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Abstract

BACKGROUND:

Multiple lines of evidence suggest that single nucleotide polymorphisms (SNPs) in genes encoding components of the microRNA processing machinery may underlie susceptibility to various human diseases, including cancer.

OBJECTIVE:

The present study aimed to investigate whether rs6877842, rs642321 and rs10719 SNPs of DROSHA, a key component of the miRNA biogenesis pathway, are associated with increased risk of breast cancer.

METHODS:

A total of 100 patients diagnosed with breast cancer and 100 healthy women were included. Following extraction of DNA, genotyping was performed by tetra primer- amplification refractory mutation system-PCR (T-ARMS-PCR) technique. Under the co-dominant, dominant and recessive inheritance models, the association between DROSHA SNPs and breast cancer risk was determined by logistic regression analysis. The association of DROSHA SNPs with patients’ clinicopathological parameters was assessed. Also, haplotype analysis was performed to evaluate the combined effect of DROSHA SNPs on breast cancer risk.

RESULTS:

We observed a statistically significant association between DROSHA rs642321 polymorphism and breast cancer susceptibility (P < 0.05). Under the dominant inheritance model, DROSHA rs642321 polymorphism was significantly associated with increased risk of breast cancer (OR: 6.091; 95% CI: 3.291–11.26; P = 0.0001). Our findings demonstrated that DROSHA rs642321 T allele can contribute to the development of breast cancer (OR: 3.125; 95% CI: 1.984–4.923; P = 0.0001). We also found that GTC and GTT haplotypes conferred significant risk for breast cancer (OR: 2.367; 95% CI: 1.453–3.856; P = 0.0001 and OR: 7.944; 95% CI: 2.073–30.43; P = 0.0001, respectively).

CONCLUSIONS:

These results provide the first evidence that DROSHA rs642321 polymorphism is associated with increased risk of breast cancer. However, further studies are needed to firmly validate these findings.

Keywords

rs6877842 rs642321 rs10719 polymorphism breast cancer

1. Introduction

By surpassing lung cancer for the first time in 2020, breast cancer has become the most commonly diagnosed cancer among women worldwide, with 2.3 million new cases (11.7% of all new cancer cases) [1,2]. Breast cancer ranks as the fifth leading cause of cancer-related deaths globally, accounting for nearly 685,000 deaths (6.9% of all cancer deaths) [2]. According to the American Cancer Society’s annual report, in 2021, it was estimated that 281,550 new cases of breast cancer will be diagnosed in the United States and 43,600 women will die from the disease [3]. Breast cancer is a heterogeneous and multifactorial disease caused by the complex interplay between multiple genetic and lifestyle/environmental factors [4,5]. Despite remarkable advances in elucidating the pathophysiological mechanisms behind development of breast cancer during the last decades, many aspects of the disease have yet to be adequately studied and remains largely unknown [6–8].

Mature microRNAs (miRNAs or miRs), a class of small (∼22 nucleotides), single-stranded non-coding RNAs, regulate gene expression post-transcriptionally through binding to the 3^′-untranslated regions (3^′UTRs) of target mRNAs and thereby suppressing protein synthesis via promoting mRNA decay and/or translational silencing [9–11]. Based on miRNA target databases, a single miRNA can target several dozen or even hundreds of different mRNAs and one mRNA can be targeted by many miRNAs [12]. Practically, this means that nearly all cellular and molecular processes are subjected to miRNA-mediated regulation of gene expression [13–16]. The majority of miRNA genes are initially transcribed by RNA polymerase II from the genome into primary miRNA (pri-miRNA) and then processed within the nucleus by the microprocessor complex to generate ∼70 nucleotide hairpin-like structure termed precursor miRNA (pre-miRNA) [17]. The core components of the microprocessor complex are Drosha, a nuclear ribonuclease (RNase) III enzyme, and its interacting partner DiGeorge syndrome critical region 8 (DGCR8), a double-stranded RNA binding protein [17–19]. The pre-miRNA is subsequently exported to the cytoplasm by Exportin5 (XPO5) in a RanGTP-dependent manner [20]. In the cytoplasm, the double-stranded pre-microRNA undergo final processing by Dicer, another member of the RNase III family, and trans-activation response (TAR) RNA-binding protein (TRBP) to produce the mature miRNA duplex [21]. Finally, one of the miRNA duplex strands (guide strand) is preferentially loaded onto Argonaute (AGO) proteins to form a ribonucleoprotein machinery called miRNA-induced silencing complex (miRISC), which recognizes and silences cognate mRNA [22].

There is now compelling evidence to show that disrupted miRNA biogenesis can alter the miRNA expression profiles and potentially drive the transformation of normal cells into cancerous cells [23–25]. Dysregulated expression levels of DROSHA, a catalytic core of microprocessor complex, is closely linked to increased risk of developing breast cancer [26–28]. Besides, single nucleotide polymorphisms (SNPs) in DROSHA gene that affect the structure, function and expression level of protein can be involved in the pathogenesis of breast cancer. To date, several studies have examined the association between DROSHA SNPs and breast cancer risk, and attained conflicting results [29–33]. To the best of the authors’ knowledge, no studies thus far have investigated the association of DROSHA polymorphisms with breast cancer susceptibility in Iranian women. Of note, distribution of genotype and allele frequencies for DROSHA rs6877842, rs642321 and rs10719 SNPs in Iranian population can be differ from those seen in other populations. Accordingly, we investigated the possible association between DROSHA rs6877842, rs642321 and rs10719 SNPs and the risk of breast cancer under the co-dominant, dominant and recessive inheritance models. We also addressed the question whether combined genotypes and haplotypes of DROSHA SNPs are associated with breast cancer. Furthermore, the association between DROSHA SNPs and patients’ clinicopathological parameters was assessed.

Table 1
List of primers used in T-ARMS-PCR procedure for genotyping of DROSHA rs6877842, rs642321 and rs10719 polymorphisms

SNP Primer type Sequence Ta (°C) Amplicon size (bp)

rs6877842 Forward outer primer 5^′-CTGCAAAACTGACGGGCGCA-3^′ 65 Outer primers: 349

Reverse outer primer 5^′-TCTGGGATTTGGGGGCAAACC-3^′ C allele: 207

Forward inner primer (C allele) 5^′-CGGAAGCCCTGACGCTCACAG-3^′ G allele: 181

Reverse inner primer (G allele) 5^′-GCGCCCTCTGCTTGGGCC-3^′

rs642321 Forward outer primer 5^′-GGCTGATAAAAGCTATAAACCC-3^′ 55 Outer primers: 515

