Published online Nov 24, 2025. doi: 10.5306/wjco.v16.i11.112349
Revised: September 4, 2025
Accepted: October 30, 2025
Published online: November 24, 2025
Processing time: 120 Days and 2.3 Hours
MicroRNAs play a key role in regulating gene expression in human cells. Single-nucleotide variants in these molecules have been linked to cancer development, particularly breast cancer (BrC).
To analyze the association of three microRNA polymorphisms with the risk of BrC in women from western Mexico.
This case-control study included 71 women diagnosed with BrC and 215 women without BrC. Genotypes were determined using a real-time polymerase chain reaction allelic discrimination assay. Multiple genetic models - dominant, recessive, over-dominant, additive, and multiple comparison - were applied to assess the risk.
The over-dominant model showed that the C/T genotype of MIR196A2 (rs11614913) is a protective factor against the ductal histological subtype of BrC in women from western Mexico [odds ratio (OR) = 0.4687, 95% confidence interval (CI): 0.2205-0.9963, P = 0.0489]. A protective effect was also observed for the C/A genotype (OR = 0.2612, 95%CI: 0.0900-0.7582, P = 0.0135) and A allele (OR = 0.2826, 95%CI: 0.0993-0.8044, P = 0.0179) of MIR618 (rs2682818). No significant association was found between MIR200C (rs73262897) and BrC risk.
The C/T genotype of rs11614913 in MIR196A2, and C/A genotype and A allele of rs2682818 in MIR618, are associated with a protective effect against BrC in women from western Mexico.
Core Tip: Breast cancer (BrC) is a complex disease influenced by both environmental and genetic factors. Among the latter, acquired or inherited single-nucleotide variants can increase the risk of developing BrC by up to 50%. single-nucleotide variants in microRNAs may alter their function or biogenesis, potentially contributing to oncogenesis. This study aimed to analyze the association of rs11614913 in MIR196A2, rs2682818 in MIR618, and rs73262897 in the regulatory region of MIR200C with the risk of developing BrC in women from western Mexico. The variants rs11614913 and rs2682818 showed a protective effect against BrC in this population.
- Citation: Victorio-De Los Santos M, Rodríguez-López AA, Gutiérrez-Franco J, Rodríguez-Trejo A, Nieves-López ZF, Torres-Valadez R, Zepeda-Carrillo EA, Flores-Reyes MF, Ayón-Pérez MF, Vázquez-Reyes A. Single-nucleotide variants in microRNAs associated with breast cancer in women from western Mexico. World J Clin Oncol 2025; 16(11): 112349
- URL: https://www.wjgnet.com/2218-4333/full/v16/i11/112349.htm
- DOI: https://dx.doi.org/10.5306/wjco.v16.i11.112349
Breast cancer (BrC) is the most common cancer worldwide[1]. In Mexico, the incidence of BrC in 2023 was 23790 cases among individuals over 20 years of age, with 8034 reported deaths. In the state of Nayarit, around 85 new cases of BrC are detected every year[2]. BrC originates in the epithelial cells of the breast and can be classified as either ductal (arising from ductal epithelium) or lobular (arising from lobular epithelium)[1,3]. Invasive ductal carcinoma is the most frequent subtype, followed by invasive lobular carcinoma, ductal/lobular carcinoma, inflammatory BrC, and other rarer types[4-6]. BrC is a complex disease influenced by both environmental and genetic factors. Among the latter, acquired or inherited single-nucleotide variants (SNVs) can increase the risk of developing BrC by up to 50%. Additionally, these variants are involved in disease pathogenesis, prognosis, and response to treatment[7]. These SNVs may occur in both coding and non-coding regions of DNA and can be associated with BrC susceptibility[8]. MicroRNAs (miRNAs) are non-coding RNAs of 20-22 nucleotides that regulate gene expression post-transcriptionally, affecting more than 50% of human genes[9]. When an SNV occurs within the “seed sequence”, a region of 2-5 nucleotides at the 5′ end that determines specificity for mRNA binding, it can alter miRNA function or biogenesis, affecting the expression levels of miRNAs or mRNA-miRNA interactions. miRNAs involved in cancer development are referred to as “oncomirs”[10]. The SNV rs11614913 (C>T) in MIR196A2 has been associated with several cancers, including BrC, particularly in Asian and African populations[11]. This variant influences miRNA concentration and disrupts binding to its mRNA targets[11,12]. Another variant, rs2682818 (C>A) in MIR618, occurs within the structural region and affects miR-618 maturation and expression. It has also been linked to various cancers, including BrC[13,14]. Finally, the SNV rs73262897 (G>A), located 2 kb upstream of MIR200C, has not previously been associated with cancer. However, miR-200c is known to be upregulated in several BrC cell lines[15] and has been associated with cell differentiation status and tumor suppression in human BrC stem cells[16].
Identifying these SNVs could help establish better strategies for the diagnosis, prognosis, and prevention of BrC and may even improve the management of patients with the disease. To date, no association studies have examined the SNVs rs11614913, rs2682818, and rs73262897 in relation to BrC in the Mexican population. This study presents a novel approach by investigating these three SNVs, notably including rs73262897, which has not been studied in any population worldwide, thereby representing an original contribution to the field of oncogenetics. Given the potential importance of these genetic variants in BrC risk and the lack of genetic studies in Latin American populations, it is imperative to investigate underrepresented groups to better understand human genetic diversity and develop personalized medicine strategies and more effective treatments for these populations. Furthermore, this research could contribute to the creation of risk prediction models based on miRNA SNVs, using BrC risk stratification algorithms adapted to the Mexican population. Thus, this study aimed to analyze the association of rs11614913 in MIR196A2, rs2682818 in MIR618, and rs73262897 in the regulatory region of MIR200C with the risk of developing BrC in women from western Mexico.
