Abstract
Breast cancer (BC) is one of the most prevalent malignancies and a leading cause of cancer-related mortality among women worldwide. Genetic variation in the SIRT1 (Sirtuin-1) gene has been implicated in cancer susceptibility; however, evidence regarding its association with breast cancer in the Bangladeshi population remains limited. This study aimed to investigate the association of SIRT1 polymorphisms (rs3758391 and rs12778366) with breast cancer susceptibility and clinicopathological characteristics, while also evaluating demographic and hematological parameters among Bangladeshi women. A hospital-based case-control study was conducted involving 199 histopathologically confirmed breast cancer patients and 100 healthy controls. Genotyping of rs3758391 and rs12778366 was performed using the polymerase chain reaction–restriction fragment length polymorphism (PCR-RFLP) technique. Logistic regression analysis was employed to estimate crude and age-adjusted odds ratios (ORs) with 95% confidence intervals (CIs). Herein, significant differences in genotype distributions were observed between cases and controls for both polymorphisms. For rs3758391, individuals carrying the TT genotype showed significantly higher odds of breast cancer under the codominant model (adjusted OR = 4.30, 95% CI: 1.60–11.55, p = 0.004) and recessive model (adjusted OR = 4.76, 95% CI: 1.80–12.59, p = 0.0003) after adjustment for age. In contrast, although the TC + CC genotype of rs12778366 demonstrated increased odds in the dominant model before adjustment (OR = 1.76, p = 0.038), the association lost statistical significance after age adjustment (p = 0.06). Haplotype analysis revealed a significant global association before age adjustment, which became non-significant after adjustment. Among clinicopathological characteristics, rs12778366 TC genotype was significantly associated with progesterone receptor positivity (OR = 3.20, 95% CI: 1.3–8.12, p = 0.012), while no associations were observed with tumor stage. Hematological analysis demonstrated significantly lower hemoglobin levels and elevated erythrocyte sedimentation rate (ESR), neutrophil percentage, plateletcrit (PCT), and mean platelet volume (MPV) in breast cancer patients compared with controls. In conclusion, SIRT1 rs3758391 may serve as a potential genetic susceptibility marker for breast cancer in Bangladeshi women, whereas the association of rs12778366 appears less robust after age adjustment. Larger studies and functional investigations are warranted to validate these findings.
Keywords: Breast cancer, SIRT1, SNP, rs3758391, rs12778366, Bangladeshi population
Highlights
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SIRT1 rs3758391 TT genotype significantly increases BC susceptibility.
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rs12778366 shows modest association, losing significance after adjustment.
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rs12778366 TC genotype linked with progesterone receptor positivity.
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Genotype frequencies differ significantly between cases and controls.
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BC patients exhibit lower Hb and elevated ESR, PCT, MPV, neutrophils.
1. Introduction
Breast cancer, a malignancy that primarily affects women, is a widely diagnosed form of cancer that accounts for nearly a quarter of all cancer cases in women worldwide, making it a significant public health issue.1 According to global cancer estimates from GLOBOCAN 2020, an estimated 2.3 million women were diagnosed with breast cancer worldwide in 2020, and approximately 685,000 women died from the disease, making it a leading cause of cancer mortality among women.2 In Bangladesh, breast cancer is the most commonly diagnosed cancer among women, with an estimated 12,989 new cases reported in 2022, accounting for approximately 18% of all new female cancer cases according to GLOBOCAN 2022 data (https://gco.iarc.who.int/media/globocan/factsheets/populations/50-bangladesh-fact-sheet.pdf).3
Research suggests that approximately 10% of breast cancer cases are linked to genetic factors.4, 5 While high-penetrance mutations in genes such as BRCA1 and BRCA2 are well-established contributors to hereditary breast cancer,6, 7, 8 studies have also highlighted the role of single nucleotide polymorphisms (SNPs) in various genes influencing disease risk9, 10, 11,12,13 particularly with respect to cancer risk.14, 15 In this context, the Sirtuin 1 (SIRT1) gene has been implicated as a possible candidate in cancer development based on its functions in the modulation of cellular homeostasis,16 metabolic processes,17 and tumorigenesis.18, 19 It should be remembered that cancer development is not solely based on mutations in these genes. The variability of polymorphic forms of these genes also appears to be a contributing factor in the pathogenesis of this disease.4 This highlights the complex nature of breast cancer and the need for further investigation into the genetic factors that contribute to its development.
SIRT1 belongs to the sirtuin family of NAD + -dependent deacetylases and has been implicated in various cellular processes, such as DNA repair,20 apoptosis,21 response to oxidative stress,22 and metabolic regulation.17 The gene translates into a protein that regulates key transcription factors, including p53, NF-κB, and FOXO, which are involved in tumor suppression and survival pathways.23 Depending on cell context and type of tumor, SIRT1 can act as an oncogene or tumor suppressor.24 Several studies have shown that overexpression of SIRT1 is associated with enhanced survival, growth, and chemotherapy resistance of cancer cells.25 Other reports indicate that activation of SIRT1 results in DNA repair and genomic stability, thus playing protective roles against cancer development.26 The dual action of SIRT1 in cancer positions it as an attractive molecule to be investigated further, especially in the context of genetic changes that can influence its expression or activity.
