# Impacts ofLOC105371267 Variants on Breast Cancer Susceptibility in Northern Chinese Han Females: A Population-Based Case-Control Study

Authors: Linna Peng; Congmei Huang; Shishi Xing; Dandan Li; Chunjuan He; Yongjun He; Wei Yang; Tianbo Jin; Li Wang
Journal: Journal of Oncology
 (2021)

---

## Abstract

Background. LOC105371267, also known as PR-lncRNA1, was reported to be a p53-regulated long noncoding RNA (lncRNA), which played an essential role in the pathogenesis of breast cancer (BC). We aimed to observe the potential association between LOC105371267 polymorphisms and BC risk in Northern Chinese Han females. Methods. Totally, 555 healthy individuals and 561 patients with BC were recruited. Five candidate SNPs (rs6499221, rs3931698, rs8044565, rs3852740, and rs111577197) of LOC105371267 were genotyped with the Agena MassARRAY system. Odds ratio (OR) and 95% confidence intervals (CIs) were applied to evaluate the relationship of LOC105371267 genetic polymorphisms with BC susceptibility. Additionally, stratification analysis based on clinical features and haplotype analysis were also conducted. Finally, multifactor dimensionality reduction (MDR) analysis was performed to assess the SNP-SNP interaction among LOC105371267 variants, and false-positive report probability (FPRP) analysis was used to validate the result of this study. Results. In this study, rs3931698 was a protective factor of BC in total (GG homozygote: OR = 0.30, 95% CI: 0.11–0.82, p=0.018; recessive model: OR = 0.30, 95% CI: 0.11–0.84, p=0.021). In stratification analysis based on the average age of 52 years and clinical characteristics (PR status, III-IV TNM stage), rs3931698 was also demonstrated to be associated with BC susceptibility. In addition, rs6499221 and rs3852740 were also associated with BC susceptibility among patients at age <52 years and patients with BC in a positive status. Thus, the haplotype analysis had a negative result for the incidence of BC (p>0.05), and haplotype consisting of rs8044565 and rs111577197 was nonsignificantly associated with the BC risk. Finally, MDR and FPRP analyses also validated the result of this study. Conclusion. Polymorphisms rs3931698, rs6499221, and rs3852740 of LOC105371267 were found to be associated with the risk of BC in total, and stratification analysis in the Northern Chinese Han females suggested that LOC105371267 variants might be helpful to predict BC progression.


---

## Body

## 1. Introduction

Breast cancer (BC), an important cause of human suffering and premature mortality among women, has been considered as one of the most prevailing cancers. At abroad, 1.7 million new cases have been diagnosed and 1.2 million people died of the disease per year [1–3]. At home, the health burden of cancer is increasing inescapably [4]. Due to its heterogeneous presentation, BC is classified into distinct subtypes, which differ in their unique biology, survival outcome, and related risk factors [5, 6]. The pathogenesis and overall prognosis of BC are multifactorial and result from interaction between modifiable factors such as breastfeeding and oral contraceptive use and nonmodifiable factors such as age, early menarche, late menopause, ethnicity, and genetic aberrations [7, 8]. Among the above-mentioned multiple factors, genetic factors were the major drivers in the genesis of BC [9–11]. BRCA1 and BRCA2 have proved to be the two classical susceptible genes in the BC hereditary [12, 13]. In addition, progesterone receptor (PGR) gene variants, MIR-375 gene, and NF-kB genetic variants were also demonstrated to be associated with BC risk [14–16]. Encouragingly, increasing attention has been concentrated on the role and susceptibility of lncRNAs in the BC pathogenesis [17–20]. For example, Ma et al. evaluated the association between BC risk and LncRNA LINC01585 using a genome-wide association study (GWAS) method, and they suggested that the lncRNA probably served as a novel therapeutic target for BC [21]. Moreover, Peng et al. pointed out that lncRNA MALAT1 polymorphisms were correlated with the risk of BC based on the association analysis in Chinese Han females [22], which indicated the crucial role of lncRNAs in BC pathogenesis. Among numerous kinds of lncRNAs, LOC105371267, a p53-regulated lncRNA [23, 24], whose RefSeq DNA sequence is NC_000016.10, was reported to be a probable new candidate susceptibility gene of BC in European women in a previous transcriptome-wide association study. However, there were no other studies on this gene, including genetic polymorphism of this gene. Thus, relative genetic roles of this gene are worth digging out. In the present study, we have a strong desire to explore the impact of LOC105371267 genetic polymorphism on the risk of BC in Northern Chinese Han females conducting a case-control study. In addition, we also investigated the association between LOC105371267 genetic polymorphisms and clinical characteristics of BC in stratification analysis. Finally, false-positive report probability (FPRP) analysis was conducted to validate the positive result in this study.


## 2. Patients and Methods

### 2.1. Study Population

In this case-control study, blood samples were collected from 561 female patients with BC and 555 female healthy individuals, who were consecutively recruited from the Shaanxi Provincial Cancer Hospital. All the BC cases were newly diagnosed as breast carcinoma through the histopathological examination, and none of them had undergone chemotherapy or radiotherapy before gathering samples. Moreover, those who had a history of other cancer or suffered from immunological, cardiovascular, or hematologic disorders were excluded. The control subjects were received from the physical examination center in the same hospital; they had no medical illness or family history of BC and were genetically unrelated to the included patients with BC. In addition, the demographic data of participants and the clinical information of patients with BC were acquired based on a standard questionnaire of clinical information, including age, estrogen receptor (ER), progesterone receptor (PR), Ki-67 status, tumor status, location and stage, lymph nodes metastasis, and distance metastasis. All participants signed informed consent, and this work was approved by the Ethics Committee of Xizang Minzu University. All experiments were conducted in accordance with the World Medical Association Declaration of Helsinki.