Reverse outer primer 5^′-ATCCTGTTTTTCCATCTGTGT-3^′ C allele: 264

Forward inner primer (C allele) 5^′-CTTCTCCCATTAGCCAATTATTA-3^′ T allele: 296

Reverse inner primer (T allele) 5^′-TGTAGAAGGAATCCGTTTATCG-3^′

rs10719 Forward outer primer 5^′-TCACCAAAGTCAAGTTTTCCG-3^′ 58 Outer primers: 288

Reverse outer primer 5^′-ATGAGGTGGGAAAGAGAGCAT-3^′ T allele: 157

Forward inner primer (T allele) 5^′-TTTATTTCAATGAGCACACTCCT-3^′ C allele: 178

Reverse inner primer (C allele) 5^′-CTAGTTTTCCTGCAGACAATGGAC-3^′

SNP	Primer type	Sequence	Ta (°C)	Amplicon size (bp)
rs6877842	Forward outer primer	5^′-CTGCAAAACTGACGGGCGCA-3^′	65	Outer primers: 349
	Reverse outer primer	5^′-TCTGGGATTTGGGGGCAAACC-3^′		C allele: 207
	Forward inner primer (C allele)	5^′-CGGAAGCCCTGACGCTCACAG-3^′		G allele: 181
	Reverse inner primer (G allele)	5^′-GCGCCCTCTGCTTGGGCC-3^′
rs642321	Forward outer primer	5^′-GGCTGATAAAAGCTATAAACCC-3^′	55	Outer primers: 515
	Reverse outer primer	5^′-ATCCTGTTTTTCCATCTGTGT-3^′		C allele: 264
	Forward inner primer (C allele)	5^′-CTTCTCCCATTAGCCAATTATTA-3^′		T allele: 296
	Reverse inner primer (T allele)	5^′-TGTAGAAGGAATCCGTTTATCG-3^′
rs10719	Forward outer primer	5^′-TCACCAAAGTCAAGTTTTCCG-3^′	58	Outer primers: 288
	Reverse outer primer	5^′-ATGAGGTGGGAAAGAGAGCAT-3^′		T allele: 157
	Forward inner primer (T allele)	5^′-TTTATTTCAATGAGCACACTCCT-3^′		C allele: 178
	Reverse inner primer (C allele)	5^′-CTAGTTTTCCTGCAGACAATGGAC-3^′

Abbreviation: T-ARMS-PCR, tetra primer-amplification refractory mutation system- polymerase chain reaction; SNP, single nucleotide polymorphism; Ta, annealing temperature.

2. Materials and methods

2.1. Subjects

In this case-control study, one hundred women with histopathologically confirmed breast cancer and one hundred ethnicity-matched healthy women with no personal and/or family history of breast cancer or any other type of cancer were recruited from Anahid Clinic, Isfahan, Iran, between August 2020 and April 2021. Histopathological analysis was conducted by pathologists at department of pathology of the respective hospitals. Patients’ demographic and clinicopathological data, including age at diagnosis, BMI, smoking status, history of taking the oral contraceptive pills, family history of breast cancer, history of chemotherapy and radiotherapy, laterality, histological type, histological grade, tumor size and metastasis were obtained from medical records and review of pathology reports. Patients who underwent neoadjuvant chemotherapy or radiotherapy were excluded from the study. The control subjects were selected from healthy volunteers during the same period, and data were collected on demographic and clinical features using a structured questionnaire. The study protocol was approved by the Institutional Review Board/Independent Ethics Committee (IRB/IEC) of the Shahid Ashrafi Esfahani University, Isfahan, Iran. Written informed consent was obtained from all subjects prior to blood sampling and the study was conducted in accordance with the Helsinki Declaration.

2.2. DNA extraction and genotyping of DROSHA SNPs

Peripheral blood samples (∼5 mL) were taken from all subjects into tubes containing ethylene diamine tetraacetic acid (EDTA), and genomic DNA was isolated from leukocytes using Genomic DNA Extraction Kit (ZEAN, Isfahan, Iran). The quality and quantity of the extracted DNA was determined by NanoDrop^® ND-1000 UV-Vis Spectrophotometer (ThermoFisher Scientific^{^TM}, Waltham, MA, USA). The rs6877842, rs642321 and rs10719 SNPs of the DROSHA were genotyped using tetra primer-amplification refractory mutation system-polymerase chain reaction (T-ARMS-PCR) assays. PCR reactions were carried out in a final volume of 20 μl containing 10 μl 2X Master Mix Red (Ampliqon, Denmark), 10 pmol of each primer and 100 ng genomic DNA. To detect the DROSHA SNPs, the specific primers were designed by Primer3 available at http://primer3.ut.ee (Table 1). The thermal cycling conditions were as follows: initial denaturation at 95 °C for 5 min; 35 cycles of denaturation at 94 °C for 40 s, annealing at temperature that are given in Table 1 for each primer pair for 40 s, and elongation at 72 °C for 2 min, followed by final extension at 72 °C for 5 min. The PCR products were separated by electrophoresis on 2% agarose gel stained with safe DNA gel stain and visualized with ultra-violet (UV) transilluminator.

2.3. Linkage disequilibrium and haplotype analysis

To compute Lewontin’s standardized coefficient (D^′) and the square of the Pearson’s correlation coefficient (r2), pairwise linkage disequilibrium (LD) between DROSHA SNPs was compared using SHEsis online server available at http://analysis.bio-x.cn/myAnalysis.php. The association between haplotypes of DROSHA SNPs and breast cancer risk was also evaluated using SHEsis platform.

2.4. Statistical analysis

Statistical analysis of data was performed by IBM SPSS Statistics for Windows, Version 22.0 (IBM Corp., Armonk, N.Y., USA) and p value less than 0.05 was considered as the threshold of statistical significance. Student’s t-test was used to compare the difference of continuous variables between breast cancer patients and healthy controls. The association between DROSHA SNPs and breast cancer risk under the co-dominant, dominant and recessive inheritance models was evaluated using logistic regression analysis. To this end, the wild-type genotype was used as the reference to calculate the odds ratio (OR) and 95% confidence intervals (CI). Departures of genotype frequencies from Hardy-Weinberg Equilibrium (HWE) among the breast cancer patients and healthy subjects was calculated using Chi-square test. The association of DROSHA SNPs with patients’ clinicopathological characteristics was also estimated by calculating the OR and 95% CI using logistic regression analysis.

3. Results

3.1. Baseline characteristics of participants

The demographic of all subjects and detailed clinicopathological characteristics of breast cancer patients are summarized in Table 2. The mean age of breast cancer patients at the time of diagnosis and healthy controls was 51.56 years (standard deviation (SD) = 11.24 years, range 29–80) and 39.01 years (SD = 10.51 years, range 17–76), respectively. There was statistically significant difference between the two groups with respect to age (P < 0.05). Patients with breast cancer had significantly higher body mass index (BMI) (mean ± SD; 28.09 ± 4.30) compared to the healthy controls (mean ± SD; 25.64 ± 4.59) (P < 0.05). No statistically significant difference was found in terms of smoking status between the two groups (P > 0.05). All breast cancer patients were married (100%) and 49% had history of taking the oral contraceptive pills. Out of 100 breast cancer patients, 30% of them had family history of breast cancer, and 98% had unilateral tumor. A substantial proportion of patients (92%) had invasive ductal carcinoma (IDC), and (8%) had invasive lobular carcinoma (ILC). Histopathologically, 12% of patients had grade I, 62% had grade II, and had 26% grade III. At the time of diagnosis, 89% of patients were diagnosed with stage I, 10% stage II and 1% stage III. Of 100 patients, 91% had a tumor size ≤2 cm, and 9% had tumor size >2 cm. Only 7% of patients had metastasis involving one or more organs.

