinct inflammatory and fibrotic patterns were identified in NAFL. (a) The 182 NAFLD progression-specific genes were analyzed using GO method, and the Circos illustrated that they were mainly enriched in five GOBPs annotated with “ECM organization”, “Blood vessel development”, “Wnt signaling pathway”, “Cell adhesion”, and “Cell morphogenesis”. (b) Based on the expression profile of the 182 NAFLD progression genes and using the NMF algorithm, the 51 NAFLs in the training cohort were divided into two subclusters (C1 = 21, C2 = 30). (c-e) Using ssGSEA quantification, we observed that the ssGSEA scores of “Inflammatory response”, “ECM organization”, and “Cell-cell adhesion” were progressively elevated from C2 to C1 to NASH. (f & g) Positive correlations between “Inflammatory response” and “ECM organization” or “Cell-cell adhesion” were observed in NAFL-C1, C2, and NASH samples. (h) A group of widely acknowledged inflammatory factors including LPAR1, PTPRE, CCR2, CCL20, CLEC5A, CXCL6, ITGB8, PDPN, and GPC3 were significantly decreased in the NAFL-C2 group compared to either NAFL-C1 or NASH. (i) The fibroblasts abundance was significantly downregulated in NAFL-C2. Th1 cell infiltration was significantly upregulated in NAFL-C2, while no significant difference of Th2 cell infiltration was observed among the three groups. (j) The NAFL-C2 group exhibited the lowest stroma score, while no significant difference of stroma score was observed between NAFL-C1 and NASH. * p < 0.05, ** p < 0.01, *** p < 0.001, # not significant

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