### 2.2. Gene Selection and SNPs Genotyping Assay

Total DNA isolation was undertaken from 5 mL of ethylenediamine tetraacetic acid (EDTA)-anticoagulated peripheral blood using the GoldMag whole blood genomic DNA purification kit (GoldMag Co. Ltd., Xi’an, China) according to the protocol of manufacturer and subsequently was stored at −80°C for the following analysis. We selectedLOC105371267, SNPs referring to the genes, and SNPs reported in BC transcriptome analysis literature [24], by which our five candidate SNPs of LOC105371267, namely, rs6499221, rs3931698, rs8044565, rs3852740, and rs111577197, were near the SNPs demonstrated. Then, we identified the above five SNPs in 1000 Genomes Project database (https://www.ncbi.nlm.nih.gov/variation/tools/1000genomes/) based on CHB (Chinese Han in Beijing) data with minor allele frequency (MAF) > 0.05 and call rate >95% [25] in order to ensure the successful genotyping and valid statistical analysis in Northern Chinese Han females. Moreover, functional prediction analysis of these SNPs was performed using the web-based HaploReg v4.1 software (https://pubs.broadinstitute.org/mammals/haploreg/haploreg.php). Thus, we selected these five SNPs for subsequent genotyping. In this study, the genotyping was carried out with the Agena MassARRAY system (Agena, San Diego, CA, USA) as described in previous research [26] by two independent investigators. In addition, 10% of samples were randomly selected as blinded duplication to evaluate the accuracy of SNPs genotyping and exhibited 100% concordance. The used primers are summarized in Table S9.


### 2.3. Statistical Analyses

The differences in demographic and clinical data between cases and controls were assessed by Pearson’sχ2 test and Student’s t-test. Hardy–Weinberg equilibrium (HWE) analysis for each SNP among controls was conducted using Fisher’s exact test for further analysis. Pearson’s χ2 test was also used to analyze the difference in the allele and genotype frequencies for each polymorphism between patients with BC and healthy subjects. Accordingly, odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using logistic regression analysis after the adjustment for age to evaluate the correlation between LOC105371267 polymorphisms and BC risk using the PLINK v1.07 software. The Stata (version 11) software was used for the forest plot making in order to partly show the result of regression analysis intuitively. Meanwhile, multiple genetic models (genotype, dominant model, recessive model, and additive model) were utilized to estimate the relationship of LOC105371267 SNPs with the susceptibility to BC. Moreover, we performed multiple stratified analyses in terms of average age at 52 years of the study population in this study (Table 1), ER (positive vs. negative), PR (positive vs. negative), lymph nodes metastasis (positive vs. negative), TNM stage [III-IV (n = 161) vs. I-II ], Ki-67 status (high vs. low), and tumor size (>2 cm vs. ≤ 2 cm). Additionally, the pairwise linkage disequilibrium (LD) was measured by the LD coefficient D′ using the Haploview v4.2 software. Haplotype analysis was conducted by logistic regression analysis using the PLINK v1.07 software. All statistical analyses were carried out using SPSS v 18.0 software (Armonk, New York City, NY, USA). Besides, p values adjusted for the false discovery rate (FDR) were also calculated in this study. Additionally, the noteworthy associations of the significant findings were evaluated using the FPRP evaluation method developed by Wacholder et al. [27] [FPRP cutoff value = 0.2, power OR = 2, and prior probability levels = (0.25, 0.1, 0.01, 0.001, 0.0001)]. Finally, the MDR analysis was carried out using MDR software (version 3.0.2) to evaluate the SNP-SNP interactions among these three candidate SNPs. All statistical tests were two-sided, and p<0.05 was considered statistically significant. The p value analyzed by the MDR analysis was calculated using χ2 test. Flow diagram of study design is shown in Figure S1.Table 1
Characteristics of breast cancer cases and cancer-free controls.
VariablesBreast cancer patients (n = 561)Control (n = 555)paAge (years) (mean ± SD)52.04 ± 9.8251.84 ± 9.760.738ER status, n (%)Positive380 (67.7)Negative172 (30.7)Unavailable9 (1.6)PR status, n (%)Positive328 (58.5)Negative224 (39.9)Unavailable9 (1.6)Ki67 status, n (%)High371 (66.1)Low154 (27.5)Unavailable36 (6.4)Tumor size (cm), n (%)>2238 (42.4)≤2206 (36.7)Unavailable117 (20.9)Tumor location, n (%)Right267 (47.6)Left284 (50.6)Bilateral8 (1.4)Unavailable2 (0.4)Lymph nodes metastasis, n (%)Positive277 (49.4)Negative279 (49.7)Unavailable5 (0.9)Distance metastasis, n (%)M0517 (92.2)M139 (7.0)Unavailable5 (0.9)TNM stage, n (%)I-II366 (65.2)III-IV161 (28.7)Unavailable34 (6.1)Primary or recurrent, n (%)Primary424 (75.6)Recurrent22 (3.9)Unavailable112 (20.5)ER: estrogen receptor; RP: progesterone receptor; SD: standard deviation. Note:ap values were calculated by independent samples t-test; empty cells indicate data loss.


## 2.1. Study Population

In this case-control study, blood samples were collected from 561 female patients with BC and 555 female healthy individuals, who were consecutively recruited from the Shaanxi Provincial Cancer Hospital. All the BC cases were newly diagnosed as breast carcinoma through the histopathological examination, and none of them had undergone chemotherapy or radiotherapy before gathering samples. Moreover, those who had a history of other cancer or suffered from immunological, cardiovascular, or hematologic disorders were excluded. The control subjects were received from the physical examination center in the same hospital; they had no medical illness or family history of BC and were genetically unrelated to the included patients with BC. In addition, the demographic data of participants and the clinical information of patients with BC were acquired based on a standard questionnaire of clinical information, including age, estrogen receptor (ER), progesterone receptor (PR), Ki-67 status, tumor status, location and stage, lymph nodes metastasis, and distance metastasis. All participants signed informed consent, and this work was approved by the Ethics Committee of Xizang Minzu University. All experiments were conducted in accordance with the World Medical Association Declaration of Helsinki.


