High mRNA Expression Levels of Heat Shock Protein Family B Member 2 (HSPB2) Are Associated with Breast Cancer Patients’ Relapse and Poor Survival

G-quadruplex formation within the promoter region of HSPB2 and its effect on transcription

G-rich sequences in DNA and RNA tend to fold into stable secondary structures called G-quadruplexes. Except for the telomere region, G-quadruplex-forming sequences are widely present in gene promoters and have been implicated in transcriptional regulation. Single nucleotide polymorphisms (SNPs) can disrupt the G-quadruplex structure of a gene promoter. In this study, we confirmed the promoter of HSPB2, a cancer-related gene, tends to form an unusual DNA secondary structure. The dual luciferase assay revealed that the SNP rs2234704 in the HSPB2 promoter with a single G > A mutation increased the transcriptional activity of the HSPB2 promoter. Circular dichroism and native PAGE revealed that the G-rich strand of the DNA in this promoter preferred to form a parallel G-quadruplex, which could be destabilized by the SNP rs2234704 (G > A) mutation. Furthermore, we found that the SNP rs2234704 (G > A) greatly increased and influenced the overexpression of HSPB2 in breast cancer samples. These results suggest SNP rs2234704 (G > A) may play a role in the occurrence of breast cancer by destroying the G-quadruplex structure and promoting the expression of HSPB2.

Computational Exploration of Fluorocyclopentenyl-purines and-pyrimidines Derivatives as Potential Inhibitors of Epidermal Growth Factor Receptor (EGFR) for the Treatment of Breast Cancer

The Epidermal Growth Factor Receptor (EGFR) is an important therapeutic target for the treatment of a variety of epithelial malignancies, including breast cancer, in which EGFR is aberrantly expressed.The fluorocyclopentenyl-purine-pyrimidines derivatives, which have previously been described as powerful compounds against breast cancer, were selected to investigate their potential against EGFR using computational tools in an effort to obtain potent inhibitors with fewer adverse effects. The molecule's chemical reactivity and stability were assessed by determining the HOMO-LUMO energy gap using density functional theory (DFT) calculations. Among all the selected compounds, PU4 displayed a HOMO-LUMO gap of 0.191 eV. Additionally, molecular docking analysis was performed to assess the binding affinities of PU4 within the active pocket of EGFR-TK. The compound PU4 showed potent interactions with EGFR exhibiting -32.3 kJ/mol binding energy which was found best as compared to gefitinib i. e., -27.4 kJ/mol which was further validated by molecular dynamics simulations and ADMET analysis. The results of these analyses indicate that the top hits obtained from the virtual screening possess the ability to act as effective EGFR inhibitor. Therefore, it is recommended to further investigate the inhibitory potential of these identified compounds using in vitro and in vivo approaches.

Attention-based GCN integrates multi-omics data for breast cancer subtype classification and patient-specific gene marker identification

Breast cancer is a heterogeneous disease and can be divided into several subtypes with unique prognostic and molecular characteristics. The classification of breast cancer subtypes plays an important role in the precision treatment and prognosis of breast cancer. Benefitting from the relation-aware ability of a graph convolution network (GCN), we present a multi-omics integrative method, the attention-based GCN (AGCN), for breast cancer molecular subtype classification using messenger RNA expression, copy number variation and deoxyribonucleic acid methylation multi-omics data. In the extensive comparative studies, our AGCN models outperform state-of-the-art methods under different experimental conditions and both attention mechanisms and the graph convolution subnetwork play an important role in accurate cancer subtype classification. The layer-wise relevance propagation (LRP) algorithm is used for the interpretation of model decision, which can identify patient-specific important biomarkers that are reported to be related to the occurrence and development of breast cancer. Our results highlighted the effectiveness of the GCN and attention mechanisms in multi-omics integrative analysis and the implement of the LRP algorithm can provide biologically reasonable insights into model decision.