This case-control study included 71 women diagnosed with BrC, all born and residing in Nayarit, with no history of any other type of cancer. Patients were recruited between June 2021 and July 2022 from the Nayarit State Cancer Center (Instituto Mexicano del Seguro Social-Bienestar) and the Hospital Dr. Aquiles Calles Ramírez of the Institute of Social Security and Services for State Workers. The control group comprised 215 women with no familial relationship to the patients with BrC, maintaining a 3:1 control-to-case ratio. For both groups, the inclusion criteria were age over 18 years, born and settled in Nayarit, and no history of any other type of cancer. Controls with a family history of any type of cancer in first-degree relatives and patients with a history of any other type of cancer were excluded. This study was conducted in the Molecular Biology and Immunology Research Laboratories at the Universidad Autónoma de Nayarit.
The protocol was reviewed, evaluated, and approved by the Research Ethics Committee of the Nayarit State Cancer Center, approval No. 010-CI-CECN-2021. In accordance with the updated version of the Declaration of Helsinki[17], all participants were informed about the objectives and scope of the research, and all of them provided written informed consent before enrolment.
Genomic DNA was extracted from peripheral blood using a modified salting-out method described in 1988 by Miller et al[18]. The DNA samples were quantified using an Eppendorf BioPhotometer™ D30 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, United States), and the DNA concentration was adjusted to 5 ng/μL for polymerase chain reaction (PCR) analysis. Samples were stored at -80 ºC until genotyping analysis.
Genotyping of rs11614913, rs2682818, and rs73262897 was performed using TaqMan™ probes via real-time PCR. Each PCR reaction was carried out in a total volume of 10 μL, consisting of 5 μL of TaqMan™ Genotyping Master Mix, 0.25 μL of a predesigned TaqMan® SNP Genotyping Assay (C__31185852_10 for rs11614913, C____286717_10 for rs2682818, or C__97728464_10 for rs73262897), 3.75 μL of nuclease-free water (all from Thermo Fisher Scientific), and 1 μL of DNA sample (5 ng/μL). Reactions were conducted in an Applied Biosystems™ 7500 thermocycler (Thermo Fisher Scientific), using the manufacturer’s recommended conditions as follows: Initial denaturation at 95 °C for 10 minutes, followed by 40 cycles at 95 °C for 15 seconds and 60 °C for 1 minute. Data were analyzed using SDS software (Thermo Fisher Scientific), with a call rate threshold of 95%. All samples were analyzed in duplicate, and both positive (reference sample) and negative (non-template) controls were included in each genotyping run.
Categorical variables are expressed as frequencies and percentages. Hardy-Weinberg equilibrium was assessed to determine the genetic equilibrium in the control group. Categorical data were compared using the χ2 test or Fisher’s exact test, as appropriate. We used SPSS software version 21.0 (IBM Corp., Armonk, NY, United States). Associations between the three variants and BrC were assessed, and odds ratios (ORs) were calculated using SNP Stats software v0.96 (Barcelona, Spain)[19]. Genetic models - including dominant, recessive, over-dominant, and log-additive - were analyzed, along with multiple-comparison tests. A 95% confidence interval (CI) was applied, and P values < 0.05 were considered statistically significant. To account for multiple testing, the Bonferroni correction method was applied (adjusted P < 0.025). Statistical power (1 - b) for the comparisons was also determined.
To establish the histopathological profile of the patients included in this study, 71 women from the western Mexican population diagnosed with BrC were classified according to histological subtype, hormone receptor (HR) status, and molecular subtype. Based on clinical records, the ductal subtype was the most prevalent, observed in 54.9% of cases. HR-positive (HR+) status was observed in 53.5% of patients, and the most frequent molecular subtype was luminal A (LA), present in 50.7% of cases (Table 1).
| Characteristic | BrC patients |
| n = 71 | |
| Age at diagnosis, mean ± SD | 54.06 ± 10.75 |
| Patients with relatives with cancer | |
| First-degree relative | 22 (31.0) |
| Second-degree relative | 10 (14.1) |
| With no relatives | 39 (54.9) |
| Subtype of BrC | |
| Lobular | 13 (18.3) |
| Ductal | 37 (52.1) |
| Mixed | 5 (7.0) |
| Not determined | 16 (22.5) |
| BrC hormone receptor status | |
| HR+ | 38 (53.5) |
| HER+ | 9 (12.7) |
| Triple-negative BrC | 5 (7.0) |
| Triple-positive BrC | 13 (18.3) |
| Not determined | 6 (8.5) |
| Molecular subtype | |
| Luminal A | 36 (50.7) |
| Luminal B | 13 (18.3) |
| HER2 | 9 (12.7) |
| Triple negative | 5 (7.0) |
| Not determined | 8 (11.3) |
To evaluate the association between the selected SNVs and BrC in the studied population, we analyzed three genetic variants - rs11614913, rs2682818, and rs73262897 - using dominant, recessive, over-dominant, log-additive, and multiple-comparison genetic models. The genotypic distributions of rs11614913, rs2682818, and rs73262897 in the control group were in accordance with Hardy-Weinberg equilibrium (P = 0.89, P = 0.81, and P = 0.8, respectively).
The allelic and genotypic frequencies of rs11614913, rs2682818, and rs73262897 are presented in Table 2. No significant association was found between rs11614913 or rs73262897 and BrC in any of the genetic models analyzed. Similarly, no significant differences in risk were detected between the reference and risk alleles for these variants (Table 2). In contrast, rs2682818 showed a significant association in the multiple-comparison model (C/C vs C/A), with an OR of 0.2612 (95%CI: 0.0900-0.7582, P = 0.0135, statistical power = 80%), indicating that the C/A genotype is a protective factor. Similarly, the A allele was also associated with a reduced risk of developing BrC, demonstrating a protective effect (OR = 0.2826, 95%CI: 0.0993-0.8044, P = 0.0179, statistical power = 69%) (Table 2).