## 2.2. Gene Selection and SNPs Genotyping Assay

Total DNA isolation was undertaken from 5 mL of ethylenediamine tetraacetic acid (EDTA)-anticoagulated peripheral blood using the GoldMag whole blood genomic DNA purification kit (GoldMag Co. Ltd., Xi’an, China) according to the protocol of manufacturer and subsequently was stored at −80°C for the following analysis. We selectedLOC105371267, SNPs referring to the genes, and SNPs reported in BC transcriptome analysis literature [24], by which our five candidate SNPs of LOC105371267, namely, rs6499221, rs3931698, rs8044565, rs3852740, and rs111577197, were near the SNPs demonstrated. Then, we identified the above five SNPs in 1000 Genomes Project database (https://www.ncbi.nlm.nih.gov/variation/tools/1000genomes/) based on CHB (Chinese Han in Beijing) data with minor allele frequency (MAF) > 0.05 and call rate >95% [25] in order to ensure the successful genotyping and valid statistical analysis in Northern Chinese Han females. Moreover, functional prediction analysis of these SNPs was performed using the web-based HaploReg v4.1 software (https://pubs.broadinstitute.org/mammals/haploreg/haploreg.php). Thus, we selected these five SNPs for subsequent genotyping. In this study, the genotyping was carried out with the Agena MassARRAY system (Agena, San Diego, CA, USA) as described in previous research [26] by two independent investigators. In addition, 10% of samples were randomly selected as blinded duplication to evaluate the accuracy of SNPs genotyping and exhibited 100% concordance. The used primers are summarized in Table S9.


## 2.3. Statistical Analyses

The differences in demographic and clinical data between cases and controls were assessed by Pearson’sχ2 test and Student’s t-test. Hardy–Weinberg equilibrium (HWE) analysis for each SNP among controls was conducted using Fisher’s exact test for further analysis. Pearson’s χ2 test was also used to analyze the difference in the allele and genotype frequencies for each polymorphism between patients with BC and healthy subjects. Accordingly, odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using logistic regression analysis after the adjustment for age to evaluate the correlation between LOC105371267 polymorphisms and BC risk using the PLINK v1.07 software. The Stata (version 11) software was used for the forest plot making in order to partly show the result of regression analysis intuitively. Meanwhile, multiple genetic models (genotype, dominant model, recessive model, and additive model) were utilized to estimate the relationship of LOC105371267 SNPs with the susceptibility to BC. Moreover, we performed multiple stratified analyses in terms of average age at 52 years of the study population in this study (Table 1), ER (positive vs. negative), PR (positive vs. negative), lymph nodes metastasis (positive vs. negative), TNM stage [III-IV (n = 161) vs. I-II ], Ki-67 status (high vs. low), and tumor size (>2 cm vs. ≤ 2 cm). Additionally, the pairwise linkage disequilibrium (LD) was measured by the LD coefficient D′ using the Haploview v4.2 software. Haplotype analysis was conducted by logistic regression analysis using the PLINK v1.07 software. All statistical analyses were carried out using SPSS v 18.0 software (Armonk, New York City, NY, USA). Besides, p values adjusted for the false discovery rate (FDR) were also calculated in this study. Additionally, the noteworthy associations of the significant findings were evaluated using the FPRP evaluation method developed by Wacholder et al. [27] [FPRP cutoff value = 0.2, power OR = 2, and prior probability levels = (0.25, 0.1, 0.01, 0.001, 0.0001)]. Finally, the MDR analysis was carried out using MDR software (version 3.0.2) to evaluate the SNP-SNP interactions among these three candidate SNPs. All statistical tests were two-sided, and p<0.05 was considered statistically significant. The p value analyzed by the MDR analysis was calculated using χ2 test. Flow diagram of study design is shown in Figure S1.Table 1
Characteristics of breast cancer cases and cancer-free controls.
VariablesBreast cancer patients (n = 561)Control (n = 555)paAge (years) (mean ± SD)52.04 ± 9.8251.84 ± 9.760.738ER status, n (%)Positive380 (67.7)Negative172 (30.7)Unavailable9 (1.6)PR status, n (%)Positive328 (58.5)Negative224 (39.9)Unavailable9 (1.6)Ki67 status, n (%)High371 (66.1)Low154 (27.5)Unavailable36 (6.4)Tumor size (cm), n (%)>2238 (42.4)≤2206 (36.7)Unavailable117 (20.9)Tumor location, n (%)Right267 (47.6)Left284 (50.6)Bilateral8 (1.4)Unavailable2 (0.4)Lymph nodes metastasis, n (%)Positive277 (49.4)Negative279 (49.7)Unavailable5 (0.9)Distance metastasis, n (%)M0517 (92.2)M139 (7.0)Unavailable5 (0.9)TNM stage, n (%)I-II366 (65.2)III-IV161 (28.7)Unavailable34 (6.1)Primary or recurrent, n (%)Primary424 (75.6)Recurrent22 (3.9)Unavailable112 (20.5)ER: estrogen receptor; RP: progesterone receptor; SD: standard deviation. Note:ap values were calculated by independent samples t-test; empty cells indicate data loss.


