Indonesian Journal of Medical Chemistry and Bioinformatics
Abstract
Background: The rs4944946 variant, located in the 5’UTR of the DHCR7 gene and upstream of NADSYN1, is biologically implicated in vitamin D and cholesterol metabolism pathways closely linked to cancer pathogenesis. This study aimed to experimentally validate the association between the rs4944946 polymorphism and breast cancer susceptibility in an independent cohort of Indonesian women. Method: A case-control study was conducted involving 36 breast cancer patients and 39 healthy adult females. Genotyping of the rs4944946 variant was performed using Polymerase Chain Reaction-Restriction Fragment Length Polymorphism (PCR-RFLP). The genetic association was evaluated across codominant, dominant, and recessive models. Luminal B was the predominant molecular subtype (36.1%) within the case group. Results: Validation results demonstrated no significant association between the rs4944946 variant and breast cancer susceptibility across all tested genetic models. The discordance between our validation cohort and prior in silico predictions is likely attributable to the low frequency of the mutant allele, limited sample size, and the "winner’s curse" phenomenon. Furthermore, technical constraints inherent to PCR-RFLP, such as the risk of incomplete enzymatic digestion, may have influenced genotyping accuracy. Conclusion: This experimental validation did not establish a significant association between the rs4944946 variant and breast cancer in the studied cohort. Future multicenter studies utilizing larger cohorts and more advanced, precise genotyping technologies (e.g., Next-Generation Sequencing) are required to definitively determine the clinical significance of this variant.
Bahasa Abstract
Latar Belakang: Varian rs4944946, yang terletak di daerah 5’UTR pada gen DHCR7 dan upstream dari NADSYN1, secara biologis terlibat dalam jalur metabolisme vitamin D dan kolesterol yang berkaitan erat dengan patogenesis kanker. Penelitian ini bertujuan untuk memvalidasi secara eksperimental asosiasi antara polimorfisme rs4944946 dengan kerentanan kanker payudara pada kohort independen wanita Indonesia. Metode: Studi kasus-kontrol dilakukan dengan melibatkan 36 pasien kanker payudara dan 39 wanita dewasa sehat. Genotiping varian rs4944946 dilakukan menggunakan metode Polymerase Chain Reaction-Restriction Fragment Length Polymorphism (PCR-RFLP). Asosiasi genetik dievaluasi melalui model kodominan, dominan, dan resesif. Luminal B merupakan subtipe molekuler yang paling dominan (36,1%) pada kelompok kasus. Hasil: Hasil validasi menunjukkan tidak adanya asosiasi yang signifikan antara varian rs4944946 dengan kerentanan kanker payudara pada semua model genetik yang diuji. Diskordansi antara hasil in vitro ini dengan prediksi in silico sebelumnya kemungkinan besar disebabkan oleh rendahnya frekuensi alel mutan, ukuran sampel yang terbatas, dan fenomena "winner’s curse". Lebih lanjut, keterbatasan teknis yang melekat pada metode PCR-RFLP, seperti risiko digesti enzimatik yang tidak sempurna, kemungkinan turut memengaruhi akurasi genotiping. Kesimpulan: Validasi eksperimental ini tidak menetapkan adanya asosiasi yang signifikan antara varian rs4944946 dan kanker payudara pada kohort yang diteliti. Studi multisentris di masa depan yang menggunakan kohort lebih besar serta teknologi genotiping yang lebih mutakhir dan presisi (misalnya, Next-Generation Sequencing) diperlukan untuk memastikan signifikansi klinis dari varian ini.
References
1. Fu, M.; Peng, Z.; Wu, M.; Lv, D.; Li, Y.; Lyu, S. Current and Future Burden of Breast Cancer in Asia: A GLOBOCAN Data Analysis for 2022 and 2050. Breast, 2025, 79. https://doi.org/10.1016/j.breast.2024.103835.
2. Loibl, S.; Poortmans, P.; Morrow, M.; Denkert, C.; Curigliano, G. Breast Cancer. The Lancet. Elsevier B.V. May 8, 2021, pp 1750–1769. https://doi.org/10.1016/S0140-6736(20)32381-3.
3. Sun, Y. S.; Zhao, Z.; Yang, Z. N.; Xu, F.; Lu, H. J.; Zhu, Z. Y.; Shi, W.; Jiang, J.; Yao, P. P.; Zhu, H. P. Risk Factors and Preventions of Breast Cancer. International Journal of Biological Sciences. Ivyspring International Publisher 2017, pp 1387–1397. https://doi.org/10.7150/ijbs.21635.