| Genetic model | Genotypes | BrC cases (n = 71) | Controls (n = 215) | OR | 95%CI | P value |
| rs11614913 C>T | ||||||
| MC | C/C | 27 (38) | 77 (36) | 1.00 | - | - |
| C/T | 26 (37) | 102 (47) | 0.7296 | 0.3932-1.3439 | 0.3091 | |
| T/T | 18 (25) | 36 (17) | 1.4259 | 0.6970-2.9171 | 0.3312 | |
| Do | C/C | 27 (38) | 77 (36) | 1.00 | - | - |
| C/T + T/T | 44 (62) | 138 (64) | 0.9093 | 0.5223-1.5829 | 0.7367 | |
| Re | C/C + C/T | 53 (75) | 179 (83) | 1.00 | - | - |
| T/T | 18 (25) | 36 (17) | 1.6887 | 0.8873-3.2173 | 0.1105 | |
| OD | C/C + T/T | 45 (63) | 113 (53) | 1.00 | - | - |
| C/T | 26 (37) | 102 (47) | 0.6401 | 0.3685-1.1117 | 0.1132 | |
| L-ad | - | - | - | 0.89 | 0.61-1.28 | 0.52 |
| Allele | ||||||
| C | 80 (56) | 256 (60) | 0.89 | 0.7770-1.6732 | 0.5024 | |
| T | 62 (44) | 174 (40) | ||||
| rs2682818 C>A | ||||||
| MC | C/C | 67 (94) | 175 (81) | 1.00 | - | - |
| C/A | 4 (6) | 40 (19) | 0.2612 | 0.0900-0.7582 | 0.0135a,b | |
| A/A | 0 (0) | 0 (0) | ND | ND | ND | |
| Allele | ||||||
| C | 138 (97) | 390 (91) | 0.2826 | 0.0993-0.8044 | 0.0179a,b | |
| A | 4 (3) | 40 (9) | ||||
| rs73262897 G>A | ||||||
| MC | G/G | 54 (76) | 171 (79.5) | 1.00 | - | - |
| G/A | 17 (24) | 43 (20) | 1.2519 | 0.6605-2.3730 | 0.4910 | |
| A/A | 0 (0) | 1 (0.5) | ND | ND | ND | |
| Allele | ||||||
| G | 125 (88) | 385 (90) | 1.1636 | 0.6428-2.1061 | 0.6168 | |
| A | 17 (12) | 45 (10) | ||||
The association of the variants was analyzed according to histological subtypes - ductal, lobular, and mixed - as well as immunohistochemical subtypes of BrC: HR+/LA, triple-positive/Luminal B, human epidermal growth factor receptor 2-positive, and triple-negative (Tables 3, 4, and 5). A significant association was found between the C/T genotype of rs11614913 in MIR196A2 and the ductal subtype of BrC under the over-dominant genetic model (C/C + T/T vs C/T) (OR = 0.4687, 95%CI: 0.2205-0.9963, P = 0.0489; not significant after Bonferroni correction). This finding suggests that the C/T genotype acts as a protective factor against BrC development in women from western Mexico, specifically in Nayarit (Table 3).
| rs11614913 C>T | Cases | Control | Model comparison | OR (95%CI) | P value | ||
| Breast cancer ductal | |||||||
| Genotype | - | n = 37 | n = 215 | - | C/C vs C/T | 0.7296 (0.3932-1.3439) | 0.3091 |
| C/C | 15 (40) | 77 (36) | MC | C/C vs T/T | 1.4259 (0.6970-2.9171) | 0.3312 | |
| C/T | 11 (30) | 102 (47) | - | C vs T | 1.1402 (0.7770-1.6732) | 0.5024 | |
| T/T | 11 (30) | 36 (17) | Do | C/C vs C/T + T/T | 0.8184 (0.4011-1.6696) | 0.5816 | |
| Allele | - | Re | C/C + C/T vs T/T | 2.1036 (0.9541-4.6383) | 0.0653 | ||
| C | 41 (55) | 256 (60) | OD | C/C + T/T vs C/T | 0.4687 (0.2205-0.9963) | 0.0489a | |
| T | 33 (45) | 174 (40) | L-ad | 0.85 (0.53-1.38) | 0.5200 | ||
| Breast cancer lobular | |||||||
| Genotype | - | n = 13 | n = 215 | MC | C/C vs C/T | - | - |
| C/C | 6 (36) | 77 (36) | MC | C/C vs T/T | - | - | |
| C/T | 5 (47) | 102 (47) | - | C vs T | - | - | |
| T/T | 2 (15) | 36 (17) | Do | C/C vs C/T + T/T | - | - | |
| Allele | - | Re | C/C + C/T vs T/T | - | - | ||
| C | 17 (65) | 256 (60) | OD | C/C + T/T vs C/T | 0.6924 (0.2195-2.1845) | 0.5306 | |
| T | 9 (35) | 174 (40) | L-ad | 1.28 (0.56-2.91) | 0.5600 | ||
| Breast cancer mixed | |||||||
| Genotype | - | n = 5 | n = 215 | - | C/C vs C/T | - | - |
| C/C | 1 (20) | 77 (36) | MC | C/C vs T/T | - | - | |
| C/T | 4 (80) | 102 (47) | - | C vs T | - | - | |
| T/T | 0 (0) | 36 (17) | Do | C/C vs C/T + T/T | - | - | |
| Allele | - | Re | C/C + C/T vs T/T | - | - | ||
| C | 6 (60) | 256 (60) | OD | C/C + T/T vs C/T | - | - | |
| T | 4 (40) | 174 (40) | L-ad | - | - | ||
| Breast cancer HR+/LA | |||||||
| Genotype | - | n = 36 | n = 215 | - | C/C vs C/T | 1.5139 (0.6968-3.2891) | 0.2948 |
| C/C | 16 (44) | 77 (36) | MC | C/C vs T/T | 0.8021 (0.2897-2.2206) | 0.6712 | |
| C/T | 14 (39) | 102 (47) | C vs T | 0.8316 (0.4954-1.3960) | 0.4853 | ||
| T/T | 6 (17) | 36 (17) | Do | C/C vs C/T + T/T | 0.6975 (0.3415-1.4244) | 0.3227 | |
| Allele | - | Re | C/C + C/T vs T/T | 0.9944 (0.3858-2.5632) | 0.9908 | ||