## 3. Results

### 3.1. Characteristics of Study Population and SNP Identification

A total of 1116 female participants (561 patients with BC and 555 controls) were recruited in the current study. The baseline characteristics of these subjects are exhibited in Table1. We noted that no significant difference was detected between BC cases and controls (p<0.05) in terms of age. Among 561 BC cases with available ER, PR, Ki-67, tumor size, tumor location, lymph nodes metastasis, distance metastasis, TNM stage, and primary or recurrent information, 380 (67.7%) cases were ER positive, 328 (58.5%) cases were PR positive, 371 (66.1%) cases had high Ki-67, 238 (42.4%) cases had tumor size > 2 cm, 267 (47.6%) cases were lymph nodes metastasis positive, 517 (92.2%) cases had distance metastasis of M0, 366 (65.2%) cases were at I-II TNM stage, and 424 (75.6%) cases were primary. Five SNPs of LOC105371267 (rs6499221, rs3931698, rs8044565, rs3852740, and rs111577197) were screened according to the criteria described above and successfully genotyped in included samples. The fundamental information of these SNPs is displayed in Table 2, and the genotypes frequency of all SNPs in control group conforms to HWE (p<0.05), which then could be used as a basis for further study. Moreover, there were no significant differences in allele frequencies between patients and healthy controls (p>0.05) with or without the FDR test, implying that these SNPs were not susceptible to BC in the allele model.Table 2
Basic characteristics aboutLOC105371267 candidate SNPs and relationship with risk of breast cancer in allele model.
SNPsChromosomePositionTypeAllele (minor/major)MAF (case/control)HWE-paOR (95% CI)pbFDR testHaploRegrs649922116q12.253036124IntronA/G0.19/0.190.491.02 (0.83–1.26)0.8721.453Enhancer histone marks, motifs changedrs393169816q12.253036913IntronG/T0.14/0.160.640.80 (0.64–1.02)0.0740.370Enhancer histone marks, DNase, motifs changedrs804456516q12.253040078IntronC/T0.25/0.241.001.01 (0.83–1.22)0.9610.961Motifs changedrs385274016q12.253044259IntronG/C0.20/0.200.690.98 (0.80–1.21)0.8741.093Promoter histone marks, enhancer histone marks, DNase, proteins bound, motifs changedrs11157719716q12.253049243IntronT/C0.22/0.210.801.09 (0.89–1.33)0.4331.083Enhancer histone marks, motifs changedSNP: single-nucleotide polymorphism; MAF: minor allele frequency; HWE: Hardy–Weinberg equilibrium; OR: odds ratio; 95% CI: 95% confidence interval. Note: OR and 95% CI were computed by logistic regression analysis with adjustments for age.ap values for the Hardy–Weinberger equilibrium (HWE) test, calculated by Fisher’s exact test. bp values were calculated by two-sided χ2 test after adjustment for age with logistic regression analysis.


### 3.2. Associations betweenLOC105371267 Polymorphisms and BC Risk

The logistic regression model was used to evaluate the associations betweenLOC105371267 SNPs and the risk of BC based on the adjustment for age. As can be seen from Table S1 and Figure 1, the homozygote of rs3931698 (GG vs. TT) had a 0.3-fold decreased BC risk (OR = 0.30, 95% CI: 0.11–0.82, p=0.018). Similarly, a 0.3-fold reduced risk was also observed for rs3931698 in the recessive model (OR = 0.30, 95% CI: 0.11–0.84, p=0.021), yet FDR test did not prove this positive result. Negatively, there was no dramatically statistical difference between BC risk and remaining SNPs (rs6499221, rs8044565, rs3852740, and rs111577197) in any genetic model (p>0.05) with or without the FDR test (p>0.05).Figure 1
Association analysis results of genetic polymorphisms ofLOC105371267 rs3931698 and breast cancer susceptibility.


### 3.3. Stratification Analyses

According to the epidemiology report of BC, the mean age at diagnosis of breast cancer in China is approximately within 45–55 years [4]; besides, combined with the average age of the study subjects enrolled in this study, the subsequent stratified analyses based on age of 52 years were performed. As shown in Table S2 and Figure 2, rs3931698 exhibited significant association with a decreased BC risk among patients at age <52 years in homozygous genotype (GG vs. TT, OR = 0.26, 95% CI: 0.71–0.97, p=0.045) and additive model (OR = 0.68, 95% CI: 0.48–0.95, p>0.025) with adjustments for age. We also found that heterozygous genotype of rs6499221 was associated with an increased BC risk at BC cases at age <52 years (AG vs. GG, OR = 1.48, 95% CI: 1.01–2.09, p=0.046); however, we got a negative result after the FDR test (p>0.05). Similarly, the above two SNPs had a negative result on the BC susceptibility analysis for patients with BC at age ≥52 years. As for the other three SNPs (rs8044565, rs3852740, and rs111577197), no significant association was detected between age and susceptibility to BC with or without the FDR test (p>0.05), either.Figure 2
Positive results of stratification analysis betweenLOC105371267 polymorphisms and breast cancer risk based on age of 52 years, PR status, TNM stage, and ER status.Additionally, the correlation between clinical characteristics and BC susceptibility was also assessed. As shown in TableS3 and Figure 2, the heterozygous genotype and dominant model of rs3931698 were correlated with an increased possibility of PR-positive BC (GT vs. TT : OR = 1.52, 95% CI: 1.01–2.29, p=0.043), and likewise rs6499221 increased the BC feasibility of ER-positive patients in the additive genetic model (OR = 1.43, 95% CI: 1.02–2.02, p=0.041). However, the additive model of rs3582740 was associated with a decreased probability of ER-positive BC (OR = 0.73, 95% CI: 0.53–0.99, p=0.043), which indicated the poor effect of rs3582740 on ER-positive BC. On the other hand, there was no correlation between the other two candidate SNPs (rs8044565 and rs111577197) of LOC105371267 and ER or PR status (Tables S4–S8), and no positive result existed after the FDR test (p>0.05) in this stratified analysis based on the PR or ER status of BC.Afterwards, we further evaluated the impacts ofLOC105371267 SNPs on the severity of BC according to TNM stage (III-IV/I-II). As shown in Table S3 and Figure 2, the results revealed that heterozygous genotype of rs3931698 was overrepresented in patients with clinical III-IV stage compared to those with I-II stage (OR = 1.58, 95% CI: 1.04–2.40, p=0.033), which indicated the poor effect of rs3931698 on TNM stage of BC. Nevertheless, after the FDR test, the positive result no longer existed. Negatively, there was no correlation between other SNPs and TNM stage of BC (Tables S5–S8). Additionally, no statistical difference was estimated between the selected five SNPs in LOC105371267 and tumor size, Ki-67 status, or lymph nodes metastasis based on the stratification analyses (Tables S4–S8) with or without the FDR test.


### 3.4. Haplotype Analysis ofLOC105371267 Polymorphisms

The LD and corresponding haplotypes analysis were further investigated by Haploview software to explore the combined effect of these five SNPs inLOC105371267 on BC risk. Our findings implied that only two SNPs, rs3931698 and rs8044565, were in high LD and formed three haplotypes (TC, GT, and TT) (Figure 3). However, none of haplotypes was related to the incidence of BC in the condition with or without the FDR test (p>0.05, Table S10).Figure 3
The LD status (D′) of two SNPs ofLOC105371267. The number in the diamonds is the LOD score of D′. The LD value is determined by D′ > 0.8 analyzed by Haploview software 4.2. LD haplotype analysis provides the basis for the selection of sites for association analysis.


