High BRCA1 gene expression increases the risk of early distant metastasis in ER+ breast cancers

Upregulation of BRCA1 and 2 protein expression is associated with dysregulation in amino acids profiles in breast cancer

BACKGROUND

The prevalence of breast cancer (BC) is high among cancers in Egypt, ranking it the most common cause of cancer mortality in women. BRCA1 and BRCA2 tumor suppressors proteins have a specific relationship with BC. Plasma free amino acids levels (PFAAs) have been reported to exhibit altered profiles among cancer patients. Thus, the present study aims to examine the alteration of the PFAAs profiles and investigate their association with BRCA1 and 2 circulating levels in Egyptian females diagnosed with BC and in females with family history of BC to establish potential early detection strategies for BC.

METHODS AND RESULTS

This study included 26 BC patients, 22 females with family history of BC (relatives) in addition to 38 healthy females as control group. Quantitative measurement of PFAAs was determined by the ion exchange separation method through high performance liquid chromatography. BRCA1 and BRCA2 concentrations were determined using ELISA. Our results showed PFAAs profiles in BC patients and in females with BC family history with significant upregulation in mean plasma levels of Alanine, Phenylalanine, Glutamate and Cysteine and downregulation of Taurine, Threonine, Serine, Glycine, Valine, Methionine and Histidine levels compared to controls. Also, a significant positive correlation was observed between plasma BRCA1 and Valine levels while a significant negative correlation was observed between BRCA2 and Lysine plasma levels.

CONCLUSION

PFAAs profile can potentially be used in early screening for BC patients and for susceptible females.

Explainable Multilayer Graph Neural Network for cancer gene prediction

MOTIVATION

The identification of cancer genes is a critical yet challenging problem in cancer genomics research. Existing computational methods, including deep graph neural networks, fail to exploit the multilayered gene-gene interactions or provide limited explanations for their predictions. These methods are restricted to a single biological network, which cannot capture the full complexity of tumorigenesis. Models trained on different biological networks often yield different and even opposite cancer gene predictions, hindering their trustworthy adaptation. Here, we introduce an Explainable Multilayer Graph Neural Network (EMGNN) approach to identify cancer genes by leveraging multiple gene-gene interaction networks and pan-cancer multi-omics data. Unlike conventional graph learning on a single biological network, EMGNN uses a multilayered graph neural network to learn from multiple biological networks for accurate cancer gene prediction.

RESULTS

Our method consistently outperforms all existing methods, with an average 7.15% improvement in area under the precision-recall curve over the current state-of-the-art method. Importantly, EMGNN integrated multiple graphs to prioritize newly predicted cancer genes with conflicting predictions from single biological networks. For each prediction, EMGNN provided valuable biological insights via both model-level feature importance explanations and molecular-level gene set enrichment analysis. Overall, EMGNN offers a powerful new paradigm of graph learning through modeling the multilayered topological gene relationships and provides a valuable tool for cancer genomics research.

AVAILABILITY AND IMPLEMENTATION

Our code is publicly available at https://github.com/zhanglab-aim/EMGNN.

Zooming into the structure-function of RING finger proteins for anti-cancer therapeutic applications

Cancer is one of the most common and widely diagnosed diseases worldwide. With an increase in prevalence and incidence, many studies in cancer biology have been looking at the role pro-cancer proteins play. One of these proteins is the Really Interesting New Gene (RING), which has been studied extensively due to its structure and functions such as apoptosis, neddylation, and its role in ubiquitination. The RING domain is a cysteine-rich domain known to bind Cysteine and Histidine residues. It also binds two zinc ions that help stabilize the protein in various patterns, often with a 'cross-brace' topology. Different RING finger proteins have been studied and found to have suitable targets for developing anti-cancer therapeutics. These identified candidate proteins include Parkin, COP1, MDM2, BARD1, BRCA-1, PIRH2, c-CBL, SIAH1, RBX1 and RNF8. Inhibiting these candidate proteins provides opportunities for shutting down pathways associated with tumour development and metastasis.

Low expression of PALB2 is associated with poor survival in Chinese women with primary breast cancer

BACKGROUND

PALB2 plays a crucial role in genome stability and the DNA repair process, and its mutation is associated with a moderate to high risk of breast cancer. However, the status and prognostic role of PALB2 expression in breast cancer are still unclear.

MATERIALS AND METHODS

The expression level of PALB2 mRNA was evaluated by using quantitative real-time polymerase chain reaction in core biopsy samples from 563 primary breast cancer tissues.

RESULTS

In the entire cohort, low expression of PALB2 mRNA was significantly associated with poor survival (low vs. intermediate: DFS, adjusted HR = 1.79, 95% CI = 1.21-2.65, P = .003; DDFS, adjusted HR = 2.07, 95% CI = 1.34-3.20, P = .001; DSS, adjusted HR = 2.59, 95% CI = 1.45-4.64, P = .001; OS, adjusted HR = 2.77, 95% CI = 1.56-4.92, P = .001; low vs. high: DFS, adjusted HR = 1.57, 95% CI = 1.06-2.35, P = .026; DDFS, adjusted HR = 1.66, 95% CI = 1.08-2.55, P = .020; DSS, adjusted HR = 1.74, 95% CI = 1.00-3.03, P = .048; OS, adjusted HR = 1.59, 95% CI = 0.95-2.67, P = .08). Notably, among hormone receptor (HR)-positive/HER2-negative subtype, patients with low PALB2 expression also had significantly worse outcomes (low vs. intermediate: DFS, adjusted HR = 2.33, 95% CI = 1.32-4.13, P = .004; DDFS, adjusted HR = 2.78, 95% CI = 1.47-5.27, P < .001; DSS, adjusted HR = 3.08, 95% CI = 1.27-7.43, P = .013; OS, adjusted HR = 3.15, 95% CI = 1.32-7.50, P = .010; low vs. high: DFS, adjusted HR = 1.84, 95% CI = 1.04-3.28, P = .04; DDFS, adjusted HR = 1.82, 95% CI = 0.99-3.36, P = .05; DSS, adjusted HR = 2.06, 95% CI = 0.87-4.86, P = .10; OS, adjusted HR = 1.54, 95% CI = 0.71-3.33, P = .28).

CONCLUSION

Breast cancer patients with low expression of mRNA have a poor survival, suggesting that patients with PALB2 low expression may be the potential beneficiaries for PARP inhibitors therapy.