Expression and prognostic value of hsa-miR-206 in non-triple-negative breast cancer

Expression and prognostic value of hsa-miR-206 in non-triple-negative breast cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Help Center Sign In Submit a Preprint Cite Share Download PDF Case Report Expression and prognostic value of hsa-miR-206 in non-triple-negative breast cancer HE Dong-Ning, Ze-Hui GU, Qi Tan, Su-Xian CHEN, WANG Ya-Di This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4507297/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective This study aims to analyze the expression and prognostic value of hsa-miR-206 in non-triple-negative breast cancer. Methods The expression of has-miR-206 in breast cancer and normal breast tissues was analyzed using the dbDEMC 2.0 database. The TCGA dataset was used to verify hsa-miR-206 expression and analyze its role in breast cancer pathways. In situ hybridization was conducted on tissue microarrays comprising 80 breast cancer specimens and corresponding paracancerous tissues. The relationship between hsa-miR-206 expression and the clinicopathological features of patients with non-triple-negative breast cancer was assessed. Patients were divided into high and low-expression groups based on hsa-miR-206 expression levels, and survival curves were plotted. Online TCGA data analysis was performed to determine intersecting genes and action pathways of hsa-miR-206, with further STRING network analysis to explore possible mechanisms involving hsa-miR-206-related intersecting genes. Results The dbDEMC 2.0 and TCGA database and in situ hybridization assay confirmed significantly lower hsa-miR-206 expression in breast cancer tissues compared to paracancerous tissues. In the luminal A subtype, hsa-miR-206 expression was markedly lower in ER-positive human breast cancer tissues than in paracancerous tissues. In the HER2+ subtype, the positive expression rate of hsa-miR-206 in cancerous tissues was 28%, while that in paracancerous tissues was 72%. Patients under 50 years old showed significantly lower positive expression rates. Additionally, hsa-miR-206 expression level correlated significantly with histological grade and Ki-67 expression but not with tumor size or sex hormone receptor status. Kaplan–Meier Plotter analysis of the TCGA and METABRIC databases indicated that patients with low hsa-miR-206 expression had longer overall survival (OS). Subtype-specific analysis showed varying OS benefits: longer OS in luminal A and B breast cancer with low hsa-miR-206 and a slight increase in OS in HER2+ breast cancer. Target genes regulated by hsa-miR-206 were linked to cell cycle and estrogen signaling pathways. Conclusion Downregulation of hsa-miR-206 expression in breast cancer may prolong patient OS. Hsa-miR-206 plays distinct roles across breast cancer subtypes, potentially through different target genes affecting cell cycle and estrogen signaling, which underscore its prognostic implications. hsa-miR-206 breast cancer prognostic biomarker non-triple-negative microRNA expression Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Breast cancer is one of the most common cancers among women worldwide [ 1 ] , affecting approximately 12% of the global female population [ 2 ] . It poses a severe threat to women’s health, affecting their quality of life and being a leading cause of death among women. Due to its high incidence and mortality rates, breast cancer remains a focal point in recent clinical research. Micro RNAs (miRNAs) have shown the ability to detect multiple human cancers of different developmental lineages and differentiation states, including breast cancer [ 3 ] . In particular, comparisons between normal and malignant breast tissues have revealed a subset of miRNAs that can distinguish between these tissues. Additionally, differentially expressed miRNAs are associated with the histopathological features of breast cancer, such as tumor size, lymph node metastasis, proliferative capacity, and vascular invasion [ 4 ] . Although specific miRNAs are thought to modulate the expression of genes within the receptor network driving breast cancer progression, miRNA profiling has yet to independently identify breast cancer subtypes clinically defined by the overexpression of ErbB2 or estrogen receptor (ER) proteins. Current methods and platforms of miRNA analysis further limit this technique. miR-206, located on human chromosome 6p12.2, is indispensable to the growth and reconstruction of skeletal muscle [ 5 ] . The dysregulation of miR-206 is linked to cancer progression, neurological disorders, and myocardial infarction [ 6 – 8 ] . Recent studies have found that ER-positive breast cancer samples exhibit significant downregulation of hsa-miR-206 expression compared to the corresponding adjacent non-cancerous tissues. Low hsa-miR-206 expression was associated with TNM stage, mitotic rate, and lymph node metastasis ( P values = 0.02, 0.01 and 0.01, respectively) [ 9 ] . Detection of hsa-miR-206 expression levels in breast cancer and normal paracancerous breast tissues using quantitative real-time polymerase chain reaction (RT-PCR) revealed that hsa-miR-206 expression was correlated with negative ER, PR, HER2 status [ 10 ] . Kondo et al. [ 11 ] found that the expression of miR-206 in ERα-positive human breast cancer tissues was significantly lower than in paracancerous tissues. It was also observed that hsa-miR-206 expression in breast cancer tissues was negatively correlated with ERα expression but not with ERβ mRNA expression. Further in vitro transfection experiments demonstrated that introducing hsa-miR-206 in estrogen-dependent MCF-7 breast cancer cells inhibited cell growth in a dose- and time-dependent manner. The hsa-miR-206 high expression group had a lower 3-year survival rate than the low expression group. Thus, hsa-miR-206 upregulation may affect the prognosis of patients with breast cancer and serve as an important indicator for predicting the prognosis of patients with this type of breast cancer [ 12 ] . Although the expression of hsa-miR-206 in breast cancer has been widely reported, these experiments were mainly conducted in vitro . Even those verified histologically lacked a systematic analysis of the molecular subtypes of non-triple-negative breast cancer. Additionally, the characteristics and molecular mechanisms of the hsa-miR-206 regulatory pathway remain poorly understood. This study aimed to analyze the expression and prognostic value of hsa-miR-206 in non-triple-negative breast cancer. Therefore, we collected tissue specimens from 80 patients with breast cancer and grouped them according to the ER, progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and Ki-67 molecular subtypes. Online analysis was performed using the TCGA and STRING databases to determine the intersecting genes and action pathways of hsa-miR-206 in breast cancer to verify the different effects and possible mechanisms of hsa-miR-206 in non-triple-negative breast cancer. Methods dbMEMC 2.0 database [ 13 ] The dbDEMC 2.0 ( http://www.picb.ac.cn/dbDEMC . https://www.biosino.org/dbDEMC/index ) is a collection of differentially expressed miRNAs (DEMs) in human cancers acquired compiled using microarray data. It contains 209 expression profile datasets for 36 cancer types and 73 subtypes, identifying 2,224 DEMs. An easy-to-use web interface allows users to swiftly search for DEMs specific to certain cancer types. The “meta-profiling” function enables viewing differential expression events based on user-defined miRNAs and cancer types. This database will continue to serve as a valuable source for cancer research and potential clinical applications related to miRNAs. METABRIC database TCGA database [ 14 ] The miRNAseq data from level 3 BCGSC miRNA Profiling in the TCGA ( https://portal.gdc.cancer.gov/ ) BRCA (Breast Invasive Carcinoma) project were used. Statistical analysis and visualization were performed using R version 3.6.3. The R package ggplot version 2.3.3.3 was mainly used for visualization. The expression of hsa-miR-206 [MIMAT0000462] in breast invasive carcinoma and normal breast tissue was analyzed. Kaplan–Meier Plotter [ 15 ] The Kaplan–Meier plotter ( http://kmplot.com/analysis/ ) is a public database that can assess the impact of 54,000 genes (mRNA, miRNA, protein) on the survival rates of 21 cancer types, including breast, ovarian, lung, and gastric cancer. The database sources include GEO, METABRIC, and TCGA. The main purpose of the tool is to discover and validate survival-associated biomarkers through meta-analysis. Information on gene expression and disease prognosis can be obtained based on this. Breast cancer specimen collection and tissue microarray construction eighty pairs of breast cancer or non-breast cancer specimens were collected from patients at the Third Affiliated Hospital of Jinzhou Medical University and Shanghai Tongren Hospital. The specimens were paraffin-embedded, stained with hematoxylin and eosin, and sent to Superbiotek Co., Ltd. (Shanghai, China) for human tissue microarrays (TMAs) construction. Tumor staging was performed according to the eighth edition of the American Joint Committee on Cancer TNM Classification. This study was commissioned by the Institutional Review Board (IRB) of the Third Affiliated Hospital of Jinzhou Medical University. All patients provided informed consent prior to participation. The IRB no. is kx2020016. In situ hybridization assay Anti-Dig-AP antibody (manufacturer: Roche, product no.: 11093274, concentration: 1:1000) and hsa-miR-206 probe (manufacturer: QIAGEN; product no.: YD00619855-BCE, concentration: 100nm, hybridization temperature: 56°C) were used. TMAs were produced by Shanghai Peide Biotechnology Co., Ltd. (no. BRC1602). Deparaffinization and hydration: The specimens were deparaffinized with xylene twice, for 10 minutes each, then immersed sequentially in 100%, 95%, 80%, and 75% ethanol and DEPC-H 2 O once, for 5 minutes each time. After treatment with 0.2N HCl at room temperature for 5 minutes, the specimens were washed thrice with PBS (phosphate buffered saline) for 5 minutes each time. These were incubated for 20 minutes in proteinase K (40 µg/ml), washed three times with PBS containing 0.2% glycine for 5 minutes each, and fixed with 4% paraformaldehyde for 10 minutes. They were washed twice with PBS for 5 minutes each time, incubated with acetic anhydride and triethanolamine solutions at room temperature for 10 minutes, and washed twice with PBS for 5 minutes each time. The specimens underwent pre-hybridization at 37°C for 2 hours and hybridization at 37°C overnight. The specimens were washed with 5×SSC twice for 20 minutes each time, 50% deionized formamide/ 2×SSC three times at 37°C for 20 minutes each time, and TBST five times for 5 minutes each time. Blocking was done for 1 hour at room temperature. The specimens were incubated with anti-digoxigenin primary antibody (1:1000) at 4°C overnight and washed with TBST four times for 10 minutes each time. They were then washed twice with BCIP/NBT staining buffer for 10 minutes each time and stained with BCIP/NBT in the dark for 4–48 hours. The reaction was stopped by washing twice with TE (pH 8.0), and the residual liquid was rinsed with water. The slides were dehydrated, mounted, observed under microscopy, and photographed. Interpretation of in situ hybridization results [ 16 ] An ordinary optical microscope was used, and five different fields of view were randomly selected for observation at high magnification (10×10). The percentage of positive cells among the total number of cells was calculated and scored (0 points, no positive cells; 1 point, 1–25%; 2 points, 26–50%; 3 points, 51–75%; 4 points, > 75%). The staining intensity was also observed (0 points, no staining; 1 point, light yellow; 2 points, light brown; 3 points, brown). The scores for the percentage of positive cells and staining intensity were multiplied to define the relative expression of miR-206 in tumor tissue. TCGA database analysis of hsa-miR-206 signaling pathway in breast cancer The online tool on the TCGA database ( http://bioinfo.life.hust.edu.cn/miR_path/index.html ) was used for analysis to obtain the action pathways of hsa-miR-206 in breast cancer. STRING network analysis The STRING version 11.0 database ( https://string-db.org/cgi/input.pl ) is a platform for analyzing predictable protein-protein regulatory networks. This database predicted the upstream and downstream regulation of proteins targeted by hsa-miR-206 and regulatory relationships between proteins. The search conditions were: ① Protein name; ② organism: homo sapiens; ③ click “analysis.” Statistical analysis Data analysis was performed using SPSS 21.0. A paired t-test was used to compare between-groups of paired samples. One-way analysis of variance (ANOVA) was used to analyze the relationship between hsa-miR-206 expression and the clinicopathological stages of breast cancer, as well as between hsa-miR-206 expression and prognosis. Survival analysis was conducted using the Kaplan–Meier model. Statistical significance was set at P < 0.05. Results Hsa-miR-206 expression in breast cancer The dbMEMC 2.0 database revealed that hsa-miR-206 expression was significantly lower in breast cancer tissue compared to normal breast tissue ( P = 3.78E-4, FC = -1.41). Further analysis of different molecular subtypes of breast cancer indicated significantly lower hsa-miR-206 expression in ER-positive ( P = 5.44E-4, FC = -1.39), luminal B ( P = 2.70E-3, FC = -1.48), HER2+ ( P = 1.97E-3, FC = -1.73) and basal-like ( P = 9.45E-4, FC = -1.50) breast cancer compared to normal breast tissues (Table 1 ). Table 1 miR-206 expression of breast cancer and paracancerous tissues in the dbMEM 2.0 database Experiment ID Cancer Type Cancer Subtype Design logFC AveExpr T value P value adj Pvalue Status EXP00034 breast cancer cancer vs normal -1.41 -1.95 -3.66 3.78e-4 1.29e-2 DOWN EXP00035 breast cancer ER positive cancer vs normal -1.39 -1.95 -3.55 5.44e-4 1.65e-2 DOWN EXP00036 breast cancer ER negative cancer vs normal -1.44 -1.95 -3.56 5.30e-4 5.58e-3 DOWN EXP00038 