### 3.5. FPRP Analysis

As recommended by Wacholder et al. [27], only FPRP value is less than the preset threshold (0.2), which means that the false-positive rate of the positive result is lower than the expected value, and the positive result is noteworthy. Thus, we set 0.2 as an FPRP threshold. Besides, according to the FPRP analysis result in this study, we assigned a prior probability of 0.1 to detect OR of 2 for an association between BC risk and genotypes under investigation, although the setting of OR value is more conservative than that suggested by Wacholder et al. (OR = 1.5) [27]. As can be seen from Table 3, when OR value was 2, the effect of additive model of rs3931698 on BC risk under the subgroup (patients at age <52 years) conformed to the notable association, whose FPRP level was <0.2 under the prior probability level of 0.1; furthermore, other positive results still had notable associations since the FPRP value was <0.2 although the prior probability level was 0.25.Table 3
FPRP evaluation for association betweenLOC105371267 variants and breast cancer risk.
Group (subgroup)/variants/genotypeOR (95% CI)Powera, OR = 2OR = 2Prior probability level0.250.10.010.0010.0001Overall/rs3931698/GT vs. TT0.30 (0.11–0.82)0.1600.2840.5440.9290.9930.999Overall/rs3931698/GG vs. GT + TT0.30 (0.11–0.84)0.1650.2620.5160.9220.9920.999Age <52 years/rs3931698/GG vs. TT0.26 (0.17–0.97)0.1650.4490.710.9640.9960.999Age <52 years/rs3931698/additive0.68 (0.48–0.95)0.9640.0690.1820.7090.9610.996Age <52 years/rs6499221/AG vs. GG1.48 (1.01–2.19)0.9340.1380.3250.8410.9820.998PR status/rs3931698/GT vs. TT1.52 (1.01–2.29)0.9050.130.310.8320.980.998PR status/rs3931698/GG +GT vs. TT1.50 (1.01–2.25)0.9180.1410.3290.8440.9820.998TNM stage/rs3931698/GT vs. TT1.58 (1.04–2.40)0.8650.10.250.7850.9740.997ER status/rs6499221/additive1.43 (1.02–2.02)0.9720.1160.2820.8120.9780.998ER status/rs3852740/additive0.73 (0.53–0.99)0.9930.1150.280.8110.9770.998OR: odd ratio; 95% CI: 95% confidence interval; ER: estrogen receptor; RP: progesterone receptor. Note: OR and 95% CI were computed by logistic regression analysis with adjustments for age.a statistical power to detect an OR of 2; FPRP value < 0.2 in bold.


### 3.6. MDR Analysis

Finally, we conducted the MDR analysis to explore the SNP-SNP interaction among five loci (rs111577197, rs3852740, rs3931698, rs6499221, rs8044565) in theLOC105371267 gene to better evaluate the effect of LOC105371267 variants on BC risk. As shown in Table 4 and Table S11, the larger the “CV consistency” value and “1/0 ratio” value, the stronger the interaction among these SNPs. A model consisting of five loci (rs111577197, rs3852740, rs3931698, rs6499221, rs8044565) with the largest “CV consistency” value (10/10) could be the best multilocus model, and the best genotype combination of this model was rs111577197-TT, rs3852740-CC, rs3931698-GT, rs6499221-AG, rs8044565-TC. At the same time, the impact of this best model on the risk of BC prediction was significant (p<0.0001). Likewise, as shown in Figure 4, the bluer the string color, the greater the redundancy effect among those five SNPs. Contrarily, the redder the color, the greater the synergy effect among those five SNPs. Furthermore, we could observe that a strong redundancy effect existed between rs3931698 and rs111577197. The redundancy effect among other loci decreased gradually, and the synergy effect increased gradually.Table 4
MDR analysis for impact ofLOC105371267 variants on risk of breast cancer.
ModelTraining bal. acc.Testing bal. acc.CV consistencyAccuracySensitivitySpecificityOR (95% CI)pars39316980.5180.4957/100.5180.7390.2951.184 (0.911, 1.538)0.206rs3931698, rs64992210.5310.4816/100.5290.4760.5831.269 (1.002, 1.607)0.054rs3852740, rs3931698, rs64992210.5440.4834/100.5420.7000.3811.437 (1.121, 1.842)0.005rs111577197, rs3852740, rs3931698, rs80445650.5590.4807/100.5560.6290.4821.576 (1.242, 2.001)0.0001rs111577197, rs3852740, rs3931698, rs6499221, rs80445650.5740.49710/100.5700.6020.5381.761 (1.389, 2.232)<0.0001MDR: multifactor dimensionality reduction; SNP: single-nucleotide polymorphism; bal. acc.: balanced accuracy; CV: cross-validation; OR: odds ratio; CI: confidence interval. Note:ainteractions were validated by 1000 permutation tests. All p values in this study were two-tailed. Bold values mean statistical significance (p<0.05).Figure 4
The dendrogram of the SNP-SNP interaction of five SNPs onLOC105371267 gene. The bluer the string color, the more redundant the effect between those five SNPs. Contrarily, the redder the color, the more the synergy effect between those five SNPs.