Table 2
Demographic and clinicopathological characteristics of breast cancer patients and healthy controls

Variables Cases (n = 100) Controls (n = 100) P value

% %

Age, years (mean ± SD) 51.56 ± 11.24 39.01 ± 10.51 0.0001

Range 29–80 17–76 -

≤40 16 54 0.0001

>40 84 46 0.0001

Weight, kg (mean ± SD) 72.71 ± 11.01 67.71 ± 11.45 0.002

Height, cm (mean ± SD) 161.13 ± 6.47 162.71 ± 5.70 0.691

BMI, kg/m² (mean ± SD) 28.09 ± 4.30 25.64 ± 4.59 0.0001

Underweight (<18.5) 1 4 0.369

Normal (18.5–24.9) 21 43 0.001

Overweight (25–29.9) 46 37 0.251

Obese (≥30) 32 16 0.013

Smoking status 0.767

Non-smoker 95 93

Smoker 5 7

Marital status 0.0001

Single 0 17

Married 100 83

History of taking the oral contraceptive pills 0.031

Yes 49 33

No 51 67

Family history of breast cancer

Yes 30

No 70

Laterality

Unilateral 98

Bilateral 2

Histological type

IDC 92

ILC 8

Histological grade

I 12

II 62

III 26

Histological stage

I 89

II 10

III 1

Tumor size, cm

≤ 2 91

>2 9

Other organ metastasis

Yes 7

No 93

Variables	Cases (n = 100)	Controls (n = 100)	P value
Age, years (mean ± SD)	51.56 ± 11.24	39.01 ± 10.51	0.0001
Range	29–80	17–76	-
≤40	16	54	0.0001
>40	84	46	0.0001
Weight, kg (mean ± SD)	72.71 ± 11.01	67.71 ± 11.45	0.002
Height, cm (mean ± SD)	161.13 ± 6.47	162.71 ± 5.70	0.691
BMI, kg/m² (mean ± SD)	28.09 ± 4.30	25.64 ± 4.59	0.0001
Underweight (<18.5)	1	4	0.369
Normal (18.5–24.9)	21	43	0.001
Overweight (25–29.9)	46	37	0.251
Obese (≥30)	32	16	0.013
Smoking status			0.767
Non-smoker	95	93
Smoker	5	7
Marital status			0.0001
Single	0	17
Married	100	83
History of taking the oral contraceptive pills			0.031
Yes	49	33
No	51	67
Family history of breast cancer
Yes	30
No	70
Laterality
Unilateral	98
Bilateral	2
Histological type
IDC	92
ILC	8
Histological grade
I	12
II	62
III	26
Histological stage
I	89
II	10
III	1
Tumor size, cm
≤ 2	91
>2	9
Other organ metastasis
Yes	7
No	93

Abbreviation: SD, standard deviation; BMI, body mass index; IDC, invasive ductal carcinoma; ILC, invasive lobular carcinoma. Values are presented as mean ± SD for age, weight, height and BMI, and as % for other variables. P values were calculated using t-test for continuous variables and Chi-square test for categorical variables. Bold values indicate statistically significant differences (P < 0.05).

3.2. Association between DROSHA SNPs and breast cancer risk under different inheritance models

Distribution of genotype and allele frequencies for rs6877842, rs642321 and rs10719 SNPs of DROSHA in patients with breast cancer and healthy controls are presented in Table 3. The genotype frequencies of two SNPs, rs6877842 and rs10719, in breast cancer patients and all SNPs in the healthy subjects were in compliance with HWE (P > 0.05) (Table 3). In patients, the distribution of the DROSHA rs642321 C/T genotypes did not conform to HWE (P < 0.05). Using logistic regression analysis, we evaluated all SNPs under co-dominant, dominant and recessive inheritance models to find their association with susceptibility to breast cancer (Table 3). Our results showed a significant association between DROSHA rs642321 and breast cancer (P < 0.05), but not the other two SNPs (P > 0.05) (Table 3). Under the co-dominant inheritance model, both CT and TT genotypes of DROSHA rs642321 polymorphism revealed a significant association with increased risk of breast cancer (OR: 6.191; 95% CI: 3.287–11.66; P = 0.0001) and (OR: 5.361; 95% CI: 1.482–19.37; P = 0.009), respectively (Table 3). The DROSHA rs642321 C/T polymorphism was found significantly associated with breast cancer susceptibility in dominant inheritance model (OR: 6.091; 95% CI: 3.291–11.26; P = 0.0001) (Table 3). Under the recessive inheritance model, DROSHA rs642321 polymorphism showed no association with breast cancer (OR: 2.087; 95% CI: 0.607–7.167; P = 0.373) (Table 3). As regards DROSHA rs642321 polymorphism, we found that T allele significantly increased the risk of breast cancer development (OR: 3.125; 95% CI: 1.984–4.923; P = 0.0001) (Table 3). No significant association was found between DROSHA rs6877842 and rs10719 polymorphisms and breast cancer risk under the co-dominant, dominant and recessive inheritance models (Table 3). However, a trend of increased risk of developing breast cancer can be considered for these polymorphisms (OR > 1) (Table 3). For DROSHA rs6877842 and rs10719 polymorphisms, the frequency of the C allele in breast cancer patients were higher than that in healthy women, but not statistically significant (P > 0.05) (Table 3). Combined genotype analysis of DROSHA rs6877842, rs642321 and rs10719 SNPs was also conducted to assess the combined effects of these polymorphisms on breast cancer risk (Table 4). Among the combined genotypes of DROSHA rs6877842/rs642321 polymorphisms, GG/CT, GG/TT and GC/CT combined genotypes were associated with a higher risk for breast cancer (GG/CT; OR: 7.425; 95% CI: 3.612–15.26; P = 0.0001, GG/TT; OR: 6.444; 95% CI: 1.736–23.92; P = 0.004, GC/CT; OR: 6.444; 95% CI: 2.116–19.62; P = 0.0004) (Table 4). Similarly, carrying the GG/TC combined genotype for DROSHA rs6877842/rs10719 polymorphisms may increase a woman’s risk of developing breast cancer (OR: 2.285; 95% CI: 1.077–4.851; P = 0.039) (Table 4). As demonstrated in Table 4, for DROSHA rs642321/rs10719 polymorphisms, individuals with CT/TT (OR: 4.292; 95% CI: 2.068–8.910; P = 0.0001), CT/TC (OR: 29.94; 95% CI: 6.387–140.3; P = 0.0001), CT/CC (OR: 10.88; 95% CI: 1.139–104.1; P = 0.028) and TT/TT (OR: 5.444; 95% CI: 1.230–24.09; P = 0.024) combined genotypes had higher breast cancer risk.

3.3. Association between DROSHA SNPs and patients’ clinicopathological features

We also analyzed the possible association of genotype and allele distribution of DROSHA rs6877842, rs642321 and rs10719 polymorphisms in breast cancer patients with demographic and clinicopathological characteristics. As shown in Tables 5–7, no statistically significant association was found between distribution of genotypes in studied SNPs of DROSHA and age, BMI, family history of breast cancer, histological type, grade, stage, tumor size and metastasis.

Fig. 1.

Linkage disequilibrium (LD) plot of DROSHA rs6877842, rs642321 and rs10719 polymorphisms in (a) breast cancer patients and (b) healthy controls.

