Differential role of glucocorticoid receptor based on its cell type specific expression on tumor cells and infiltrating lymphocytes.

BACKGROUND: The glucocorticoid receptor (GR) is frequently expressed in breast cancer (BC), and its prognostic implications are contingent on estrogen receptor (ER) status. To address conflicting reports and explore therapeutic potential, a GR signature (GRsig) independent of ER status was developed. We also investigated cell type-specific GR protein expression in BC tumor epithelial cells and infiltrating lymphocytes. METHODS: GRsig was derived from Dexamethasone treated cell lines through a bioinformatic pipeline. Immunohistochemistry assessed GR protein expression. Associations between GRsig and tumor phenotypes (proliferation, cytolytic activity (CYT), immune cell distribution, and epithelial-to-mesenchymal transition (EMT) were explored in public datasets. Single-cell RNA sequencing data evaluated context-dependent GR roles, and a cell type-specific prognostic role was assessed in an independent BC cohort. RESULTS: High GRsig levels were associated with a favorable prognosis across BC subtypes. Tumor-specific high GRsig correlated with lower proliferation, increased CYT, and anti-tumorigenic immune cells. Single-cell data analysis revealed higher GRsig expression in immune cells, negatively correlating with EMT while a positive correlation was observed with EMT primarily in tumor and stromal cells. Univariate and multivariate analyses demonstrated the robust and independent predictive capability of GRsig for favorable prognosis. GR protein expression on immune cells in triple-negative tumors indicated a favorable prognosis. CONCLUSION: This study underscores the cell type-specific role of GR, where its expression on tumor cells is associated with aggressive features like EMT, while in infiltrating lymphocytes, it predicts a better prognosis, particularly within TNBC tumors. The GRsig emerges as a promising independent prognostic indicator across diverse BC subtypes.

An androgen receptor regulated gene score is associated with epithelial to mesenchymal transition features in triple negative breast cancers.

BACKGROUND: Androgen receptor (AR) is considered a marker of better prognosis in hormone receptor positive breast cancers (BC), however, its role in triple negative breast cancer (TNBC) is controversial. This may be attributed to intrinsic molecular differences or scoring methods for AR positivity. We derived AR regulated gene score and examined its utility in BC subtypes. METHODS: AR regulated genes were derived by applying a bioinformatic pipeline on publicly available microarray data sets of AR+ BC cell lines and gene score was calculated as average expression of six AR regulated genes. Tumors were divided into AR high and low based on gene score and associations with clinical parameters, circulating androgens, survival and epithelial to mesenchymal transition (EMT) markers were examined, further evaluated in invitro models and public datasets. RESULTS: 53% (133/249) tumors were classified as AR gene score high and were associated with significantly better clinical parameters, disease-free survival (86.13 vs 72.69 months, log rank p = 0.032) when compared to AR low tumors. 36% of TNBC (N = 66) were AR gene score high with higher expression of EMT markers (p = 0.024) and had high intratumoral levels of 5α-reductase, enzyme involved in intracrine androgen metabolism. In MDA-MB-453 treated with dihydrotestosterone, SLUG expression increased, E-cadherin decreased with increase in migration and these changes were reversed with bicalutamide. Similar results were obtained in public datasets. CONCLUSION: Deciphering the role of AR in BC is difficult based on AR protein levels alone. Our results support the context dependent function of AR in driving better prognosis in ER positive tumors and EMT features in TNBC tumors.

Premenopausal women with breast cancer in the early post-partum period show molecular profiles of invasion and are associated with poor prognosis.