## 3.1. Characteristics of Study Population and SNP Identification

A total of 1116 female participants (561 patients with BC and 555 controls) were recruited in the current study. The baseline characteristics of these subjects are exhibited in Table1. We noted that no significant difference was detected between BC cases and controls (p<0.05) in terms of age. Among 561 BC cases with available ER, PR, Ki-67, tumor size, tumor location, lymph nodes metastasis, distance metastasis, TNM stage, and primary or recurrent information, 380 (67.7%) cases were ER positive, 328 (58.5%) cases were PR positive, 371 (66.1%) cases had high Ki-67, 238 (42.4%) cases had tumor size > 2 cm, 267 (47.6%) cases were lymph nodes metastasis positive, 517 (92.2%) cases had distance metastasis of M0, 366 (65.2%) cases were at I-II TNM stage, and 424 (75.6%) cases were primary. Five SNPs of LOC105371267 (rs6499221, rs3931698, rs8044565, rs3852740, and rs111577197) were screened according to the criteria described above and successfully genotyped in included samples. The fundamental information of these SNPs is displayed in Table 2, and the genotypes frequency of all SNPs in control group conforms to HWE (p<0.05), which then could be used as a basis for further study. Moreover, there were no significant differences in allele frequencies between patients and healthy controls (p>0.05) with or without the FDR test, implying that these SNPs were not susceptible to BC in the allele model.Table 2
Basic characteristics aboutLOC105371267 candidate SNPs and relationship with risk of breast cancer in allele model.
SNPsChromosomePositionTypeAllele (minor/major)MAF (case/control)HWE-paOR (95% CI)pbFDR testHaploRegrs649922116q12.253036124IntronA/G0.19/0.190.491.02 (0.83–1.26)0.8721.453Enhancer histone marks, motifs changedrs393169816q12.253036913IntronG/T0.14/0.160.640.80 (0.64–1.02)0.0740.370Enhancer histone marks, DNase, motifs changedrs804456516q12.253040078IntronC/T0.25/0.241.001.01 (0.83–1.22)0.9610.961Motifs changedrs385274016q12.253044259IntronG/C0.20/0.200.690.98 (0.80–1.21)0.8741.093Promoter histone marks, enhancer histone marks, DNase, proteins bound, motifs changedrs11157719716q12.253049243IntronT/C0.22/0.210.801.09 (0.89–1.33)0.4331.083Enhancer histone marks, motifs changedSNP: single-nucleotide polymorphism; MAF: minor allele frequency; HWE: Hardy–Weinberg equilibrium; OR: odds ratio; 95% CI: 95% confidence interval. Note: OR and 95% CI were computed by logistic regression analysis with adjustments for age.ap values for the Hardy–Weinberger equilibrium (HWE) test, calculated by Fisher’s exact test. bp values were calculated by two-sided χ2 test after adjustment for age with logistic regression analysis.


## 3.2. Associations betweenLOC105371267 Polymorphisms and BC Risk

The logistic regression model was used to evaluate the associations betweenLOC105371267 SNPs and the risk of BC based on the adjustment for age. As can be seen from Table S1 and Figure 1, the homozygote of rs3931698 (GG vs. TT) had a 0.3-fold decreased BC risk (OR = 0.30, 95% CI: 0.11–0.82, p=0.018). Similarly, a 0.3-fold reduced risk was also observed for rs3931698 in the recessive model (OR = 0.30, 95% CI: 0.11–0.84, p=0.021), yet FDR test did not prove this positive result. Negatively, there was no dramatically statistical difference between BC risk and remaining SNPs (rs6499221, rs8044565, rs3852740, and rs111577197) in any genetic model (p>0.05) with or without the FDR test (p>0.05).Figure 1
Association analysis results of genetic polymorphisms ofLOC105371267 rs3931698 and breast cancer susceptibility.


## 3.3. Stratification Analyses

According to the epidemiology report of BC, the mean age at diagnosis of breast cancer in China is approximately within 45–55 years [4]; besides, combined with the average age of the study subjects enrolled in this study, the subsequent stratified analyses based on age of 52 years were performed. As shown in Table S2 and Figure 2, rs3931698 exhibited significant association with a decreased BC risk among patients at age <52 years in homozygous genotype (GG vs. TT, OR = 0.26, 95% CI: 0.71–0.97, p=0.045) and additive model (OR = 0.68, 95% CI: 0.48–0.95, p>0.025) with adjustments for age. We also found that heterozygous genotype of rs6499221 was associated with an increased BC risk at BC cases at age <52 years (AG vs. GG, OR = 1.48, 95% CI: 1.01–2.09, p=0.046); however, we got a negative result after the FDR test (p>0.05). Similarly, the above two SNPs had a negative result on the BC susceptibility analysis for patients with BC at age ≥52 years. As for the other three SNPs (rs8044565, rs3852740, and rs111577197), no significant association was detected between age and susceptibility to BC with or without the FDR test (p>0.05), either.Figure 2
Positive results of stratification analysis betweenLOC105371267 polymorphisms and breast cancer risk based on age of 52 years, PR status, TNM stage, and ER status.Additionally, the correlation between clinical characteristics and BC susceptibility was also assessed. As shown in TableS3 and Figure 2, the heterozygous genotype and dominant model of rs3931698 were correlated with an increased possibility of PR-positive BC (GT vs. TT : OR = 1.52, 95% CI: 1.01–2.29, p=0.043), and likewise rs6499221 increased the BC feasibility of ER-positive patients in the additive genetic model (OR = 1.43, 95% CI: 1.02–2.02, p=0.041). However, the additive model of rs3582740 was associated with a decreased probability of ER-positive BC (OR = 0.73, 95% CI: 0.53–0.99, p=0.043), which indicated the poor effect of rs3582740 on ER-positive BC. On the other hand, there was no correlation between the other two candidate SNPs (rs8044565 and rs111577197) of LOC105371267 and ER or PR status (Tables S4–S8), and no positive result existed after the FDR test (p>0.05) in this stratified analysis based on the PR or ER status of BC.Afterwards, we further evaluated the impacts ofLOC105371267 SNPs on the severity of BC according to TNM stage (III-IV/I-II). As shown in Table S3 and Figure 2, the results revealed that heterozygous genotype of rs3931698 was overrepresented in patients with clinical III-IV stage compared to those with I-II stage (OR = 1.58, 95% CI: 1.04–2.40, p=0.033), which indicated the poor effect of rs3931698 on TNM stage of BC. Nevertheless, after the FDR test, the positive result no longer existed. Negatively, there was no correlation between other SNPs and TNM stage of BC (Tables S5–S8). Additionally, no statistical difference was estimated between the selected five SNPs in LOC105371267 and tumor size, Ki-67 status, or lymph nodes metastasis based on the stratification analyses (Tables S4–S8) with or without the FDR test.