breast cancer Luminal B cancer vs normal -1.48 -1.95 -3.06 2.70e-3 2.46e-2 DOWN EXP00039 breast cancer HER2+ cancer vs normal -1.73 -1.95 -3.17 1.97e-3 2.99e-2 DOWN EXP00040 breast cancer Basal-like cancer vs normal -1.50 -1.95 -3.39 9.45e-4 1.29e-2 DOWN Hsa-miR-206 expression of breast cancer in the TCGA database The TCGA-BRCA includes 1,227 breast cancer specimens and 104 normal breast tissues, which were used to analyze the expression of hsa-miR-206 in breast cancer and paracancerous tissues. A normality test indicated that the sample was not normally distributed ( P < 0.05), so the Mann–Whitney U test was performed. The results showed that hsa-miR-206 expression was lower in the breast cancer group compared to the normal breast tissue group, and the difference was statistically significant ( P < 0.001) (shown in Fig. 1 ). Relationship of hsa-miR-206 with TCGA and METABRIC breast cancer prognosis The relationship between hsa-miR-206 and breast cancer prognosis was analyzed online using the Kaplan–Meier Plotter with data from the METABRIC database. The results of the analysis showed that in the METABRIC database (n = 1,262), patients with low hsa-miR-206 expression had a longer median overall survival (OS) than those with high expression (89 months vs. 77.52 months, P = 0.02). Further analysis of the molecular subtypes showed no significant difference in median OS between patients with low and high hsa-miR-206 expressions (Luminal A, n = 546, 250.06 months vs.189.3 months, P = 0.28) (shown in Fig. 2 ). Hsa-miR-206 expression in breast cancer TMA In situ hybridization assay results showed that miR-206 was expressed in the cytoplasm. Under microscopy, the products of positive expression showed a granular or patchy distribution, appearing yellow, light brown, or brown depending on staining intensity (shown in Fig. 3 ). Comparison of hsa-miR-206 expression in breast cancer and paracancerous tissues In situ hybridization results indicated that hsa-miR-206 expression was positive in 25 of the 80 breast cancer tissue specimens, with a positive expression rate of 31.25%. In contrast, hsa-miR-206 expression was positive in 55 of the 80 paracancerous tissue specimens, with a positive expression rate of 68.75%. Thus, the positive expression rate of hsa-miR-206 was significantly lower in breast cancer tissues compared to paracancerous breast tissues, with a statistically significant difference ( P < 0.01) (Table 2 ). Table 2 miR-206 expression in breast cancer and normal paracancerous tissues Tissue n miR−206 P positive negative Breast cancer 80 25(31.25%) 55(68.75%) < 0.01 Normal breast 80 55(68.75%) 25(31.25%) Hsa-miR-206 expression in cancerous and paracancerous tissues in breast cancer subtypes Among the 80 specimens of breast cancer tissues, the relationship of hsa-miR-206 expression with clinical molecular subtypes was as follows: In the luminal A subtype, the expression of hsa-miR-206 in ER-positive human breast cancer tissues was significantly lower than that of paracancerous tissues, with a positive expression rate of 21.74% in cancerous tissues compared to 73.91% in paracancerous tissues ( χ 2 = 12.545, P = 0.000). In the HER2 + subtype, the positive expression rate of hsa-miR-206 in cancerous tissues was 28%, while in paracancerous tissues, it was 72% ( χ 2 = 9.68, P = 0.002). Pearson’s χ 2 test revealed a statistically significant difference between the cancerous and paracancerous groups in the luminal A and HER2 + molecular subtypes (Table 3 ). Table 3 miR-206 expression in breast cancer and normal paracancerous tissues in different molecular subtypes Molecular subtypes n Breast cancer Breast normal χ 2 P positive negative positive negative Lumina A 25 7(21.74%) 18(78.26%) 17(73.91%) 8(26.09%) 12.545 < 0.000 Lumina B Her−2(-) 13 6(46.15%) 7(53.84%) 9(69.23%) 4(30.77%) 1.418 0.214 Lumina B Her−2(+) 19 7(36.84%) 12(63.16%) 11(57.89%) 8(42.11%) 1.689 0.165 Her−2(+) 25 7(28%) 18(72%) 18(72%) 7(28%) 9.680 0.002 Relationship between hsa-miR-206 expression and clinicopathological features of patients with non-triple-negative breast cancer Relationship between hsa-miR-206 expression and patient-related clinicopathological data in invasive ductal carcinoma was examined using in situ hybridization. The results revealed that breast cancer patients aged < 50 years showed a significantly lower positive expression rate for hsa-miR-206 compared to patients aged ≥ 50 years ( P = 0.019). Additionally, the expression level of hsa-miR-206 was significantly associated with histological grade ( P = 0.048) and Ki-67 expression ( P = 0.000) ( P 0.05) (Table 4 ). Table 4 Relationship between hsa-miR-206 expression and clinicopathological features of patients with non-triple-negative breast cancer Feature n Positive expression rate/% Negative expression rate/% χ 2 P value 80 25 (31.25) 55 (68.75) 1. Age at onset/ years 5.545 0.019 < 50 38 7 (18.42) 31 (81.58) ≥ 50 42 18 (42.86) 24 (57.14) 2. Tumor size 2.431 0.297 5cm 3 0 (0) 3 (100) 3. Location 0.179 0.673 Right breast 42 14 (33.33) 28 (66.67) Left breast 38 11 (28.95) 27 (71.05) 4. Clinical stage 2.021 0.364 IA 29 7 (24.14) 22 (75.86) IIA-B 30 9 (30) 21 (70) IIIA-C 21 9 (42.86) 12 (57.14) 5. Histological grade 3.903 0.048 I–II 51 12 (23.53) 39 (76.47) II–III 29 13 (44.83) 16 (55.17) 6. Differentiation grade T stage 2.747 0.432 T1 36 10 (27.78) 26 (72.22) T2 40 15 (37.5) 25 (62.5) T3 2 0 (0) 2 (100) T4 2 0 (0) 2 (100) 7. Differentiation grade N stage 3.006 0.391 N0 48 12 (25) 36 (75) N1 12 4 (33.33) 8 (66.67) N2 12 6 (50) 6 (50) N3 8 3 (37.5) 5 (62.5) 8. Lymph node metastasis 1.734 0.188 Negative 47 12 (25.53) 35 (74.47) Positive 33 13 (39.39) 20 (60.61) 8. Ki-67 expression 3.970 0.046 < 14% 38 16 (42.11) 22 (57.89) ≥ 14% 42 9 (21.43) 33 (78.57) 10. p53 expression 1.343 0.247 Low expression 34 13 (38.24) 21 (61.76) High expression 46 12 (26.09) 34 (73.91) 11. Molecular subtype 2.712 0.438 Luminal A 23 5 (21.74) 18 (78.26) Luminal B Her2(-) 13 6 (46.15) 7 (53.85) Luminal B Her2(+) 19 7 (36.84) 12 (63.16) Her2(+) 25 7 (28) 18 (72) Relationship between hsa-miR-206 expression and prognosis of patients with non-triple-negative breast cancer Postoperative cumulative survival rates of the hsa-miR-206 high and low expression groups were 56.0% and 81.8%, respectively. Compared to the hsa-miR-206 high expression group, the median OS of the low expression group was 2 months longer (83 months vs. 85 months, χ 2 = 5.586, P = 0.018). The 5-year OS of the hsa-miR-206 high and low expression groups were 66.4% and 85.68%, respectively. In luminal A breast cancer, the median OS of the hsa-miR-206 high expression group was 10.5 months longer than that of the low expression group (115 months vs. 104.5 months, P = 0.598). Conversely, in luminal B breast cancer, the median OS of the hsa-miR-206 low expression group was 30 months longer than that of the high expression group (84 months vs. 54 months, P = 0.002), while the 5-year OS of the hsa-miR-206 high and low expression groups were 74.95% and 75.35%, respectively. In HER2 + breast cancer, the median OS