Research Paper Volume 12, Issue 24 pp 25916—25938

Exploring diagnostic m6A regulators in endometriosis

Figure 5. Functional annotation of HNRNPA2B1 and HNRNPC in EMs. (AD) Differentially expressed genes (DEGs) between low-HNRNPA2B1 vs. high-HNRNPA2B1 EU samples, low-HNRNPC vs. high-HNRNPC EU samples, low-HNRNPA2B1 vs. high-HNRNPA2B1 EC samples, and low-HNRNPC vs. high-HNRNPC EC samples in the training dataset (Green dots, DEGs with log2FC < -0.5 and p < 0.05; Red dots, DEGs with log2FC > 0.5 and p < 0.05; Black dots, |log2FC| < 0.05 or p > 0.05). (EH) The GSEA analysis of HNRNPA2B1 and HNRNPC in EMs. (IL) Classical GSEA plots of the top 3 BP terms in each contrast matrix. (M) 48 shared DEGs (|log2FC| > 0.5 and p < 0.05) between low-HNRNPA2B1 vs. high-HNRNPA2B1 and low-HNRNPC vs. high-HNRNPC in EU and EC samples (N) The enriched Reactome pathways of 48 shared DEGs. (Grey circles, Reactome pathways; Red rectangles, up-regulated DEGs; Green rectangles, down-regulated DEGs). (O), (P) The correlation between PGR and HNRNPA2B1, HNRNPC in training and validation datasets. EMs, endometriosis; EU, eutopic endometrium; EC, ectopic endometrium; GSEA, the gene set enrichment analysis; BP, biological process; PGR, progesterone receptor.