## 3.4. Haplotype Analysis ofLOC105371267 Polymorphisms

The LD and corresponding haplotypes analysis were further investigated by Haploview software to explore the combined effect of these five SNPs inLOC105371267 on BC risk. Our findings implied that only two SNPs, rs3931698 and rs8044565, were in high LD and formed three haplotypes (TC, GT, and TT) (Figure 3). However, none of haplotypes was related to the incidence of BC in the condition with or without the FDR test (p>0.05, Table S10).Figure 3
The LD status (D′) of two SNPs ofLOC105371267. The number in the diamonds is the LOD score of D′. The LD value is determined by D′ > 0.8 analyzed by Haploview software 4.2. LD haplotype analysis provides the basis for the selection of sites for association analysis.


## 3.5. FPRP Analysis

As recommended by Wacholder et al. [27], only FPRP value is less than the preset threshold (0.2), which means that the false-positive rate of the positive result is lower than the expected value, and the positive result is noteworthy. Thus, we set 0.2 as an FPRP threshold. Besides, according to the FPRP analysis result in this study, we assigned a prior probability of 0.1 to detect OR of 2 for an association between BC risk and genotypes under investigation, although the setting of OR value is more conservative than that suggested by Wacholder et al. (OR = 1.5) [27]. As can be seen from Table 3, when OR value was 2, the effect of additive model of rs3931698 on BC risk under the subgroup (patients at age <52 years) conformed to the notable association, whose FPRP level was <0.2 under the prior probability level of 0.1; furthermore, other positive results still had notable associations since the FPRP value was <0.2 although the prior probability level was 0.25.Table 3
FPRP evaluation for association betweenLOC105371267 variants and breast cancer risk.
Group (subgroup)/variants/genotypeOR (95% CI)Powera, OR = 2OR = 2Prior probability level0.250.10.010.0010.0001Overall/rs3931698/GT vs. TT0.30 (0.11–0.82)0.1600.2840.5440.9290.9930.999Overall/rs3931698/GG vs. GT + TT0.30 (0.11–0.84)0.1650.2620.5160.9220.9920.999Age <52 years/rs3931698/GG vs. TT0.26 (0.17–0.97)0.1650.4490.710.9640.9960.999Age <52 years/rs3931698/additive0.68 (0.48–0.95)0.9640.0690.1820.7090.9610.996Age <52 years/rs6499221/AG vs. GG1.48 (1.01–2.19)0.9340.1380.3250.8410.9820.998PR status/rs3931698/GT vs. TT1.52 (1.01–2.29)0.9050.130.310.8320.980.998PR status/rs3931698/GG +GT vs. TT1.50 (1.01–2.25)0.9180.1410.3290.8440.9820.998TNM stage/rs3931698/GT vs. TT1.58 (1.04–2.40)0.8650.10.250.7850.9740.997ER status/rs6499221/additive1.43 (1.02–2.02)0.9720.1160.2820.8120.9780.998ER status/rs3852740/additive0.73 (0.53–0.99)0.9930.1150.280.8110.9770.998OR: odd ratio; 95% CI: 95% confidence interval; ER: estrogen receptor; RP: progesterone receptor. Note: OR and 95% CI were computed by logistic regression analysis with adjustments for age.a statistical power to detect an OR of 2; FPRP value < 0.2 in bold.


## 3.6. MDR Analysis

Finally, we conducted the MDR analysis to explore the SNP-SNP interaction among five loci (rs111577197, rs3852740, rs3931698, rs6499221, rs8044565) in theLOC105371267 gene to better evaluate the effect of LOC105371267 variants on BC risk. As shown in Table 4 and Table S11, the larger the “CV consistency” value and “1/0 ratio” value, the stronger the interaction among these SNPs. A model consisting of five loci (rs111577197, rs3852740, rs3931698, rs6499221, rs8044565) with the largest “CV consistency” value (10/10) could be the best multilocus model, and the best genotype combination of this model was rs111577197-TT, rs3852740-CC, rs3931698-GT, rs6499221-AG, rs8044565-TC. At the same time, the impact of this best model on the risk of BC prediction was significant (p<0.0001). Likewise, as shown in Figure 4, the bluer the string color, the greater the redundancy effect among those five SNPs. Contrarily, the redder the color, the greater the synergy effect among those five SNPs. Furthermore, we could observe that a strong redundancy effect existed between rs3931698 and rs111577197. The redundancy effect among other loci decreased gradually, and the synergy effect increased gradually.Table 4
MDR analysis for impact ofLOC105371267 variants on risk of breast cancer.
ModelTraining bal. acc.Testing bal. acc.CV consistencyAccuracySensitivitySpecificityOR (95% CI)pars39316980.5180.4957/100.5180.7390.2951.184 (0.911, 1.538)0.206rs3931698, rs64992210.5310.4816/100.5290.4760.5831.269 (1.002, 1.607)0.054rs3852740, rs3931698, rs64992210.5440.4834/100.5420.7000.3811.437 (1.121, 1.842)0.005rs111577197, rs3852740, rs3931698, rs80445650.5590.4807/100.5560.6290.4821.576 (1.242, 2.001)0.0001rs111577197, rs3852740, rs3931698, rs6499221, rs80445650.5740.49710/100.5700.6020.5381.761 (1.389, 2.232)<0.0001MDR: multifactor dimensionality reduction; SNP: single-nucleotide polymorphism; bal. acc.: balanced accuracy; CV: cross-validation; OR: odds ratio; CI: confidence interval. Note:ainteractions were validated by 1000 permutation tests. All p values in this study were two-tailed. Bold values mean statistical significance (p<0.05).Figure 4
The dendrogram of the SNP-SNP interaction of five SNPs onLOC105371267 gene. The bluer the string color, the more redundant the effect between those five SNPs. Contrarily, the redder the color, the more the synergy effect between those five SNPs.


