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Record W4412726432 · doi:10.1038/s41598-025-13411-7

Genetic and computational analysis of AKR1C4 gene rs17134592 polymorphism in breast cancer among the Bangladeshi population

2025· article· en· W4412726432 on OpenAlexaff
Md. Akeruzzaman Shaon, Farzana Ansari, Zimam Mahmud, Sonia Tamanna, Abdullah Al Saba, Rushafi Sikder, Tabassum Howlader, Md. Zakir Hossain Howlader

Bibliographic record

VenueScientific Reports · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAldose Reductase and Taurine
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsBreast cancerGenotypingGenotypeOncologyPopulationBiologyInternal medicineRisk factors for breast cancerBioinformaticsMedicineGeneticsCancerGene

Abstract

fetched live from OpenAlex

Breast cancer is characterized by the unchecked proliferation of breast cells. Variations in the metabolism of steroid hormones can influence the risk of this disease by modifying the concentration and the potency of these hormones. The enzyme AKR1C4, primarily found in the liver, is crucial for regulating these hormone levels in the bloodstream. This research examined the relationship between a polymorphic variant (rs17134592) of the AKR1C4 gene and the susceptibility to breast cancer among the Bangladeshi population. A case-control study was conducted with 310 breast cancer patients and 310 healthy individuals from Bangladesh. DNA was extracted using an organic process, followed by genotyping through the PCR-RFLP technique. To validate the accuracy of genotyping results, a subset of PCR products was randomly selected and confirmed using Sanger sequencing. Statistical assessments were conducted to analyze the association of polymorphism, while molecular dynamics simulation and diverse computational methods were employed to predict the structural and functional impacts of the SNP. The results indicate that rs17134592 in the AKR1C4 gene is linked with a heightened risk for breast cancer (p < 0.0001, OR = 3.39, 95% CI = 1.80 to 6.50 for the GG genotype in additive model 2). The recessive model (GG vs. CC + CG) also showed an enhanced risk of susceptibility to breast malignancy (p < 0.0001, OR = 3.25, 95% CI = 1.78 to 6.08). In the subgroup of post-menopausal women, the risk of developing breast cancer was significantly higher for carriers of the GG genotype, with relative risks of 4.02 and 3.92, in the additive model 2 and recessive model, respectively. However, no significant correlations were observed between these genotypes and tumor grade or size in breast cancer patients. Computational analysis suggested that the L311V mutation (rs17134592) could potentially reduce the stability of the protein. Additionally, molecular dynamics simulations indicated that the L311V mutation introduces notable conformational instability to the AKR1C4 enzyme, potentially impacting its biological functionality and catalytic efficiency. In conclusion, the genetic variant rs17134592 has been identified as significantly correlated with the prevalence of breast cancer in the Bangladeshi population. Computational studies suggest that the L311V mutation in the AKR1C4 gene, corresponding to rs17134592, results in marked conformational instability and changes in enzyme functionality.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.231

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.005
GPT teacher head0.243
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2025
Admission routes1
Has abstractyes

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