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Article

Impacts of DROSHA (rs10719) and DICER (rs3742330) Variants on Breast Cancer Risk and Their Distribution in Blood and Tissue Samples of Egyptian Patients

by
Aly A. M. Shaalan
1,2,
Essam Al Ageeli
3,
Shahad W. Kattan
4,
Amany I. Almars
5,6,
Nouf A. Babteen
7,
Abdulmajeed A. A. Sindi
8,
Eman A. Toraih
9,10,*,
Manal S. Fawzy
11,12,* and
Marwa Hussein Mohamed
13
1
Department of Anatomy, Faculty of Medicine, Jazan University, Jazan 45142, Saudi Arabia
2
Department of Histology and Cell Biology, Faculty of Medicine, Suez Canal University, Ismailia 41522, Egypt
3
Department of Basic Medical Sciences, Faculty of Medicine, Jazan University, Jazan 45141, Saudi Arabia
4
Department of Medical Laboratory, College of Applied Medical Sciences, Taibah University, Yanbu 46423, Saudi Arabia
5
Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah 21589, Saudi Arabia
6
Hematology Research Unit, King Fahd Medical Research Center, King Abdulaziz University, Jeddah 21589, Saudi Arabia
7
Department of Biochemistry, College of Science, University of Jeddah, Jeddah 80203, Saudi Arabia
8
Department of Basic Medical Sciences, Faculty of Applied Medical Sciences, Al-Baha University, Al-Baha 65779, Saudi Arabia
9
Department of Surgery, School of Medicine, Tulane University, New Orleans, LA 70112, USA
10
Genetics Unit, Department of Histology and Cell Biology, Faculty of Medicine, Suez Canal University, Ismailia 41522, Egypt
11
Department of Biochemistry, Faculty of Medicine, Northern Border University, Arar 91341, Saudi Arabia
12
Center for Health Research, Northern Border University, Arar 91431, Saudi Arabia
13
Department of Medical Biochemistry and Molecular Biology, Faculty of Medicine, Suez Canal University, Ismailia 41522, Egypt
 
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*
Authors to whom correspondence should be addressed.
Curr. Issues Mol. Biol. 2024, 46(9), 10087-10111; https://doi.org/10.3390/cimb46090602
Submission received: 22 August 2024 / Revised: 6 September 2024 / Accepted: 9 September 2024 / Published: 12 September 2024
(This article belongs to the Special Issue Advances in Molecular Pathogenesis Regulation in Cancer 2024)
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Abstract

:
MicroRNAs (miRNAs) are small, noncoding RNAs that regulate gene expression and play critical roles in tumorigenesis. Genetic variants in miRNA processing genes, DROSHA and DICER, have been implicated in cancer susceptibility and progression in various populations. However, their role in Egyptian patients with breast cancer (BC) remains unexplored. This study aims to investigate the association of DROSHA rs10719 and DICER rs3742330 polymorphisms with BC risk and clinical outcomes. This case–control study included 209 BC patients and 106 healthy controls. Genotyping was performed using TaqMan assays in blood, tumor tissue, and adjacent non-cancerous tissue samples. Associations were analyzed using logistic regression and Fisher’s exact test. The DROSHA rs10719 AA genotype was associated with a 3.2-fold increased risk (95%CI = 1.23–9.36, p < 0.001), and the DICER rs3742330 GG genotype was associated with a 3.51-fold increased risk (95%CI = 1.5–8.25, p = 0.001) of BC. Minor allele frequencies were 0.42 for rs10719 A and 0.37 for rs3742330 G alleles. The risk alleles were significantly more prevalent in tumor tissue than adjacent normal tissue (rs10719 A: 40.8% vs. 0%; rs3742330 G: 42.7% vs. 0%; p < 0.001). However, no significant associations were observed with clinicopathological features or survival outcomes over a median follow-up of 17 months. In conclusion, DROSHA rs10719 and DICER rs3742330 polymorphisms are associated with increased BC risk and more prevalent in tumor tissue among our cohort, suggesting a potential role in miRNA dysregulation during breast tumorigenesis. These findings highlight the importance of miRNA processing gene variants in BC susceptibility and warrant further validation in larger cohorts and different ethnic populations.

1. Introduction

Breast cancer (BC) represents a significant global health challenge and stands as one of the foremost causes of cancer-related deaths among women globally [1]. In 2022, it was estimated that there were around 2.308 million cases of BC, making it the most common cancer diagnosed worldwide [2]. Additionally, BC accounted for nearly 665,684 deaths in 2022, underscoring its significant contribution to morbidity and mortality among women [2]. The high incidence rate, along with the associated mortality, reinforces the necessity for ongoing research into risk factors and genetic predispositions related to BC.
Despite advances in diagnosis and treatment, the molecular mechanisms underlying BC development/progression remain incompletely understood [2]. Identifying novel genetic and epigenetic markers associated with BC risk and outcomes is essential for improving preventive measurements, early detection, and personalized therapeutic strategies [3,4].
MicroRNAs (miRNAs) are a family of “small noncoding RNAs” that modulate gene expression after transcription and are crucial for a variety of biological processes, such as cell proliferation/differentiation and apoptosis [5]. Aberrant miRNA expression has been involved in the pathogenesis of several cancers, including BC [6,7]. The biogenesis of miRNAs is a multi-step process involving several enzymes, with the ribonucleases DROSHA and DICER being two critical components [8]. DROSHA, as part of the microprocessor complex, cleaves the primary miRNA transcript into precursor miRNA in the nucleus. DICER subsequently converts the precursor miRNA into a double-stranded molecule that includes the mature miRNA guide strand and the passenger strand in the cytoplasm [9].
The dysregulated expression of DROSHA and DICER has emerged as a critical factor in BC pathogenesis [10], and their altered expression levels have been reported to impact several stages of BC development and progression [11]. This dysregulation can affect crucial cellular processes such as cell growth, invasion, metastasis, and apoptosis, all contributing to the BC malignant phenotype [12]. The intricate interplay between DICER, DROSHA, and miRNAs underscores their significance in BC biology, highlighting them as potential diagnostic and therapeutic targets for managing this disorder [13].
Genetic variants in the DROSHA (Gene ID: 29102) and DICER (Gene ID: 23405) have been investigated for their potential influence on miRNA processing and cancer susceptibility [14]. “Single-nucleotide polymorphisms (SNPs)” in these genes, such as rs10719 in DROSHA and rs3742330 in DICER, have been associated with altered risk and/or survival outcomes in several malignancies, including esophageal, ovarian, and colorectal cancers [15]. However, these SNPs’ role in BC remains poorly characterized, with limited and inconsistent evidence [16,17].
To address this gap, we conducted a case–control study to comprehensively unravel the association of DROSHA rs10719 and DICER rs3742330 polymorphisms with BC risk and clinicopathological characteristics in a sample of Egyptian women. We genotyped both SNPs in paired breast tumor and adjacent non-cancerous tissue samples, as well as in peripheral blood samples from patients with BC and healthy controls. To the best of our knowledge, this is the first study to investigate these SNPs in a BC population and compare the genotype distributions between tumor tissues and matched normal tissues.
Our findings provide new insights into the potential contributions of miRNA processing gene variants to BC susceptibility and clinical outcomes. Understanding these associations could help to identify novel biomarkers for BC risk assessment, ultimately enabling more precise and effective management of this heterogeneous disease.

2. Materials and Methods

2.1. Study Variant Selection, In Silico Analysis, and Literature Review

DROSHA and DICER genes’ genomic structures and variants were obtained from the “Ensembl Genomic database (www.ensembl.org)”. After reviewing and sorting the list, the prevalent biallelic variants, DROSHA rs10719 (A/G) and DICER rs3742330 (A/G), were identified for further study. The potential regulatory roles of these variants, influenced by their spatial genomic structures and their chromatin loop-mediated interactions with other genes and variants, were sourced from the 3DSNP database and depicted through three-dimensional visualizations showcasing gene interactions, regulatory enhancers, promoters, transcription factors, and conservation metrics (https://omic.tech/3dsnpv2/) [18]. The relevant literature was collated from the “GeneCards human gene database (www.genecards.org)” and the “National Center for Biotechnology Information (NCBI) (https://www.ncbi.nlm.nih.gov/)”, with all referenced databases last accessed on 30 March 2024.
To ensure a comprehensive understanding of the relationship between DROSHA rs10719 and DICER rs3742330 variants and their implications in various cancers, we conducted a systematic literature review. This review aimed to identify, analyze, and synthesize existing research on the associations of these variants with BC and other malignancies. The literature search used electronic databases, including “PubMed, Scopus, and Web of Science”. Relevant articles were selected based on the following criteria: studies that investigated “DROSHA rs10719” and/or “DICER rs3742330” in the context of cancer, published in peer-reviewed journals, and involving human subjects. The search strategy utilized keywords such as “DROSHA”, “DICER”, “rs10719”, “rs3742330”, and “cancer risk” in various combinations.