of the hsa-miR-206 low expression group was 1 month longer than that of the high expression group (82 months vs. 81 months, P = 0.845) (shown in Fig. 4 ). Pathway analysis of hsa-miR-206 expression in breast cancer combined with TCGA data The online tool on the TCGA database was used to analyze the significant regulatory pathways of hsa-miR-206 in breast cancer. It was mainly enriched in the roles of BRCA1, BRCA2, and ATR in tumor susceptibility, cell cycle, G2/M checkpoint, blockade of neurotransmitter release by botulinum toxin, CARM1, and ER regulation, regulation of cell cycle progression by PLK3, CDC25C, and CHEK1 regulatory pathway response to DNA damage, and so on. The target genes of hsa-miR-206 that intersected with the pathways above included: BRCA1, BRCA2, CHEK1, CDC25C, STX1A, SNAP25, CCND1, ESR1, CXCR3, CXCR4, SLC25A22, and WT1 (Table 5 ). Table 5 Significant regulation of 17 pathways by hsa-miR-206 in breast invasive carcinoma Interactions of hsa-miR-206 intersecting genes Pathway alias pathway name Pvalue Targets exp. ATRBRC Role of BRCA1, BRCA2 and ATR in Cancer Susceptibility 0.0001 BRCA1, BRCA2, CHEK1, G2 Cell Cycle: G2/M Checkpoint 0.0001 BRCA1, CDC25C, CHEK1, BOTULIN Blockade of Neurotransmitter Relase by Botulinum Toxin 0.0001 STX1A, SNAP25 CARM-ER CARM1 and Regulation of the Estrogen Receptor 0.0002 BRCA1, CCND1, ESR1 PLK3 Regulation of cell cycle progression by Plk3 0.0004 CDC25C, CHEK1 CDC25 cdc25 and chk1 Regulatory Pathway in response to DNA damage 0.0006 CDC25C, CHEK1 RB RB Tumor Suppressor/Checkpoint Signaling in response to DNA damage 0.0013 CDC25C, CHEK1 ATM ATM Signaling Pathway 0.0031 BRCA1, CHEK1 NK Selective expression of chemokine receptors during T-cell polarization 0.0034 CXCR3, CXCR4, VIF HIV-1 defeats host-mediated resistance by CEM15 0.0125 CXCR4 RHODOPSIN Visual Signal Transduction 0.0166 SLC25A22 PELP1 Pelp1 Modulation of Estrogen Receptor Activity 0.0290 ESR1 TER Overview of telomerase protein component gene hTert Transcriptional Regulation 0.0331 WT1 BARD1 BRCA1-dependent Ub-ligase activity 0.0331 BRCA1 PTC1 Sonic Hedgehog (SHH) Receptor Ptc1 Regulates cell cycle 0.0412 CDC25C SRCR Activation of Src by Protein-tyrosine phosphatase alpha 0.0452 CDC25C BTG2 BTG family proteins and cell cycle regulation 0.0452 CCND1 The interactions between the intersecting genes of hsa-miR-206 target genes and the pathways above, along with their related signaling pathways, were analyzed. Among which, the interaction scores for STX1A and SNAP25, CDC25 and CHEK1, BRCA2 and BRCA1, ESR1 and BRCA1, CCND1 and ESR1, and CHEK1 and BRCA1 were all 0.99. Based on the biological functions and KEGG enrichment, these intersecting genes were mainly involved in the proliferation of breast cancer, cell cycle, and homologous recombination (Table 6 , shown in Fig. 5 ). Hence, these genes played a crucial role in breast cancer. Table 6 Interaction coefficients of intersecting genes between hsa-miR-206 target genes and pathways node1 node2 node1_external_id node2_external_id combined_score STX1A SNAP25 ENSP00000222812 ENSP00000254976 0.999 CDC25C CHEK1 ENSP00000321656 ENSP00000391090 0.999 BRCA2 BRCA1 ENSP00000369497 ENSP00000418960 0.999 ESR1 BRCA1 ENSP00000405330 ENSP00000418960 0.999 CCND1 ESR1 ENSP00000227507 ENSP00000405330 0.997 CHEK1 BRCA1 ENSP00000391090 ENSP00000418960 0.996 CXCR3 CXCR4 ENSP00000362795 ENSP00000386884 0.969 BRCA2 CHEK1 ENSP00000369497 ENSP00000391090 0.963 CCND1 BRCA1 ENSP00000227507 ENSP00000418960 0.922 CCND1 BRCA2 ENSP00000227507 ENSP00000369497 0.861 WT1 ESR1 ENSP00000368370 ENSP00000405330 0.736 CDC25C BRCA1 ENSP00000321656 ENSP00000418960 0.735 BRCA2 ESR1 ENSP00000369497 ENSP00000405330 0.723 CCND1 CXCR4 ENSP00000227507 ENSP00000386884 0.706 CXCR4 ESR1 ENSP00000386884 ENSP00000405330 0.702 WT1 BRCA1 ENSP00000368370 ENSP00000418960 0.683 CCND1 CDC25C ENSP00000227507 ENSP00000321656 0.638 CCND1 CHEK1 ENSP00000227507 ENSP00000391090 0.593 CDC25C BRCA2 ENSP00000321656 ENSP00000369497 0.479 WT1 BRCA2 ENSP00000368370 ENSP00000369497 0.469 CHEK1 ESR1 ENSP00000391090 ENSP00000405330 0.455 CCND1 WT1 ENSP00000227507 ENSP00000368370 0.446 Differential expression of hsa-miR-206 target genes in breast cancer Compared to paracancerous tissues, breast cancer tissues exhibited higher expressions of BRCA1, BRCA2, CHEK1, SNAP25, STX1A, CCND1, ESR1, CXCR3, CXCR4, SLC25A22, and WT1, and the differences were statistically significant ( P < 0.05); SNAP25 was lower than that of paracancerous tissues, and the difference was statistically significant ( P < 0.05) (shown in Fig. 6 ). Correlation analysis of hsa-miR-206-related target genes By combining the results from Sections 2.9, 2.10, and 2.11, we found through correlation analysis positive correlations between STX1A and SNAP25, CDC25C and CHEK1, BRCA2 and BRCA1, ESR1 and BRCA1, CCND1 and ESR1, and CHEK1 and BRCA1. Notably, the strongest positive correlations were between CDC25C and CHEK1, and CCND1 and ESR1(shown in Fig. 7 ). Discussion miRNAs (miR-206s) are a series of endogenous non-coding RNAs that inhibit the expression of protein-coding genes at transcriptional and post-transcriptional levels by binding to the 3’-untranslated region (3’-UTR) of specific target mRNA molecules, leading to their degradation or translation inhibition. miR-206 has been found to play a crucial role in a variety of malignancies, such as lung, gastric, colorectal, and kidney cancers, and endometrioid adenocarcinoma, glioma, and neuroblastoma [ 17 – 21 ] . Sun et al. [ 22 ] demonstrated that miR-206 could bind directly to the 3’-UTR of c-Met and Bcl-2, thus affecting the proliferation, migration, and colony formation of lung cancer cells. miR-206 can suppress the proliferation and invasion of clear cell renal cell carcinoma by targeting vascular endothelial growth factor A [ 23 ] . Furthermore, cell cycle arrest and inhibition of epithelial-mesenchymal transition (EMT) also contribute to the miR-206-mediated suppression of tumor growth [ 24 ] . Its differential expression in breast cancer can either promote or inhibit cancer progression [ 25 , 26 ] . Moreover, miR-206 expression in breast tumors is downregulated, correlating with tumor size and pathological stage. Therefore, miR-206 has been suggested as a tumor suppressor in breast cancer [ 27 ] . However, previous studies on breast cancer have found that the expression of hsa-miR-206 was downregulated in patients with breast cancer [ 28 – 30 ] . In contrast, others have reported that it was upregulated in patients with breast cancer [ 31 ] . In this study, GSE microarray analysis was first performed on the dbDEMC 2.0 database, which revealed the significantly low expression of hsa-miR-206 in breast cancer. The differential expression of hsa-miR-206 was then analyzed online on the TCGA database, and the verification results were consistent with those of the dbDEMC 2.0 database. Following this, we performed in situ hybridization and found that the positive expression rate of hsa-miR-206 among 80 specimens of breast cancer tissues was 31.25%, while that among paracancerous