| C | 46 (64) | 256 (60) | OD | C/C + T/T vs C/T | 0.7050 (0.3426-1.4506) | 0.3424 | |
| T | 26 (36) | 174 (40) | L-ad | 1.20 (0.72-1.99) | 0.4900 | ||
| Breast cancer TP/LB | |||||||
| Genotype | - | n = 13 | n = 215 | - | C/C vs C/T | - | - |
| C/C | 4 (31) | 77 (36) | MC | C/C vs T/T | - | - | |
| C/T | 3 (23) | 102 (47) | - | C vs T | 2.0063 (0.9001-4.4718) | 0.0886 | |
| T/T | 6 (46) | 36 (17) | Do | C/C vs C/T + T/T | - | - | |
| Allele | - | Re | C/C + C/T vs T/T | - | - | ||
| C | 11 (42) | 256 (60) | OD | C/C + T/T vs C/T | 0.33 (0.089-1.241) | 0.1013 | |
| T | 15 (58) | 174 (40) | L-ad | 0.51 (0.230-1.130) | 0.0940 | ||
| Breast cancer HER2+ | |||||||
| Genotype | - | n = 9 | n = 215 | - | C/C vs C/T | - | - |
| C/C | 8 (89) | 77 (36) | MC | C/C vs T/T | - | - | |
| C/T | 1 (11) | 102 (47) | - | C vs T | 1.4713 (0.5725-3.7808) | 0.4226 | |
| T/T | 0 (0) | 36 (17) | Do | C/C vs C/T + T/T | - | - | |
| Allele | - | Re | C/C + C/T vs T/T | - | - | ||
| C | 17 (94) | 256 (60) | OD | C/C + T/T vs C/T | - | - | |
| T | 1 (6) | 174 (40) | L-ad | 0.69 (0.27-1.74) | 0.4300 | ||
| Breast cancer TN | |||||||
| Genotype | - | n = 5 | n = 215 | - | C/C vs C/T | - | - |
| C/C | 5 (100) | 77 (36) | MC | C/C vs T/T | - | - | |
| C/T | 0 (0) | 102 (47) | - | C vs T | - | - | |
| T/T | 0 (0) | 36 (17) | Do | C/C vs C/T + T/T | - | - | |
| Allele | - | Re | C/C + C/T vs T/T | - | - | ||
| C | 10 (100) | 256 (60) | OD | C/C + T/T vs C/T | - | - | |
| T | 0 (0) | 174 (40) | L-ad | 0.69 (0.20-2.37) | 0.5500 | ||
| rs2682818 C>A | Cases | Control | Model comparison | OR (95%CI) | P value | ||
| Breast cancer ductal | |||||||
| Genotype | - | n = 37 | n = 215 | - | C/C vs C/A | 0.1215 (0.0162-0.9129) | 0.0405a |
| C/C | 36 (97) | 175 (81) | MC | C/C vs A/A | - | - | |
| C/A | 1 (3) | 40 (19) | - | C vs A | 0.1336 (0.0181-0.9869) | 0.0485a | |
| A/A | 0 (0) | 0 (0) | Do | C/C vs C/A + A/A | - | - | |
| Allele | Re | C/C+C/A vs A/A | - | - | |||
| C | 73 (99) | 390 (91) | OD | C/C+A/A vs C/A | - | - | |
| A | 1 (1) | 40 (9) | L-ad | - | - | ||
| Breast cancer lobular | |||||||
| Genotype | - | n = 13 | n = 215 | - | C/C vs C/A | - | - |
| C/C | 12 (92) | 175 (81) | MC | C/C vs A/A | - | - | |
| C/A | 1 (8) | 40 (19) | - | C vs A | - | - | |
| A/A | 0 (0) | 0 (0) | Do | C/C vs C/A + A/A | - | - | |
| Allele | - | Re | C/C+C/A vs A/A | - | - | ||
| C | 25 (96) | 390 (91) | OD | C/C+A/A vs C/A | - | - | |
| A | 1 (4) | 40 (9) | L-ad | - | - | ||
| Breast cancer mixed | |||||||
| Genotype | - | n = 5 | n = 215 | - | C/C vs C/A | - | - |
| C/C | 5 (100) | 175 (81) | MC | C/C vs A/A | - | - | |
| C/A | 1 (8) | 40 (19) | - | C vs A | - | - | |
| A/A | 0 (0) | 0 (0) | Do | C/C vs C/A + A/A | - | - | |
| Allele | - | Re | C/C + C/A vs A/A | - | - | ||
| C | 10 (100) | 390 (91) | OD | C/C + A/A vs C/A | - | - | |
| A | 0 (0) | 40 (9) | L-ad | - | - | ||
| Breast cancer HR+/LA | |||||||
| Genotype | - | n = 36 | n = 215 | - | C/C vs C/A | 0.1250 (0.0166-0.9397) | 0.0433a |
| C/C | 35 (97) | 175 (81) | MC | C/C vs A/A | - | - | |
| C/A | 1 (3) | 40 (19) | - | C vs A | 0.1373 (0.0186-1.0151) | 0.0517 | |
| A/A | 0 (0) | 0 (0) | Do | C/C vs C/A + A/A | - | - | |
| Allele | - | Re | C/C + C/A vs A/A | - | - | ||
| C | 71 (99) | 390 (91) | OD | C/C + A/A vs C/A | - | - | |
| A | 1 (1) | 40 (9) | L-ad | - | - | ||
| Breast cancer TP/LB | |||||||
| Genotype | - | n = 13 | n = 215 | - | C/C vs C/A | - | - |
| C/C | 12 (92) | 175 (81) | MC | C/C vs A/A | - | - | |
| C/A | 1 (8) | 40 (19) | - | C vs A | - | - | |
| A/A | 0 (0) | 0 (0) | Do | C/C vs C/A + A/A | - | - | |
| Allele | - | Re | C/C + C/A vs A/A | - | - | ||
| C | 25 (96) | 390 (91) | OD | C/C + A/A vs C/A | - | - | |
| A | 1 (4) | 40 (9) | L-ad | - | - | ||
| Breast cancer HER2+ | |||||||
| Genotype | - | n = 9 | n = 215 | - | C/C vs C/A | - | - |
| C/C | 8 (89) | 175 (81) | MC | C/C vs A/A | - | - | |
| C/A | 1 (11) | 40 (19) | - | C vs A | - | - | |
| A/A | 0 (0) | 0 (0) | Do | C/C vs C/A + A/A | - | - | |
| Allele | - | Re | C/C + C/A vs A/A | - | - | ||
| C | 17 (94) | 390 (91) | OD | C/C + A/A vs C/A | - | - | |
| A | 1 (6) | 40 (9) | L-ad | - | - | ||
