Clinical outcomes linked to expression of gene subsets for protein hormones and their cognate receptors from LCM-procured breast carcinoma cells

Investigation of mRNA Expression Levels of Tip60 and Related DNA Repair Genes in Molecular Subtypes of Breast Cancer

INTRODUCTION

Studies in breast cancer (BC) have been shown that many tumor cells carry mutations that disrupt the DNA damage response mechanism. In eukaryotic cells, the overexpression or deprivation of DSBs repair genes is linked closely to a higher risk of cancer.

PATIENTS AND METHODS

In this study, mRNA expression levels of some genes, such as Tip60, ATM, p53, CHK2, BRCA1, H2AX, which are associated with DNA damage repair, were measured using RT-PCR method in tumor and matched-normal tissues of 58 patients with BC.

RESULTS

According to the study results, 55% in Tip60, 59% in ATM, 57% in BRCA1, 48% in H2AX, 66% in CHK2, and 43% in p53 decreased in tumor tissue of patients compared to the matched normal tissue. When evaluated according to molecular subtypes, expression of all genes in the pathway was found significantly higher in normal tissues than in tumor tissues especially in Luminal B and Luminal B+HER2 groups. One of the most important results of the study is that CHK2 mRNA expressions in normal tissues were higher than tumor tissue in 90% of patients in Luminal B and Luminal B-HER2 + groups. This is the first study showing DNA repair genes' expressions in molecular subtypes of breast cancer. In general, the decrease in the expression of DNA damage repair genes in tumor tissue indicates that these genes may have a role in the development of BC. Our study results also suggest that CHK2 may be a candidate marker in the molecular classification of breast cancer.

Relationships of protein biomarkers of the urokinase plasminogen activator system with expression of their cognate genes in primary breast carcinomas

Background

uPA, its receptor uPAR, and inhibitors PAI‐1 and PAI‐2 play key roles in membrane remodeling/invasion and in predicting response to chemotherapy. We identified novel relationships of these biomarkers with ER/PR that indicate clinical utility for assessing breast carcinoma outcomes.

Methods

Retrospective studies were performed with de‐identified results of (a) uPA, uPAR, and PAI‐1; (b) estrogen (ER) and progestin receptor (PR); and (c) clinical outcomes. Relative expression of 22 000 genes from microarray of RNA from LCM‐procured breast cancer cells was used with R Studio version 3.4.1.

Results

Primary ER/PR status was related to uPA, uPAR, or PAI‐1 levels. ER− or PR− cancers expressed elevated uPA, uPAR, and PAI2 mRNA compared to ER+ or PR+ cells. Inverse relationships between ER/PR protein and expression of uPA, uPAR, and PAI‐2 were observed, whereas HER2 status was unrelated. qPCR analyses showed RERG and NQO‐1 expressions were elevated in uPA− lesions, while CD34 and EDG‐1 were elevated in uPAR− cancers. ERBB4 was overexpressed in PAI‐1+ carcinomas. Cox regression analyses revealed relationships of ER/PR status and uPA system members with regard to clinical outcomes of breast cancer.

Conclusions

uPA, uPAR, PAI1, or PAI2 expression was increased in either ER− or PR− cancers similar to that of protein content in ER−/PR− carcinomas, suggesting sex hormones regulate the uPA system in breast cancer. Results revealed protein content of uPA system members was related to ER/PR status of primary lesions. Use of LCM‐procured carcinoma cells uncovered relationships between expression of known cancer−associated genes and protein content of uPA system members. Collectively, results indicate evaluation of ER and PR protein of primary breast cancers combined with analyses of uPA, uPAR, and PAI‐1 protein content improves assessment of clinical outcomes.

High-Dimensional LASSO-Based Computational Regression Models: Regularization, Shrinkage, and Selection

Regression models are a form of supervised learning methods that are important for machine learning, statistics, and general data science. Despite the fact that classical ordinary least squares (OLS) regression models have been known for a long time, in recent years there are many new developments that extend this model significantly. Above all, the least absolute shrinkage and selection operator (LASSO) model gained considerable interest. In this paper, we review general regression models with a focus on the LASSO and extensions thereof, including the adaptive LASSO, elastic net, and group LASSO. We discuss the regularization terms responsible for inducing coefficient shrinkage and variable selection leading to improved performance metrics of these regression models. This makes these modern, computational regression models valuable tools for analyzing high-dimensional problems.

Expression of Genes for Methylxanthine Pathway-Associated Enzymes Accompanied by Sex Steroid Receptor Status Impacts Breast Carcinoma Progression

Consumption of methylxanthine alkaloids appears to induce activities by antagonizing adenosine receptors, implicated in breast cancer behavior in vitro. Our goal was to evaluate expression of genes for methylxanthine receptors and metabolizing enzymes to assess risk of breast carcinoma recurrence. Clinical outcomes, estrogen/progestin receptor results, and gene expression assays guided selection. RNA was isolated from laser capture microdissection-procured carcinoma cells for microarray using established protocols. Gene expression levels of eight methylxanthine receptors, eight metabolizing enzymes, and various phosphodiesterases were retrieved from microarray results. Univariable Cox regressions and Kaplan-Meier plots were determined for each gene with R software. Individually, lower expressions of PDE4A, CYP2A6, or CYP2E were related to decreased progression-free survival (PFS) and overall survival (OS). PDE1A over-expression predicted decreased PFS and OS. ADORA2B and RYR1 over-expressions predicted diminished OS. ER+ cancers exhibited lower ADORA1, ADORA2B, and RYR1 and elevated PDE4A, CYP2A6, and CYP2E expressions. Of PR+ carcinomas, diminished ADORA2B and RYR1 and elevated expressions of ADORA3, PDE4A, CYP2C8, and CYP2E were noted. Least absolute shrinkage and selection operator (LASSO) revealed that CYP2E, PDE1A, and PDE4A expressions collectively predicted PFS whereas ADORA1, CYP2E, PDE1A, PDE1B, and PDE4A expressions jointly predicted OS. Models were clinically significant when validated externally. LASSO also derived a six-gene model and five-gene model that predicted PFS of ER- or PR- carcinomas, respectively. Similarly, five-gene and four-gene models predicted OS in ER- or PR- carcinomas, respectively. Collectively, expression of genes involved in methylxanthine action and metabolism in single-cell types predicted clinical outcomes of breast carcinoma indicating promise for developing diagnostics and design of new therapeutics.