2.2. Study Population

A total of 209 female patients with BC were enrolled in the current study after obtaining the ethical approval of the institutional ethical committee (# 5027, 29 September 2022). These included (1) 103 archived paired tumor and adjacent non-tumor formalin-fixed paraffin-embedded (FFPE) tissue samples recruited from the pathology laboratory of Suez Canal University, Ismailia, and AlByan Laboratory in Port Said, Egypt, as well as (2) 106 blood samples of patients with BC obtained at the time of surgery. They had no prior history of radiotherapy or chemotherapy before tumor resection. Another 106 age-matched control blood samples were retrieved from the blood bank. Written consent was obtained from participants before they took part in this study.

2.3. Pathological and Clinical Assessment

A pathologist performed a post-operative pathological assessment of BC tissue specimens to determine the histopathological type, tumor size, grade, and lymph node infiltration. The Elston and Ellis modification of the Scarff–Bloom–Richardson grading system was used to grade the cancer cells, which assigns scores ranging from 1 to 3 to three parameters: tubule formation, nuclear pleomorphism, and mitotic index. Scores of 3–5 indicate well-differentiated cancer cells (grade 1), scores of 6–7 indicate moderately differentiated cancer cells (grade 2), and scores of 8–9 indicate poor or undifferentiated cancer cells (grade 3). Clinical staging was classified according to the “International Union Against Cancer (UICC)” and the “American Joint Committee on Cancer (AJCC) tumor-lymph node-metastasis (TNM)” staging system. Immunohistochemistry analysis was used to evaluate the hormone receptor status “(estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2/neu))” of the tumor tissues. Patients were then classified into four molecular tumor subtypes based on the results of the immunohistochemical analysis: “(1) Luminal A: ER+, PR+, HER2−, (2) Luminal B: ER+, PR+, HER2+, (3) HER2+ subset: ER−, PR−, HER2+, and (4) Basal-like (triple negative): ER−, PR−, HER2−”.
An evaluation of the prognosis of each patient was conducted using the “Nottingham Prognostic Index (NPI)” and “Immunohistochemical Prognostic Index (IHPI)” [19]. The NPI was based on three prognostic factors: (i) tumor size, (ii) histological grade, and (iii) lymph node status; it was grouped into three prognostic classes according to the NPI results—good, moderate, and poor. The IHPI scoring system complemented the NPI and was based on the HER2, ER, and PR statuses. Patients were then ranked from 0 to 4 points and divided into three classes—good, moderate, and poor. In addition, the “European Society of Medical Oncology (ESMO)” clinical recommendations for the follow-up of primary BC was used to predict the risk of recurrence and divided into three categories—low, intermediate, and high [20]. The follow-up of the patients was conducted to assess loco-regional recurrence, disease-free survival, and overall survival.

2.4. Sample Collection

FFPE tissue and blood samples (3 mL) on EDTA-coated vacutainers were collected from patients with BC. FFPE tissue samples included both tumor tissue and adjacent non-tumor tissue. Blood samples were obtained at the time of surgery prior to any treatment. Control blood samples were age-matched to the cases and retrieved from the blood bank.

2.5. Allelic Discrimination Analysis

After genomic DNA isolation using a commercially available DNA extraction kit, “QIAamp DNA Blood Mini kit (Cat. No. 51104, QIAGEN, Hilden, Germany)”, following the procedures outlined by the supplier’s guidelines, measurements to assess the quality/quantity of the isolated genetic material were conducted through “Nanodrop-1000 spectrophotometer (NanoDrop Tech., Wilmington, NC, USA)”. The DNA samples were then carefully preserved at −80 degrees Celsius for subsequent allelic discrimination polymerase chain reaction (PCR) analysis. The rs10719 in DROSHA and rs3742330 in DICER were genotyped using TaqMan SNP genotyping assays on the “StepOne™ Real-Time PCR system” (Applied Biosystems, Foster City, CA, USA). The assays C___7761648_10 and C__27475447_10 with the catalog numbers 4351379 and 4351379, respectively (Applied Biosystems), probed the wild/mutant alleles in the following context sequences: “[VIC/FAM]TATTTTATTTCAATGAGCACACTTC[A/G]TTCATTGTCTGCAGGAAAC AGGC and CTTCAATCTTGTGTAAAGGGATTAG[A/G]CACCCTAACAGAGCAAGA TCCAATA”, respectively; these sequences aligned with the reference genome build GRCh38. The exact formulations and the concentrations of the reagents used in each PCR have been described in prior studies [21]. PCR was accomplished in a 25 μL volume mixture containing 1× TaqMan Genotyping Master Mix, 1× SNP genotyping assay mix, and 20 ng genomic DNA. The PCR cycling protocol consisted of an initial step at 95 °C for 10 min, followed by 40 cycles comprising 15 s at 95 °C and 1 min at 60 °C [22]. To mitigate the risk of contamination, no-template controls accompanied each batch of experiments, and to ensure reliability, a subset of the samples, amounting to 10%, was subsequently retested, achieving total agreement with the initial results. The post-amplification analysis was executed using specialized software provided by the PCR system’s manufacturer.

2.6. Statistical Analysis

SNP analysis, including the Hardy–Weinberg equilibrium, allele, and genotype frequencies, was performed using SNPStat (www.snpstats.net). The Chi-square test was used for comparison. Adjusted odds ratios (ORs) with 95% confidence intervals (CI) were calculated using logistic regression models for multiple genetic association models, adjusting for relevant covariates [21]. The association of the SNPs with clinical and pathological markers was assessed using Fisher’s exact test for categorical variables and Student’s t-test for continuous variables. A paired t-test was applied to compare genotypes between paired tumor and non-tumor tissue samples. Univariate and multivariate logistic regression analyses were performed to estimate the impacts of SNPs on BC risk, calculating ORs and 95% CIs. Survival analyses were not conducted as preliminary assessments yielded insignificant associations between the studied variants and survival outcomes. All statistical analyses were conducted with a two-sided approach, and a p-value of less than 0.05 was deemed statistically significant. SPSS software version 27.0 (IBM Corporation, Armonk, NY, USA) was applied for statistical analysis.

3. Results

3.1. Selection of Single-Nucleotide Polymorphisms and In Silico Analysis

Genetic variants in DROSHA and DICER genes at 5p13.3 and 14q32.13 were screened. The top cited single-nucleotide variants were selected, and the variant with the highest citation and minor allele frequency over 0.1 was analyzed in the current study (Supplementary Table S1). Allelic frequencies of DROSHA rs10719 and DICER rs3742330 across different ethnic populations are shown in Figure 1.
The DROSHA ribonuclease III gene (ENSG00000113360) is located on chromosome 5p13.3: 31,400,494–31,532,093 (reverse strand, Figure 2A). It is implicated in the initial processing step of microRNA (miRNA) biogenesis (gene ontology (GO): 0006396). The studied variant rs10719 (NC_000005.10: g.31401340 A: G) is present in the three prime untranslated region. A customizable ‘Circos plotting system’ was established utilizing “3DSNP 2.0′ (https://omic.tech/3dsnpv2/)” (accessed 30 March 2024) to visualize the 3D chromatin structure along with a selection of crucial chromatin marks around the SNP of interest (Figure 2B). For predicting transcription factors, breast tissue was chosen as the cell type, while epithelial cells were selected for the assessment of histone modification. The primary subcellular localization of DROSHA, with a high confidence level, is found in the nucleoplasm and cytosol (Figure 2C). Its “PhyloP conservation score” is 2.277 (Figure 2D). The absolute values of this score correspond to “−log(p-value) under a null hypothesis of neutral evolution”. Positive scores indicate predicted conserved sites, whereas negative scores are assigned to regions anticipated to evolve rapidly.
Regarding the DICER ribonuclease III gene (ENSG00000100697), it is located on chromosome 14q32.13: 95,086,228–95,158,010 (reverse strand, Figure 3A). It is implicated in short dsRNA-mediated post-transcriptional gene silencing (GO:0006396). The studied variant rs3742330 (NC_000014.9:95087024 A: G) is present in the three prime untranslated region. A customizable Circos plotting system was created utilizing “3DSNP 2.0 (https://omic.tech/3dsnpv2/)” (accessed 30 March 2023) to visualize the 3D chromatin topology, along with crucial chromatin marks surrounding the SNP of interest (Figure 3B), based on the previously mentioned selection criteria. The primary subcellular localization of DICER, with a high degree of confidence, is found in the cytosol (Figure 3C). Its “PhyloP conservation score” is −0.023 (Figure 3D).

