In silico SNP analysis of the breast cancer antigen NY-BR-1

Abstract

BackgroundBreast cancer is one of the most common malignancies with increasing incidences every year and a leading cause of death among women. Although early stage breast cancer can be effectively treated, there are limited numbers of treatment options available for patients with advanced and metastatic disease. The novel breast cancer associated antigen NY-BR-1 was identified by SEREX analysis and is expressed in the majority (>70%) of breast tumors as well as metastases, in normal breast tissue, in testis and occasionally in prostate tissue. The biological function and regulation of NY-BR-1 is up to date unknown.MethodsWe performed an in silico analysis on the genetic variations of the NY-BR-1 gene using data available in public SNP databases and the tools SIFT, Polyphen and Provean to find possible functional SNPs. Additionally, we considered the allele frequency of the found damaging SNPs and also analyzed data from an in-house sequencing project of 55 breast cancer samples for recurring SNPs, recorded in dbSNP.ResultsOver 2800 SNPs are recorded in the dbSNP and NHLBI ESP databases for the NY-BR-1 gene. Of these, 65 (2.07%) are synonymous SNPs, 191 (6.09%) are non-synoymous SNPs, and 2430 (77.48%) are noncoding intronic SNPs. As a result, 69 non-synoymous SNPs were predicted to be damaging by at least two, and 16 SNPs were predicted as damaging by all three of the used tools. The SNPs rs200639888, rs367841401 and rs377750885 were categorized as highly damaging by all three tools. Eight damaging SNPs are located in the ankyrin repeat domain (ANK), a domain known for its frequent involvement in protein-protein interactions. No distinctive features could be observed in the allele frequency of the analyzed SNPs.ConclusionConsidering these results we expect to gain more insights into the variations of the NY-BR-1 gene and their possible impact on giving rise to splice variants and therefore influence the function of NY-BR-1 in healthy tissue as well as in breast cancer.

In silico analysis of nonsynonymous single‐nucleotide polymorphisms (nsSNPs) of the SMPX gene

“…Structure‐based methods are limited to a known 3D structure; on the other hand, sequence‐based approaches can be implemented in proteins with unknown 3D structures (Doss & Rajith, ; Marín‐Martín, Soler‐Rivas, Martín‐Hernández, & Rodriguez‐Casado, ). A combination of multiple predictors showed better predictions in many recent reports for classifying deleterious nsSNPs in SLX4/FANCP (Landwehr et al., ), MACC1 (Muendlein et al., ), NY‐BR‐1 (Kosaloglu et al., ), BARD1 (Alshatwi, Hasan, Syed, Shafi, & Grace, ), MBL2 (Kalia, Sharma, Kaur, Kamboj, & Singh, ), BCL11A (Abdulazeez et al., ), HBA1 (AbdulAzeez & Borgio, ), AHSP (Borgio et al., ), MTHFR (Karimian & Hosseinzadeh Colagar, ), MKRN3 (Neocleous et al., ), and PALB2 (Phuah et al., ). From the dbSNP database of NCBI, we found 26 SNPs as missense and three SNPs as nonsense for SMPX .…”

Frequent Molecular Subtype Switching and Gene Expression Alterations in Lung and Pleural Metastasis From Luminal A–Type Breast Cancer

“…The latter is a breast differentiation gene that is frequently expressed in the breast and in receptor-positive breast cancer. 22 Other molecular changes in metastases were linked to proliferation ( CDC6 , CCNB1 , MKI67 , TOP2A , AURKB , PTTG1 ), cell cycle checkpoints ( BRCA2 , BUB1 , BUB1B , CHEK1 ), and epithelial-mesenchymal transition (EMT) genes. Only one gene, SCRG1 , was upregulated in BCLPM with a fold change of > 2, which is a marker of mesenchymal stem cells, 23 pointing toward EMT in BCLPM.…”

Unmasking of novel mutations within XK gene that could be used as Diagnostic Markers to Predict McLeod syndrome: Using in silico analysis

“…In silico analysis has been done for many disorders for cancer related genes and other disorders (e.g: Huntington disease). (50)(51)(52) It was not easy to predict the pathogenic effect of SNPs using single method. Therefore, multiple methods were used to compare and rely on the results predicted.…”