Table 3

Genotype and allele distribution of DROSHA rs6877842, rs642321 and rs10719 polymorphisms in breast cancer patients and healthy controls

Genotypes	Cases (n = 100)	Controls (n = 100)	OR (95% CI)	P value
	%	%
rs6877842				0.492
GG	79	85	1.000	-
GC	17	13	1.407 (0.642–3.082)	0.431
CC	4	2	2.151 (0.383–12.07)	0.435
Dominant (GG versus GC+CC)	-	-	1.506 (0.725–3.125)	0.357
Recessive (GG+GC versus CC)	-	-	2.042 (0.365–11.40)	0.682
C allele	12.5	8.5	1.538 (0.803–2.946)	0.253
HWE P value	0.083	0.259	-	-
rs642321				0.0001
CC	25	67	1.000	-
CT	67	29	6.191 (3.287–11.66)	0.0001
TT	8	4	5.361 (1.482–19.37)	0.009
Dominant (CC versus CT+TT)	-	-	6.091 (3.291–11.26)	0.0001
Recessive (CC+CT versus TT)	-	-	2.087 (0.607–7.167)	0.373
T allele	41.5	18.5	3.125 (1.984–4.923)	0.0001
HWE P value	0.0001	0.929	-	-
rs10719				0.125
TT	65	78	1.000	-
TC	30	19	1.894 (0.977–3.674)	0.068
CC	5	3	2.001 (0.460–8.687)	0.472
Dominant (TT versus TC+CC)	-	-	1.909 (1.020–3.572)	0.059
Recessive (TT+TC versus CC)	-	-	1.701 (0.395–7.320)	0.720
C allele	20.0	12.5	1.750 (1.016–3.014)	0.057
HWE P value	0.822	0.421	-	-

Abbreviation: OR, odds ratio; CI, confidence interval; HWE, Hardy-Weinberg equilibrium. P value were calculated by Chi-square test. Bold values indicate statistically significant differences (P < 0.05).

Table 4

Distribution of DROSHA rs6877842, rs642321 and rs10719 polymorphisms combined genotypes in breast cancer patients and healthy controls

Combined genotypes	Cases (n = 100)	Controls (n = 100)	OR (95% CI)	P value
	%	%
rs6877842/rs642321
GG/CC	18	58	1.000	-
GG/CT	53	23	7.425 (3.612–15.26)	0.0001
GG/TT	8	4	6.444 (1.736–23.92)	0.004
GC/CC	5	7	2.301 (0.650–8.143)	0.286
GC/CT	12	6	6.444 (2.116–19.62)	0.0004
GC/TT	0	0	ND	ND
CC/CC	2	2	3.222 (0.423–24.53)	0.567
CC/CT	2	0	ND	ND
CC/TT	0	0	ND	ND
rs6877842/rs10719
GG/TT	51	68	1.000	-
GG/TC	24	14	2.285 (1.077–4.851)	0.039
GG/CC	4	3	1.777 (0.381–8.295)	0.698
GC/TT	10	9	1.481 (0.561–3.911)	0.463
GC/TC	6	4	2.000 (0.536–7.458)	0.335
GC/CC	1	0	ND	ND
CC/TT	4	1	5.333 (0.578–49.16)	0.169
CC/TC	0	1	ND	ND
CC/CC	0	0	ND	ND
rs642321/rs10719
CC/TT	18	49	1.000	-
CC/TC	6	16	1.020 (0.345–3.014)	1.000
CC/CC	1	2	1.361 (0.116–15.94)	1.000
CT/TT	41	26	4.292 (2.068–8.910)	0.0001
CT/TC	22	2	29.94 (6.387–140.3)	0.0001
CT/CC	4	1	10.88 (1.139–104.1)	0.028
TT/TT	6	3	5.444 (1.230–24.09)	0.024
TT/TC	2	1	5.444 (0.464–63.76)	0.194
TT/CC	0	0	ND	ND

Abbreviation: OR, odds ratio; CI, confidence interval; ND, not determined. P value calculated by Chi-square test. Bold values indicate statistically significant differences (P < 0.05).

Table 5

Association of DROSHA rs6877842 polymorphism with age, BMI, family history of breast cancer, histological type, histological grade, tumor size and other organ metastasis in breast cancer patients

Variables	Genotypes	n (%)		OR (95% CI)	P value
Age, years		≤40	>40
	GG	14 (87.50)	65 (77.39)	1.000	-
	GC	2 (12.50)	15 (17.85)	1.615 (0.331–7.877)	0.729
	CC	0 (0.00)	4 (4.76)	ND	ND
	Dominant (GG versus GC+CC)	-	-	2.046 (0.426–9.808)	0.511
	Recessive (GG+GC versus CC)	-	-	ND	ND
	C allele	2 (6.25)	23 (13.70)	2.379 (0.532–10.63)	0.381
BMI, kg/m²		≤25	>25
	GG	20 (90.90)	59 (75.64)	1.000	-
	GC	2 (9.10)	15 (19.24)	2.542 (0.534–12.10)	0.343
	CC	0 (0.00)	4 (5.12)	ND	ND
	Dominant (GG versus GC+CC)	-	-	3.220 (0.688–15.06)	0.148
	Recessive (GG+GC versus CC)	-	-	ND	ND
	C allele	2 (4.54)	23 (14.74)	3.243 (0.736–14.29)	0.119
Family history of breast cancer		Yes	No
	GG	25 (80.33)	54 (77.15)	1.000	-
	GC	4 (13.33)	13 (18.57)	0.664 (0.196–2.244)	0.576
	CC	1 (3.34)	3 (4.28)	0.721 (0.071–7.270)	0.628
	Dominant (GG versus GC+CC)	-	-	0.675 (0.222–2.049)	0.597
	Recessive (GG+GC versus CC)	-	-	0.770 (0.076–7.718)	0.652
	C allele	6 (10.00)	19 (13.57)	0.707 (0.267–1.870)	0.641
Histological type		IDC	ILC
	GG	74 (80.44)	5 (62.50)	1.000	-
	GC	14 (15.21)	3 (37.50)	3.171 (0.679–14.81)	0.146
	CC	4 (4.35)	0 (0.00)	ND	ND
	Dominant (GG versus GC+CC)	-	-	2.466 (0.538–11.29)	0.359
	Recessive (GG+GC versus CC)	-	-	ND	ND
	C allele	22 (11.95)	3 (18.75)	1.699 (0.448–6.438)	0.698
Histological grade		Grade I and II	Grade III
	GG	59 (79.73)	20 (76.92)	1.000	-
	GC	12 (16.22)	5 (19.24)	1.229 (0.385–3.921)	0.764
	CC	3 (4.05)	1 (3.84)	0.983 (0.096–9.999)	1.000
	Dominant (GG versus GC+CC)	-	-	1.181 (0.403–3.454)	0.783
	Recessive (GG+GC versus CC)	-	-	0.946 (0.094–9.524)	1.000
	C allele	18 (12.16)	7 (13.46)	1.123 (0.440–2.866)	0.488
Histological stage		Stage I	Stage II and III
	GG	70 (78.65)	9 (81.81)	1.000	-
	GC	15 (16.86)	2 (18.19)	1.037 (0.203–5.295)	1.000
	CC	4 (4.49)	0 (0.00)	ND	ND
	Dominant (GG versus GC+CC)	-	-	0.818 (0.163–4.111)	1.000
	Recessive (GG+GC versus CC)	-	-	ND	ND
	C allele	23 (12.92)	2 (9.09)	0.673 (0.147–3.075)	0.747
Tumor size, cm		≤2	>2
	GG	74 (81.32)	5 (55.56)	1.000	-
	GC	14 (15.39)	3 (33.33)	3.171 (0.679–14.81)	0.146
	CC	3 (3.29)	1 (1.11)	4.993 (0.431–56.74)	0.263
	Dominant (GG versus GC+CC)	-	-	3.482 (0.844–14.35)	0.089
	Recessive (GG+GC versus CC)	-	-	3.666 (0.340–39.46)	0.318
	C allele	20 (10.98)	5 (27.77)	3.115 (1.005–9.655)	0.055