Sirtuins, also referred to as SIRTs, are deacetylases that rely on NAD for their function, and their acknowledged involvement in various pathophysiological processes makes them potential targets for further validation in diagnostic applications.27, 28 SIRT1 (sirtuin 1) is a constituent of the sirtuin protein family, which holds significant responsibilities in regulating cellular processes.29 It functions as a deacetylase that relies on NAD+ (nicotinamide adenine dinucleotide) for its activity, enabling it to eliminate acetyl groups from specific proteins.29, 30 This deacetylation activity governs various physiological phenomena, such as aging, inflammation, stress tolerance, and energy utilization. The impact of SIRT1 extends to gene expression, cell aging, and the response of cells to DNA damage. Furthermore, it has been observed that SIRT1 can modulate metabolic control by altering pathways involved in the body's reaction to calorie restriction. Its potential influence on longevity and its possible role as a preventive measure against age-related diseases underscores the significance of SIRT1 in maintaining cellular homeostasis and overall health.30, 31
Genetic polymorphisms within the SIRT1 gene have been investigated in several diseases, such as type 2 diabetes,32 cardiovascular disease,33 and neurodegenerative disease.34 SIRT1 SNPs have more recently been investigated for roles in cancer susceptibility, with certain polymorphisms strongly associated with breast, colorectal, and lung cancers. Of the SNPs identified, rs3758391 (T > C) and rs12778366 (C > T) are noteworthy for their possible influence on SIRT1 gene expression and regulation.35 The rs3758391 (T > C) polymorphism in the promoter region of the SIRT1 gene has been shown to affect transcriptional activity.36 Moreover, genetic variations in promoter activity may influence the protein level of SIRT1 and, thus, its tumor-suppressing or oncogenic activity. Previous studies have reported conflicting associations between rs3758391 and various cancers, warranting further investigation.37 rs12778366 (C > T) is also situated in a regulatory region of SIRT1 and has been associated with differences in SIRT1 expression. The T allele has been found in some studies to be associated with lower SIRT1 expression, which can impact cellular aging and tumorigenesis.
Polymorphisms within the SIRT1 gene have recently been the subject of rigorous investigation concerning its implications for breast cancer. A study conducted in Egypt revealed that specific SIRT1 gene polymorphisms, namely rs3758391 and rs12778366, exhibit associations with both the susceptibility to and prognosis of breast cancer within the Egyptian population.38 Notably, the T allele of rs3758391, particularly the TT genotype was significantly associated with an increased risk of breast cancer, and individuals carrying this genotype exhibited significantly higher serum SIRT1 levels compared with carriers of other genotypes within the breast cancer cohort.38
Breast cancer studies in Bangladesh are still underdeveloped, and genetic studies investigating susceptibility markers are sparse. Although numerous studies have investigated SIRT1 polymorphisms in different populations, information on their association with breast cancer susceptibility in the Bangladeshi population is lacking. Owing to the genetic heterogeneity among ethnic groups, studying these SIRT1 polymorphisms in a population-specific context is necessary to understand their utility as biomarkers for early detection and risk assessment.38, 39, 40 Limited awareness and dense population in Bangladesh are the etiologic reasons for the rising trend of breast cancer with a reported incidence of 22.5 per 100,000 women.41 Nearly 63% of patients (n = 987) present with grade III tumors,42 and delayed presentation (90% at stage III-IV) is standard. While earlier investigations have evaluated SIRT1 SNPs in other populations, their functions in Bangladeshi patients remain unknown. Environmental and lifestyle factors prevalent in Bangladesh may also interact with genetic susceptibility in intricate manners to modulate breast cancer risk. As far as we know, no investigation has hitherto examined SIRT1 polymorphisms and breast cancer relationships in Bangladeshi individuals.
The current research aims to fill this knowledge gap by investigating the correlation between SIRT1 polymorphisms (rs3758391 and rs12778366) and susceptibility to breast cancer in Bangladeshi women. Through the comparison of the frequency of these SNPs among breast cancer patients and controls, we seek to determine if SIRT1 polymorphisms can serve as potential biomarkers in the risk stratification of the disease. Given the urgent need for genetic exploration in this group, our study can provide sorely needed insight into disease pathogenesis and the implementation of individualized medicine approaches.
2. Methods and materials
2.1. Study design and subject group
A total of 199 breast cancer patients were selected from the National Institute of Cancer Research & Hospital (NICRH) in Dhaka, Bangladesh. 100 healthy females at Ibn Sina Hospital in Dhaka were included as control samples. The Ethical Review Committee of the Department of Biochemistry and Molecular Biology, University of Dhaka, approved the ethical clearance of the study (Approval No: BMBDU-ERC/EC/21/02). Informed written consent was obtained from the patient for the publication of all images, clinical data, and any other relevant information. This study adhered to the principles outlined in the declaration of Helsinki and its subsequent revisions and complies with all relevant regulations and guidelines required for research involving human samples.
2.2. Sample and data collection
Approximately 5 mL of blood sample was collected in EDTA-containing tubes from each subject and immediately preserved in an icebox. Following blood collection, serum was separated from blood cells by centrifuging for 15 min at 3000 rpm and stored separately at −20 °C for further analysis. Demographic data were obtained through individual interviews, facilitating subsequent analysis.