4. Pérez-Losada, J.; Castellanos-Martín, A.; Mao, J. H. Cancer Evolution and Individual Susceptibility. Integrative Biology. April 2011, pp 316–328. https://doi.org/10.1039/c0ib00094a.
5. Sun, S.; Yin, S.; Huang, J.; Zhou, D.; Tan, Q.; Man, X.; Wang, W.; Zhang, J.; Li, H. Identification of Significant Single-Nucleotide Polymorphisms Associated with Breast Cancer Recurrence and Metastasis Using GWAS. Cancer Innovation, 2025, 4 (1). https://doi.org/10.1002/cai2.142.
6. O’Brien, K. M.; Sandler, D. P.; Kinyamu, H. K.; Taylor, J. A.; Weinberg, C. R. Single-Nucleotide Polymorphisms in Vitamin D–Related Genes May Modify Vitamin D–Breast Cancer Associations. Cancer Epidemiology Biomarkers and Prevention, 2017, 26 (12), 1761–1771. https://doi.org/10.1158/1055-9965.EPI-17-0250.
7. Cavalieri, R.; de Oliveira, H. F.; Louvain de Souza, T.; Kanashiro, M. M. Single Nucleotide Polymorphisms as Biomarker Predictors of Oral Mucositis Severity in Head and Neck Cancer Patients Submitted to Combined Radiation Therapy and Chemotherapy: A Systematic Review. Cancers. Multidisciplinary Digital Publishing Institute (MDPI) March 1, 2024. https://doi.org/10.3390/cancers16050949.
8. Wu, M.; Li, Q.; Wang, H. Identification of Novel Biomarkers Associated With the Prognosis and Potential Pathogenesis of Breast Cancer via Integrated Bioinformatics Analysis. Technol. Cancer Res. Treat., 2021, 20. https://doi.org/10.1177/1533033821992081.
9. Zeng, X.; Shi, G.; He, Q.; Zhu, P. Screening and Predicted Value of Potential Biomarkers for Breast Cancer Using Bioinformatics Analysis. Sci. Rep., 2021, 11 (1). https://doi.org/10.1038/s41598-021-00268-9.
10. Pandey, S. C.; Gangola, S.; Kumar, S.; Debborma, P.; Suyal, D. C.; Punetha, A.; Joshi, T.; Bhatt, P.; Samant, M. DNA Microarray Analysis of Leishmania Parasite: Strengths and Limitations. In Pathogenesis, Treatment and Prevention of Leishmaniasis; Elsevier, 2021; pp 85–101. https://doi.org/10.1016/B978-0-12-822800-5.00003-2.
11. Michalska, D.; Jaguszewska, K.; Liss, J.; Kitowska, K.; Mirecka, A.; Łukaszuk, K. Comparison of Whole Genome Amplification and Nested-PCR Methods for Preimplantation Genetic Diagnosis for BRCA1 Gene Mutation on Unfertilized Oocytes-a Pilot Study. Hered. Cancer Clin. Pract., 2013, 11 (1). https://doi.org/10.1186/1897-4287-11-10.
12. Hashim, H. O.; Al-Shuhaib, M. B. S. Exploring the Potential and Limitations of PCR-RFLP and PCR-SSCP for SNP Detection: A Review. Journal of Applied Biotechnology Reports. Baqiyatallah University of Medical Sciences September 1, 2019, pp 137–144. https://doi.org/10.29252/JABR.06.04.02.
13. Gallagher, M. D.; Chen-Plotkin, A. S. The Post-GWAS Era: From Association to Function. American Journal of Human Genetics. Cell Press May 3, 2018, pp 717–730. https://doi.org/10.1016/j.ajhg.2018.04.002.
14. Widiana, I. K.; Irawan, H. Clinical and Subtypes of Breast Cancer in Indonesia. Asian Pacific Journal of Cancer Care, 2020, 5 (4), 281–285. https://doi.org/10.31557/apjcc.2020.5.4.281-285.
15. Hudjashov, G.; Karafet, T. M.; Lawson, D. J.; Downey, S.; Savina, O.; Sudoyo, H.; Lansing, J. S.; Hammer, M. F.; Cox, M. P. Complex Patterns of Admixture across the Indonesian Archipelago. Mol. Biol. Evol., 2017, 34 (10), 2439–2452. https://doi.org/10.1093/molbev/msx196.
16. Fatumo, S.; Chikowore, T.; Choudhury, A.; Ayub, M.; Martin, A. R.; Kuchenbaecker, K. A Roadmap to Increase Diversity in Genomic Studies. Nat. Med., 2022, 28 (2), 243–250. https://doi.org/10.1038/s41591-021-01672-4.