3.2. Pooled Analysis for the Role of DROSHA rs10719 and DICER rs3742330 SNPs in Cancer

We found 10 and 16 original articles on DROSHA rs10719 and DICER rs3742330 and different types of cancers (Supplementary Table S2). Pairwise comparison for the G allele versus the A allele showed rs10719 G to be associated with a lower risk of chronic lymphocytic leukemia (OR = 0.65, 95%CI = 0.44–0.97, p = 0.038). However, rs3742330 G was associated with contradictory results, showing higher risk in laryngeal cancer (OR = 1.41, 95%CI = 1.01–1.98, p = 0.047) and conferred protection against gastric cancer (OR = 0.76, 95%CI = 0.64–0.91, p = 0.002) and cervical precancerous lesions (OR = 0.73, 95%CI = 0.57–0.92, p = 0.009) (Supplementary Table S3).

3.3. Characteristics of the Tissue Samples

This study included two different cohorts. For the first cohorts, 103 paired FFPE tissue samples were compared. Patient characteristics are shown in Table 1.

3.4. Genotype/Allele Frequencies in Tissue Samples

Allelic discrimination analysis of 103 paired tissue samples of women with BC revealed minor allele frequencies of 0.39 for DROSHA G and 0.46 for DICER A. Genotype frequencies followed the Hardy–Weinberg equilibrium (p > 0.05). Screening the overall frequencies of DROSHA rs10719 and DICER rs3742330 genotypes showed the association of both variants with disease susceptibility. Carrying rs10719 G or rs3742330 A conferred protection against the development of BC under heterozygote comparison, homozygote comparison, and allelic models (Table 2).
Similarly, patients with both DROSHA A and DICER G genotype combinations were associated with the risk of BC (Figure 4).

3.5. Somatic Mutation Rate in Paired Tissue Samples

Paired analysis of tumor/non-tumor tissues per each patient displayed significant differences between the two types of tissue samples (p <0.001). The rates of conversion and somatic mutation load are depicted in Figure 5 For the DROSHA gene, 28 tumor samples (27.2%) acquired a single A allele, while 14 samples (13.6%) showed the conversion of the two alleles from G/G to A/A with a total rate of conversion from G to A of 40.8%. In contrast, 60 patients (58.3) have similar genotypes to the adjacent non-cancer tissues (Figure 5A). Regarding the DICER gene, they exhibited a similar conversion rate (42.7%); indeed, 33 (32%) and 11 (10.7%) tumor samples showed one and two hit mutations from A to G, respectively, while 53 patients (51.5%) had the same genotype of neighboring tissues (Figure 5B). One and six samples represented a reverse direction of allelic conversion in DROSHA and DICER genes. The seven cohorts with the reverse direction exhibited the acquisition of risk alleles in the alternative gene. The de novo acquisition of risky alleles in both genes accounted for 64 women (37.9%) (Figure 5C).

3.6. Assessment of the Prognostic Value of DROSHA and DICER Genotyping in BC Tissues

Univariate analysis showed no significant association of DROSHA and DICER gene polymorphisms with clinical and pathological outcomes (Table 3 and Table 4).
Similarly, no significant difference existed between tumors with and without conversion to risky alleles and their paired adjacent non-cancer samples (Table 5).

3.7. Survival Analysis

Median disease-free survival was 15 months (IQR = 8.0–20.0), and the overall survival time was 17 months (IQR = 14.0–20.0). Comparison between cohorts with and without conversion of risky alleles showed insignificant results. For DROSHA, the median disease-free survival/overall survival times were 15.2 months (95%CI = 12.9–17.4) in the mutant group versus 15.6 (95%CI = 13.7–17.4) months in non-mutant group (p = 0.76) and 19.3 months (95%CI = 18.4–20.1) in the mutant group versus 19.4 (95%CI = 18.6–20.1) months in non-mutant group (p = 0.75), respectively. Regarding DICER SNP, the median disease-free survival and overall survival times were 15.8 months (95%CI = 13.5–17.9) in the mutant group versus 15.2 (95%CI = 13.3–17.1) months in non-mutant group (p = 0.76) and 19.3 months (95%CI = 18.6–20.1) in the mutant group versus 19.3 (95%CI = 18.4–20.1) months in non-mutant group (p = 0.68), respectively.

3.8. Characteristics of the Study Population with Blood Samples

For the second cohort, the blood samples of patients and controls were compared. The characteristics of the study populations are depicted in Table 6. Blood analysis was performed to pinpoint their role in the cancer screening of inherited risk alleles.
The most common presenting symptoms at the time of presentation were breast lump (41.5%) and mastalgia (14.6%), followed by nipple (8.5%) and skin changes (7.5%). Most lesions were presented on the right side (n = 66, 62.3%) and in the upper outer quadrant (n = 52, 49.1%). Of the affected women, 28 (26.4%) presented with multiple masses, and 71% had positive lymph node infiltration (Figure 6).

3.9. Genotype and Allele Frequencies in Blood Samples

Minor allele frequencies were 0.42 for the DROSHA A and 0.37 for the DICER G alleles. As shown in Figure 7, the most common genotype in patients was DROSHA*G/G, accounting for 46.2% (n = 49), whereas in controls, the heterozygote form A/G was the most prevalent (n = 59, 55.7%); p < 0.001. As for the DICER genotype, A/A was the most frequent in patients (n = 49, 46.2%) and controls (n = 56, 52.8%). However, G/G carriers were twice as common as in controls (30.2% vs. 15.1%, p < 0.001).

3.10. Association of DICER and DROSHA Polymorphisms in Blood with BC Risk

Genetic association model analysis showed that cohorts who were carrying DROSHA A/A were at higher risk of developing BC under the recessive model (OR = 6.3, 95%CI = 1.23–8.36, p < 0.001) and homozygote comparison model (OR = 3.2, 95%CI = 1.23–9.36, p < 0.001). In contrast, carrying the heterozygote form (A/G) ameliorates the impact of the risk allele under the heterozygote comparison model (OR = 0.20, 95%CI = 0.09–0.46, p < 0.001) and over-dominant model (OR = 0.14, 95%CI = 0.07–0.31, p < 0.001) (Figure 8A). For DICER gene risk assessment, G/G cohorts had over three times more risk of disease susceptibility under the recessive model (OR = 3.73,95%CI = 1.66–8.38, p = 0.001) and homozygote comparison model (OR = 3.51, 95%CI = 1.5–8.25) (Figure 8B). Carriers of both risk alleles (DROSHA A and DICER G) conferred higher susceptibility for developing BC (OR = 2.18, 95%CI = 1.23–3.89); p = 0.008 (Table 7).

3.11. DICER and DROSHA Polymorphisms as a Prognostic Marker

For the BC cohorts with blood samples, we did not find a significant association with the DROSHA and DICER genotypes or risk alleles with clinical and pathological features in cancer-affected women using univariate and multivariate analyses. Adjusted variables for logistic regression models were age, a family history of cancer, prior breast problems, smoking, body mass index, diabetes, hypertension, and hepatitis C virus infection (Table 8).

