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Relationship Between Genetic Polymorphisms in Cell Cycle Regulatory Gene TP53 and Polycystic Ovarian Syndrome: A Case–Control Study and In Silico Analyses

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Abstract

Polycystic ovarian syndrome (PCOS) is a complex endocrine and metabolic condition with several potential causes. Insulin resistance is a hallmark of PCOS that often coexists with hirsutism, hyperandrogenism, being overweight, and hormonal imbalances. The functioning of multiple replication and transcription factors is regulated by tumor suppressor genes (TSGs), which play a crucial role in maintaining genomic integrity and controlling the cell cycle of granulosa cells. In the present study, we examined how three single nucleotide polymorphisms (SNPs) in TP53, a cell cycle regulatory gene, affect the risk of developing PCOS in a sample of an Iranian population. Genomic DNA was extracted from 200 PCOS patients and 200 healthy women to analyze TP53 rs17880604, rs1625895, and rs1042522 SNPs using the polymerase chain reaction-restriction fragment length polymorphism (PCR–RFLP) method. Our findings revealed that the majority of PCOS cases were overweight [25 < body mass index (BMI) < 30]. A positive association was observed between the TP53 rs1042522 SNP and the risk of PCOS under codominant heterozygous and overdominant genetic patterns (odds ratio > 1). Meanwhile, a negative association was observed between TP53 SNPs (rs1625895, rs17880604) and susceptibility to PCOS under codominant heterozygous and dominant models of inheritance (odds ratio < 1). Moreover, different genotype and haplotype combinations of rs17880604/rs1625895/rs1042522 conferred a decreased risk of PCOS in our population. We found no statistical difference in the frequency of TP53 genotypes between PCOS cases and/or controls in terms of BMI, waist circumference, prolactin level, and markers of lipid and carbohydrate profile (P > 0.05). Molecular dynamic prediction showed that the missense substitution in the 17p13.1 position (rs1042522) could change the properties and secondary structure of the p53 protein. As inherited risk factors, TP53 variations may play a  pivotal role in the pathogenesis of PCOS among Iranian women. Replicated population-based studies on other ethnicities are required to find the genetic contribution of variants of TP53, or SNPs located in other TSGs, to the etiology of this endocrine disease.

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Data Availability

The data presented in this manuscript will be available by the corresponding author upon reasonable request.

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Acknowledgements

We sincerely thank the cooperation of Bu-Ali hospital and Zahedan University of Medical Sciences. We would also thank Mrs. Narjes Keikha for her assistance.

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University of Sistan and Baluchestan provided financial support for the current study.

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SS contributed to conceptualization; GB-Z and MM performed experiments; SS, GB-Z, MM, and RS performed writing—original draft preparation; SS and MM performed writing—review and editing; MG and SM carried out clinical assessments; SS and MG  supervised the project. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Saman Sargazi or Marzieh Ghasemi.

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10528_2023_10349_MOESM1_ESM.docx

Supplementary file1 (DOCX 471 KB)—Table 1. Primer sequences used for genotyping TP53 polymorphisms. Table 2. Association between TP53 SNPs and clinical-demographic features of PCOS patients and healthy subjects. Figure 1. Pairwise LD analysis of TP53 rs17880604, rs1625895, and rs1042522 polymorphisms. No strong LD was found between the tested variations. Figure 2. Schematic representation of DNA sequence conservation using the WebLogo tool around the three TP53 rs17880604 and rs1625895 and rs1042522 loci. Red vertical line shows the position of locus variations in humans and wild allele conservation among mammalian species. Smaller and more varied nucleotides have less conservation.

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Biglari-Zadeh, G., Sargazi, S., Mohammadi, M. et al. Relationship Between Genetic Polymorphisms in Cell Cycle Regulatory Gene TP53 and Polycystic Ovarian Syndrome: A Case–Control Study and In Silico Analyses. Biochem Genet 61, 1827–1849 (2023). https://doi.org/10.1007/s10528-023-10349-1

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