| Breast cancer TN | |||||||
| Genotype | - | n = 5 | n = 215 | - | C/C vs C/A | - | - |
| C/C | 5 (100) | 175 (81) | MC | C/C vs A/A | - | - | |
| C/A | 0 (0) | 40 (19) | - | C vs A | - | - | |
| A/A | 0 (0) | 0 (0) | Do | C/C vs C/A + A/A | - | - | |
| Allele | - | Re | C/C + C/A vs A/A | - | - | ||
| C | 10 (100) | 390 (91) | OD | C/C + A/A vs C/A | - | - | |
| A | 0 (0) | 40 (9) | L-ad | - | - | ||
| rs73262897 G>A | Cases | Control | Model comparison | OR (95%CI) | P value | ||
| Breast cancer ductal | |||||||
| Genotype | - | n = 37 | n = 215 | - | G/G vs G/A | 1.0970 (0.4683-2.5696) | 0.8311 |
| G/G | 29 (78) | 171 (80) | MC | G/G vs A/A | - | - | |
| G/A | 8 (22) | 43 (20) | - | G vs A | 1.0370 (0.4678-2.2990) | 0.9287 | |
| A/A | 0 (0) | 1 (0) | Do | G/G vs G/A + A/A | - | - | |
| Allele | - | Re | G/G + G/A vs A/A | - | - | ||
| G | 66 (89) | 385 (90) | OD | G/G + A/A vs G/A | - | - | |
| A | 8 (11) | 45 (10) | L-ad | - | - | ||
| Breast cancer lobular | |||||||
| Genotype | - | n = 13 | n = 215 | - | G/G vs G/A | - | - |
| G/G | 10 (77) | 171 (80) | MC | G/G vs A/A | - | - | |
| G/A | 3 (23) | 43 (20) | - | G vs A | - | - | |
| A/A | 0 (0) | 1 (0) | Do | G/G vs G/A + A/A | - | - | |
| Allele | - | Re | G/G + G/A vs A/A | - | - | ||
| G | 23 (88) | 385 (90) | OD | G/G + A/A vs G/A | - | - | |
| A | 3 (12) | 45 (10) | L-ad | - | - | ||
| Breast cancer mixed | |||||||
| Genotype | - | n = 5 | n = 215 | - | G/G vs G/A | - | - |
| G/G | 2 (40) | 171 (80) | MC | G/G vs A/A | - | - | |
| G/A | 3 (60) | 43 (20) | - | G vs A | - | - | |
| A/A | 0 (0) | 1 (0) | Do | G/G vs G/A + A/A | - | - | |
| Allele | - | Re | G/G + G/A vs A/A | - | - | ||
| G | 7 (70) | 385 (90) | OD | G/G + A/A vs G/A | - | - | |
| A | 3 (30) | 45 (10) | L-ad | - | - | ||
| Breast cancer HR+/LA | |||||||
| Genotype | - | n = 36 | n = 215 | - | G/G vs G/A | 1.5295 (0.6857-3.4119) | 0.2992 |
| G/G | 26 (72) | 171 (80) | MC | G/G vs A/A | - | - | |
| G/A | 10 (28) | 43 (20) | - | G vs A | 1.3799 (0.6611-2.8802) | 0.3910 | |
| A/A | 0 (0) | 1 (0) | Do | G/G vs G/A + A/A | - | - | |
| Allele | - | Re | G/G + G/A vs A/A | - | - | ||
| G | 62 (86) | 385 (90) | OD | G/G + A/A vs G/A | - | - | |
| A | 10 (14) | 45 (10) | L-ad | - | - | ||
| Breast cancer TP/LB | |||||||
| Genotype | - | n = 13 | n = 215 | - | G/G vs G/A | - | - |
| G/G | 10 (77) | 171 (80) | MC | G/G vs A/A | - | - | |
| G/A | 3 (23) | 43 (20) | - | G vs A | - | - | |
| A/A | 0 (0) | 1 (0) | Do | G/G vs G/A + A/A | - | - | |
| Allele | - | Re | G/G + G/A vs A/A | - | - | ||
| G | 23 (88) | 385 (90) | OD | G/G + A/A vs G/A | - | - | |
| A | 3 (12) | 45 (10) | L-ad | - | - | ||
| Breast cancer HER2+ | |||||||
| Genotype | - | n = 9 | n = 215 | - | G/G vs G/A | - | - |
| G/G | 7 (78) | 171 (80) | MC | G/G vs A/A | - | - | |
| G/A | 2 (22) | 43 (20) | - | G vs A | - | - | |
| A/A | 0 (0) | 1 (0) | Do | G/G vs G/A + A/A | - | - | |
| Allele | - | Re | G/G + G/A vs A/A | - | - | ||
| G | 16 (89) | 385 (90) | OD | G/G + A/A vs G/A | - | - | |
| A | 2 (11) | 45 (10) | L-ad | - | - | ||
| Breast cancer TN | |||||||
| Genotype | - | n = 5 | n = 215 | - | G/G vs G/A | - | - |
| G/G | 5 (100) | 171 (80) | MC | G/G vs A/A | - | - | |
| G/A | 0 (0) | 43 (20) | - | G vs A | - | - | |
| A/A | 0 (0) | 1 (0) | Do | G/G vs G/A + A/A | - | - | |
| Allele | - | Re | G/G + G/A vs A/A | - | - | ||
| G | 10 (100) | 385 (90) | OD | G/G + A/A vs G/A | - | - | |
| A | 0 (0) | 45 (10) | L-ad | - | - | ||
Additionally, rs2682818 in MIR618 was associated with ductal BrC, where the C/A genotype acted as a protective factor (C/C vs C/A; OR = 0.1215, 95%CI: 0.0162–0.9129, P = 0.0405; not significant after Bonferroni correction). The A allele also showed a protective effect against ductal BrC (OR = 0.1336, 95%CI: 0.0181-0.9869, P = 0.0485; not significant after Bonferroni correction). Furthermore, the C/A genotype of rs2682818 was associated with the HR+/LA BrC subtype in the multiple-comparison model (C/C vs C/A; OR = 0.1250, 95%CI: 0.0166-0.9397, P = 0.0433; not significant after Bonferroni correction), indicating a protective effect of this genotype as well (Table 4).