2.3. DNA extraction
DNA was extracted from blood cells using an in-house-optimized alkaline lysis method as described previously43, 44.45 This method involved lysing red blood cells (RBC) with RBC lysis buffer, liberating genomic DNA with nucleic lysis buffer, and using saturated NaCl, phenol-chloroform-isoamyl alcohol, and ethanol for the selective precipitation of DNA. The purity and quantity of the DNA were assessed by measuring the absorbance of the diluted DNA solution at 260 nm and 280 nm, and consistency was examined through gel electrophoresis.
2.4. Genotyping of SIRT1 gene
The genotype of the SIRT1 gene was determined using the PCR-RFLP method diseases,44.46 Genomic DNA was amplified in a thermal cycler (Applied Biosystems) using the primer sets outlined in Supplementary Table 1. Genomic DNA (2.0 μL) was added to a mixture of PCR grade water (1.6 μL), Master Mix (5.0 μL), 10% DMSO (1.0 μL), and 10 μM of forward and reverse primers (0.2 μL each).
Subsequent to the PCR amplification of rs3758391 and rs12778366 in both the case and control samples, the PCR products underwent digestion using NlaIII and HpyCH4III, respectively, for durations of 15 and 60 min at 37 °C. Following the restriction digestion, the resultant digested products were analyzed on a 2% agarose gel with a 100 bp DNA ladder and visualized using a gel documentation system. The polymorphic nature of rs3758391 leads to the generation of two distinct DNA fragments, measuring 148 bp and 93 bp, when subjected to NlaIII digestion (Supplementary Fig. 1.a). Similarly, the polymorphism observed in rs12778366 yields two separate DNA fragments of 177 bp and 35 bp upon treatment with HpyCH4III (Supplementary Fig. 1.b).
2.5. Statistical analysis
The acquired data underwent analysis utilizing GraphPad Prism 8.0 and R, and the outcomes were presented as mean ± standard error of the mean (SEM). Odds ratios (OR) and 95% confidence intervals (95% CI) were computed using logistic regression models. The statistical methodologies included applying the Chi-square test and one-way analysis of variance (ANOVA). Statistical significance was ascribed to instances where p-values were equal to or less than 0.05 (p ≤ 0.05). To determine the independent risk associated with SIRT1 polymorphisms, Multivariate Logistic Regression was performed using a model that included age as covariate. This allowed for the calculation of Adjusted Odds Ratios (ORa) and 95% Confidence Intervals (CI). By adjusting for these confounding variables, we ensured that the reported genetic risk estimates for rs3758391 and rs12778366 are independent of the age differences between cases and controls. Multinomial logistic regression was conducted using nnet package in R to investigate the association between genotypic distribution of rs3758391 and rs12778366 with breast cancer stage.
3. Results
3.1. Sample size calculation for case–control study
The sample size was calculated using Schlesselman's formula for unmatched case-control studies with unequal group sizes, using G*Power software (version 3.1.9.7). Detailed sample size calculation is shown in Supplementary File 1.
The calculation indicated a minimum requirement of 172 cases to be compared against 86 controls (maintaining the 0.5 ratio). The present study included 199 cases and 100 controls, confirming that the sample size is sufficient to achieve more than 80% statistical power at a 95% confidence level to detect the expected associations.
3.2. Demographic characteristics of the study population
The demographic characteristics of 199 patients diagnosed with breast cancer and 100 healthy controls are shown in Table 1. Age was significantly different between breast cancer patients and healthy controls as shown in Table 1. On the other hand, no significant differences were found regarding the place of residence (p = 0.287) or blood group (p = 0.700), which indicates that these two factors may not play an essential role in the development of breast cancer among the respondents in this study.
Table 1.
Baseline demographic and clinical characteristics of the study population.
| Parameters | Case (n = 199) n (%) | Control (n = 100) n (%) | p-value |
|---|---|---|---|
| Age (years) (Mean ± SD) | 45.14 (±9.58) | 39.95 (±16.68) | 0.005⁎ |
| Age groups | <0.0001 | ||
| 19–30 | 16 (8.04) | 40 (40.00) | |
| 31–40 | 60 (30.15) | 16 (16.00) | |
| 41–50 | 69 (34.67) | 18 (18.00) | |
| 50+ | 54 (27.14) | 26 (26.00) | |
| Smoking Habits | 0.653 | ||
| Non-smoker | 98 (64.82) | 52 (52.00) | |
| Smoker | 101 (35.68) | 48 (48.00) | |
| Residential Area | 0.287 | ||
| Urban | 52 (26.13) | 32 (32.00) | |
| Rural | 147 (73.87) | 68 (68.00) | |
| Blood Group | 0.700† | ||
| A- | 1 (0.50) | 2 (2.00) | |
| A+ | 25 (12.56) | 11 (11.00) | |
| AB+ | 11 (5.53) | 3 (3.00) | |
| B- | 2 (1.01) | 1 (1.00) | |
| B+ | 24 (12.06) | 14 (14.00) | |
| O- | 3 (1.51) | 0 (0.00) | |
| O+ | 133 (68.63) | 69 (69.00) | |
P-values with * were calculated using t-test. P-values with † were calculated using Fischer exact test. Otherwise, p-values were calculated using chi-square test.