17. Politi, C.; Roumeliotis, S.; Tripepi, G.; Spoto, B. Sample Size Calculation in Genetic Association Studies: A Practical Approach. Life. MDPI January 1, 2023. https://doi.org/10.3390/life13010235.
18. Ioannidis, J. P. A.; Thomas, G.; Daly, M. J. Validating, Augmenting and Refining Genome-Wide Association Signals. Nature Reviews Genetics. May 2009, pp 318–329. https://doi.org/10.1038/nrg2544.
19. Zöllner, S.; Pritchard, J. K. Overcoming the Winner’s Curse: Estimating Penetrance Parameters from Case-Control Data. Am. J. Hum. Genet., 2007, 80 (4), 605–615. https://doi.org/10.1086/512821.
20. Xiao, R.; Boehnke, M. Quantifying and Correcting for the Winner’s Curse in Genetic Association Studies. Genet. Epidemiol., 2009, 33 (5), 453–462. https://doi.org/10.1002/gepi.20398.
21. Tian, X.; Wei, Z.; Khan, M.; Zhou, Z.; Zhang, J.; Huang, X.; Yang, Y.; Wang, S.; Wang, H.; Cai, X.; et al. Refining Lineage Classification and Updated RFLP Patterns of PRRSV-2 Revealed Viral Spatiotemporal Distribution Characteristics in China in 1991–2023. Transbound. Emerg. Dis., 2025, 2025 (1). https://doi.org/10.1155/tbed/9977088.
22. Egert, M.; Friedrich, M. W. Formation of Pseudo-Terminal Restriction Fragments, a PCR-Related Bias Affecting Terminal Restriction Fragment Length Polymorphism Analysis of Microbial Community Structure. Appl. Environ. Microbiol., 2003, 69 (5), 2555–2562. https://doi.org/10.1128/AEM.69.5.2555-2562.2003.
23. Cheng, Y. H.; Kuo, C. N.; Lai, C. M. Effective Natural PCR-RFLP Primer Design for SNP Genotyping Using Teaching-Learning-Based Optimization with Elite Strategy. IEEE Trans. Nanobioscience, 2016, 15 (7), 657–665. https://doi.org/10.1109/TNB.2016.2597867.
24. Singer, R. S.; Sischo, W. M.; Carpenter, T. E. Exploration of Biases That Affect the Interpretation of Restriction Fragment Patterns Produced by Pulsed-Field Gel Electrophoresis. J. Clin. Microbiol., 2004, 42 (12), 5502–5511. https://doi.org/10.1128/JCM.42.12.5502-5511.2004.
25. Wu, Y.-Y.; Delgado, R.; Costello, R.; Sunderland, T.; Dukoff, R.; Csako, G. Quantitative Assessment of Apolipoprotein E Genotypes by Image Analysis of PCR-RFLP Fragments a a a b; 2000; Vol. 293.
26. Fekete, M.; Lehoczki, A.; Szappanos, Á.; Zábó, V.; Kaposvári, C.; Horváth, A.; Farkas, Á.; Fazekas-Pongor, V.; Major, D.; Lipécz, Á.; et al. Vitamin D and Colorectal Cancer Prevention: Immunological Mechanisms, Inflammatory Pathways, and Nutritional Implications. Nutrients, 2025, 17 (8), 1351. https://doi.org/10.3390/nu17081351.
27. Dorling, L.; Carvalho, S.; Allen, J.; Gonzáles-Neira, A.; Luccarini, C.; Wahlstörm, C.; Karen, P.; Parsons, M.; Fortuno, C.; Wang, Q.; et al. Breast Cancer Risk Genes — Association Analysis in More than 113,000 Women. New England Journal of Medicine, 2021, 384 (5), 428–439. https://doi.org/10.1056/NEJMoa1913948.
Recommended Citation
Wahyudi, Dian Tri; Paramita, Rafika Indah; and Panigoro, Sonar Soni
(2026)
"Evaluation of the DHCR7/NADSYN1 rs4944946 Variant and Breast Cancer Susceptibility: A Case-Control Study in an Indonesian Cohort,"
Indonesian Journal of Medical Chemistry and Bioinformatics: Vol. 5:
No.
1, Article 4.
DOI: 10.7454/ijmcb.v5i1.1065
Available at:
https://scholarhub.ui.ac.id/ijmcb/vol5/iss1/4
Included in
Bioinformatics Commons, Genomics Commons, Molecular Biology Commons