Finally, the C/T genotype of rs11614913 and the C/A genotype and A allele of rs2682818 were statistically significantly associated with a protective effect against BrC. In contrast, no significant association was observed between rs73262897 and BrC, or with any of its histological or immunohistochemical subtypes, in the western Mexican population (Table 5).
In this study, we analyzed the association of the SNVs rs11614913 in MIR196A2, rs2682818 in MIR618, and rs73262897 in the regulatory region of MIR200C with BrC and its histological and immunohistochemical subtypes in a population from western Mexico. Our findings revealed that rs11614913 and rs2682818 were associated with a protective effect against BrC, while rs73262897 showed no significant association with the disease. The identification of protective genetic variants in miRNA genes represents a meaningful step forward in understanding BrC susceptibility within the Mexican population. These results carry potential clinical relevance for risk stratification and the advancement of personalized medicine, especially considering the population-specific effects of genetic variants.
The SNV rs11614913 (C>T) in MIR196A2 demonstrated a protective effect specifically for the ductal histological subtype under the over-dominant genetic model. This variant may influence miRNA maturation, leading to changes in concentration and alterations in the mature sequence of miR-196a-3p[11,12], thereby reducing its ability to regulate target genes such as HOX, HOXB3, HOXC3, HOXB5, HOXB8, GADD45G, INHBB, HMGA1, HMGA2, ANXA1, S100A9, SPRR2C, KRTS, and ERG, among others. These genes have been implicated in key malignancy-related processes, including proliferation, differentiation, and metastasis[20-23]. Evidence also indicates that MIR196A2 serves as a prognostic biomarker in HR+ BrC[12], which aligns with our findings, as 53.5% of the study population presented with HR+ tumors. Moreover, rs11614913 has been associated with improved prognosis in various cancers, including lung cancer[11], suggesting that the protective effect observed in our population may not only contribute to BrC prevention but may also play a role in influencing disease progression and outcomes.
The association of rs11614913 (C>T) with BrC has been extensively studied across various populations worldwide, yielding heterogeneous results that underscore the importance of population-specific research. In contrast to our findings, several studies have reported no association between rs11614913 and BrC in Australian[24], Chilean[22], and Iranian[25] populations. Conversely, a meta-analysis conducted in 2022 by Aziz et al[12] revealed a strong association between this SNV and BrC as a protective factor - consistent with our observation that the C/T genotype of rs11614913 was associated with the ductal histological subtype under the over-dominant model. These discrepancies may be explained by differences in ethnic and genetic backgrounds, which can influence the functional impact of genetic variants. Such variability has been observed between Indian and European populations[11], as well as within different groups in western Mexico[26].
The functional impact of rs11614913 in MIR196A2 has been well documented across multiple cancer types, particularly in Asian and African populations[11]. This variant alters miRNA concentration and disrupts binding to its mRNA targets[11,12]. In vitro studies have shown that cells carrying the T allele express lower levels of mature miRNAs compared to those carrying the C allele, significantly affecting their ability to function as gene silencers and regulatory molecules[27]. This differential expression pattern may help explain the protective effect observed in our western Mexican population, where the heterozygous C/T genotype was associated with a reduced risk of the ductal BrC subtype.
Regarding rs2682818 in MIR618, our study showed that the A allele acts as a protective factor against BrC, particularly for the ductal subtype and the HR+/LA subtypes. This finding is clinically significant because it represents the first report of this association in a Mexican population and contributes to the growing body of evidence supporting the role of MIR618 variants in cancer susceptibility. The rs2682818 variant has been associated with reduced maturation of miR-618[22] and with BrC tumorigenesis, and it was previously considered a risk biomarker for BrC development[13]. The protective effect of the A allele in our population may be related to altered miRNA processing or regulation of target genes, potentially affecting key oncogenic or tumor suppressor pathways involved in BrC. Our results are consistent with those reported in 2021 by Nguyen et al[28] in a Vietnamese population, where no risk association was found and the A allele of rs2682818 was suggested to play a protective role against malignancy. This concordance between Mexican and Vietnamese populations may reflect shared genetic mechanisms, possibly related to similar ancestral backgrounds or convergent evolutionary pressures influencing miRNA function in these populations.
The results of our study differ from those of other reports that identified an association between rs2682818 and increased risk of BrC. In 2016, Morales et al[22] identified this variant as being associated with a higher risk of early-onset BrC in a Chilean population, with a 1.6-fold increased risk in carriers (P = 0.04). Similarly, in 2021, Najafian-Najafabady et al[29] reported an association between this SNV and BrC risk in an Iranian population, suggesting that it could serve as a novel risk marker for the disease. In a meta-analysis, Feng et al[13] evaluated the association of rs2682818 with several types of cancer. Specifically for BrC, they found that the C/A genotype increased the risk of developing the malignancy, while the C/C genotype - under the dominant model - also conferred increased risk. They concluded that this variant is linked to tumorigenesis and could be considered a potential biomarker for BrC development[13]. These contrasting results, compared with our finding that the C/A genotype was associated with a protective effect, underscore the importance of genetic ancestry in modulating the functional impact of miRNA polymorphisms. The discrepancy may, in part, be explained by population structure - Feng et al’s meta-analysis[13] included a predominantly Asian (70%) population, whereas the genetic makeup of the Nayarit population comprises approximately 51% Native Amerindian, 37% European, and 12% African ancestry[30]. This unique admixed background in western Mexico may lead to distinct patterns of linkage disequilibrium and functional interactions that influence the effect of rs2682818 on BrC susceptibility.