Table 5 (Continued)

Variables	Genotypes	n (%)		OR (95% CI)	P value
Other organ metastasis		Yes	No
	GG	7 (100.0)	72 (77.41)	1.000	-
	GC	0 (0.00)	17 (18.28)	ND	ND
	CC	0 (0.00)	4 (4.31)	ND	ND
	Dominant (GG versus GC+CC)	-	-	ND	ND
	Recessive (GG+GC versus CC)	-	-	ND	ND
	C allele	0 (0.00)	25 (13.44)	ND	ND

Abbreviation: BMI, body mass index; IDC, invasive ductal carcinoma; ILC, invasive lobular carcinoma; ND, not determined. Values are presented as number (%). P values were calculated using Chi-square test.

Table 6

Association of DROSHA rs642321 polymorphism with age, BMI, family history of breast cancer, histological type, histological grade, tumor size and other organ metastasis in breast cancer patients

Variables	Genotypes	n (%)		OR (95% CI)	P value
Age, years		≤40	>40
	CC	2 (12.50)	23 (27.38)	1.000	-
	CT	13 (81.25)	54 (64.29)	2.768 (0.577–13.26)	0.224
	TT	1 (6.25)	7 (8.33)	1.642 (0.128–20.94)	1.000
	Dominant (CC versus CT+TT)	-	-	2.639 (0.556–12.52)	0.344
	Recessive (CC+CT versus TT)	-	-	0.733 (0.084–6.404)	1.000
	T allele	15 (46.87)	68 (40.47)	0.770 (0.360–1.647)	0.559
BMI, kg/m²		≤25	>25
	CC	5 (22.72)	20 (25.65)	1.000	-
	CT	15 (68.19)	52 (66.66)	1.153 (0.370–3.593)	1.000
	TT	2 (9.09)	6 (7.69)	1.333 (0.204–8.708)	1.000
	Dominant (CC versus CT+TT)	-	-	1.172 (0.382–3.590)	1.000
	Recessive (CC+CT versus TT)	-	-	1.201 (0.224–6.408)	1.000
	T allele	19 (43.18)	64 (41.02)	0.915 (0.465–1.801)	0.863
Family history of breast cancer		Yes	No
	CC	8 (26.67)	17 (24.28)	1.000	-
	CT	19 (63.33)	48 (68.57)	0.841 (0.311–2.273)	0.798
	TT	3 (10.00)	5 (7.15)	1.275 (0.242–6.704)	1.000
	Dominant (CC versus CT+TT)	-	-	0.882 (0.332–2.341)	0.999
	Recessive (CC+CT versus TT)	-	-	1.444 (0.322–6.474)	0.693
	T allele	25 (41.66)	58 (41.42)	0.992 (0.536–1.829)	1.000
Histological type		IDC	ILC
	CC	22 (23.92)	3 (37.50)	1.000	-
	CT	63 (68.48)	4 (50.00)	0.465 (0.096–2.246)	0.384
	TT	7 (7.60)	1 (12.50)	1.047 (0.093–11.75)	1.000
	Dominant (CC versus CT+TT)	-	-	0.523 (0.115–2.369)	0.675
	Recessive (CC+CT versus TT)	-	-	1.734 (0.186–16.17)	0.999
	T allele	77 (44.84)	6 (37.50)	1.199 (0.418–3.440)	0.797
Histological grade		Grade I and II	Grade III
	CC	19 (25.68)	6 (23.07)	1.000	-
	CT	49 (66.21)	18 (69.23)	1.163 (0.401–3.375)	0.777
	TT	6 (8.11)	2 (7.70)	1.055 (0.166–6.678)	1.000
	Dominant (CC versus CT+TT)	-	-	1.151 (0.402–3.293)	0.791
	Recessive (CC+CT versus TT)	-	-	0.944 (0.178–5.001)	1.000
	T allele	61 (41.21)	22 (42.30)	0.956 (0.504–1.813)	0.887

Table 6 (Continued)

Variables	Genotypes	n (%)		OR (95% CI)	P value
Histological stage		Stage I	Stage II and III
	CC	20 (22.47)	5 (45.45)	1.000	-
	CT	62 (69.67)	5 (45.45)	0.322 (0.084–1.299)	0.127
	TT	7 (7.86)	1 (9.10)	0.571 (0.056–5.775)	1.000
	Dominant (CC versus CT+TT)	-	-	0.347 (0.096–1.258)	0.136
	Recessive (CC+CT versus TT)	-	-	1.171 (0.130–10.52)	1.000
	T allele	76 (42.69)	7 (31.81)	0.626 (0.243–1.611)	0.367
Tumor size, cm		≤2	>2
	CC	22 (24.17)	3 (33.34)	1.000	-
	CT	62 (68.13)	5 (55.55)	0.591 (0.130–2.681)	0.678
	TT	7 (7.70)	1 (1.11)	1.047 (0.093–11.75)	1.000
	Dominant (CC versus CT+TT)	-	-	0.637 (0.147–2.764)	0.686
	Recessive (CC+CT versus TT)	-	-	1.501 (0.163–13.77)	1.000
	T allele	76 (41.75)	7 (38.88)	0.887 (0.329–2.394)	0.806
Other organ metastasis		Yes	No
	CC	2 (28.57)	23 (24.73)	1.000	-
	CT	5 (77.43)	62 (66.66)	0.927 (0.168–5.118)	1.000
	TT	0 (0.00)	8 (8.61)	ND	ND
	Dominant (CC versus CT+TT)	-	-	0.821 (0.149–4.524)	1.000
	Recessive (CC+CT versus TT)	-	-	ND	ND
	T allele	5 (35.71)	78 (41.93)	0.862 (0.248–2.384)	0.781

Table 7

Association of DROSHA rs10719 polymorphism with age, BMI, family history of breast cancer, histological type, histological grade, tumor size and other organ metastasis in breast cancer patients