3.3. Clinical characteristics of the study population
Analysis of the clinical and treatment characteristics of 199 breast cancer cases revealed that most patients were diagnosed at stage II (77.89%), with stage I and stage III accounting for 8.04% and 14.07%, respectively. The most common type of tumor was ductal carcinoma (96.98%), and lobular carcinoma was found in 3.02% of them. HER2, ER + PR, and ER were the most frequent subtypes, constituting 19.12%, 15.58%, and 13.57%, respectively. The overall percentage of triple-negative breast cancer was 21.61% of the cases. Regarding treatment modalities, 95.98% of patients received some medication, wherein chemotherapy was found to be the most common form of treatment used. The most frequent chemotherapeutic agents employed in this group were doxorubicin (133) and cyclophosphamide (114). The pattern in Table 2 presents the fact that stage II is the most common way that breast cancer presents. Ductal carcinoma is the most frequent type of tumor with a high rate of HR-positive and HER2-positive subtypes. The quite frequent use of chemotherapy may indicate the great importance of this treatment method for breast cancer management in this population.
Table 2.
Clinical and treatment characteristics of breast cancer cases.
| Parameter | Case; n (%) |
|---|---|
| Cancer Stage | |
| Stage - I | 16 (8.04) |
| Stage - II | 155 (77.89) |
| Stage - III | 28 (14.07) |
| Tumor Type | |
| Ductal | 193 (96.98) |
| Lobular | 6 (3.02) |
| Receptor Status | |
| ER | 27 (13.57) |
| HER2 | 38 (19.1) |
| PR | 17 (8.545) |
| ER + HER2 | 6 (3.02) |
| ER + PR | 33 (15.58) |
| HER2 + PR | 2 (1.01) |
| Triple Negative | 43 (21.61) |
| Triple Positive | 16 (8.04) |
| Medication | |
| None | 8 (4.02) |
| Recipient | 191 (95.98) |
| Tamoxifen | 9⁎ |
| Doxorubicin | 133⁎ |
| Docetaxel | 12⁎ |
| Paclitaxel | 17⁎ |
| Cyclophosphamide | 114⁎ |
| Epirubicin | 6⁎ |
| Zoledronic Acid | 3⁎ |
| Trastuzumab | 3⁎ |
| Fluorouracil | 3⁎ |
| Carboplatin | 1⁎ |
| Gemcitabine | 4⁎ |
| Filgrastim | 2⁎ |
| Therapy Approach | |
| None | 8 (4.02) |
| Recipient | 191 (95.98) |
| Single | 84 (42.21) |
| Combination | 107 (53.77) |
: not cumulated.
3.4. Assessment of blood parameters
Fig. 1 shows hematological parameters of breast cancer patients and healthy controls. Thus, total WBC count was significantly elevated among the cases (p < 0.05), possibly in response to malignancy as an inflammatory or leukocytosis reaction. The neutrophil percentage in cases was relatively high compared to the control group (p < 0.01). Also, the eosinophilic percentage was considerably raised (p < 0.05), indicative of systemic immune modulations and possible tumor-mediated inflammation. In contrast, lymphocyte percentage is statistically unchanged (ns), indicating a stable lymphocyte population.
Fig. 1.
Comparison of hematological parameters between breast cancer cases and healthy controls. Results from blood tests for case and control groups are broken down in this graph. These graphs compare different blood parameters and assess them by comparing breast cancer patients and healthy subjects. ESR: Erythrocyte Sedimentation Rate.
Monocyte percentage is significantly increased in cases (p < 0.01), in concordance with monocyte infiltration in the tumor microenvironment. Hemoglobin is significantly decreased in cases (p < 0.01), which may be compatible with cancer-related anemia. ESR is also markedly increased in cases (p < 0.001), showing increased systemic inflammation and disease activity [49, 53, 54]. No significant differences were seen between cases and controls regarding total red blood cell (RBC) and platelet count (ns), thus indicating these parameters were unaffected in this cohort. Taken together, our data suggest the existence of differential hematology alterations among patients with breast cancer—explicitly regarding inflammatory biomarkers and anemia—as valuable indicators that can aid in the monitoring of this malignancy.
This Fig. 2 shows the comparison of platelet and red blood cell indices between breast cancer cases and healthy controls. The plateletcrit (PCT) and mean platelet volume (MPV) are significantly higher in the patient group (p < 0.01), indicating an increase in platelet activation and turnover, which is often associated with systemic inflammation and tumor progression. Similarly, mean corpuscular hemoglobin concentration (MCHC) and red cell distribution width-coefficient of variation (RDW-CV) are significantly higher in cases (p < 0.05), which may indicate erythrocyte alterations probably related to cancer-related anemia or oxidative stress.
Fig. 2.