4. Discussion

In this study, we investigated the association of two common polymorphisms in the miRNA processing genes DROSHA (rs10719) and DICER (rs3742330) with BC risk and clinical outcomes in an Egyptian population. We found that both SNPs were significantly associated with altered BC susceptibility, with the DROSHA rs10719 A allele and the DICER rs3742330 G allele conferring increased risk. Furthermore, a higher frequency of the risk alleles was detected in breast tumor tissues compared to adjacent normal tissues, suggesting a potential role for these variants in driving miRNA dysregulation during breast tumorigenesis. However, neither SNP showed significant associations with clinicopathological characteristics or survival outcomes.
Initially, in our observations regarding the lateralization of BC, there was a predominance of cases (approximately two-thirds of cases) affecting the right side, although they showed insignificant association with the clinicopathological characteristics of the study population (Table 3, Table 4 and Table 5); this may provide additional insights into the biological factors influencing tumorigenesis. This lateralization could reflect anatomical, hormonal, or environmental factors, which may warrant further investigation [24]. Understanding whether the affected side correlates with specific genetic makeup or tumor biology may enhance our comprehension of BC heterogeneity. Although the predominance of right-sided tumors does not alter our primary findings regarding the associations of the studied variants with BC risk, it suggests an avenue for future research to explore the implications of tumor location concerning genetic predispositions [25].
DROSHA and DICER are essential endoribonucleases involved in the biogenesis of microRNAs (miRNAs), which play pivotal roles in regulating gene expression and various cellular processes [26]. DROSHA processes primary miRNA transcripts (pri-miRNAs) into precursor miRNAs (pre-miRNAs) in the nucleus. The functioning of DROSHA is vital as it ensures that the remaining pre-miRNA is appropriately sized for subsequent processing by DICER. Any alterations in DROSHA activity, such as those potentially conferred by the rs10719 variant, might disrupt this initial step of miRNA maturation, leading to aberrant levels of downstream miRNAs [27]. DICER, on the other hand, processes pre-miRNAs into mature miRNAs in the cytoplasm and is also involved in the generation of small interfering RNAs (siRNAs) [28]. The DICER rs3742330 variant may influence DICER’s enzymatic efficiency, affecting the quantity and quality of generated mature miRNAs. This disruption can have significant downstream consequences, including the altered expression of target mRNAs involved in critical cellular pathways such as apoptosis, proliferation, and differentiation [29]. For instance, the dysregulation of miRNAs processed through these pathways can lead to the misregulation of oncogenes and tumor suppressor genes, thereby promoting tumorigenesis. Specific miRNAs that DROSHA and DICER can potentially impact include oncogenic miRNAs like miR-21 and tumor suppressor miRNAs such as let-7. Elevated levels of oncogenic miRNAs can facilitate cancer cell proliferation and survival by inhibiting pro-apoptotic signals. Conversely, a deficit in tumor suppressor miRNAs can lead to the inhibition of oncogenes, further driving cancer progression [30]. Thus, understanding the genetic variants in miRNA processing genes like DROSHA and DICER not only provides insights into BC susceptibility but also highlights the importance of downstream miRNA-mediated regulatory networks in the development and progression of BC.
Our findings on the association of DROSHA rs10719 with increased BC risk align with a previous study by Jiang et al., which reported a higher frequency of the AA genotype in patients with BC compared to healthy controls in a Chinese population [16]. Also, it has been associated with colorectal and bladder cancers in previous reports [31,32]. Similarly, the association of the DICER rs3742330 G allele with cancer risk has been observed in several other malignancies, including colorectal [31,33], gastric [34,35], hepatocellular [36], prostate [37], larynx [38], and thyroid [39] cancers, as well as precancerous cervical lesion [40].
The functional consequences of these SNPs for miRNA processing efficiency and target gene regulation remain to be fully elucidated. However, it has been proposed that the rs10719 variant may affect DROSHA’s mRNA stability and alter its subcellular localization [41]. Interestingly, this SNP is located in the miR-27b binding site within DROSHA 3′UTR [42], which has been proven to be oncogenic in MCF7 BC cells and may have tumor suppressive activity under certain conditions, as evidenced by a “CRISPR/Cas9 deletion study” conducted by Hannafon et al. [43]. At the same time, the rs3742330 variant may influence DICER mRNA expression and enzymatic activity [42]. As this SNP is located within the potential target sequences of miR-632, miR-3622a-5p, and miR-5582-5p, it may affect cellular processes like apoptosis, cell growth, and migration/invasion [44]. Mir-632 was found to be a putative epigenetic down-regulator of DNAJB6, a constitutive member of the heat shock protein 40 family, which supports BC oncogenesis and progression [45]. MiR-3622b-5p has been reported to impact the Her2-positive BC cell line negatively, and miR-5582-5p, via the long noncoding RNA LUCAT1/miR-5582-3p/TCF7L2 axis, was associated with the regulatory mechanisms of BC stemness [46]. The DICER rs3742330 G variant enhances the affinity of these microRNAs for the DICER 3′ UTR, reducing DICER expression. This decrease results in diminished RNA cleavage and translation repression, along with heightened cell migration, invasion, and angiogenesis, all of which may impact the progression of BC [47]. This could support the present study findings that carrying the G allele and the GG genotype confer higher susceptibility to developing BC in our cohort.
The higher frequency of risk alleles in tumor tissues than matched normal tissues suggests that the studied variants may undergo positive selection during BC development, potentially conferring a growth advantage to tumor cells. This finding is consistent with the concept of “onco-miRNAs”, whereby dysregulated miRNA networks can promote various hallmarks of cancer, such as sustained proliferation, the evasion of apoptosis, and metastatic dissemination [48]. However, it is essential to note that the observed differences in genotype frequencies between tumor and normal tissues could also be influenced by factors such as tumor purity, genetic heterogeneity, and tissue-specific mosaicism [49,50,51,52].
The lack of significant associations between the studied SNPs and clinicopathological features or survival outcomes in our cohort suggests that these variants may primarily influence BC initiation rather than progression [52,53,54]. However, it is also possible that the impacts of these SNPs on clinical outcomes may be modulated by other genetic, epigenetic, or environmental factors not accounted for in our analysis [55,56]. Additionally, the relatively small sample size and short follow-up duration of our study may have limited our ability to detect subtle associations with survival endpoints.
Our study has several strengths, including the comprehensive evaluation of both blood and tissue samples, including age-matched healthy controls, and the detailed clinicopathological annotation of BC cases. However, we acknowledge certain limitations that should be considered when interpreting our results. First, our study was conducted in a single institution and may not fully represent the Egyptian population. Second, we did not have information on potential confounding factors such as lifestyle habits and environmental exposures, which could have influenced the observed associations. Third, we did not perform functional experiments to validate the biological consequences of the studied SNPs on miRNA processing efficiency or target gene expression.

5. Conclusions

Our study presents evidence linking the DROSHA rs10719 and DICER rs3742330 polymorphisms to an elevated risk of breast cancer in an Egyptian cohort, suggesting that these variants may play a role in miRNA dysregulation during breast tumorigenesis. These findings underscore the significance of exploring genetic variability within miRNA processing pathways and its influence on cancer susceptibility and progression. To validate our results and clarify the associated molecular mechanisms, further large-scale research across diverse populations, along with functional analyses of the identified variants, is essential. Ultimately, enhancing our understanding of the genetic factors contributing to miRNA dysregulation in breast cancer could facilitate the development of novel risk assessment tools, prognostic biomarkers, and targeted therapeutic interventions.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/cimb46090602/s1, Table S1: Bibliography screening of DROSHA and DICER gene polymorphisms in cancer; Table S2: Characteristics of studies screened for identifying the influence of DROSHA and DICER genotypes on cancer risk; Table S3: The association between DROSHA rs10719 and DICER rs3742330 polymorphisms and cancer risk. References [57,58,59,60,61,62,63,64,65] are cited in the Supplementary Materials.

Author Contributions

Conceptualization, A.A.M.S., E.A.T. and M.S.F.; Data curation, A.A.M.S., S.W.K. and A.A.A.S.; Formal analysis, E.A.T.; Funding acquisition, M.S.F.; Investigation, M.H.M.; Methodology, E.A.T., M.S.F. and M.H.M.; Resources, A.A.M.S., E.A.A., S.W.K., A.I.A., N.A.B., A.A.A.S. and M.H.M.; Software, E.A.T.; Validation, E.A.A., A.I.A., N.A.B. and A.A.A.S.; Visualization, E.A.T.; Writing—original draft, E.A.T. and M.H.M.; Writing—review and editing, A.A.M.S., E.A.A., S.W.K., A.I.A., N.A.B., A.A.A.S. and M.S.F. All authors have read and agreed to the published version of the manuscript.

Funding

The deanship of Scientific Research at Northern Border University, Arar, KSA, funded this research work through the project NBU-FFR-2024–1442-06.

Institutional Review Board Statement

This study was conducted following the Declaration of Helsinki and approved by the Ethics Committee of Suez Canal University (protocol code 5027, 29 September 2022).

Informed Consent Statement

Informed consent was obtained from the subjects who provided blood samples in this study. However, in the case of working on FFPE tissue samples, patient consent was waived due to the retrospective nature of this type of work.