Variables	Genotypes	n (%)		OR (95% CI)	P value
Age, years		≤40	>40
	TT	12 (75.00)	53 (63.10)	1.000	-
	TC	3 (18.75)	27 (32.14)	2.037 (0.529–7.839)	0.374
	CC	1 (6.25)	4 (4.76)	0.905 (0.092–8.847)	1.000
	Dominant (TT versus TC+CC)	-	-	1.754 (0.520–5.915)	0.408
	Recessive (TT+TC versus CC)	-	-	0.751 (0.078–7.185)	1.000
	C allele	5 (15.62)	35 (20.83)	1.141 (0.510–3.957)	0.632
BMI, kg/m²		≤25	>25
	TT	13 (59.10)	52 (66.66)	1.000	-
	TC	8 (36.36)	22 (28.21)	0.687 (0.249–1.891)	0.595
	CC	1 (4.54)	4 (5.13)	1.000 (0.102–9.718)	1.000
	Dominant (TT versus TC+CC)	-	-	0.722 (0.273–1.908)	0.614
	Recessive (TT+TC versus CC)	-	-	1.135 (0.120–10.70)	1.000
	C allele	10 (22.72)	30 (19.23)	0.809 (0.360–1.819)	0.670
Family history of breast cancer		Yes	No
	TT	18 (60.00)	47 (67.14)	1.000	-
	TC	9 (30.00)	21 (30.00)	1.119 (0.432–2.897)	0.999
	CC	3 (10.00)	2 (2.86)	3.916 (0.603–25.40)	0.312
	Dominant (TT versus TC+CC)	-	-	1.362 (0.562–3.299)	0.647
	Recessive (TT+TC versus CC)	-	-	3.777 (0.597–23.88)	0.318
	C allele	15 (25.00)	25 (17.85)	1.533 (0.741–3.172)	0.334

Table 7 (Continued)

Variables	Genotypes	n (%)		OR (95% CI)	P value
Histological type		IDC	ILC
	TT	58 (63.05)	7 (87.50)	1.000	-
	TC	29 (31.52)	1 (12.50)	3.501 (0.410–29.81)	0.275
	CC	5 (5.43)	0 (0.00)	ND	ND
	Dominant (TT versus TC+CC)	-	-	4.103 (0.483–34.79)	0.254
	Recessive (TT+TC versus CC)	-	-	ND	ND
	C allele	39 (21.19)	1 (6.25)	4.303 (0.553–33.46)	0.203
Histological grade		Grade I and II	Grade III
	TT	52 (70.27)	13 (50.00)	1.000	-
	TC	19 (25.67)	11 (42.30)	2.315 (0.887–6.045)	0.126
	CC	3 (4.05)	2 (7.70)	2.666 (0.403–17.64)	0.577
	Dominant (TT versus TC+CC)	-	-	2.363 (0.945–5.908)	0.093
	Recessive (TT+TC versus CC)	-	-	1.972 (0.310–12.51)	0.602
	C allele	25 (16.89)	15 (28.84)	1.994 (0.953–4.172)	0.071
Tumor size, cm		≤2	>2
	TT	59 (64.84)	6 (66.67)	1.000	-
	TC	27 (29.67)	3 (33.33)	1.092 (0.254–4.699)	1.000
	CC	5 (5.49)	0 (0.00)	ND	ND
	Dominant (TT versus TC+CC)	-	-	0.921 (0.216–3.934)	1.000
	Recessive (TT+TC versus CC)	-	-	ND	ND
	C allele	37 (20.32)	3 (16.66)	0.783 (0.215–2.850)	0.774
Other organ metastasis		Yes	No
	TT	4 (57.14)	61 (65.59)	1.000	-
	TC	3 (42.86)	27 (29.03)	1.694 (0.354–8.096)	0.674
	CC	0 (0.00)	5 (5.37)	ND	ND
	Dominant (TT versus TC+CC)	-	-	1.429 (0.301–6.782)	0.692
	Recessive (TT+TC versus CC)	-	-	ND	ND
	C allele	3 (21.42)	37 (20.10)	1.098 (0.291–4.137)	1.000

3.4. Association between haplotypes of DROSHA SNPs and breast cancer risk

On the basis of the genotype frequencies of DROSHA rs6877842, rs642321 and rs10719 SNPs among breast cancer patients and healthy controls, we computed pairwise LD between each pair of the three SNP loci (Table 8), and plotted LD values between all pairs of SNP (Fig. 1). The observed low LD coefficient (D^′) values and low r² in patients with breast cancer and healthy subjects revealed that the studied DROSHA SNPs were not in high linkage disequilibrium with each other (Table 8) (Fig. 1). The association between haplotypes of DROSHA SNPs and breast cancer susceptibility are displayed in Table 9. We found that the GTC and GTT haplotypes (DROSHA rs6877842, rs642321 and rs10719) were significantly associated with increased risk of breast cancer (OR: 2.367; 95% CI: 1.453–3.856; P = 0.0001 and OR: 7.944; 95% CI: 2.073–30.43; P = 0.0001, respectively) (Table 9). There was no significant difference between the two groups regarding the distribution of the CCC, GCC and GCT haplotypes (P > 0.05) (Table 9).

Table 8
Pairwise LD of DROSHA rs6877842, rs642321 and rs10719 polymorphisms in breast cancer patients and healthy controls

Cases Controls

Locus pair D^′ r ² D^′ r ²

rs6877842/rs642321 0.553 0.031 0.133 0.001

rs6877842/rs10719 0.388 0.005 0.107 0.007

rs642321/rs10719 0.056 0.001 0.426 0.006

	Cases	Controls
rs6877842/rs642321	0.553	0.031	0.133	0.001
rs6877842/rs10719	0.388	0.005	0.107	0.007
rs642321/rs10719	0.056	0.001	0.426	0.006

Abbreviation: LD, linkage disequilibrium. r² reflects statistical power to detect LD.

Table 9

Haplotype distribution of DROSHA rs6877842, rs642321 and rs10719 polymorphisms in breast cancer patients and healthy controls

Haplotype			Frequency		OR (95% CI)	P value
rs6877842	rs642321	rs10719	Cases	Controls
C	C	C	0.089	0.051	1.815 (0.818–4.026)	0.138
G	C	C	0.387	0.651	0.324 (0.214–0.491)	0.776
G	C	T	0.094	0.093	1.006 (0.513–1.971)	0.987
G	T	C	0.304	0.157	2.367 (1.453–3.856)	0.0001
G	T	T	0.091	0.012	7.944 (2.073–30.43)	0.0001

Abbreviation: SNPs, single nucleotide polymorphisms; OR, odds ratio; CI, confidence interval. P value were calculated using Pearson’s chi-square test. Bold values indicate statistically significant differences (P < 0.05).