breast tissues was 68.75%. The difference was statistically significant, which implies that hsa-miR-206 exhibits low expression in breast cancer tissues. Further analysis of the relationship between hsa-miR-206 and different clinicopathological features revealed that hsa-miR-206 expression in breast cancer tissues was associated with age, histological grade, and Ki-67 expression, suggesting high hsa-miR-206 expression in breast cancer may promote the proliferation, invasion, and metastasis of breast cancer cells, and is associated with poor prognosis. Based on this, we speculate that hsa-miR-206 can be an important factor in the prognostic evaluation of breast cancer. In the analysis of breast cancer subtypes, we observed that hsa-miR-206 expression was significantly lower in ER-positive breast cancer tissues compared to paracancerous tissues. This may be because hsa-miR-206 inhibits ERα expression via two binding sites in the 3’UTR [ 32 ] , while ERα agonists can also inhibit hsa-miR-206 but not by progesterone or ERβ agonists. These results suggest there is a feedback loop between hsa-miR-206 and ERα. In addition, hsa-miR-206 has been shown to induce cell cycle arrest and suppress estrogen-induced proliferation and migration [ 33 – 36 ] . Since aberrant ERα expression is generally considered a hallmark of breast cancer, hsa-miR-206 may serve as a new candidate gene for endocrine therapy in breast cancer. Survival analysis revealed that the median OS of the hsa-miR-206 low expression group was 2 months longer than that of the high expression group ( P = 0.018). In luminal A breast cancer, the median OS of the hsa-miR-206 low expression group did not differ significantly from that of the high expression group. In luminal B breast cancer, the median OS of the hsa-miR-206 low expression group was 30 months longer ( P = 0.002). In HER2 + breast cancer, the median OS of the hsa-miR-206 low expression group was 1 month longer, but the difference was not statistically significant. These findings suggest that the differential expression of hsa-miR-206 is associated with different prognoses in patients with breast cancer with different molecular subtypes. This may have been because hsa-miR-206 can bind to multiple target genes, and these target genes can perform different functions, including as transcription factors, secretory proteins, receptors, transporters, and so on, or that the 3’-UTR of individual genes have multiple binding sites for several miRNAs. This implies that there are complex combinations in the regulation of gene expression by miRNAs. Another possibility is that miRNAs may regulate multiple signaling pathways, and the deletion or aberrant expression of these small RNAs may be closely associated with the pathogenesis of diseases [ 37 ] . Although numerous studies have been conducted on hsa-miR-206, the mechanisms underlying its regulation of breast cancer remain unclear [ 38 ] . To clarify the role of hsa-miR-206 in breast cancer-related protein networks and how it regulates the malignant progression of breast cancer, we incorporated TCGA data to analyze the target genes of hsa-miR-206 in breast cancer pathways. The intersecting target genes of the various pathways were integrated, and our analysis revealed that the target genes of hsa-miR-206 included BRCA1, BRCA2, CHEK1, CDC25C, STX1A, SNAP25, CCND1, ESR1, CXCR3, CXCR4, SLC25A22, WT1, and other interactions. Comparisons were performed with paracancerous tissues for the target genes above, and the results showed that all target genes were highly expressed in breast cancer tissues, except for WT1, which was lowly expressed. The target genes of hsa-miR-206 were mainly involved in signaling pathways and biological processes, such as breast cancer, cell cycle, and homologous recombination. By performing correlation analysis, we found positive correlations between CDC25C and CHEK1 and between CCND1 and ESR1, indicating crucial roles in breast cancer pathogenesis. CDC25C, an important cell cycle regulatory protein, has shown significantly elevated expressions in various tumor tissues compared to normal tissues and is also associated with tumor invasion, recurrence, and low survival rate [ 39 – 42 ] . CHEK1 is involved in cell cycle regulation and DNA damage repair [ 43 ] . Recent research has shown that CHEK1 is upregulated in malignancies such as ovarian cancer [ 44 ] and breast cancer [ 45 ] and acts as an oncogene that can promote the malignant progression of tumors. Some researchers have proposed that the greater activation of cell cycle checkpoint proteins (e.g., cyclin-dependent kinases, CDKs) may be related to increased stem cell activity in triple-negative breast cancer [ 46 , 47 ] , as the accumulation of mutations in the p53 tumor suppressor gene and BRCA1 gene may trigger the ATM/ATR-CHK1/2-CDC25 pathway [ 48 , 49 ] . Interactions between the ER (ESR1) and cyclin D1 (CCND1) pathways can also be found. ER generally targets CCND1 transcriptionally to promote the formation of CCND1-CDK4/6 complexes, activate the transcription factor E2F, and play a role in promoting tumor cell proliferation [ 50 ] . Research ideas are abundant concerning regulating breast cancer development by hsa-miR-206 expression. Therefore, further in-depth investigations are needed on the specific mechanisms involved. In conclusion, by conducting bioinformatics analysis using the TCGA database and TCGA online software, combined with clinical in situ hybridization, this study demonstrated the low expression of hsa-miR-206 in the cancerous tissues of patients with non-triple-negative breast cancer and its higher expression in paracancerous tissues. Furthermore, patients with breast cancer with low hsa-miR-206 expression showed good survival and prognosis. Therefore, hsa-miR-206 is a promising candidate as a novel tumor marker for the prognostic evaluation of non-triple-negative breast cancer. However, the limited sample size of this study should be further expanded to verify this claim. Declarations A cknowledgement Thanks are due to the laboratory colleagues for their great help in technology. Statement of Ethics All patient samples were collected with their or their family members' informed consent, and written informed consent was provided prior to enrollment, which was approved by the Ethics Committee of the Third Affiliated Hospital of Jinzhou Medical University (approval No. KX2020016), in accordance with the Declaration of Helsinki. Conflict of Interest Statement The authors declare that they have no competing interests. Funding Sources The present study was supported by the Youth Project of the Education Department of Liaoning Province (Project No. JYTQN2020021) and the Natural Science Foundation of Liaoning Province (Fund No. 2022-BS-316). Author Contributions Throughout the study, HDN and CSX conceived and designed the study. HDN and WYD wrote the manuscript. Experiments were conducted on GZH. CSX, TQ and GZH were analyzed. WYD and GZH create charts and graphs. HDN and CSX oversaw the study. HDN and CSX confirm the authenticity of all raw data. All authors read and approved the final version of the manuscript. Data Availability Statement The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Patient consent for publication Not applicable. References Katsura C, Ogunmwonyi I, Kankam HK, et al . Breast cancer: presentation, investigation and management. Br J Hosp Med (Lond). 