The observed population-specific effects of both SNVs - rs11614913 (C>T) in MIR196A2 and rs2682818 in MIR618 - carry important implications for personalized medicine approaches in the prevention and management of BrC. The protective effects identified in our western Mexican population may be attributed to specific haplotype structures, gene-gene interactions, or population-specific environmental factors that influence miRNA function. These findings underscore the importance of conducting large-scale, population-based studies to develop accurate genetic risk assessment tools that can be effectively applied in diverse healthcare settings.
Finally, our statistical analysis revealed no association between rs73262897, located in the regulatory region of MIR200C, and BrC or any of its histological or immunohistochemical subtypes in the western Mexican population. This study represents the first investigation of this SNV in a population diagnosed with BrC. These findings further highlight the importance of population-specific genetic association studies, as the functional impact of genetic variants may differ significantly across ethnic and geographic groups.
Although a protective association was observed in a relatively small sample of women from western Mexico, these results should be considered preliminary. Therefore, future studies with larger sample sizes are essential to validate and strengthen these results.
The present study contributes meaningfully to the understanding of genetic variants associated with BrC risk in understudied and admixed populations. Although the SNVs related to BrC remain to be identified, our findings highlight two variants acting as protective factors for BrC development in the western Mexican population: The C/T genotype of rs11614913 in MIR196A2 and the C/A genotype and A allele of rs2682818 in MIR618. Despite the relatively small sample size, the consistency of our results with protective effects reported in other populations (Asian for rs11614913 and Vietnamese for rs2682818) supports the biological plausibility of these associations. Continued identification and analysis of genetic variants influencing BrC risk will deepen our understanding of the disease and support the development of evidence-based public health strategies to improve its prevention, diagnosis, and management in diverse populations.
As personalized medicine continues to gain traction for its potential to deliver targeted cancer therapies with minimal side effects, our findings offer a valuable contribution to the advancement of precision oncology in underrepresented populations. By shedding light on genetic factors unique to the western Mexican population, this study helps bridge existing disparities in cancer care and outcomes.
We thank all participants and patients for their valuable contributions and willingness to take part in the study.
| 1. | Guo L, Kong D, Liu J, Zhan L, Luo L, Zheng W, Zheng Q, Chen C, Sun S. Breast cancer heterogeneity and its implication in personalized precision therapy. Exp Hematol Oncol. 2023;12:3. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 23] [Cited by in RCA: 163] [Article Influence: 81.5] [Reference Citation Analysis (0)] |
| 2. | Angelica Martinez G G. Estadísticas a propósito del día internacional de la lucha contra el cáncer de mama (19 de octubre). Oct 16, 2024. [cited 24 July 2025]. Available from: https://metropolibahia.com/archivos/47143. |
| 3. | Manson EN, Achel DG. Fighting breast cancer in low-and-middle-income countries – What must we do to get every woman screened on regular basis? Sci Afr. 2023;21:e01848. [DOI] [Full Text] |
| 4. | Bydoun M, Marcato P, Dellaire G. Breast Cancer Genomics. In: Dellaire G, Berman JN, Arceci RJ, editors. Cancer Genomics. Amsterdam: Elsevier, 2014: 213-232. |
| 5. | Loibl S, Poortmans P, Morrow M, Denkert C, Curigliano G. Breast cancer. Lancet. 2021;397:1750-1769. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 391] [Cited by in RCA: 976] [Article Influence: 244.0] [Reference Citation Analysis (0)] |
| 6. | Waks AG, Winer EP. Breast Cancer Treatment: A Review. JAMA. 2019;321:288-300. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 1800] [Cited by in RCA: 2962] [Article Influence: 493.7] [Reference Citation Analysis (0)] |
| 7. | Rivenbark AG, O'Connor SM, Coleman WB. Molecular and cellular heterogeneity in breast cancer: challenges for personalized medicine. Am J Pathol. 2013;183:1113-1124. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 144] [Cited by in RCA: 225] [Article Influence: 18.8] [Reference Citation Analysis (0)] |
| 8. | Bose M, Benada J, Thatte JV, Kinalis S, Ejlertsen B, Nielsen FC, Sørensen CS, Rossing M. A catalog of curated breast cancer genes. Breast Cancer Res Treat. 2022;191:431-441. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in RCA: 11] [Reference Citation Analysis (0)] |
| 9. | Johanson TM, Skinner JP, Kumar A, Zhan Y, Lew AM, Chong MM. The role of microRNAs in lymphopoiesis. Int J Hematol. 2014;100:246-253. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 26] [Cited by in RCA: 29] [Article Influence: 2.6] [Reference Citation Analysis (0)] |
| 10. | Esquela-Kerscher A, Slack FJ. Oncomirs - microRNAs with a role in cancer. Nat Rev Cancer. 2006;6:259-269. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 5384] [Cited by in RCA: 5627] [Article Influence: 296.2] [Reference Citation Analysis (0)] |
| 11. | Liu Y, He A, Liu B, Zhong Y, Liao X, Yang J, Chen J, Wu J, Mei H. rs11614913 polymorphism in miRNA-196a2 and cancer risk: an updated meta-analysis. Onco Targets Ther. 2018;11:1121-1139. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 22] [Cited by in RCA: 24] [Article Influence: 3.4] [Reference Citation Analysis (0)] |
| 12. | Aziz MA, Akter T, Islam MS. Effect of miR-196a2 rs11614913 Polymorphism on Cancer Susceptibility: Evidence From an Updated Meta-Analysis. Technol Cancer Res Treat. 2022;21:15330338221109798. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in RCA: 7] [Reference Citation Analysis (0)] |
