Fig. 8

Prognostic effects for our identified candidate biomarkers utilizing multiple luminal-like breast cancer clinical databases. Using systems biology, we analyzed metastasis transformation and survival/expression data for our identified candidate biomarkers using clinical tumor expression data. A Candidate biomarkers associated with a disrupted CmPn signaling network in Luminal-A T47D cells during tumorigenesis. The table illustrated in the left panel details the shared DEGs/DEPs under hormone treatments (panel A1, left column) and hormone treatments combined with CSC-KD (panel A1, right column) in this study. Treatments are colored green in the table, genes/proteins are color-coded red (up-regulated) and blue (down-regulated), compared to vehicle controls. Additionally, we further examined the candidate biomarkers and generated comparable EMT score signatures for epithelial to mesenchymal transition (EMT) potential for our identified candidate biomarkers using 7 publicly available pan-Breast cancer cohorts (panel A2). B Summarized RNAseq expression profiling of identified diagnostic candidate biomarkers for Luminal-like breast cancer tissues using TCGA. Candidate biomarkers shared at both the RNA/Protein levels under mPR-specific PRG treatments (columns 1) were further analyzed utilizing the TCGA database to assess their potential as diagnostic biomarkers between luminal-like breast cancers. Abbreviations: BC, Breast Cancer; Norm, Normal breast cancer subtype; LumA, Luminal-A subtype; LumB, Luminal-B subtype; Y, yes to significant expression differences; N, no to significant expression differences. C1–3 Prognostic effects for identified candidate biomarkers was assessed utilizing microarray data from Luminal-A breast cancer patients from TCGA. Publicly available microarray data from 1,236 breast cancer patients was analyzed to integrate gene expression and clinical data simultaneously to generate the displayed Kaplan–Meier (KM) survival curves. Breast cancer patients were filtered to only analyze patient samples classified as Luminal-A subtype (identical to T47D cells). C4–7 Prognostic effects for identified candidate biomarkers utilizing microarray data of normal-like breast cancer tumors from TCGA. Breast cancer patients were filtered to only analyze patient samples classified as Normal Breast cancer subtype [ER(+)/nPR(+)/HER2(-)]. For all survival curves, logrank P-values are calculated and displayed as well as logrank test statistics which were automatically calculated by the software using default parameters. D Summarized prognostic effects for our identified candidate biomarkers utilizing microarray data of breast cancer patients using KMplotter. Publicly available microarray data (22,277 probes) from 1,809 breast cancer patients was analyzed using kmplotter to integrate gene expression and clinical data simultaneously to generate the displayed KM survival curves. Breast cancer patients were filtered to only analyze patient samples classified as either ER(+)/nPR(+)/HER2(-)/Luminal-A subtype (T47D cells) or ER(+)/nPR(+)/HER2(−)/Normal subtype (identical receptors’ status as Luminal-A). Trends associated with decreased survival for Luminal-A breast cancer tissues are shown in column 2, followed by the corresponding P-values (automatically calculated by the software using default parameters) and number of patients(n) in columns 3 and 4, respectively. Trends associated with decreased survival for normal breast cancer tissues are shown in column 5, followed by the corresponding P-values and number of patients(n) in columns 6 and 7, respectively. For Panels A1, C1–7, and D, red color indicates up-regulation of gene expression, while blue color indicates down-regulation of gene expression