Prognostic value of Maspin protein level in patients with triple negative breast cancer.

The search for prognostic markers in breast cancer has bumped into a typical feature of these tumors, intra and intertumoral heterogeneity. Changes in the expression profile, localization of these proteins or shedding to the surrounding stroma can be useful in the search for new markers. In this context, classification by molecular subtypes can bring perspectives for both diagnosis and screening for appropriate treatments. However, the Triple Negative (TN) subtype, which is already the one with the worst prognosis, lacks appropriate and consistent molecular markers. In this work, we analyzed 346 human breast cancer samples in tissue microarrays (TMA) from cases diagnosed with invasive breast carcinoma to assess the expression and localization pattern of Maspin and their correlation with clinical parameters. To complement our findings, we also used TCGA data to analyze the mRNA levels of these respective genes. Our data suggests that the TN subtype demonstrates a higher level of cytoplasmic Maspin compared to the other subtypes. Maspin transcript levels follow the same trend. However, TN patients with lower Maspin expression tend to have worse overall survival and free-survival metastasis rates. Finally, we used Maspin expression data to verify possible relationships with the clinicopathological information of our cohort. Our univariate analyses indicate that Maspin is related to the expression of estrogen receptor (ER) and progesterone receptor (PR). Furthermore, Maspin expression levels also showed correlation with Scarff-Bloom-Richardson (SBR) parameter, and stromal Maspin showed a relationship with lymph node involvement. Our data is not consistently robust enough to categorize Maspin as a prognostic marker. However, it does indicate a change in the expression profile within the TN subtype.

Altered Expression of CYSLTR1 is Associated With Adverse Clinical Outcome in Triple Negative Breast Tumors: An In Silico Approach.

Objective: Triple negative breast cancer (TNBC) has high relapse rates due to dysregulated inflammatory signaling pathways and significant changes in the tumor microenvironment, probably influencing the failure of several therapies. The Cysteinyl Leukotriene Receptor 1 (CYSLTR1), a leukotriene modulator of inflammation, has been shown to play an important role in cancer pathogenesis and survival but few studies have been reported on its role in breast cancer. Materials and Methods: The present work was conducted using publicly available platforms that have omics data to assess the clinical potential of CYSLTR1 expression and its prognostic validation in large cohorts of samples from breast cancer patients. Web platforms containing clinical information, RNA-seq and protein data were selected to perform in silico analyses of the potential marker CYLSTR1. Added together, the platforms included modules for correlation, expression, prognosis, drug interactions, and construction of gene networks. Results: Kaplan-Meier curves revealed that reduced levels of CYSLTR1 corresponded to an unfavorable outcome for overall survival (p<0.005) as well as relapse-free survival (p<0.001) in the basal subtype. Additionally, CYSLTR1 was downregulated in breast tumor samples compared to adjacent healthy tissue (p<0.01) and the basal subtype exhibited the lowest expression of CYSLTR1 relative to the other subtypes (p<0.0001). Furthermore, gene networking analysis showed strong associations of CYSLTR1 with two protein-coding genes (P2RY10 and XCR1) when tested on a TNBC dataset. Conclusion: Our data highlighted the relevance of CYSLTR1 since it may play an important role in TNBC therapy. However, further in vitro and in vivo studies should be directed towards validating our findings in an effort to improve our understanding of TNBC pathology.

An In Silico Analysis Identified Members of the Pleckstrin Homology-Like Domain, Family B (PHLDB family) as Potential Prognostic and Predictive Biomarkers of Treatment Response in Breast Cancer Patients.

Objective: Breast cancer is the leading cause of morbidity and mortality in women worldwide. This malignant neoplasm can be classified into four clinically relevant subtypes according to the expression of a number of biomarkers. However, these tumors show considerable intratumoral heterogeneity and multidrug resistance. Members of the pleckstrin homology-like domain, family B (PHLDB) play a critical role in the regulation of p53 and AKT signaling pathways, important for cancer and cellular metabolism. The present study was performed to evaluate the expression pattern of PHLDB family members in breast cancer and its potential prognostic and predictive value for therapeutic response using bioinformatics tools. Materials and Methods: This in silico analysis was performed using several online repositories, including UALCAN, GEPIA2, bc-GenExMiner, KM Plotter, PrognoScan and ROC Plotter. Results: PHLDB family genes were found to be differentially expressed in tumor samples when compared to healthy breast tissue samples. Furthermore, epigenetic regulation may be one of the regulatory mechanisms for the expression of these markers. The PHLDB family of genes proved to be potential markers for predicting the development of lymph node metastasis (p<0.0001) and poor clinical outcome. All members of the PHLDB family were significantly correlated with hormone receptors. High levels of PHLDBs expression were associated with worse overall survival and recurrence-free survival in breast cancer patients. Finally, our data demonstrate that members of the PHLDB family can be promising markers in the stratification of patients who may or may not respond to different available therapies. Conclusion: Our cumulative results demonstrate that PHLDB family members may be promising biomarkers for predicting prognosis and therapeutic response in breast cancer patients.