Comparison of platelet and red blood cell indices between breast cancer cases and healthy controls. These graphs assess the abundance of various RBC indices in case and control groups and describe a relationship between the disease stage and the parameters mentioned. PCT: Plateletcrit; MCHC: Mean Corpuscular Hemoglobin Concentration; RDW-CV: Red Cell Distribution Width - Coefficient of Variation; MPV: Mean Platelet Volume; P-LCR: Platelet Large Cell Ratio; PDW: Platelet Distribution Width. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
On the other hand, the large platelet ratio (P-LCR) and platelet distribution width (PDW) did not show any statistically significant differences (ns) between cases and controls, which may suggest that these platelet metrics are not sensitive to pathological alterations associated with breast cancer. Such findings emphasize the role of some hematological parameters as potential biomarkers indicative of the inflammatory and hematological consequences of breast cancer. The association of hematological parameters with breast cancer patients and controls is shown in Supplementary Table 2.
3.5. Association of allelic and genotypic distributions for rs3758391 within SIRT1 with breast cancer susceptibility
The PCR-RFLP banding pattern for rs3758391 is shown in Supplementary Fig. 1a. The distribution of C and T alleles for rs3758391 in breast cancer patients and healthy individuals is shown in Table 3. Individuals harboring the T allele of rs3758391 had significantly increased risk of developing breast cancer compared to those harboring C allele with or without adjusting for age (Table 3).
Table 3.
Association of allelic and genotypics frequencies of rs3758391 and rs12778366 with breast cancer susceptibility.
| rs3758391 | |||||||
|---|---|---|---|---|---|---|---|
| Models | Genotypes | Case (n = 199) | Control (n = 100) | OR (95% CI) | p | ORa (95% CI) | pa |
| Co-dominant | CC | 117 (58.8%) | 63 (63%) | 1 | 1 | ||
| CT | 41 (20.6%) | 32 (32%) | 0.69 (0.40–1.20) | 0.218 | 0.71 (0.40–1.26) | 0.267 | |
| TT | 41 (20.6%) | 5 (5%) | 4.42 (1.66–11.74) | 0.003 | 4.30 (1.60–11.55) | 0.004 | |
| Dominant | CC | 117 (58.8%) | 63 (63%) | 1 | – | 1 | – |
| CT + TT | 82 (41.2%) | 37 (37%) | 1.19 (0.73–1.96) | 0.48 | 1.20 (0.73–1.99) | 0.47 | |
| Recessive | CC + CT | 158 (79.4%) | 95 (95%) | 1 | – | 1 | – |
| TT | 41 (20.6%) | 5 (5%) | 4.93 (1.88–12.91) | 0.0001 | 4.76 (1.80–12.59) | 0.0003 | |
| Over-dominant | CC + TT | 158 (79.4%) | 68 (68%) | 1 | – | 1 | – |
| CT | 41 (20.6%) | 32 (32%) | 0.55 (0.32–0.95) | 0.033 | 0.57 (0.33–1.00) | 0.05 | |
| Allele | C/T | 275/123 | 114/16 | 3.19 (1.86–5.79) | 0.00006 | 3.01 (1.75–5.49) | 0.0001 |
| rs12778366 | |||||||
| Co-dominant | TT | 128 (64.3%) | 76 (76%) | 1 | – | 1 | – |
| TC | 48 (24.1%) | 16 (16%) | 1.78 (0.95–3.35) | 0.074 | 1.77 (0.93–3.37) | 0.082 | |
| CC | 23 (11.6%) | 8 (8%) | 1.71 (0.73–4.01) | 0.219 | 1.52 (0.64–3.62) | 0.346 | |
| Dominant | TT | 128 (64.3%) | 76 (76%) | 1 | – | 1 | – |
| TC + CC | 71 (35.7%) | 24 (24%) | 1.76 (1.02–3.02) | 0.038 | 1.69 (0.97–2.93) | 0.06 | |
| Recessive | TT + TC | 176 (88.4%) | 92 (92%) | 1 | – | 1 | – |
| CC | 23 (11.6%) | 8 (8%) | 1.50 (0.65–3.49) | 0.33 | 1.34 (0.57–3.16) | 0.5 | |
| Over-dominant | TT + CC | 151 (75.9%) | 84 (84%) | 1 | – | 1 | – |
| TC | 48 (24.1%) | 16 (16%) | 1.67 (0.89–3.12) | 0.1 | 1.68 (0.89–3.18) | 0.1 | |
| Allele | T/C | 304/94 | 168/32 | 1.62 (1.05–2.56) | 0.0321 | 1.53 (0.98–2.43) | 0.0648 |
OR: Odds ratio; CI: Confidence intervals; ORa: Odds ratio adjusted for age; pa: p-value adjusted.
The frequencies of genotypes CC, CT and TT with respect to rs3758391 were 180 (60.2%), 73 (24.4%) and 46 (15.4%), respectively. The distribution of these genotypes in breast cancer patients and healthy individuals is shown in Table 3. The genotypic analysis revealed the T allele was associated with breast cancer susceptibility in homozygous codominant, recessive and over-dominant models without adjusting for age (Table 3). After adjusting for age, the association remained significant in homozygous codominant and recessive models (Table 3). In recessive model, it was found that the individuals harboring the TT genotypes had increased risk of having breast cancer compared to those having CC and CT genotypes (ORa (95%CI): 4.76 (1.80–12.59), pa: 0.0003).
