Formal modeling and analysis of ER-α associated Biological Regulatory Network in breast cancer

Prediction of Bone Metastasis in Breast Cancer Based on Minimal Driver Gene Set in Gene Dependency Network

Bone is the most frequent organ for breast cancer metastasis, and thus it is essential to predict the bone metastasis of breast cancer. In our work, we constructed a gene dependency network based on the hypothesis that the relation between one gene and the risk of bone metastasis might be affected by another gene. Then, based on the structure controllability theory, we mined the driver gene set which can control the whole network in the gene dependency network, and the signature genes were selected from them. Survival analysis showed that the signature could distinguish the bone metastasis risks of cancer patients in the test data set and independent data set. Besides, we used the signature genes to construct a centroid classifier. The results showed that our method is effective and performed better than published methods.

Pharmacophore modeling for identification of anti-IGF-1R drugs and in-vitro validation of fulvestrant as a potential inhibitor

Insulin-like growth factor 1 receptor (IGF-1R) is an important therapeutic target for breast cancer treatment. The alteration in the IGF-1R associated signaling network due to various genetic and environmental factors leads the system towards metastasis. The pharmacophore modeling and logical approaches have been applied to analyze the behaviour of complex regulatory network involved in breast cancer. A total of 23 inhibitors were selected to generate ligand based pharmacophore using the tool, Molecular Operating Environment (MOE). The best model consisted of three pharmacophore features: aromatic hydrophobic (HyD/Aro), hydrophobic (HyD) and hydrogen bond acceptor (HBA). This model was validated against World drug bank (WDB) database screening to identify 189 hits with the required pharmacophore features and was further screened by using Lipinski positive compounds. Finally, the most effective drug, fulvestrant, was selected. Fulvestrant is a selective estrogen receptor down regulator (SERD). This inhibitor was further studied by using both in-silico and in-vitro approaches that showed the targeted effect of fulvestrant in ER+ MCF-7 cells. Results suggested that fulvestrant has selective cytotoxic effect and a dose dependent response on IRS-1, IGF-1R, PDZK1 and ER-α in MCF-7 cells. PDZK1 can be an important inhibitory target using fulvestrant because it directly regulates IGF-1R.

Identification of key pathways and genes in response to trastuzumab treatment in breast cancer using bioinformatics analysis

Breast cancer (BC) is one of the leading causes of death among women worldwide. The gene expression profile GSE22358 was downloaded from the Gene Expression Omnibus (GEO) database, which included 154 operable early-stage breast cancer samples treated with neoadjuvant capecitabine plus docetaxel, with (34) or without trastuzumab (120), to identify gene signatures during trastuzumab treatment and uncover their potential mechanisms. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were performed, and a protein–protein interaction (PPI) network of the differentially expressed genes (DEGs) was constructed by Cytoscape software. There were 2284 DEGs, including 1231 up-regulated genes enriched in DNA replication, protein N-linked glycosylation via asparagine, and response to toxic substances, while 1053 down-regulated genes were enriched in axon guidance, protein localization to plasma membrane, protein stabilization, and protein glycosylation. Eight hub genes were identified from the PPI network, including GSK3B, RAC1, PXN, ERBB2, HSP90AA1, FGF2, PIK3R1 and RAC2. Our experimental results showed that GSK3B was also highly expressed in breast cancer tissues and was associated with poor survival, as was β-catenin. In conclusion, the present study indicated that the identified DEGs and hub genes further our understanding of the molecular mechanisms underlying trastuzumab treatment in BC and highlighted GSK3B, which might be used as a molecular target for the treatment of BC.

Association of rs1801157 single nucleotide polymorphism of CXCL12 gene in breast cancer in Pakistan and in-silico expression analysis of CXCL12-CXCR4 associated biological regulatory network

Background

C-X-C chemokine ligand 12 (CXCL12) has important implications in breast cancer (BC) pathogenesis. It is selectively expressed on B and T lymphocytes and is involved in hematopoiesis, thymocyte trafficking, stem cell motility, neovascularization, and tumorigenesis. The single nucleotide polymorphism (SNP) rs1801157 of CXCL12 gene has been found to be associated with higher risk of BC.

Methods

Our study focuses on the genotypic and allelic distribution of SNP (rs1801157; G/A) in Pakistani population as well as its association with the clinico-pathological features. The association between rs1801157 genotypes (G/A) and BC risks was assessed by a multivariate logistic regression (MLR) analysis. Genotyping was performed in both healthy individuals and patients of BC using PCR-restriction fragment length polymorphism (PCR-RFLP) method. Furthermore, in-silico approaches were adapted to investigate the association of CXCL12 and its receptor CXCR4 with genes/proteins involved in BC signalling.

Results

Significant differences in allelic and genotypic distribution between BC patients and healthy individuals of genotype (G/G) and (A/G) (p < 0.05) were observed. The frequency of the allele G in the BC group (77%) was significantly higher as compared to control group (61%) (p = 0.01). The association of genotype GG with clinico-pathological features including age, stages of cancer and organ (lung, liver, bones and brain) metastasis (p > 0.05) was assessed. In a MLR analysis, a number of variables including age, weight of an individual, affected lymph nodes, hormonal status (estrogen and progesterone receptor), alcohol consumption and family history associated with the GG genotype (GG:AA, odds ratio (OR) = 1.30, 95% CI [1.06–1.60]) were found to be independent risk factors for BC. Our in-vitro results suggest that genotype GG is possibly increasing the risk of BC in Pakistani cohorts. in-silico analysis finds that CXCL12–CXCR4 is associated with an increased expression of PDZK1, PI3k and Akt which lead the breast tumor towards metastasis.

Conclusion

Multiple targets such as CXCL12, CXCR4, PDZK1, PI3k and Akt can be inhibited in combined strategies to treat BC metastasis.