Data Availability Statement

The original contributions presented in this study are included in the article/Supplementary Materials; further inquiries can be directed to the corresponding authors.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. The allele frequencies of DROSHA rs10719 and DICER rs3742330 variants in different populations. (A) DROSHA (B) DICER. AMR: American; AFR: African; EAS: East Asian; EUR: European; SAS: South Asians. Data source: 1000 Genomes Project Phase 3 allele frequencies [Ensembl.org] (last accessed on 20 March 2024)”. *rs: reference sequence.
Figure 1. The allele frequencies of DROSHA rs10719 and DICER rs3742330 variants in different populations. (A) DROSHA (B) DICER. AMR: American; AFR: African; EAS: East Asian; EUR: European; SAS: South Asians. Data source: 1000 Genomes Project Phase 3 allele frequencies [Ensembl.org] (last accessed on 20 March 2024)”. *rs: reference sequence.
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Figure 2. Analysis of the DROSHA gene structure and its associated 3D interactions with other genes and variants mediated by chromatin loops. (A) The DROSHA gene is on the short arm of chromosome 5 on the forward strand, following the ‘GRCh38.p14’ assembly. The rs10719 variant is positioned at 5:31,401,340 (highlighted), where the ancestral nucleotide ‘A’ is replaced by the alternative (minor) allele ‘G’ (https://www.ncbi.nlm.nih.gov/snp/rs10719). (B) A Circos plot illustrating the chromatin loops and other 2D characteristics related to the variant of interest, generated using 3DSNP 2.0 (https://omic.tech/3dsnpv2/). The plot displays, from the outer edge to the inner section, the chromatin states, annotated genes, the current SNP of interest and associated SNPs, and 3D chromatin interactions. A color key corresponding to the chromatin states and loops for twelve distinct cell types has been detailed previously [23]. (C) DROSHA’s subcellular localization can be accessed via https://www.proteinatlas.org/ENSG00000113360-DROSHA/subcellular. (D) The conservation score for the variant of interest is recorded as 2.277, derived from multiple alignments of vertebrate (n = 46) and mammalian (n = 33) genomes. All databases were last accessed on 30 March 2024.
Figure 2. Analysis of the DROSHA gene structure and its associated 3D interactions with other genes and variants mediated by chromatin loops. (A) The DROSHA gene is on the short arm of chromosome 5 on the forward strand, following the ‘GRCh38.p14’ assembly. The rs10719 variant is positioned at 5:31,401,340 (highlighted), where the ancestral nucleotide ‘A’ is replaced by the alternative (minor) allele ‘G’ (https://www.ncbi.nlm.nih.gov/snp/rs10719). (B) A Circos plot illustrating the chromatin loops and other 2D characteristics related to the variant of interest, generated using 3DSNP 2.0 (https://omic.tech/3dsnpv2/). The plot displays, from the outer edge to the inner section, the chromatin states, annotated genes, the current SNP of interest and associated SNPs, and 3D chromatin interactions. A color key corresponding to the chromatin states and loops for twelve distinct cell types has been detailed previously [23]. (C) DROSHA’s subcellular localization can be accessed via https://www.proteinatlas.org/ENSG00000113360-DROSHA/subcellular. (D) The conservation score for the variant of interest is recorded as 2.277, derived from multiple alignments of vertebrate (n = 46) and mammalian (n = 33) genomes. All databases were last accessed on 30 March 2024.
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Figure 3. Structural analysis of the DICER gene and its associated 3D interactions with other genes and variants mediated by chromatin loops. (A) The DICER gene is situated on the long arm of chromosome 14 on the reverse strand, aligning with the ‘GRCh38.p14’ assembly. The rs3742330 variant is found at position 14: 95,087,025 (highlighted), where the ancestral nucleotide ‘A’ is replaced by the alternative (minor) allele ‘G’ (https://www.ncbi.nlm.nih.gov/snp/rs3742330). (B) A Circos plot displaying the chromatin loops and other 2D features related to the variant of interest was created using “3DSNP 2.0 (https://omic.tech/3dsnpv2/)”. The plot illustrates, from the outermost section to the inner, the chromatin states, annotated genes, the currently examined SNP and its associated SNPs, and 3D chromatin interactions. The color key for chromatin states and loops across twelve different cell types is provided in a previous work [23]. (C) Information regarding the subcellular distribution of DICER can be accessed at https://www.proteinatlas.org/ENSG00000100697-DICER1/subcellular. (D) The conservation score for the variant of interest is reported as −0.023, derived from multiple alignments of vertebrate (n = 46) and mammalian (n = 33) genomes. All databases were last accessed on 30 March 2024.
Figure 3. Structural analysis of the DICER gene and its associated 3D interactions with other genes and variants mediated by chromatin loops. (A) The DICER gene is situated on the long arm of chromosome 14 on the reverse strand, aligning with the ‘GRCh38.p14’ assembly. The rs3742330 variant is found at position 14: 95,087,025 (highlighted), where the ancestral nucleotide ‘A’ is replaced by the alternative (minor) allele ‘G’ (https://www.ncbi.nlm.nih.gov/snp/rs3742330). (B) A Circos plot displaying the chromatin loops and other 2D features related to the variant of interest was created using “3DSNP 2.0 (https://omic.tech/3dsnpv2/)”. The plot illustrates, from the outermost section to the inner, the chromatin states, annotated genes, the currently examined SNP and its associated SNPs, and 3D chromatin interactions. The color key for chromatin states and loops across twelve different cell types is provided in a previous work [23]. (C) Information regarding the subcellular distribution of DICER can be accessed at https://www.proteinatlas.org/ENSG00000100697-DICER1/subcellular. (D) The conservation score for the variant of interest is reported as −0.023, derived from multiple alignments of vertebrate (n = 46) and mammalian (n = 33) genomes. All databases were last accessed on 30 March 2024.
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Figure 4. Genotype combination analysis of DICER and DROSHA genes in tissues of BC women. (A) Distribution of DROSHA genotypes in patients and controls. (B) Distribution of DICER genotypes in patients and controls. (C) Distribution of combined DROSHA and DICER genotypes in patients and controls. *rs: reference sequence.
Figure 4. Genotype combination analysis of DICER and DROSHA genes in tissues of BC women. (A) Distribution of DROSHA genotypes in patients and controls. (B) Distribution of DICER genotypes in patients and controls. (C) Distribution of combined DROSHA and DICER genotypes in patients and controls. *rs: reference sequence.
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Figure 5. Somatic mutation analyses of DICER and DROSHA polymorphisms in paired tissues of women with BC. (A) Genotype alteration of the DROSHA gene in cancer and non-cancer tissues. The A allele is considered the risky variant. (B) Genotype alteration of DICER gene in cancer and non-cancer tissues. G allele is considered the risky variant. (C) Genotype alteration of combined DROSHA and DICER genes in cancer/non-cancer tissues. *rs: reference sequence.
Figure 5. Somatic mutation analyses of DICER and DROSHA polymorphisms in paired tissues of women with BC. (A) Genotype alteration of the DROSHA gene in cancer and non-cancer tissues. The A allele is considered the risky variant. (B) Genotype alteration of DICER gene in cancer and non-cancer tissues. G allele is considered the risky variant. (C) Genotype alteration of combined DROSHA and DICER genes in cancer/non-cancer tissues. *rs: reference sequence.
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Figure 6. Clinical presentation of BC women with blood samples (n = 106). (A) Counts of affected sides. (B) Counts of affected locations. (C) Counts of patients according to the number of masses at the time of presentation. (D) The different histopathological types. (E)Pathology-related data of patients with BC and provided blood samples. G3: Grade 3, T3-4: stage 3-4, LNM: Lymph node metastasis, LVI: lymphovascular infiltration.
Figure 6. Clinical presentation of BC women with blood samples (n = 106). (A) Counts of affected sides. (B) Counts of affected locations. (C) Counts of patients according to the number of masses at the time of presentation. (D) The different histopathological types. (E)Pathology-related data of patients with BC and provided blood samples. G3: Grade 3, T3-4: stage 3-4, LNM: Lymph node metastasis, LVI: lymphovascular infiltration.