3.6. Association of allelic and genotypic distributions for rs12778366 within SIRT1 with breast cancer susceptibility
The PCR-RFLP banding pattern for rs12778366 is shown in Supplementary Fig. 1b. The distribution of T and C alleles for rs12778366 in breast cancer patients and healthy individuals is shown in Table 3. Individuals harboring the C allele of rs12778366 had significantly increased risk of developing breast cancer compared to those harboring T allele without adjusting for age (Table 3). However, after adjusting for age the association was no longer significant (pa = 0.0648).
The frequencies of genotypes TT, TC and CC with respect to rs12778366 were 204 (68.2%), 64 (21.4%) and 31 (10.4%), respectively. The distribution of these genotypes in breast cancer patients and healthy individuals is shown in Table 3. The genotypic analysis revealed the C allele was associated with breast cancer susceptibility in dominant without adjusting for age (Table 3). After adjusting for age, the association was not significant (Table 3).
3.7. Hardy-Weinberg equilibrium for the genotypic distribution of rs3758391 and rs12778366 in healthy individuals
The genotype distributions of the investigated SNPs were tested for Hardy-Weinberg Equilibrium (HWE) in the control group. The rs3758391 polymorphism was found to be in HWE (P = 0.647). However, the rs12778366 polymorphism showed a borderline deviation from HWE (P = 0.047), characterized by a slight excess of homozygotes and a deficit of heterozygotes. For rs12778366, the deviation could be due to small sample size, population stratification, or selection bias.
3.8. Association of genotypic distribution for rs3758391 and rs12778366 with receptor status of breast cancer patients
The distribution of genotypes of rs3758391 and rs12778366 with respect to HER2, ER and PR positive and negative is shown in Table 4. Breast cancer patients carrying the TC genotype of rs12778366 showed a higher likelihood of being progesterone receptor positive. However, no significant association was observed between the other genotypes of rs3758391 or rs12778366 and the receptor status of the breast cancer patients (Table 4).
Table 4.
Association of genotypic distribution of rs3758391 and rs12778366 with receptor status of breast cancer patients.
| rsID | Genotypes | Receptor Status |
|||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| HER2 |
ER |
PR |
|||||||||||
| + | − | OR (95% CI) | p | + | − | OR (95% CI) | p | + | − | OR (95% CI) | p | ||
| rs3758391 | CC | 81 | 36 | 1 | − | 74 | 43 | 1 | − | 82 | 35 | 1 | − |
| CT | 25 | 17 | 1.59 (0.76–3.32) | 0.214 | 22 | 20 | 1.64 (0.80–3.37) | 0.178 | 25 | 17 | 1.66 (0.79–3.46) | 0.178 | |
| TT | 31 | 9 | 0.63 (0.26–1.42) | 0.284 | 21 | 19 | 1.49 (0.72–3.06) | 0.284 | 24 | 16 | 1.50 (0.71–3.14) | 0.285 | |
| rs12778366 | TT | 86 | 42 | 1 | − | 82 | 46 | 1 | − | 91 | 37 | 1 | − |
| TC | 38 | 10 | 1.58 (0.63–3.88) | 0.324 | 26 | 22 | 2.32 (0.95–5.83) | 0.672 | 31 | 17 | 3.20 (1.3–8.12) | 0.012 | |
| CC | 13 | 10 | 0.54 (0.23–1.15) | 0.124 | 10 | 13 | 1.51 (0.77–2.96) | 0.231 | 10 | 13 | 1.35 (0.66–2.71) | 0.405 | |
3.9. Association of genotypic distribution for rs3758391 and rs12778366 with cancer stage of breast cancer patients
The distribution of genotypes of rs3758391 and rs12778366 with respect to cancer stage of breast cancer patients is shown in Table 5. No significant association was found between the genotypic distributions of either rs3758391 or rs12778366 and the cancer stage of the patients (Table 5).
Table 5.
Association of genotypic distribution of rs3758391 and rs12778366 with cancer stage of breast cancer patients.
| rsID | Genotype | Stage |
SII VS SI |
SIII VS SI |
||||
|---|---|---|---|---|---|---|---|---|
| SI | SII | SIII | OR (95% CI) | p | OR (95%CI) | p | ||
| rs3758391 | CC | 10 | 91 | 16 | 1 | – | 1 | – |
| CT | 4 | 32 | 5 | 0.88 (0.26–3.00) | 0.84 | 0.78 (0.17–3.62) | 0.75 | |
| TT | 2 | 32 | 7 | 1.81 (0.38–8.72) | 0.46 | 1.88 (0.31–11.18) | 0.49 | |
| rs12778366 | TT | 9 | 100 | 19 | 1 | – | 1 | – |
| TC | 5 | 36 | 7 | 0.65 (0.2–2.06) | 0.46 | 0.66 (0.16–2.68) | 0.56 | |
| CC | 2 | 19 | 2 | 0.86 (0.17–4.27) | 0.85 | 0.47 (0.06–3.93) | 0.49 | |
SI: Stage-1, SII: Stage-2, SIII: Stage-3, OR: Odds ratio.
3.10. Linkage disequilibrium between rs3758391 and rs12778366 and haplotype analysis
The two SNPs, rs3758391 and rs12778366, showed weak linkage disequilibrium (r2 = 0.021, D′ = 0.172, p = 0.0004), suggesting that while they were statistically associated, they were largely inherited independently of each other.
