Fig 2: Correlation of fallopian tube tissue transcriptomics profile with a hFTE sEV proteomics profile. (A) Comparison between hFTE sEV proteins with hFT tissue transcripts (CTA database, GeoMx) of ciliated and secretory cells identified by GeoMx digital spatial imaging showing 61 common gene IDs. (B) Protein-protein interaction network of the 61 common gene IDs via STRING. (C) Gene enrichment analysis of 61 common genes showing molecular functions, biological processes, Reactome pathways, and cellular components associated with 61 common genes, using the FUNRICH software. The percentage in the pie chart description represents the percentage of genes out of 61 common genes responsible for particular events. (D) The differential expression of genes in ciliated vs. secretory cells in fallopian tube tissue. The green dots represent 61 common gene IDs between the hFTE transcriptome and hFTE sEV proteomics. Four (FLNA, TUBB, JUP, and FLNC) out of 61 genes were significantly upregulated in secretory cells. Blue and red dots are differentially expressed genes in secretory and ciliated cells, respectively. (E) IHC staining of representative proteins of 61 common genes expressed in fallopian tube tissue and sEVs. Scale bar = 10 µm.

Fig 3: S100A8/A9-RAGE system increases the migration of TNBC cells. A-B Representative images (left) and relative quantification (right) of transwell migration in MDA-MB231 cells overexpressing RAGE and treated for 4 hours with 100 ng/ml rhS100A8/A9 alone or in combination with 1 µM RAGE antagonist FPS-ZM1. C-D Representative images (left) and relative quantification (right) of transwell migration in MDA-MB231 cells overexpressing RAGE and treated for 4 hours with 100 ng/ml rhS100A8/A9 alone or in combination with 1 µM FAK inhibitor VS-4718. E-F Representative images (left) and relative quantification (right) of transwell migration in BT-549 cells overexpressing RAGE and treated for 4 hours with 100 ng/ml rhS100A8/A9 alone or in combination with 1 µM RAGE antagonist FPS-ZM1. G-H Representative images (left) and relative quantification (right) of transwell migration in BT-549 cells overexpressing RAGE and treated for 4 hours with 100 ng/ml rhS100A8/A9 alone or in combination with 1 µM FAK inhibitor VS-4718. I-J Representative images (left) and relative quantification (right) of transwell migration in BT-549 cells overexpressing RAGE transfected with siRNA control (20 nM) and siRNA targeting FLNA (20 nM) and stimulated for 4 hours with 100 ng/ml rhS100A8/A9. K FLNA knockdown efficiency is shown. Error bars represent mean ± SD. * indicates p-value < 0.05. Results shown are representative of three independent experiments performed in triplicate

Fig 4: Hippo pathway is significantly enriched in high RAGE expression breast cancer cohort. A Differentially expressed genes (DEGs) between high (Z-score > 1) and low (Z-score = 1) RAGE expression groups in breast cancer and estimation of GSEA Hallmark in DEGs using cBioportal and Webgestalt programs. B Putative significant pathways identified among the high RAGE (Z-score > 1) expression breast cancer group as indicated by the KEGG pathway analysis for the predicted TF genes based on the DEGs. C Volcano plot for TF genes prediction by Enrichr program in high RAGE (Z-score > 1) expression breast cancer cohort. The Volcano plot shows the significance of each potential TF gene predicted by Enrichr program based on the position weight matrices from JASPAR & TRANSFAC databases. The x-axis measures the odds ratio (0, inf) calculated for the TF genes, while the y-axis gives the -log10 (p-value) of the TF genes. Larger blue points represent significant TF genes (p-value < 0.05); smaller gray points represent non-significant TF genes. The darker blue color points means the highest significance. D Venn diagram of the shared up-regulated genes by TEAD2 and TEAD4 Hippo pathway TFs in high RAGE (Z-score > 1) expression breast cancer cohort. E Gene Expression Profiling Interactive Analysis (GEPIA) box plot of FLNA expression levels in TNBC, Luminal-A and Luminal-B breast cancer subtypes