• Interactive Networking of SNPs in Zinc Absorption Pathways (ZIP/Znt) and Prostate Cancer Prognosis: A Systems Biology Approach
  • Ghazaleh Eslamian,1 Roshan Nouri,2,* Majid Mesgar Tehranni,3 Dariush Nowrouzian,4
    1. Faculty of Nutrition and Food Technology Shahid Beheshti University of Medical Sciences
    2. Islamic Azad University Najafabad Branch
    3. Member of the Core Committee of the National Genomics Hub, Shahid Beheshti University of Medical Sciences, Tehran, Iran
    4. Genomics laboratory


  • Introduction: Prostate cancer (PCa) is the second most common cancer in men globally, and there are significant differences in the occurrence and death rates of this pathology depending on the region. The latest facts clearly demonstrate that the interaction between Zn regulation and PCa origin/progression, which is mainly performed by ZIP (Zrt-/Irt-like proteins) and Znt (Zn transporter) families, is extremely important. A systems biology approach was applied in this paper to figure out Zn pathway SNPs and PCa prognosis and thus leads to the identification of new biomarkers and of targets for therapy. MY paper focused on the Interactive Networking of SNPs in Zinc Absorption Pathways (ZIP/Znt) and Prostate Cancer, revealing that genes and SNPs linked to zink may play a significant role in the development of prostate cancer. The information I obtained indicates a large number of polymorphisms that affect prostate cancer as well as other disease.
  • Methods: Data were collected from several reputable databases, among which the NCBI served as the primary source of information. The polymorphisms were first extracted from the NCBI database and then organized into a comprehensive table using Microsoft Excel, categorized according to their gene location and associated disease. This table was subsequently imported into the MegaGen pharmacogenetic software, which generated detailed tables containing relevant information for each polymorphism. The data were then subjected to statistical analysis, enabling a systematic evaluation of gene–SNP interactions. The software was a particularly employed for the analysis of polymorphism data and for the identification of potential side effects with genetic origins. Network meta-analysis enables comprehensive, simultaneous comparison and ranking of multiple treatments using both direct and indirect evidence, offering greater precision and broader decision-making power than traditional meta-analysis.
  • Results: Initially, genetic tests should be performed to examine polymorphisms in common genes, including CYP27B10, CYP27B11, CYP27B12, CYP27B13, CYP27B14, CYP27B15, CYP27B16, CYP27B17, CYP27B18, CYP27B3, CYP27B6, CYP27B9, PIK3CA, and PTEN. Based on the data analyses performed using the MegaGen software and the review of polymorphisms identified in previous studies and reported in the literature, it was found that among the polymorphisms obtained, three of the most common ones, including RS11568822, RS3755967, and RS17467825, showed a significant association with the development of prostate cancer.
  • Conclusion: Based on the studies I conducted and the polymorphisms that were analyzed, it was observed that, in addition to the polymorphisms I specifically investigated, there are other related polymorphisms that may also be associated with different diseases. This indicates that these variants could play roles not only in prostate cancer but also in other disorders, which may broaden the implications and outcomes of my work.
  • Keywords: MegaGen, polymorphisms, genes, prostate cancer, and statistical data