Four haplotypes were identified in the study participants (Table 6). Most frequent haplotype was haplotype1 having C allele in case of rs3758391 and T allele in case of rs12778366 (Total frequency = 0.5978). The haplotype2 having T allele in case of rs3758391 and T allele in case of rs12778366 was significantly associated with breast cancer susceptibility without adjusting for age. After adjusting for age, the association was no longer significant (Table 6). The global haplotype association p-value of 0.04 without adjusting for age revealed haplotype distribution of rs3758391 and rs12778366 was significantly associated with breast cancer susceptibility before adjusting for age. However, the global haplotype p-value of 0.08 after adjusting for age meant the association was no longer significant.
Table 6.
Estimation of haplotype frequencies with respect to rs3758391 and rs12778366 and association of haplotypes with breast cancer susceptibility.
| Haplotype | rs3758391 | rs12778366 | Frequency (Total) | Frequency (Case) | Frequency (Control) | OR (95% CI) | p | ORa (95% CI) | pa |
|---|---|---|---|---|---|---|---|---|---|
| 1 | C | T | 0.5978 | 0.6909 | 0.5515 | 1 | – | 1 | – |
| 2 | T | T | 0.1915 | 0.1491 | 0.2123 | 1.58 (1.01–2.45) | 0.044 | 1.55 (0.99–2.44) | 0.058 |
| 3 | C | C | 0.1263 | 0.0991 | 0.1394 | 1.59 (0.93–2.70) | 0.09 | 1.48 (0.86–2.54) | 0.16 |
| 4 | T | C | 0.0844 | 0.0609 | 0.0967 | 1.56 (0.85–2.88) | 0.16 | 1.55 (0.84–2.88) | 0.17 |
OR: Odds ratio; CI: Confidence intervals; ORa: Odds ratio adjusted for age, pa: p-value adjusted for age.
4. Discussion
Breast cancer is a multifaceted ailment influenced by the interplay of genetic and environmental determinants.47, 48 While specific genetic mutations can increase susceptibility to breast cancer, it is crucial to emphasize that the majority of instances are not attributable to hereditary genetic predispositions.47 Concurrently, environmental influences, encompassing age, gender, reproductive history, exposure to radiation, and lifestyle choices, significantly contribute to the etiology of breast cancer.48
The profound effects of mutations in BRCA1, BRCA2, PALB2, PTEN, etc. genes have been well documented.49, 50 Nevertheless, there has been a recent surge of scholarly interest in elucidating the effects of mutations within the sirtuin gene family, with particular emphasis on SIRT1 and SIRT6.28, 51, 52 There are reports that rs3758391 and rs12778366 can cause breast cancer disposition,38 hence, we investigated this association with the Bangladeshi population.
In this study, while rs3758391 conformed to HWE, a borderline deviation was observed for rs12778366 (P = 0.047). Such deviations in control groups are not uncommon in genetic epidemiological studies and can be attributed to several factors. Firstly, the relatively modest sample size of the control group (n = 100) may increase the sensitivity of the chi-square test to minor fluctuations in genotype counts.
Secondly, population stratification or a high rate of consanguinity within the study population (common in the South Asian demographic) can lead to an increase in homozygosity (the Wahlund effect). Lastly, this borderline result may reflect subtle selection bias in the hospital-based control group. Given that the P-value is near the threshold of 0.05 and the genotyping quality was strictly maintained, this SNP was retained for further association analysis.
The analysis of demographic and lifestyle factors revealed no significant association between smoking and breast cancer in this study population (p = 0.653). This indicates that smoking, in isolation, did not differentiate cases from controls in the present cohort. Similarly, no statistically significant differences were observed for residential area (p = 0.287) or blood group distribution (p = 0.700), suggesting that these variables are unlikely to serve as independent predictors of breast cancer susceptibility in this dataset. In contrast, a significant difference in age distribution was observed between cases and controls (p = 0.005 for mean age; p < 0.0001 for age groups), with a higher proportion of breast cancer cases occurring in middle-aged individuals. These finding highlights age as an important demographic factor that may contribute to disease susceptibility and also underscores the need for appropriate adjustment for age in genetic and multivariable analyses to reduce confounding effects. Overall, these findings suggest that classical demographic variables such as smoking status, residence, and blood group show limited discriminatory value in this cohort, whereas age appears to have a more relevant role in distinguishing cases from controls. Collectively, the results emphasize that breast cancer in this population is likely influenced by a complex interplay of factors, with stronger contributions from genetic and molecular determinants rather than isolated demographic characteristics.
The very high prevalence of stage II breast cancer at 77.89% demonstrates that the majority are diagnosed in an intermediate stage and hence underscores the urgent necessity of enhanced early detection initiatives. Furthermore, the overwhelming dominance of ductal carcinoma at 96.98% is consistent with global trends and hence supports its clinical significance. Additionally, the high prevalence of HER2-positive and hormone receptor-positive subtypes underscores the necessity of targeted therapy in treatment protocols. The overall application of chemotherapy, specifically doxorubicin and cyclophosphamide, highlights the conventional approaches in treatment. Nevertheless, the high rate of triple-negative cases, with 21.61%, emphasizes the critical need for additional therapeutic options since such patients tend to have poor treatment options and prognoses.