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Figure 7. The genotype and allele frequencies of the DICER and DROSHA genes in blood samples of BC and non-cancer women: (A) DROSHA rs10719; (B) DICER rs3742330. Pie charts represented the percentage of each allele in the overall cohorts (cases and controls). The bar chart showed the frequencies (counts) of cohorts per allele or genotype. A two-sided Chi-square test was used. Significance was set at p < 0.05. *rs: reference sequence.
Figure 7. The genotype and allele frequencies of the DICER and DROSHA genes in blood samples of BC and non-cancer women: (A) DROSHA rs10719; (B) DICER rs3742330. Pie charts represented the percentage of each allele in the overall cohorts (cases and controls). The bar chart showed the frequencies (counts) of cohorts per allele or genotype. A two-sided Chi-square test was used. Significance was set at p < 0.05. *rs: reference sequence.
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Figure 8. Genetic association models for disease risk assessment: (A) DROSHA rs10719; (B) DICER rs3742330. Multivariate regression models were performed and shown as odds ratios (ORs) and 95% confidence intervals (95%CI). Adjusted variables were patient age at diagnosis, marital status, occupation, a family history of cancer, prior breast problems, smoking, body mass index, diabetes, hypertension, and hepatitis C virus infection. The red line is a risky genotype, the blue line is a protective genotype, and the black line is insignificant. ** indicate significance at p-value < 0.05.
Figure 8. Genetic association models for disease risk assessment: (A) DROSHA rs10719; (B) DICER rs3742330. Multivariate regression models were performed and shown as odds ratios (ORs) and 95% confidence intervals (95%CI). Adjusted variables were patient age at diagnosis, marital status, occupation, a family history of cancer, prior breast problems, smoking, body mass index, diabetes, hypertension, and hepatitis C virus infection. The red line is a risky genotype, the blue line is a protective genotype, and the black line is insignificant. ** indicate significance at p-value < 0.05.
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Table 1. Patients with FFPE tissue sample demographics and clinical features.
Table 1. Patients with FFPE tissue sample demographics and clinical features.
CharacteristicsLevelsTissue Cohorts (n = 103)
Age<45 years51 (49.5)
≥45 years52 (50.5)
Affected sideLeft38 (36.9)
Right65 (63.1)
SiteRetro24 (23.3)
LOQ10 (9.7)
UIQ23 (22.3)
UOQ46 (44.7)
Number of massesSingle84 (81.6)
Multiple19 (18.4)
Histopathological typeDuct carcinoma39 (37.9)
Lobular carcinoma23 (22.3)
Invasive medullary carcinoma17 (16.5)
Mucinous carcinoma13 (12.6)
Tubular carcinoma6 (5.8)
Metaplastic carcinoma5 (4.9)
GradeG282 (79.6)
G321 (20.4)
T stageT253 (51.5)
T322 (21.4)
T428 (27.2)
N stageN022 (21.4)
N138 (36.9)
N237 (35.9)
N36 (5.8)
M stageM047 (45.6)
M143 (41.7)
Mx13 (12.6)
LVINegative54 (52.4)
Positive49 (47.6)
Skin infiltrationNegative85 (82.5)
Positive18 (17.5)
Clinical stageIIA16 (15.5)
IIB15 (14.6)
IIIA13 (12.6)
IIIB16 (15.5)
IV43 (41.7)
NPIGood48 (46.6)
Poor55 (53.4)
ESMOLow risk36 (35)
High risk67 (65)
ER/PRNegative39 (37.9)
Positive64 (62.1)
HER2+Negative81 (78.6)
Positive22 (21.4)
Molecular subtypeLuminal A50 (48.5)
Luminal B14 (13.6)
HER2+8 (7.8)
TNBC31 (30.1)
IHPGood64 (62.1)
Moderate31 (30.1)
Poor8 (7.8)
RecurrenceNegative48 (46.6)
Positive55 (53.4)
SurvivalAlive42 (40.8)
Dead61 (59.2)
Data are presented as counts (percentages). LVI: lymphovascular infiltration; ER/PR: estrogen and progesterone receptor; HER2+: HER2/neu receptor; NPI: Nottingham Prognostic Index, calculated as [0.2 × tumor size in cm] + tumor grade [1,2,3] + lymph node stage [1–3, according to stages A–C]; ESMO: European Society of Medical Oncology; IHPI: Immunohistochemical Prognostic Index estimated based on the three-receptor status (HER2, ER, and PR). A two-sided Chi-square test was used. Significance was set at p < 0.05.
Table 2. Genotype and allele frequencies of DICER and DROSHA genes in tissues of BC women.
Table 2. Genotype and allele frequencies of DICER and DROSHA genes in tissues of BC women.
SNP IDVariantControls (n = 103)Patients (n = 103)p-ValueAdjusted OR (95%CI)
DROSHA rs10719Genotypes
A/A23 (22.3%)57 (55.3%)<0.001Reference
A/G51 (49.5%)39 (37.9%) 0.31 (0.16–0.58)
G/G29 (28.2%)7 (6.8%) 0.10 (0.04–0.25)
Alleles
A97 (48)153 (74)<0.001Reference
G107 (52)53 (26) 0.32 (0.21–0.48)
DICER rs3742330Genotypes
G/G18 (17.5%)51 (49.5%)<0.001Reference
A/G51 (49.5%)34 (33%) 0.23 (0.11–0.46)
A/A34 (33%)18 (17.5%) 0.18 (0.08–0.40)
Alleles
G87 (42)136 (66)<0.001Reference
A119 (58)70 (34) 0.37 (0.25–0.56)
DROSHA (A/G)|DICER (A/G)Allele combination
A|G0.18990.4724<0.001Reference
A|A0.2810.2703 0.43 (0.23–0.81)
G|G0.23250.1878 0.34 (0.17–0.72)
G|A0.29660.0695 0.10 (0.05–0.21)
Data are presented as counts (percentages) or proportions. A two-sided Chi-square test was used. The regression model was adjusted by patient age at diagnosis. Heterozygote comparison, homozygote comparison, and allelic models are shown as odds ratios (ORs) and 95% confidence intervals (95%CIs). Bold values indicate significance at p < 0.05.
Table 3. Association of the DROSHA variant with clinical and pathological features.
Table 3. Association of the DROSHA variant with clinical and pathological features.
CharacteristicsLevelsA/A Genotype (n = 57)A/G Genotype (n = 39)G/G Genotype (n = 7)p-Value
Age<45 years30 (52.6)19 (48.7)2 (28.6)0.48
≥45 years27 (47.4)20 (51.3)5 (71.4)
Affected sideLeft9 (31)20 (39.2)9 (39.1)0.74
Right20 (69)31 (60.8)14 (60.9)
SiteRetro12 (21.1)11 (28.2)1 (14.3)0.90
LOQ5 (8.8)4 (10.3)1 (14.3)
UIQ15 (26.3)7 (17.9)1 (14.3)
UOQ25 (43.9)17 (43.6)4 (57.1)
Number of massesSingle46 (80.7)31 (79.5)7 (100)0.42
Multiple 11 (19.3)8 (20.5)0 (0)
Histopathological typeDuct carcinoma 22 (38.6)15 (38.5)2 (28.6)0.91
Lobular carcinoma13 (22.8)8 (20.5)2 (28.6)
Invasive medullary carcinoma9 (15.8)6 (15.4)2 (28.6)
Mucinous carcinoma 6 (10.5)6 (15.4)1 (14.3)
Tubular carcinoma5 (8.8)1 (2.6)0 (0)
Metaplastic carcinoma2 (3.5)3 (7.7)0 (0)
Grade G249 (86)28 (71.8)5 (71.4)0.21
G38 (14)11 (28.2)2 (28.6)
T stageT234 (59.6)15 (38.5)4 (57.1)0.35
T310 (17.5)11 (28.2)1 (14.3)
T413 (22.8)13 (33.3)2 (28.6)
N stageN013 (22.8)8 (20.5)1 (14.3)0.71
N119 (33.3)17 (43.6)2 (28.6)
N223 (40.4)11 (28.2)3 (42.9)
N32 (3.5)3 (7.7)1 (14.3)
M stage M027 (47.4)19 (48.7)1 (14.3)0.19
M121 (36.8)18 (46.2)4 (57.1)
Mx9 (15.8)2 (5.1)2 (28.6)
LVINegative29 (50.9)22 (56.4)3 (42.9)0.75
Positive 28 (49.1)17 (43.6)4 (57.1)
Skin infiltrationNegative48 (84.2)31 (79.5)6 (85.7)0.81
Positive 9 (15.8)8 (20.5)1 (14.3)
Clinical stageIIA10 (17.5)5 (12.8)1 (14.3)0.87
IIB9 (15.8)6 (15.4)0 (0)
IIIA9 (15.8)3 (7.7)1 (14.3)
IIIB8 (14)7 (17.9)1 (14.3)
IV21 (36.8)18 (46.2)4 (57.1)
NPIGood27 (47.4)19 (48.7)2 (28.6)0.61
Poor30 (52.6)20 (51.3)5 (71.4)
ESMOLow risk20 (35.1)14 (35.9)2 (28.6)0.93
High risk37 (64.9)25 (64.1)5 (71.4)
ER/PRNegative23 (40.4)13 (33.3)3 (42.9)0.75
Positive34 (59.6)26 (66.7)4 (57.1)
HER2+Negative45 (78.9)31 (79.5)5 (71.4)0.89
Positive12 (21.1)8 (20.5)2 (28.6)
Molecular subtypeLuminal A27 (47.4)20 (51.3)3 (42.9)0.97
Luminal B7 (12.3)6 (15.4)1 (14.3)
HER2+5 (8.8)2 (5.1)1 (14.3)
TNBC18 (31.6)11 (28.2)2 (28.6)
IHPGood34 (59.6)26 (66.7)4 (57.1)0.89
Moderate18 (31.6)11 (28.2)2 (28.6)
Poor5 (8.8)2 (5.1)1 (14.3)
RecurrenceNegative28 (49.1)17 (43.6)3 (42.9)0.85
Positive29 (50.9)22 (56.4)4 (57.1)
SurvivalAlive25 (43.9)15 (38.5)2 (28.6)0.69
Dead32 (56.1)24 (61.5)5 (71.4)
Data are presented as counts (percentage). LVI: lymphovascular infiltration; ER/PR: estrogen and progesterone receptors; HER2+: HER2/neu receptor; NPI: Nottingham Prognostic Index, calculated as [0.2 × tumor size in cm] + tumor grade [1,2,3] + lymph node stage [1–3, according to stages A–C]; ESMO: European Society of Medical Oncology; IHPI: Immunohistochemical Prognostic Index estimated based on the three-receptor status (HER2, ER, and PR). A two-sided Chi-square test was used. Significance was set at p < 0.05.
Table 4. Association of the DICER polymorphism with clinical and pathological features.
Table 4. Association of the DICER polymorphism with clinical and pathological features.
CharacteristicsLevelsA/A Genotype (n = 18)A/G Genotype (n = 34)G/G Genotype (n = 51)p-Value
Age<45 years10 (55.6)18 (52.9)23 (45.1)0.66
≥45 years8 (44.4)16 (47.1)28 (54.9)
Affected sideLeft11 (32.4)20 (39.2)7 (38.9)0.79
Right23 (67.6)31 (60.8)11 (61.1)
SiteRetro2 (11.1)9 (26.5)13 (25.5)0.79
LOQ2 (11.1)2 (5.9)6 (11.8)
UIQ4 (22.2)7 (20.6)12 (23.5)
UOQ10 (55.6)16 (47.1)20 (39.2)
Number of massesSingle15 (83.3)26 (76.5)43 (84.3)0.64
Multiple 3 (16.7)8 (23.5)8 (15.7)
Histopathological typeDuct carcinoma 8 (44.4)11 (32.4)20 (39.2)0.95
Lobular carcinoma4 (22.2)9 (26.5)10 (19.6)
Invasive medullary carcinoma2 (11.1)5 (14.7)10 (19.6)
Mucinous carcinoma 2 (11.1)5 (14.7)6 (11.8)
Tubular carcinoma2 (11.1)2 (5.9)2 (3.9)
Metaplastic carcinoma0 (0)2 (5.9)3 (5.9)
Grade G216 (88.9)24 (70.6)42 (82.4)0.24
G32 (11.1)10 (29.4)9 (17.6)
T stageT29 (50)15 (44.1)29 (56.9)0.29