4. Discussion

The biogenesis of miRNAs is delicately controlled at multiple steps by enzymes and regulatory proteins involved in the miRNA synthesis pathway [34,35]. Some genetic polymorphisms in the genes encoding miRNA biogenesis components could lead to aberrant expression of miRNAs which are closely related to the development of cancers [36–38]. The SNPs located in the promoter region of gene may affect the expression level of mRNA through altering DNA methylation, histone modifications and chromatin conformational changes, and subsequently transcription factor binding [39]. The SNPs falling in the 3^′UTR of gene may modify mRNA secondary structure, mRNA stability and miRNA-mRNA interaction and thereby affect gene expression [39]. Accordingly, we hypothesized that SNPs located in promoter (rs6877842) and 3^′UTR (rs642321 and rs10719) of DROSHA gene are contributing factor to breast cancer in Iranian women. To test this hypothesis, the present study was designed to assess the possible association of these SNPs with susceptibility to breast cancer under the co-dominant, dominant and recessive inheritance models. The association between these SNPs and patients’ clinicopathological characteristics was analyzed. We also determined the effects of haplotypes and combined genotypes of DROSHA rs6877842, rs642321 and rs10719 SNPs on breast cancer risk. As far as we know, this is the first report investigating the association between DROSHA SNPs and breast cancer in Iranian women. Overall, a statistically significant association was observed between DROSHA rs642321 SNP and breast cancer risk (P < 0.05). Under the dominant inheritance model, this SNP was found to be associated with increased risk of breast cancer development (OR: 6.091; 95% CI: 3.291–11.26; P = 0.0001). The results revealed that DROSHA rs642321 T allele can affect the susceptibility to breast cancer (OR: 3.125; 95% CI: 1.984–4.923; P = 0.0001). We also found that GTC and GTT haplotypes conferred significant risk for breast cancer (OR: 2.367; 95% CI: 1.453–3.856; P = 0.0001 and OR: 7.944; 95% CI: 2.073–30.43; P = 0.0001, respectively). Based on the literature review, only one study has so far investigated the association between DROSHA rs642321 SNP and breast cancer [30]. In 2013, Jiang et al. conducted the first study on the association between DROSHA rs642321 SNP and risk of breast cancer in the Chinese women [30]. In contrary to our results, they indicated that DROSHA rs642321 SNP was not associated with susceptibility to breast cancer [30]. In another study conducted by Yuan et al. on 685 patients with bladder cancer and 730 healthy controls, no significant association was found between DROSHA rs642321 SNP and susceptibility to bladder cancer in the Chinese population [40]. Although, no details were given on the reasons for the inconsistency in the results of genetic association studies so far, these discrepancies could be attributed in large part to ethnic/racial disparities, limited sample size and multifactorial nature of cancers.

To date, there is no published report regarding the association between DROSHA rs6877842 SNP and breast cancer development. Our results indicate for the first time that this SNP was not significantly associated with susceptibility to breast cancer in Iranian women (P > 0.05). In line with our findings, Kim et al. failed to find any association between DROSHA rs6877842 SNP and lung cancer [41]. Moreover, when Horikawa et al. conducted a case-control study on 279 Caucasian patients with renal cell carcinoma and 278 healthy controls, similar results were obtained [42]. Osuch-Wojcikiewicz et al. demonstrated that there was no significant association between DROSHA rs6877842 SNP and larynx cancer risk [43]. Kim et al. found that DROSHA rs6877842 polymorphism was not associated with pathogenesis of hepatocellular carcinoma in the Korean population [44]. No statistically significant associations between DROSHA rs6877842 SNP and risk of papillary thyroid carcinoma was observed by Mohammadpour-Gharehbagh et al. [45]. In contrast, a meta-analysis by Wen and collaborators demonstrated that DROSHA rs6877842 SNP was associated with an increased cancer risk and may be potential biomarker for cancer-forewarning [46]. These findings lead us to conclude that DROSHA rs6877842 polymorphism is not meaningfully related with development and progression of human cancers. However, these results should be interpreted with caution due to very limited studies.

To our knowledge, only one study has investigated the association between DROSHA rs10719 SNP and risk of breast cancer development [30]. As regards DROSHA rs10719 polymorphism, we did not find any significant association between this SNP and breast cancer risk (P > 0.05). These results reflect those of Jiang et al. who demonstrated that DROSHA rs10719 SNP may not contribute to breast cancer susceptibility [30]. For other types of cancer, some studies have found a significant association between DROSHA rs10719 SNP and cancer risk in diverse populations, while others have not. For instance, Yuan et al. showed that DROSHA rs10719 SNP was significantly associated with bladder cancer susceptibility [40]. Additionally, Cho et al. revealed a significant association between DROSHA rs10719 SNP and colorectal cancer risk in the Korean Population [36]. A meta-analysis by Wen et al. revealed that DROSHA rs10719 SNP is associated with cancer risk [46]. By contrast, Horikawa et al. concluded that there is no significant association between DROSHA rs10719 SNP and risk of renal cell carcinoma [42]. Similarly, Kim et al. failed to find any association between DROSHA rs10719 SNP and hepatocellular carcinoma [44]. No statistically significant associations between DROSHA rs10719 SNP and increased risk of gastric cancer in the Chinese Han population was found by Song et al. [47]. Given these contradictory results, it is far too early to conclude that DROSHA rs10719 SNP contributes to cancer susceptibility.

In conclusion, these results provide the first evidence that DROSHA rs642321 polymorphism is associated with susceptibility to breast cancer in Iranian women.

Footnotes

Acknowledgements

Authors would like to express their sincerest appreciation to all subjects for participating in this study.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committee.

Informed consent

The study was approved by the Institutional Review Board /Independent Ethics Committee (IRB/IEC) of the Shahid Ashrafi Esfahani University, Isfahan, Iran and informed written consent was filled in by all participants.

Conflict of interest

The authors declare that they have no conflict of interest.

References

Torre

, Islami

, Siegel

, Global cancer in women: Burden and trends, Cancer Epidemiol Biomarkers Prev , 26(4): 444–457, 2017.

Sung

, Ferlay

, Siegel

, Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries, CA Cancer J Clin , 71(3): 209–249, 2021.

Siegel

, Miller

, Fuchs

, Cancer statistics 2021, CA Cancer J Clin , 71(1): 7–33, 2021.

Mavaddat

, Antoniou

, Easton

, Genetic susceptibility to breast cancer, Mol Oncol , 4(3): 174–191, 2010.

Rudolph

, Chang-Claude

, Schmidt

, Gene-environment interaction and risk of breast cancer, Br J Cancer , 114(2): 125–133, 2016.

Russo

, Hu

, Yang

, Developmental, cellular, and molecular basis of human breast cancer, J Natl Cancer Inst Monogr (27): 17–37, 2000.

Feng

, Spezia

, Huang

, Breast cancer development and progression: Risk factors, cancer stem cells, signaling pathways, genomics, and molecular pathogenesis, Genes Dis , 5(2): 77–106, 2018.

Scully

, Bay

, Yip

, Breast cancer metastasis, Cancer Genomics Proteomics , 9(5): 311–320, 2012.

Mohr

, Mott

, Overview of microRNA biology, Semin Liver Dis , 35(1): 3–11, 2015.

10.

Huang

, Shen

, Zou

, Biological functions of microRNAs: A review, J Physiol Biochem , 67(1): 129–139, 2011.

11.

Fabian

, Sundermeier

, Sonenberg

, Understanding how miRNAs post-transcriptionally regulate gene expression, Prog Mol Subcell Biol , 50: 1–20, 2010.

12.

, Leng

, Dynamic miRNA-mRNA paradigms: New faces of miRNAs, Biochem Biophys Rep , 4: 337–341, 2015.

13.

Bueno

, Malumbres

, MicroRNAs and the cell cycle, Biochim Biophys Acta , 1812(5): 592–601, 2011.

14.

Bueno

, Pérez de Castro

, Malumbres

, Control of cell proliferation pathways by microRNAs, Cell Cycle , 7(20): 3143–3148, 2008.

15.

Song

, Tuan

, MicroRNAs and cell differentiation in mammalian development, Birth Defects Res C Embryo Today , 78(2): 140–149, 2006.

16.

Subramanian

, Steer

, MicroRNAs as gatekeepers of apoptosis, J Cell Physiol , 223(2): 289–298, 2010.

17.

Liu

, Fortin

, Mourelatos

, MicroRNAs: Biogenesis and molecular functions, Brain Pathol , 18(1): 113–121, 2008.