| 13. | Feng X, Ji D, Liang C, Fan S. Does miR-618 rs2682818 variant affect cancer susceptibility? Evidence from 10 case-control studies. Biosci Rep. 2019;39:BSR20190741. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 6] [Cited by in RCA: 10] [Article Influence: 1.7] [Reference Citation Analysis (0)] |
| 14. | Zheng Y, Lu T, Xie X, He Q, Lu L, Zhong W. miR-618 rs2682818 C>A polymorphism decreases Hirschsprung disease risk in Chinese children. Biosci Rep. 2020;40:BSR20193989. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 1] [Cited by in RCA: 7] [Article Influence: 1.4] [Reference Citation Analysis (0)] |
| 15. | Wang J, Zhao H, Tang D, Wu J, Yao G, Zhang Q. Overexpressions of microRNA-9 and microRNA-200c in human breast cancers are associated with lymph node metastasis. Cancer Biother Radiopharm. 2013;28:283-288. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 29] [Cited by in RCA: 34] [Article Influence: 2.8] [Reference Citation Analysis (0)] |
| 16. | Shimono Y, Zabala M, Cho RW, Lobo N, Dalerba P, Qian D, Diehn M, Liu H, Panula SP, Chiao E, Dirbas FM, Somlo G, Pera RA, Lao K, Clarke MF. Downregulation of miRNA-200c links breast cancer stem cells with normal stem cells. Cell. 2009;138:592-603. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 972] [Cited by in RCA: 958] [Article Influence: 59.9] [Reference Citation Analysis (0)] |
| 17. | World Medical Association. WMA Declaration Of Helsinki – Ethical Principles For Medical Research Involving Human Subjects. [cited 24 July 2025]. Available from: https://www.e-dmj.org/file/WMA-Declaration_of_Helsinki-2013.pdf. |
| 18. | Miller SA, Dykes DD, Polesky HF. A simple salting out procedure for extracting DNA from human nucleated cells. Nucleic Acids Res. 1988;16:1215. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 13387] [Cited by in RCA: 14504] [Article Influence: 392.0] [Reference Citation Analysis (0)] |
| 19. | Solé X, Guinó E, Valls J, Iniesta R, Moreno V. SNPStats: a web tool for the analysis of association studies. Bioinformatics. 2006;22:1928-1929. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 1299] [Cited by in RCA: 1556] [Article Influence: 81.9] [Reference Citation Analysis (0)] |
| 20. | Chen C, Zhang Y, Zhang L, Weakley SM, Yao Q. MicroRNA-196: critical roles and clinical applications in development and cancer. J Cell Mol Med. 2011;15:14-23. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 175] [Cited by in RCA: 179] [Article Influence: 12.8] [Reference Citation Analysis (0)] |
| 21. | Kozomara A, Birgaoanu M, Griffiths-Jones S. miRBase: from microRNA sequences to function. Nucleic Acids Res. 2019;47:D155-D162. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 1834] [Cited by in RCA: 3094] [Article Influence: 618.8] [Reference Citation Analysis (0)] |
| 22. | Morales S, Gulppi F, Gonzalez-Hormazabal P, Fernandez-Ramires R, Bravo T, Reyes JM, Gomez F, Waugh E, Jara L. Association of single nucleotide polymorphisms in Pre-miR-27a, Pre-miR-196a2, Pre-miR-423, miR-608 and Pre-miR-618 with breast cancer susceptibility in a South American population. BMC Genet. 2016;17:109. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 48] [Cited by in RCA: 57] [Article Influence: 6.3] [Reference Citation Analysis (0)] |
| 23. | Yekta S, Shih IH, Bartel DP. MicroRNA-directed cleavage of HOXB8 mRNA. Science. 2004;304:594-596. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 1267] [Cited by in RCA: 1291] [Article Influence: 61.5] [Reference Citation Analysis (0)] |
| 24. | Jedlinski DJ, Gabrovska PN, Weinstein SR, Smith RA, Griffiths LR. Single nucleotide polymorphism in hsa-mir-196a-2 and breast cancer risk: a case control study. Twin Res Hum Genet. 2011;14:417-421. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 36] [Cited by in RCA: 43] [Article Influence: 3.3] [Reference Citation Analysis (0)] |
| 25. | Mashayekhi S, Saeidi Saedi H, Salehi Z, Soltanipour S, Mirzajani E. Effects of miR-27a, miR-196a2 and miR-146a polymorphisms on the risk of breast cancer. Br J Biomed Sci. 2018;75:76-81. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 22] [Cited by in RCA: 28] [Article Influence: 4.0] [Reference Citation Analysis (0)] |
| 26. | Torres-Valadez R, Roman S, Ojeda-Granados C, Gonzalez-Aldaco K, Panduro A. Differential distribution of gene polymorphisms associated with hypercholesterolemia, hypertriglyceridemia, and hypoalphalipoproteinemia among Native American and Mestizo Mexicans. World J Hepatol. 2022;14:1408-1420. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 3] [Cited by in RCA: 5] [Article Influence: 1.7] [Reference Citation Analysis (1)] |
| 27. | Hoffman AE, Zheng T, Yi C, Leaderer D, Weidhaas J, Slack F, Zhang Y, Paranjape T, Zhu Y. microRNA miR-196a-2 and breast cancer: a genetic and epigenetic association study and functional analysis. Cancer Res. 2009;69:5970-5977. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 285] [Cited by in RCA: 297] [Article Influence: 18.6] [Reference Citation Analysis (0)] |
| 28. | Nguyen TNT, Huynh HL, Nguyen TH. Association of single nucleotide polymorphisms in miR-101 and Pre- miR618 with breast cancer susceptibility in Vietnamese population. Res J Biotech. 2021;16:95-102. |
| 29. | Najafian-Najafabady A, Ebrahimi N, Vallian S. rs2682818/MiR-618 is a novel marker associated with increased risk of breast cancer in the Iranian population. Arch Biol Sci (Beogr). 2021;73:457-463. [RCA] [DOI] [Full Text] [Cited by in RCA: 1] [Reference Citation Analysis (0)] |
| 30. | Goné-Vázquez I, Barquera R, Arellano-Prado FP, Hernández-Zaragoza DI, Escobedo-Ruíz A, Clayton S, Arrieta-Bolaños E, García-Arias VE, Rodríguez-López ME, Bravo-Acevedo A, Sánchez-Fernández MGJ, Sandoval-Sandoval MJ, Gómez-Navarro B, Zúñiga J, Yunis EJ, Bekker-Méndez C, Granados J. Genetic diversity of HLA system in two populations from Nayarit, Mexico: Tepic and rural Nayarit. Hum Immunol. 2020;81:499-501. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 2] [Cited by in RCA: 4] [Article Influence: 0.8] [Reference Citation Analysis (0)] |