T32 (11.1)11 (32.4)9 (17.6)
T47 (38.9)8 (23.5)13 (25.5)
N stageN05 (27.8)6 (17.6)11 (21.6)0.52
N17 (38.9)10 (29.4)21 (41.2)
N26 (33.3)14 (41.2)17 (33.3)
N30 (0)4 (11.8)2 (3.9)
M stage M010 (55.6)14 (41.2)23 (45.1)0.75
M15 (27.8)16 (47.1)22 (43.1)
Mx3 (16.7)4 (11.8)6 (11.8)
LVINegative11 (61.1)14 (41.2)29 (56.9)0.26
Positive 7 (38.9)20 (58.8)22 (43.1)
Skin infiltrationNegative15 (83.3)27 (79.4)43 (84.3)0.84
Positive 3 (16.7)7 (20.6)8 (15.7)
Clinical stageIIA4 (22.2)4 (11.8)8 (15.7)0.43
IIB1 (5.6)4 (11.8)10 (19.6)
IIIA3 (16.7)6 (17.6)4 (7.8)
IIIB5 (27.8)4 (11.8)7 (13.7)
IV5 (27.8)16 (47.1)22 (43.1)
NPIGood10 (55.6)12 (35.3)26 (51)0.26
Poor8 (44.4)22 (64.7)25 (49)
ESMOLow risk6 (33.3)10 (29.4)20 (39.2)0.64
High risk12 (66.7)24 (70.6)31 (60.8)
ER/PRNegative6 (33.3)17 (50)16 (31.4)0.20
Positive12 (66.7)17 (50)35 (68.6)
HER2+Negative12 (66.7)29 (85.3)40 (78.4)0.30
Positive6 (33.3)5 (14.7)11 (21.6)
Molecular subtypeLuminal A7 (38.9)15 (44.1)28 (54.9)0.29
Luminal B5 (27.8)2 (5.9)7 (13.7)
HER2+1 (5.6)3 (8.8)4 (7.8)
TNBC5 (27.8)14 (41.2)12 (23.5)
IHPGood12 (66.7)17 (50)35 (68.6)0.47
Moderate5 (27.8)14 (41.2)12 (23.5)
Poor1 (5.6)3 (8.8)4 (7.8)
RecurrenceNegative11 (61.1)14 (41.2)23 (45.1)0.37
Positive7 (38.9)20 (58.8)28 (54.9)
SurvivalAlive7 (38.9)15 (44.1)20 (39.2)0.89
Dead11 (61.1)19 (55.9)31 (60.8)
Data are presented as counts (percentage). A two-sided Chi-square test was used. Significance was set at p < 0.05.
Table 5. Impacts of DROSHA and DICER mutagenesis in tissue samples with clinicopathological features.
Table 5. Impacts of DROSHA and DICER mutagenesis in tissue samples with clinicopathological features.
CharacteristicsLevelsDROSHADICER
No Conversion to A (n = 61)Acquire New A Allele (n = 42)p-ValueNo Conversion to G (n = 59)Acquire New G Allele (n = 44)p-Value
Age<45 years29 (47.5)22 (52.4)0.6927 (45.8)24 (54.5)0.43
≥45 years32 (52.5)20 (47.6) 32 (54.2)20 (45.5)
Affected sideLeft26 (42.6)12 (28.6)0.2123 (39)15 (34.1)0.68
Right35 (57.4)30 (71.4) 36 (61)29 (65.9)
Number of massesSingle53 (86.9)31 (73.8)0.1245 (76.3)39 (88.6)0.13
Multiple 8 (13.1)11 (26.2) 14 (23.7)5 (11.4)
Histopathological typeDuct carcinoma 23 (37.7)16 (38.1)0.3523 (39)16 (36.4)0.91
Lobular 11 (18)12 (28.6) 12 (20.3)11 (25)
Invasive medullary 12 (19.7)5 (11.9) 11 (18.6)6 (13.6)
Mucinous 10 (16.4)3 (7.1) 7 (11.9)6 (13.6)
Tubular 2 (3.3)4 (9.5) 4 (6.8)2 (4.5)
Metaplastic 3 (4.9)2 (4.8) 2 (3.4)3 (6.8)
Grade G245 (73.8)37 (88.1)0.0947 (79.7)35 (79.5)0.98
G316 (26.2)5 (11.9) 12 (20.3)9 (20.5)
T stageT231 (50.8)22 (52.4)0.2626 (44.1)27 (61.4)0.15
T316 (26.2)6 (14.3) 13 (22)9 (20.5)
T414 (23)14 (33.3) 20 (33.9)8 (18.2)
N stageN012 (19.7)10 (23.8)0.6313 (22)9 (20.5)0.85
N1–349 (80.3)32 (76.2) 46 (78)35 (79.5)
M stage M027 (44.3)20 (47.6)0.4529 (49.2)18 (40.9)0.71
M128 (45.9)15 (35.7) 23 (39)20 (45.5)
Mx6 (9.8)7 (16.7) 7 (11.9)6 (13.6)
LVINegative33 (54.1)21 (50)0.6932 (54.2)22 (50)0.67
Positive 28 (45.9)21 (50) 27 (45.8)22 (50)
Skin infiltrationNegative53 (86.9)32 (76.2)0.1946 (78)39 (88.6)0.16
Positive 8 (13.1)10 (23.8) 13 (22)5 (11.4)
Clinical stageIIA10 (16.4)6 (14.3)0.708 (13.6)8 (18.2)0.68
IIB9 (14.8)6 (14.3) 8 (13.6)7 (15.9)
IIIA6 (9.8)7 (16.7) 9 (15.3)4 (9.1)
IIIB8 (13.1)8 (19) 11 (18.6)5 (11.4)
IV28 (45.9)15 (35.7) 23 (39)20 (45.5)
NPIGood28 (45.9)20 (47.6)0.8629 (49.2)19 (43.2)0.55
Poor33 (54.1)22 (52.4) 30 (50.8)25 (56.8)
ESMOLow risk23 (37.7)13 (31)0.5321 (35.6)15 (34.1)0.87
High risk38 (62.3)29 (69) 38 (64.4)29 (65.9)
ER/PRPositive43 (70.5)21 (50)0.04136 (61)28 (63.6)0.83
HER2+Positive13 (21.3)9 (21.4)0.9812 (20.3)10 (22.7)0.81
Molecular subtypeLuminal A35 (57.4)15 (35.7)0.0927 (45.8)23 (52.3)0.48
Luminal B8 (13.1)6 (14.3) 9 (15.3)5 (11.4)
HER2+5 (8.2)3 (7.1) 3 (5.1)5 (11.4)
TNBC13 (21.3)18 (42.9) 20 (33.9)11 (25)
IHPGood43 (70.5)21 (50)0.0636 (61)28 (63.6)0.37
Moderate13 (21.3)18 (42.9) 20 (33.9)11 (25)
Poor5 (8.2)3 (7.1) 3 (5.1)5 (11.4)
RecurrenceNegative29 (47.5)19 (45.2)0.8427 (45.8)21 (47.7)0.84
Positive32 (52.5)23 (54.8) 32 (54.2)23 (52.3)
SurvivalAlive24 (39.3)18 (42.9)0.8325 (42.4)17 (38.6)0.70
Dead37 (60.7)24 (57.1) 34 (57.6)27 (61.4)
Data are presented as counts (percentages). LVI: lymphovascular infiltration; ER/PR: estrogen and progesterone receptors; HER2+: HER2/neu receptor, NPI: Nottingham Prognostic Index, calculated as [0.2 × tumor size in cm] + tumor grade [1,2,3] + lymph node stage [1–3, according to stages A–C]; ESMO: European Society of Medical Oncology; IHPI: Immunohistochemical Prognostic Index estimated based on the three-receptor status (HER2, ER, and PR). A two-sided Chi-square test was used. The bold value indicates significance at p < 0.05.
Table 6. The demographics and comorbidities of the study groups.
Table 6. The demographics and comorbidities of the study groups.
Demographic DataTotal (n = 212)Controls (n = 106)Patients (n = 106)p-Value
Age <45 years103 (48.6)49 (46.2)54 (50.9)0.58
≥45 years109 (51.4)57 (53.8)52 (49.1)
Residence Rural110 (51.9)47 (44.3)63 (59.4)0.039
Urban102 (48.1)59 (55.7)43 (40.6)
Marital statusDivorced44 (20.8)25 (23.6)19 (17.9)0.09
Married121 (57.1)64 (60.4)57 (53.8)
Single 47 (22.2)17 (16)30 (28.3)
Occupation Housewife140 (66)64 (60.4)76 (71.7)0.07
Retired 3 (1.4)3 (2.8)0 (0)
Worker 69 (32.5)39 (36.8)30 (28.3)
Family history of cancerNegative179 (84.4)106 (100)73 (68.9)0.215
Positive33 (15.6)0 (0)33 (31.1)
Breast problemsNegative204 (96.2)106 (100)98 (92.5)0.007
Positive8 (3.8)0 (0)8 (7.5)
Smoking Negative196 (92.5)101 (95.3)95 (89.6)0.192
Positive16 (7.5)5 (4.7)11 (10.4)
BMI, Kg/m2Underweight12 (5.7)0 (0)12 (11.3)0.002
Normal weight50 (23.6)23 (21.7)27 (25.5)
Overweight65 (30.7)41 (38.7)24 (22.6)
Obesity70 (33)35 (33)35 (33)
Morbid obesity15 (7.1)7 (6.6)8 (7.5)
Diabetes mellitusNegative153 (72.2)73 (68.9)80 (75.5)0.358
Positive59 (27.8)33 (31.1)26 (24.5)
HypertensionNegative198 (93.4)99 (93.4)99 (93.4)1.000
Positive14 (6.6)7 (6.6)7 (6.6)
HCV cirrhosisNegative200 (94.3)98 (92.5)102 (96.2)0.374
Positive12 (5.7)8 (7.5)4 (3.8)
Data are presented as counts (percentages). A two-sided Chi-square test was used. Bold values indicate significance at p < 0.05. BMI: body mass index; HCV: hepatitis C virus.
Table 7. Combined genotype association with disease risk.
Table 7. Combined genotype association with disease risk.
DROSHADICERTotal Controls Patients Adjusted OR (95%CI)p-Value
1GA0.43360.43110.41571---
2AG0.21430.1340.27422.18 (1.23–3.89)0.008
3AA0.20080.25760.16450.72 (0.38–1.38)0.33
4GG0.15130.17740.14561.02 (0.54–1.94)0.94
Global haplotype association of DROSHA rs10719 and DICER rs3742330; p-value: 0.013. Multivariate regression models were used and are shown as the odds ratio (OR) and 95% confidence interval (95%CI). Adjusted variables were patient age at diagnosis, marital status, occupation, a family history of cancer, prior breast problems, smoking, body mass index, diabetes, hypertension, and hepatitis C virus infection. Bold values indicate significance at p < 0.05.
Table 8. Multiple regression analysis for the role of DROSHA and DICER variants in disease outcomes.
Table 8. Multiple regression analysis for the role of DROSHA and DICER variants in disease outcomes.
CharacteristicsDROSHAAdjusted OR (95%CI)p-ValueDICERAdjusted OR (95%CI)p-Value
A/G-G/GA/AA/G-A/AG/G
Grade ≤257 (81.4)31 (86.1)Reference 62 (83.8)26 (81.3)Reference
>213 (18.6)5 (13.9)0.55 (0.14–2.07)0.3812 (16.2)6 (18.8)1.17 (0.32–4.30)0.80
Masses Single51 (72.9)27 (75)Reference 56 (75.7)22 (68.8)Reference
Multiple19 (27.1)9 (25.0)0.61 (0.19–1.94)0.4118 (24.3)10 (31.3)2.54 (0.77–8.38)0.12
T stage≤233 (47.1)19 (52.8)Reference 37 (50.0)15 (46.9)Reference
>237 (52.9)17 (47.2)0.72 (0.27–1.86)0.4937 (50.0)17 (53.1)1.44 (0.53–3.89)0.47
N stageN018 (25.7)13 (36.1)Reference 20 (27.0)11 (34.4)Reference
N1-352 (74.3)23 (63.9)0.68 (0.24–1.86)0.4554 (73.0)21 (65.6)0.83 (0.28–2.38)0.72
NPI Good33 (47.1)20 (55.6)Reference 38 (51.4)15 (46.9)Reference
Poor 37 (52.9)16 (44.4)0.64 (0.24–1.65)0.3536 (48.6)17 (53.1)1.38 (0.51–3.74)0.52
LVINegative31 (44.3)23 (63.9)Reference 39 (552.7)15 (46.9)Reference
Positive 39 (55.7)13 (36.1)0.33 (0.12–1.09)0.3435 (47.3)17 (53.1)1.91 (0.66–5.5)0.22
SkinNegative53 (75.7)30 (83.3)Reference 57 (77.0)26 (81.3)Reference
Positive 17 (24.3)6 (16.7)0.43 (0.11–1.61)0.2117 (23.0)6 (18.8)0.96 (0.26–3.52)0.95
Data are presented as numbers (percentages). Binary logistic regression analysis was applied. NPI: Nottingham Prognostic Index, calculated as [0.2 × tumor size in cm] + tumor grade [1,2,3] + lymph node stage [1–3, according to stages A–C]; LVI: lymphovascular invasion; skin: skin infiltration; OR: odds ratio; CI: confidence interval. Each prognostic outcome represented a separate model. Adjusted variables for each logistic regression model were age, a family history of cancer, prior breast problems, smoking, body mass index, diabetes, hypertension, and hepatitis C virus infection.
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MDPI and ACS Style

