Prognostic model of invasive ductal carcinoma of the breast based on differentially expressed glycolysis-related genes

Protooncogenic Role of ARHGAP11A and ARHGAP11B in Invasive Ductal Carcinoma: Two Promising Breast Cancer Biomarkers

Invasive duct carcinoma (IDC) is one of the most common types of breast cancer (BC) in women worldwide, with a high risk of malignancy, metastasis, recurrence, and death. So far, molecular patterns among IDC cases have not been fully defined. However, extensive evidence has shown that dysregulated Rho family small GTPases (Rho GTPases) including Rho GTPase activating proteins (RhoGAPs) have important roles in the invasive features of IDCs. In the current study, we analyzed the expression levels of two RhoGAP genes, ARHGAP11A and ARHGAP11B, in The Cancer Genome Atlas (TCGA) breast cancer (BRCA) and also our 51 IDC tumors compared to their matched normal tissues using quantitative polymerase chain reaction (qPCR). Our TCGA data analysis revealed higher expression of ARHGAP11A and ARHGAP11B in various cancers comprising BCs. Also, we found correlations between these genes and other genes in TCGA-BRCA. Moreover, our methylation analysis showed that their promotor methylation had a negative correlation with their overexpression. QPCR revealed their significant upregulation in our tumor samples. Furthermore, we found that the expression level of ARHGAP11A was considerably lower in women who were breastfeeding. Moreover, it had overexpression in cases who had regular menstrual cycles and early age (younger than 14) at menarche. However, ARHGAP11B had a higher expression in HER2-positive tumors versus HER2-positive and ER-positive tumors. Our study found possible protooncogenic roles for these genes and their involvement in IDC pathogenesis and malignancy. Therefore, they can be considered novel prognostic and diagnostic biomarkers for IDC.

A multi-omics signature to predict the prognosis of invasive ductal carcinoma of the breast

BACKGROUND

Precisely evaluating the prognosis of invasive ductal carcinoma (IDC) of the breast is challenging as most prognostic signatures use single-omics data based on gene or clinical information.

METHODS

Whole-slide images (WSIs), transcriptome, and clinical data of breast IDC were collected from the Cancer Genome Atlas Database. The cancer-associated fibroblast (CAF) gene sets were downloaded from the Molecular Signatures Database. The WSI feature was extracted by artificial feature engineering. The CAF prognostic genes were determined by the Gene Set Enrichment Analysis, the Wilcoxon test, and univariate Cox regression. The IDC patients were divided into the training and test sets. The prognostic signatures based on WSIs, IDC-CAFs, bi-omics, and tri-omics were constructed using multivariate Cox regression. The samples were divided into low- and high-risk groups according to the median risk score. The Kaplan-Meier survival and receiver operating characteristic curves were applied to validate the prediction performance of the four signatures.

RESULTS

In total, 508 IDC patients with complete data were included. The area under the curve (AUC) of single-omics signature based on WSI characteristics and CAFs was 0.765 and 0.775, whereas the AUC of bi-omics was 0.823. The tri-omics signature based on WSIs, CAFs, and lymph node status demonstrated the best predictive value with an AUC of 0.897.

CONCLUSION

The multi-omics signature based on WSIs, CAFs, and clinical characteristics showed excellent prediction ability in breast IDC patients, whose risk factors can also provide a valuable diagnostic reference for the clinical course.

BRCA1 overexpression attenuates breast cancer cell growth and migration by regulating the pyruvate kinase M2-mediated Warburg effect via the PI3K/AKT signaling pathway

This work explored the mechanism of the effect of breast-cancer susceptibility gene 1 (BRCA1) on the metabolic characteristics of breast cancer cells, including the Warburg effect and its specific signaling. We transfected MCF-7 cells with a BRCA1-encoding LXSN plasmid or PKM2 siRNA and examined cancer cell metabolism using annexin V staining, inhibitory concentration determination, Western blotting, glucose uptake and lactic acid content measurements, and Transwell assays to assess glycolytic activity, cell apoptosis, and migration, and sensitivity to anti-cancer treatment. The BRCA1-expressing MCF-7 cells demonstrated low PKM2 expression and decreased glycolytic activity (downregulated hexokinase 2 (HK2) expression, upregulated isocitrate dehydrogenase 1 (IDH1) expression, and reduced O₂ and glucose consumption and lactate production) via regulation of PI3K/AKT pathway compared with the empty LXSN group. BRCA1 transfection slightly increased apoptotic activity, decreased cell migration, and increased the IC₅₀ index for doxorubicin, paclitaxel, and cisplatin. Inhibiting PKM2 using siRNA attenuated the IC₅₀ index for doxorubicin, paclitaxel, and cisplatin compared with the control. Inhibiting PKM2 activated PI3K/AKT signaling, increased apoptosis, and decreased MCF-7 cell migration. Our data suggest that BRCA1 overexpression reverses the Warburg effect, inhibits cancer cell growth and migration, and enhances the sensitivity to anti-cancer treatment by decreasing PKM2 expression regulated by PI3K/AKT signaling. These novel metabolic findings represent a potential mechanism by which BRCA1 exerts its inhibitory effect on breast cancer.