BMC Cancer (Sep 2020)

MassARRAY-based single nucleotide polymorphism analysis in breast cancer of north Indian population

  • Divya Bakshi,
  • Ashna Nagpal,
  • Varun Sharma,
  • Indu Sharma,
  • Ruchi Shah,
  • Bhanu Sharma,
  • Amrita Bhat,
  • Sonali Verma,
  • Gh. Rasool Bhat,
  • Deepak Abrol,
  • Rahul Sharma,
  • Samantha Vaishnavi,
  • Rakesh Kumar

DOI
https://doi.org/10.1186/s12885-020-07361-8
Journal volume & issue
Vol. 20, no. 1
pp. 1 – 8

Abstract

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Abstract Background Breast Cancer (BC) is associated with inherited gene mutations. High throughput genotyping of BC samples has led to the identification and characterization of biomarkers for the diagnosis of BC. The most common genetic variants studied are SNPs (Single Nucleotide Polymorphisms) that determine susceptibility to an array of diseases thus serving as a potential tool for identifying the underlying causes of breast carcinogenesis. Methods SNP genotyping employing the Agena MassARRAY offers a robust, sensitive, cost-effective method to assess multiple SNPs and samples simultaneously. In this present study, we analyzed 15 SNPs of 14 genes in 550 samples (150 cases and 400 controls). We identified four SNPs of genes TCF21, SLC19A1, DCC, and ERCC1 showing significant association with BC in the population under study. Results The SNPs were rs12190287 (TCF21) having OR 1.713 (1.08–2.716 at 95% CI) p-value 0.022 (dominant), rs1051266 (SLC19A1) having OR 3.461 (2.136–5.609 at 95% CI) p-value 0.000000466 (dominant), rs2229080 (DCC) having OR 0.6867 (0.5123–0.9205 at 95% CI) p-value 0.0116 (allelic) and rs2298881 (ERCC1) having OR 0.669 (0.46–0.973 at 95% CI), p-value 0.035 (additive) respectively. The in-silico analysis was further used to fortify the above findings. Conclusion It is further anticipated that the variants should be evaluated in other population groups that may aid in understanding the genetic complexity and bridge the missing heritability.

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