Shaalan, A.A.M.; Al Ageeli, E.; Kattan, S.W.; Almars, A.I.; Babteen, N.A.; Sindi, A.A.A.; Toraih, E.A.; Fawzy, M.S.; Mohamed, M.H. Impacts of DROSHA (rs10719) and DICER (rs3742330) Variants on Breast Cancer Risk and Their Distribution in Blood and Tissue Samples of Egyptian Patients. Curr. Issues Mol. Biol. 2024, 46, 10087-10111. https://doi.org/10.3390/cimb46090602

AMA Style

Shaalan AAM, Al Ageeli E, Kattan SW, Almars AI, Babteen NA, Sindi AAA, Toraih EA, Fawzy MS, Mohamed MH. Impacts of DROSHA (rs10719) and DICER (rs3742330) Variants on Breast Cancer Risk and Their Distribution in Blood and Tissue Samples of Egyptian Patients. Current Issues in Molecular Biology. 2024; 46(9):10087-10111. https://doi.org/10.3390/cimb46090602

Chicago/Turabian Style

Shaalan, Aly A. M., Essam Al Ageeli, Shahad W. Kattan, Amany I. Almars, Nouf A. Babteen, Abdulmajeed A. A. Sindi, Eman A. Toraih, Manal S. Fawzy, and Marwa Hussein Mohamed. 2024. "Impacts of DROSHA (rs10719) and DICER (rs3742330) Variants on Breast Cancer Risk and Their Distribution in Blood and Tissue Samples of Egyptian Patients" Current Issues in Molecular Biology 46, no. 9: 10087-10111. https://doi.org/10.3390/cimb46090602

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