2022 Feb 2;83(2):1-7. A. Mcguire, J.A.L. Brown, C. Malone, et al . Effects of age on the detection and management of breast cancer, Cancers. 7 (2015) 908-929. Lu J, Getz G, Miska EA, et al . MicroRNA expression profiles classify human cancers. Nature 2005, 435:834-838. Iorio MV, Ferracin M, Liu CG, et al .Croce CM: MicroRNA gene expression deregulation in human breast cancer. Cancer Res 2005, 65:7065-7070. Sharma S, Umar S, Centala A, et al . Role of miR-206 in genistein-induced rescue of pulmonary hypertension in monocrotaline model. J Appl Physiol.2015;119:1374–82. 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Biochemical and Biophysical Research Communications. 2013;433(2):207-12. Romero-Cordoba S,Rodriguez-Cuevas S, Rebollar-Vega R, et al . Identification and pathway analysis of microRNAs with no previous involvement in breast cancer.PLoS One 2012;73(3) . Chaudhari R, Nasra S, Meghani N, et al . MiR-206 conjugated gold nanoparticle based targeted therapy in breast cancer cells. Sci Rep. 2022 Mar 18;12(1):4713. doi: 10.1038/s41598-022-08185-1. Jang JY, Kim YS, Kang KN, et al . Multiple microRNAs as biomarkers for early breast cancer diagnosis. Mol Clin Oncol. 2021 Feb;14(2):31. doi: 10.3892/mco.2020.2193. Lee CH, Kuo WH, Lin CC, et al .MicroRNA-regulated protein-protein interaction networks and their functions in breast cancer.Int J Mol Sci 2013 May 30;146(6). B.D. Adams, H. Furneaux, B.A. White, The micro-ribonucleic acid (mirna) mir-206 targets the human estrogen receptor-α (erα) and represses erα messenger rna and protein expression in breast cancer cell lines, Mol. Endocrinol. 21 (2007)1132–1147. S.F. Tavazoie, A. Claudio, O. Thordur, et al .Endogenous human micrornas that suppress breast cancer metastasis, Nature. 451(2008) 147–152. O.D. Elizabeth, L. Ashish, Micrornas and their target gene networks in breast cancer, Breast Cancer Res. 12 (2010) 1-10. N. Kondo, T. Toyama, H. Sugiura, et al . Mir-206 expression is down-regulated in estrogen receptor alpha-positive human breast cancer, Cancer Res. 68 (2008) 5004. Z. Kos, D.J. Dabbs.Biomarker assessment and molecular testing for prognostication in breast cancer, Histopathology. 68 (2016) 70-85. Esquela-Kerscher A, Slack FJ.Oncomirs - microRNAs with a role in cancer.Nat Rev Cancer 2006 Apr;6(4). Ge X, Lyu P, Cao Z, et al . Overexpression of miR-206 suppresses glycolysis, proliferation and migration in breast cancer cells via PFKFB3 targeting. Biochemical and biophysical research communications. 2015;463(4):1115-21. Saini G, Joshi S, Garlapati C, et al . Polyploid giant cancer cell characterization: New frontiers in predicting response to chemotherapy in breast cancer.Semin Cancer Biol. 2022 Jun;81:220-231. Qingfan Mo, Ke Xu , Chenghao Luo, et al. BTNL9 is frequently downregulated and inhibits proliferation and metastasis via the P53/CDC25C and P53/GADD45 pathways in breast cancer[J].Biochem Biophys Res Commun.2021,14:553:17-24. Kai Liu, Rui Lu, Qi Zhao, et al. Association and clinicopathologic significance of p38MAPK-ERK-JNK-CDC25C with polyploid giant cancer cell formation.Med Oncol. 2019 Nov 16;37(1):6. Huayong Jiang, Bin Wang, Fuli Zhang, et al .The Expression and Clinical Outcome of pCHK2-Thr68 and pCDC25C-Ser216 in Breast Cancer.Int J Mol Sci. 2016 Nov; 17(11): 1803. HAINLEY L E,HUGHSON M S, NARENDRAN A, et al . Chk1 and the host cell DNA damage response as a potential antiviral target in BK polyomavirus infection[J].Viruses,2021,13(7):1353-1362. JIANGJ,WANGS,WANGZ, et al . HOTAIR promotes paclitaxel resistance by regulating CHEK1 in ovarian cancer[J].Cancer Chemother Pharmacol,2020,86(2): 295-305. Mei Wu, Jin-Shu Pang, Qi Sun, et al .The clinical significance of CHEK1 in breast cancer: a high-throughput data analysis and immunohistochemical study[J].Int J Clin Exp Pathol. 2019,12(1):1-20. Horiuchi D, Kusdra L, Huskey N.E, et al . MYC pathway activation in triple-negative breast cancer is synthetic lethal with CDK inhibition. J. Exp. Med. 2012;209:679-696. Tarasewicz E, Rivas L, Hamdan R, et al . Inhibition of CDK-mediated phosphorylation of Smad3 results in decreased oncogenesis in triple negative breast cancer cells. Cell Cycle. 2014;13:3191-3201. Liu S, Ginestier C, Charafe-Jauffret E, et al . BRCA1 regulates human mammary stem/progenitor cell fate. Proc. Natl. Acad. Sci. USA. 2008;105:1680-1685. Deng C.X. Brca1: Cell cycle checkpoint, genetic instability, DNA damage response and cancer evolution. Nucleic Acids Res. 2006;34:1416-1426. BILIRAN H JR, WANG Y, BANERJEE S, et al . Overexpression of cyclin D1 promotes tumor cell growth and confers resistance to cisplatin-mediated apoptosis in an elastase- myc transgene-expressing pancreatic tumor cell line [J]. Clin Cancer Res, 2005, 11(16):6075-86. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4507297","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Case Report","associatedPublications":[],"authors":[{"id":311392030,"identity":"fd5d9988-610c-4285-bcb9-eda24a8bf888","order_by":0,"name":"HE Dong-Ning","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"HE","middleName":"","lastName":"Dong-Ning","suffix":""},{"id":311392031,"identity":"4d0998eb-492e-4fa0-8d66-3db7376d6aa9","order_by":1,"name":"Ze-Hui GU","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ze-Hui","middleName":"","lastName":"GU","suffix":""},{"id":311392040,"identity":"c8057976-7686-4ae7-a3b0-53391f441972","order_by":2,"name":"Qi Tan","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qi","middleName":"","lastName":"Tan","suffix":""},{"id":311392041,"identity":"56690bc8-3752-4adb-bbac-0d27b3c59cb1","order_by":3,"name":"Su-Xian CHEN","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Su-Xian","middleName":"","lastName":"CHEN","suffix":""},{"id":311392043,"identity":"143248c0-1b15-497f-b486-428de1db25b5","order_by":4,"name":"WANG Ya-Di","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYHACgwMfDCTk+Jn5Hz5IqLAhTsvBGRU2xpLtPMwGD86kEaeFmedMWqLBeR42yYdth4hQf/zwxgO8bYcTGA7zHqtIYDvAwN/enYBfy5m0ggOSbYfzGJv50m4k8NxhkDhzdgN+LQdyDA4Yth0uZmZmMLuRIPGMwUAil4CW828MDiS2HU5sA2opSDA4TISWG0BbDgC938PMY8aQkECEFskbzwoONgADWYKZLVki4UAaD0G/8J1P3vz5DzAq7c8fPvjx5z8bOf72XvxaFA6gCfDgVQ4C8g0ElYyCUTAKRsGIBwDw2VL57CtYNwAAAABJRU5ErkJggg==","orcid":"","institution":"","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"WANG","middleName":"","lastName":"Ya-Di","suffix":""}],"badges":[],"createdAt":"2024-05-31 08:14:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4507297/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4507297/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58515284,"identity":"0ff266c0-fa52-4114-9fbd-86ca02fccfe6","added_by":"auto","created_at":"2024-06-17 16:40:40","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":18982,"visible":true,"origin":"","legend":"\u003cp\u003eHsa-miR-206 expression of breast invasive carcinoma in the TCGA database\u003c/p\u003e\n\u003cp\u003eNote: The miRNAseq data expressed in reads per million mapped reads (RPM) were log2 transformed.