## 4. Discussion

BC is a serious threat to women’s health. Genetic factors played an important role in the etiology of BC. Fortunately, numerous researchers have concentrated on elucidating the correlations between lncRNAs and susceptibility to BC in recent years. For example, Li et al. conducted a GWAS-based association analysis and concluded that polymorphism rs12325489-C.T in the LncRNA ENST00000515084 Exon was found to modulate BC risk in populations including the Northern Chinese population [28]. Besides, Zheng et al. reported that LncRNA MEG3 rs3087918 was associated with a decreased BC risk in the Chinese population [29]. Liu et al. concluded that LncRNA H19 variants were associated with the BC risk by a meta-analysis [30].In this study, we analyzed the relationship between lncRNALOC105371267, with the accession number of NC_000016, and BC susceptibility among the Northern Chinese Han females. To the best of our knowledge, this is the first study to explore the association between LOC105371267 SNPs (rs6499221, rs3931698, rs8044565, rs3852740, and rs111577197) and BC risk. In this study, five candidate SNPs (rs6499221, rs3931698, rs8044565, rs3852740, and rs111577197) were successfully genotyped. We found that carriers with rs3931698-G allele might have a decreased incidence of BC in total. Stratified by age, rs6499221 was associated with an increased BC risk while rs3931698 was associated with a decreased BC risk among patients with BC at age <52 years. Meanwhile, an increased risk was also observed between rs3931698 and other PR-positive and III-IV stage BC. Polymorphisms of rs6499221 and rs3852740 played protective and dangerous roles in the additive model in ER-positive patients, respectively. Besides, the FDR test and FPRP analysis were also conducted to validate our result. Unfortunately, all the results of FDR test were negative. Encouragingly, certain FPRP analysis result could validate our result, which can be seen in the Results. Likewise, the MDR analysis also concluded that the model (rs111577197, rs3852740, rs3931698, rs6499221, rs8044565) with the largest “CV consistency” value (10/10) could be the best multilocus model, which showed a significant association with BC risk. Therefore, SNPs (rs3931698, rs6499221, and rs3852740) of LOC105371267 might be associated with the occurrence and development of BC. However, no significant relationship was found for rs111577197 and rs8044565 of LOC105371267 with BC risk in this study.Notably,LOC105371267 is a p53-regulated lncRNA. It has been much demonstrated that tumor suppressor p53 played an essential role in molecular mechanisms of cancer progression [31–33]. Noteworthily, p53-regulated lncRNAs were reported to contribute to the occurrence of different types of cancers [34]. For example, Liu et al. highlighted the fact that LncRNA LOC285194, a p53-regulated lncRNA, served as a tumor suppressor in colon cancer via mediating the expression of miR-211 [35].Most importantly, Sánchez et al. highlighted the fact thatLOC105371267 could enhance cell apoptosis and cell cycle arrest by promoting the p53 signaling activation. Specifically, they argued that PR-lncRNA1 regulated the p53 transcriptional network by the efficient binding of p53 to some of its target genes [23]. Furthermore, Li and Richard previously also pinpointed that PR-lncRNA1 interacted with a sequence-specific RNA binding protein, Sam68, and this complex could promote the p53-mediated transcription in human colon carcinoma cell lines [36]. These lines of evidence suggest that LOC105371267 could be of pathogenic importance in the occurrence and development of BC. For the susceptibility of LOC105371267 SNPs to cancer or noncancer diseases, there was no other specific research on it so far, but a transcriptome-wide association study is available [24]. According to the retrieval of the potential function of these five SNPs (rs6499221, rs3931698, rs8044565, rs3852740, and rs111577197) on LOC105371267 gene in the dbSNP database (https://www.ncbi.nlm.nih.gov/snp/), all of the candidate SNPs in this study are located in the intron sequence. Several studies have proved that the intronic SNPs conferred susceptibilities by affecting gene expression [37], and the expression and function of lncRNA were affected by SNPs [38], so these SNPs might have potential function in LOC105371267 expression level. Nevertheless, the detailed roles of LOC105371267 or SNPs (rs6499221, rs3931698, rs8044565, rs3852740, and rs111577197) in BC risk remain to be explored in further study.There is no doubt that age factor has been identified as a prominent risk factor in the BC initiation [39]. Recently, a significant increase in BC rates has been observed among premenopausal subjects [40]. There were also some studies showing that patients with BC at the oldest age were more vulnerable to rapid deterioration [41]. For example, Unlu et al. pointed out that older women tended to have a higher BC risk compared with younger ones [42]. However, in this study, the findings that LOC105371267 SNPs rs3931698 and rs6499221 were related to the decreased and increased risk of BC, respectively, among patients at age <52 years indicated that women at age <52 years with rs3931698 and rs6499221 variants were more susceptible to BC. Importantly, the ER and PR were the decisive therapeutic targets in the treatment of BC. Additionally, Ki-67, lymph nodes metastasis, TNM stage, and tumor size were also linked with the BC pathogenesis [43–45]. Moreover, we also found that LOC105371267 SNPs (rs3931698, rs6499221, and rs3852740) of LOC105371267 might be associated with ER status, PR status, and TNM stage of BC in this study, which can be seen in the Results.Although several positive associations were observed in this study, some limitations still should be considered. First of all, since all participants were enrolled in the same hospital and were Northern Chinese females, the inherent selection bias cannot be excluded and our results cannot permit extrapolation of the results to other ethnic groups. Then, the comprehensive clinical information and environmental factors should be included. Next, the precise molecular mechanisms ofLOC105371267 polymorphisms in BC progression remain to be deciphered. Last but not least, due to the limited sample size, the statistical effect is not enough, and thus larger sample size and in vitro functional experiment are needed to evaluate the association of LOC105371267 polymorphisms and BC susceptibility in Northern Chinese Han females. Despite the limitations mentioned above, our study was only a preliminary study which shed light on the relationship between LOC105371267 polymorphisms and BC risk. The results of our study might provide a foundation for future studies on the relationship of LOC105371267 polymorphisms with BC pathogenesis.


## 5. Conclusion

In summary, this study first shed light on the impact of polymorphisms rs3931698, rs6499221, and rs3852740 ofLOC105371267 on BC susceptibility in Northern Chinese Han females, suggesting that LOC105371267 variants might be genetic markers of BC risk, which is benefit for the diagnosis and prognosis of BC.



---

*Source: 4990695-2021-08-29.xml*