The comparison of hematological parameters revealed highly significant differences between breast cancer patients and healthy controls in key markers including Hemoglobin (Hb), ESR, MPV, RDW-CV, Neutrophils, Monocytes, and Eosinophils (p < 0.01 for all). Although certain changes, such as the markedly elevated ESR, align with the expected systemic inflammatory response in malignancy, the overall profile should be interpreted cautiously given that patients were concurrently undergoing chemotherapy.
The significantly lower mean Hb levels are a classic indicator of anemia, a common and expected manifestation of chemotherapy-induced myelosuppression. Similarly, the complex shifts observed in WBC components (high Neutrophils, low Monocytes/Lymphocytes, and extreme range in Total WBC Count) reflect the cytotoxic effects of the treatment, which disrupts hematopoietic cell production and turnover.
The elevated MPV (Mean Platelet Volume) also suggests abnormal platelet kinetics, such as increased platelet turnover/destruction due to the combined effects of chronic inflammation and treatment-related bone marrow stress. Therefore, the observed hematological abnormalities primarily represent a combined effect of the underlying cancer burden and the aggressive therapeutic regimen rather than being solely markers of cancer progression.
Identification of notable associations of certain genotypic variations with the susceptibility to breast cancer is important in informing the potential genetic susceptibility markers to breast cancer.45 In the present study, the T allele of rs3758391 was significantly associated with an increased risk of breast cancer compared to the C allele, and this association persisted after adjustment for age. Genotypic analysis further demonstrated that the TT genotype confers substantially elevated susceptibility under homozygous codominant and recessive inheritance models. Notably, individuals carrying the TT genotype exhibited nearly five-fold higher odds of developing breast cancer compared to those with CC or CT genotypes (ORa = 4.76, 95% CI: 1.80–12.59, p = 0.0003) in the recessive model, highlighting a strong recessive effect of the T allele. These findings suggest that rs3758391 may serve as a meaningful genetic risk marker for breast cancer in this population. In contrast, the C allele of rs12778366 showed an association with increased breast cancer risk in the allelic and dominant models without age adjustment; however, this association lost statistical significance after adjusting for age (p = 0.0648), indicating that the observed effect may be partly confounded by age. Overall, these results support a contributory role of genetic variation at the rs3758391 locus in breast cancer susceptibility, particularly through the recessive TT genotype, while the role of rs12778366 appears weaker and age-dependent. Further validation in larger, independent cohorts is warranted to confirm these associations and explore potential functional mechanisms.
However, we cannot currently assert that this association is a trend in the Bangladeshi population, considering the various limitations of this study. These limitations encompass a restricted number of cases and controls and the selection of hospitals exclusively within Dhaka city. Consequently, these findings may not accurately reflect the association between rs3758391 and rs12778366 and the predisposition to breast cancer. Conducting further investigations with a larger sample size and including case subjects from diverse regions of Bangladesh will be essential to elucidating the definitive association of rs3758391 and rs12778366 with breast cancer.
This study has a few limitations that should be acknowledged. The sample size, although significant, may still be small to allow for the detection of smaller effect sizes with sufficient statistical power. In addition, since this study was conducted in one specific population, the findings may not be generally related to other populations. Replication and extension of these findings with larger samples and different populations are warranted.
In conclusion, this study provides supporting evidence that polymorphisms residing at rs3758391 and rs12778366 may be associated with altered susceptibility to breast cancer. These findings must be further studied to unravel the underlying mechanisms and their possible clinical implications in assessing and managing breast cancer risk.
CRediT authorship contribution statement
Fariya Aboni: Writing – original draft, Software, Methodology, Formal analysis, Data curation. Md Rasel Khan Manik: Writing – original draft, Software, Methodology, Investigation, Formal analysis, Data curation. Zimam Mahmud: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. Sonia Tamanna: Writing – review & editing, Validation, Software, Methodology, Investigation, Formal analysis. Abdullah Al Saba: Data curation, Formal analysis, Methodology, Software, Validation, Writing – review & editing. Farzana Ansari: Writing – review & editing, Validation, Software, Methodology, Investigation, Formal analysis. Md. Zakir Hossain Howlader: Writing – review & editing, Visualization, Validation, Resources, Methodology, Investigation, Formal analysis.
Ethics statement
The Ethical Review Committee of the Department of Biochemistry and Molecular Biology, University of Dhaka, approved the ethical clearance of the study (Approval No: BMBDU-ERC/EC/21/02). Informed written consent was obtained from the patient for the publication of all images, clinical data, and any other relevant information included in this manuscript. The study adhered to the principles outlined in the declaration of Helsinki and its subsequent revisions. The study complies with all relevant regulations and guidelines required for research involving human samples.
Funding
This work is partially funded by Ministry of Science and Technology, Bangladesh. Grant no: ID-472.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
Not Applicable
Footnotes
Supplementary data to this article can be found online at https://doi.org/10.1016/j.jgeb.2026.100833.
Appendix A. Supplementary data
Data availability
All data generated or analyzed during this study are included in this manuscript [and its supplementary material files].
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
All data generated or analyzed during this study are included in this manuscript [and its supplementary material files].