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4507297/v1/d34cdea27c180d5e8064ec56.jpg"},{"id":58515282,"identity":"eb142ac7-85ef-4c84-9f78-cb87570c0f80","added_by":"auto","created_at":"2024-06-17 16:40:40","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":58285,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship of hsa-miR-206 with breast cancer prognosis and molecular subtype in METABRIC\u003c/p\u003e\n\u003cp\u003eNote: 2A. Relationship of hsa-miR-206 with METABRIC breast cancer prognosis. 2B. Relationship of hsa-miR-206 with METABRIC breast cancer molecular subtypes\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4507297/v1/1e9f72687b838f4822dc10da.jpg"},{"id":58516489,"identity":"2a2c3083-d839-40d6-935a-78a3a804ea17","added_by":"auto","created_at":"2024-06-17 16:48:40","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":184145,"visible":true,"origin":"","legend":"\u003cp\u003eExpression of miR-206 in non triple negative breast invasive ductal carcinoma tissues and adjacent tissues (×200)\u003c/p\u003e\n\u003cp\u003eNote: 3A: non triple negative breast cancer; 3B: Lumina type a breast cancer; 3C: Lumina type B breast cancer; 3D: Her2+ breast cancer; 3E: Paracancerous; 3F: Lumina a type a para carcinoma; 3G: Lumina type B para carcinoma; 3H: Her2+ paracancerous\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4507297/v1/b52a7b277c9c37ffcf4401fc.jpg"},{"id":58515283,"identity":"33fb2b2a-c5cd-4af4-abb8-4fbbe6303240","added_by":"auto","created_at":"2024-06-17 16:40:40","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":71484,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between the differential expression of hsa-miR-206 in non-triple-negative breast cancer and OS\u003c/p\u003e\n\u003cp\u003eNote:4 A: Relationship between the differential expression of hsa-miR-206 in non-triple-negative breast cancer and OS. 4B: Relationship between the differential expression of hsa-miR-206 in luminal A breast cancer and OS. 4C: Relationship between the differential expression of hsa-miR-206 in luminal B breast cancer and OS. 4D: Relationship between the differential expression of hsa-miR-206 in HER2+ breast cancer and OS.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4507297/v1/62c03fe82eed54e2426bc177.jpg"},{"id":58515286,"identity":"25c857ac-492d-45d8-a125-d08433f13ac7","added_by":"auto","created_at":"2024-06-17 16:40:40","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":60936,"visible":true,"origin":"","legend":"\u003cp\u003eBubble plot of signaling pathway enrichment analysis\u003c/p\u003e\n\u003cp\u003eNote: The size of the circles indicates the number of genes enriched in the pathway; the color represents the extent of enrichment, with a darker color indicating more significant enrichment. BP indicates GO-enriched biological processes; CC indicates GO-enriched cellular components; MF indicates enriched molecular functions.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4507297/v1/516ac3720c00c716992db0b6.jpg"},{"id":58515288,"identity":"73467022-cd26-47c7-9d84-e4da3d99e13f","added_by":"auto","created_at":"2024-06-17 16:40:41","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":98641,"visible":true,"origin":"","legend":"\u003cp\u003eExpression of hsa-miR-206-related target genes in breast invasive carcinoma in the TCGA database\u003c/p\u003e\n\u003cp\u003eNote: 6A. Expression analysis of BRCA1 in breast cancer and paracancerous tissues. 6B. Expression analysis of BRCA2 in breast cancer and paracancerous tissues. 6C. Expression analysis of CHEK1 in breast cancer and paracancerous tissues. 6D. Expression analysis of CDC25C in breast cancer and paracancerous tissues. 6E. Expression analysis of STX1A in breast cancer and paracancerous tissues. 6F. Expression analysis of SNAP25 in breast cancer and paracancerous tissues. 6G. Expression analysis of CCND1 in breast cancer and paracancerous tissues. 6H. Expression analysis of ESR1 in breast cancer and paracancerous tissues. 6I. Expression analysis of CXCR3 in breast cancer and paracancerous tissues. 6J. Expression analysis of CXCR4 in breast cancer and paracancerous tissues. 6K. Expression analysis of SLC25A22 in breast cancer and paracancerous tissues.6L. Expression analysis of WT1 in breast cancer and paracancerous tissues.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4507297/v1/054d361e232d03568b25dc02.jpg"},{"id":58515287,"identity":"228b5e3d-d384-4a91-bcd9-1224f9075d46","added_by":"auto","created_at":"2024-06-17 16:40:41","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":137932,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis of hsa-miR-206-related target genes\u003c/p\u003e\n\u003cp\u003eNote: 7A. Correlation analysis between STX1A and SNAP25; 7B. Correlation analysis between CDC2C and CHEK1; 7C. Correlation analysis between BRCA2 and BRCA1;7D. Correlation analysis between ESR1 and BRCA1; 7E. Correlation analysis between CCND1 and ESR1; 7F. Correlation analysis between CHEK1 and BRCA1.\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4507297/v1/302b76deb0d9d9f6c139a619.jpg"},{"id":58517869,"identity":"83abb81c-8003-411e-b0d2-2d6c558879b5","added_by":"auto","created_at":"2024-06-17 17:04:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1845257,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4507297/v1/172690ea-e187-4a38-8394-8d032fdeecef.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Expression and prognostic value of hsa-miR-206 in non-triple-negative breast cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBreast cancer is one of the most common cancers among women worldwide \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e, affecting approximately 12% of the global female population \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. It poses a severe threat to women\u0026rsquo;s health, affecting their quality of life and being a leading cause of death among women. Due to its high incidence and mortality rates, breast cancer remains a focal point in recent clinical research. Micro RNAs (miRNAs) have shown the ability to detect multiple human cancers of different developmental lineages and differentiation states, including breast cancer \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. In particular, comparisons between normal and malignant breast tissues have revealed a subset of miRNAs that can distinguish between these tissues. Additionally, differentially expressed miRNAs are associated with the histopathological features of breast cancer, such as tumor size, lymph node metastasis, proliferative capacity, and vascular invasion \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Although specific miRNAs are thought to modulate the expression of genes within the receptor network driving breast cancer progression, miRNA profiling has yet to