18.

, Kim

, Regulation of microRNA biogenesis, Nat Rev Mol Cell Biol , 15(8): 509–524, 2014.

19.

Finnegan

, Pasquinelli

, MicroRNA biogenesis: Regulating the regulators, Crit Rev Biochem Mol Biol , 48(1): 51–68, 2013.

20.

Okada

, Yamashita

, Lee

, A high-resolution structure of the pre-microRNA nuclear export machinery, Science , 326(5957): 1275–1279, 2009.

21.

Yoshida

, Asano

, Ui-Tei

, Modulation of MicroRNA processing by dicer via its associated dsRNA binding proteins, Noncoding RNA , 7(3): 57, 2021.

22.

Medley

, Panzade

, Zinovyeva

, microRNA strand selection: Unwinding the rules, Wiley Interdiscip Rev RNA , 12(3): e1627, 2021.

23.

Romero-Cordoba

, Salido-Guadarrama

, Rodriguez-Dorantes

, miRNA biogenesis: Biological impact in the development of cancer, Cancer Biol Ther , 15(11): 1444–1455, 2014.

24.

Hata

, Kashima

, Dysregulation of microRNA biogenesis machinery in cancer, Crit Rev Biochem Mol Biol , 51(3): 121–134, 2016.

25.

Winter

, Diederichs

, MicroRNA biogenesis and cancer, Methods Mol Biol , 676: 3–22, 2011.

26.

Poursadegh Zonouzi

, Shekari

, Nejatizadeh

, Impaired expression of Drosha in breast cancer, Breast Dis , 37(2): 55–62, 2017.

27.

Yan

, Huang

, Wang

, Dysregulated expression of dicer and drosha in breast cancer, Pathol Oncol Res , 18(2): 343–348, 2012.

28.

Avery-Kiejda

, Braye

, Forbes

, The expression of Dicer and Drosha in matched normal tissues, tumours and lymph node metastases in triple negative breast cancer, BMC Cancer , 14: 253, 2014.

29.

Sung

, Lee

, Choi

, Common genetic polymorphisms of microRNA biogenesis pathway genes and risk of breast cancer: A case-control study in Korea, Breast Cancer Res Treat , 130(3): 939–951, 2011.

30.

Jiang

, Chen

, Wu

, Evaluation of genetic variants in microRNA biosynthesis genes and risk of breast cancer in Chinese women, Int J Cancer , 133(9): 2216–2224, 2013.

31.

Sung

, Jeon

, Lee

, Common genetic polymorphisms of microRNA biogenesis pathway genes and breast cancer survival, BMC Cancer , 12: 195, 2012.

32.

Qian

, Feng

, Zheng

, Genetic variants in microRNA and microRNA biogenesis pathway genes and breast cancer risk among women of African ancestry, Hum Genet , 135(10): 1145–1159, 2016.

33.

Bermisheva

, Takhirova

, Gilyazova

, MicroRNA biogenesis pathway gene polymorphisms are associated with breast cancer risk, Russ J Genet , 54: 568–575, 2018.

34.

Wahid

, Shehzad

, Khan

, Kim

, MicroRNAs: Synthesis, mechanism, function, and recent clinical trials, Biochim Biophys Acta , 1803(11): 1231–1243, 2010.

35.

Peng

, Croce

, The role of MicroRNAs in human cancer, Signal Transduct Target Ther , 1: 15004, 2016.

36.

Cho

, Ko

, Kim

, 3^′-UTR polymorphisms in the MiRNA machinery genes DROSHA, DICER1, RAN, and XPO5 are associated with colorectal cancer risk in a Korean population, PLoS One , 10(7): e0131125, 2015.

37.

Osuch-Wojcikiewicz

, Bruzgielewicz

, Niemczyk

, Association of polymorphic variants of miRNA processing genes with larynx cancer risk in a polish population, Biomed Res Int , 2015: 298378, 2015.

38.

Yang

, Dinney

, Ye

, Evaluation of genetic variants in microRNA-related genes and risk of bladder cancer, Cancer Res , 68(7): 2530–2537, 2008.

39.

Deng

, Zhou

, Fan

, Single nucleotide polymorphisms and cancer susceptibility, Oncotarget , 8(66): 110635–110649, 2017.

40.

Yuan

, Chu

, Wang

, Genetic variation in DROSHA 3^′UTR regulated by hsa-miR-27b is associated with bladder cancer risk, PLoS One , 8(11): e81524, 2013.

41.

Kim

, Choi

, Jin

, Association of a common AGO1 variant with lung cancer risk: A two-stage case-control study, Mol Carcinog , 49(10): 913–921, 2010.

42.

Horikawa

, Wood

, Yang

, Single nucleotide polymorphisms of microRNA machinery genes modify the risk of renal cell carcinoma, Clin Cancer Res , 14(23): 7956–7962, 2008.

43.

Osuch-Wojcikiewicz

, Bruzgielewicz

, Niemczyk

, Association of polymorphic variants of miRNA processing genes with larynx cancer risk in a polish population, Biomed Res Int , 2015: 298378, 2015.

44.

Kim

, Kim

, Lee

, Variation in the dicer and RAN genes are associated with survival in patients with hepatocellular carcinoma, PloS one , 11(9): e0162279, 2016.

45.

Mohammadpour-Gharehbagh

, Heidari

, Eskandari

, Association between genetic polymorphisms in microRNA machinery genes and risk of papillary thyroid carcinoma, Pathol Oncol Res , 26(2): 1235–1241, 2020.

46.

Wen

, Lv

, Ding

, Association of miRNA biosynthesis genes DROSHA and DGCR8 polymorphisms with cancer susceptibility: A systematic review and meta-analysis, Biosci Rep , 38(3): BSR20180072, 2018.

47.

Song

, Zhong

, Wu

, Association between SNPs in microRNA machinery genes and gastric cancer susceptibility, invasion, and metastasis in Chinese Han population, Oncotarget , 8(49): 86435–86446, 2017.

	Cases		Controls
Locus pair	D^′	r ²	D^′	r ²
rs6877842/rs642321	0.553	0.031	0.133	0.001
rs6877842/rs10719	0.388	0.005	0.107	0.007
rs642321/rs10719	0.056	0.001	0.426	0.006

Association of DROSHA rs6877842,rs642321 and rs10719 polymorphisms with increased susceptibility to breast cancer: A case-control study with genotype and haplotype analysis

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSIONS:

Keywords

1. Introduction

2.1. Subjects

2.2. DNA extraction and genotyping of DROSHA SNPs

2.3. Linkage disequilibrium and haplotype analysis

2.4. Statistical analysis

3. Results

3.1. Baseline characteristics of participants

3.3. Association between DROSHA SNPs and patients’ clinicopathological features

Table 8 Pairwise LD of DROSHA rs6877842, rs642321 and rs10719 polymorphisms in breast cancer patients and healthy controls Cases Controls Locus pair D ′ r 2 D ′ r 2 rs6877842/rs642321 0.553 0.031 0.133 0.001 rs6877842/rs10719 0.388 0.005 0.107 0.007 rs642321/rs10719 0.056 0.001 0.426 0.006

Footnotes

Acknowledgements

Ethical approval

Informed consent

Conflict of interest

References