PURPOSE: Young premenopausal women develop breast cancer (BC) within 5-10 years of the last childbirth, known as post-partum breast cancers (PPBC), often present with aggressive disease. The exact mechanisms that lead to poor prognosis in these patients are largely unknown. METHODS: We have evaluated the association of clinical and reproductive factors with BC in a cohort of women ≤ 45 years (N = 155) with long-term follow-up. Based on duration since last childbirth (LCB), grouped patients into PPBC1 (LCB ≤ 5 years), PPBC2 (LCB between 6 and 10 years), PPBC3 (LCB > 10 years), and NPBC (age-matched nulliparous BC patients). We compared disease-free survival and hazard associated with recurrence/metastasis between the groups. RNA sequencing of tumor samples was performed from three parous groups (n = 10), and transcriptomic data were analyzed for differentially expressed genes and altered pathways. RESULTS: Women in the PPBC1 group had an early menarche and late age at first and last childbirth compared to other groups. Survival analysis within lymph node-positive tumors showed that PPBC1 tumors had a worse prognosis than PPBC2 and NPBC tumors (p = 0.015 and p = 0.026, respectively). Clustering of the differentially expressed genes between the groups showed distinct expression in early PPBC (E-PPBC) tumors. Pathway analysis revealed upregulation of invasive-related pathways along with T cell exhaustion, extracellular matrix remodeling, angiogenesis, and epithelial-to-mesenchymal transition in E-PPBC tumors. CONCLUSION: Early PPBC is a unique subtype with aggressive clinical features and distinct biology. Further research is needed to accurately project the risk of recurrence and optimal treatment strategies in these young patients.

Implying Analytic Measures for Unravelling Rheumatoid Arthritis Significant Proteins Through Drug-Target Interaction.

Rheumatoid arthritis (RA) is a systemic autoimmune and inflammatory disease that mainly alters the synovial joints and ultimately leads to their destruction. The involvement of the immune system and its related cells is a basic trademark of autoimmune-associated diseases. The present work focuses on network analysis and its functional characterization to predict novel targets for RA. The interactive model called as rheumatoid arthritis drug-target-protein (RA-DTP) is built of 1727 nodes and 7954 edges followed the power-law distribution. RA-DTP comprised of 20 islands, 55 modules and 123 submodules. Good interactome coverage of target-protein was detected in island 2 (Q-Score 0.875) which includes 673 molecules with 20 modules and 68 submodules. The biological landscape of these modules was examined based on the participation molecules in specific cellular localization, molecular function and biological pathway with favourable p value. Functional characterization and pathway analysis through KEGG, Biocarta and Reactome also showed their involvement in relation to the immune system and inflammatory processes and biological processes such as cell signalling and communication, glucosamine metabolic process, renin-angiotensin system, BCR signals, galactose metabolism, MAPK signalling, complement and coagulation system and NGF signalling pathways. Traffic values and centrality parameters were applied as the selection criteria for identifying potential targets from the important hubs which resulted into FOS, KNG1, PTGDS, HSP90AA1, REN, POMC, FCER1G, IL6, ICAM1, SGK1, NOS3 and PLA2G4A. This approach provides an insight into experimental validation of these associations of potential targets for clinical value to find their effect on animal studies.

Implying analytic measures for unraveling rheumatoid arthritis significant proteins through drug target interaction.

Rheumatoid arthritis (RA) is a systemic auto-immune and inflammatory disease that mainly alters the synovial joints and ultimately leads to their destruction. The involvement of the immune system and its related cells is a basic trademark of auto-immune associated diseases. The present work focuses on network analysis and its functional characterization to predict novel targets for RA. The interactive model called as Rheumatoid Arthritis Drug-Target-Protein (RA-DTP) is built of 1727 nodes and 7954 edges followed the power law distribution. RADTP comprised of 20 islands, 55 modules and 123 sub modules. Good interactome coverage of target-protein was detected in Island 2 (Q-Score 0.875) which includes 673 molecules with 20 modules and 68 sub modules. The biological landscape of these modules was examined based on the participation molecules in specific cellular localization, molecular function and biological pathway with favourable p value. Functional characterization and pathway analysis through KEGG, Biocarta and Reactome also showed their involvement in relation to the immune system and inflammatory processes and biological processes such as cell signalling and communication, glucosamine metabolic process, Renin Angiotensin system, BCR signals, Galactose metabolism, MAPK signalling, Complement and Coagulation system and NGF signalling pathways. Traffic values and centrality parameters were applied as the selection criteria for identifying potential targets from the important hubs which resulted into FOS, KNG1, PTGDS, HSP90AA1, REN, POMC, FCER1G, IL6, ICAM1, SGK1, NOS3 and PLA2G4A. This approach provides an insight to experimental validation of these associations of potential targets for clinical value to find their effect on animal studies.