independently identify breast cancer subtypes clinically defined by the overexpression of ErbB2 or estrogen receptor (ER) proteins. Current methods and platforms of miRNA analysis further limit this technique. miR-206, located on human chromosome 6p12.2, is indispensable to the growth and reconstruction of skeletal muscle \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. The dysregulation of miR-206 is linked to cancer progression, neurological disorders, and myocardial infarction \u003csup\u003e[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Recent studies have found that ER-positive breast cancer samples exhibit significant downregulation of hsa-miR-206 expression compared to the corresponding adjacent non-cancerous tissues. Low hsa-miR-206 expression was associated with TNM stage, mitotic rate, and lymph node metastasis (\u003cem\u003eP\u003c/em\u003e values\u0026thinsp;=\u0026thinsp;0.02, 0.01 and 0.01, respectively) \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Detection of hsa-miR-206 expression levels in breast cancer and normal paracancerous breast tissues using quantitative real-time polymerase chain reaction (RT-PCR) revealed that hsa-miR-206 expression was correlated with negative ER, PR, HER2 status \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Kondo et al. \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e found that the expression of miR-206 in ERα-positive human breast cancer tissues was significantly lower than in paracancerous tissues. It was also observed that hsa-miR-206 expression in breast cancer tissues was negatively correlated with ERα expression but not with ERβ mRNA expression. Further \u003cem\u003ein vitro\u003c/em\u003e transfection experiments demonstrated that introducing hsa-miR-206 in estrogen-dependent MCF-7 breast cancer cells inhibited cell growth in a dose- and time-dependent manner. The hsa-miR-206 high expression group had a lower 3-year survival rate than the low expression group. Thus, hsa-miR-206 upregulation may affect the prognosis of patients with breast cancer and serve as an important indicator for predicting the prognosis of patients with this type of breast cancer \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAlthough the expression of hsa-miR-206 in breast cancer has been widely reported, these experiments were mainly conducted \u003cem\u003ein vitro\u003c/em\u003e. Even those verified histologically lacked a systematic analysis of the molecular subtypes of non-triple-negative breast cancer. Additionally, the characteristics and molecular mechanisms of the hsa-miR-206 regulatory pathway remain poorly understood. This study aimed to analyze the expression and prognostic value of hsa-miR-206 in non-triple-negative breast cancer. Therefore, we collected tissue specimens from 80 patients with breast cancer and grouped them according to the ER, progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and Ki-67 molecular subtypes. Online analysis was performed using the TCGA and STRING databases to determine the intersecting genes and action pathways of hsa-miR-206 in breast cancer to verify the different effects and possible mechanisms of hsa-miR-206 in non-triple-negative breast cancer.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e \u003cb\u003edbMEMC 2.0 database\u003c/b\u003e \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe dbDEMC 2.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.picb.ac.cn/dbDEMC\u003c/span\u003e\u003cspan address=\"http://www.picb.ac.cn/dbDEMC\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.biosino.org/dbDEMC/index\u003c/span\u003e\u003cspan address=\"https://www.biosino.org/dbDEMC/index\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is a collection of differentially expressed miRNAs (DEMs) in human cancers acquired compiled using microarray data. It contains 209 expression profile datasets for 36 cancer types and 73 subtypes, identifying 2,224 DEMs. An easy-to-use web interface allows users to swiftly search for DEMs specific to certain cancer types. The \u0026ldquo;meta-profiling\u0026rdquo; function enables viewing differential expression events based on user-defined miRNAs and cancer types. This database will continue to serve as a valuable source for cancer research and potential clinical applications related to miRNAs.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMETABRIC database\u003c/h2\u003e \u003cp\u003e \u003cb\u003eTCGA database\u003c/b\u003e \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe miRNAseq data from level 3 BCGSC miRNA Profiling in the TCGA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) BRCA (Breast Invasive Carcinoma) project were used. Statistical analysis and visualization were performed using R version 3.6.3. The R package ggplot version 2.3.3.3 was mainly used for visualization. The expression of hsa-miR-206 [MIMAT0000462] in breast invasive carcinoma and normal breast tissue was analyzed.\u003c/p\u003e \u003cp\u003e \u003cb\u003eKaplan\u0026ndash;Meier Plotter\u003c/b\u003e \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe Kaplan\u0026ndash;Meier plotter (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://kmplot.com/analysis/\u003c/span\u003e\u003cspan address=\"http://kmplot.com/analysis/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is a public database that can assess the impact of 54,000 genes (mRNA, miRNA, protein) on the survival rates of 21 cancer types, including breast, ovarian, lung, and gastric cancer. The database sources include GEO, METABRIC, and TCGA. The main purpose of the tool is to discover and validate survival-associated biomarkers through meta-analysis. Information on gene expression and disease prognosis can be obtained based on this.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eBreast cancer specimen collection and tissue microarray construction\u003c/h2\u003e \u003cp\u003eeighty pairs of breast cancer or non-breast cancer specimens were collected from patients at the Third Affiliated Hospital of Jinzhou Medical University and Shanghai Tongren Hospital. The specimens were paraffin-embedded, stained with hematoxylin and eosin, and sent to Superbiotek Co., Ltd. (Shanghai, China) for human tissue microarrays (TMAs) construction. Tumor staging was performed according to the eighth edition of the American Joint Committee on Cancer TNM Classification. This study was commissioned by the Institutional Review Board (IRB) of the Third Affiliated Hospital of Jinzhou Medical University. All patients provided informed consent prior to participation. The IRB no. is kx2020016.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eIn situ hybridization assay\u003c/h2\u003e \u003cp\u003eAnti-Dig-AP antibody (manufacturer: Roche, product no.: 11093274, concentration: 1:1000) and hsa-miR-206 probe (manufacturer: QIAGEN; product no.: YD00619855-BCE, concentration: 100nm, hybridization temperature: 56\u0026deg;C) were used. TMAs were produced by Shanghai Peide Biotechnology Co., Ltd. (no. BRC1602). Deparaffinization and hydration: The specimens were deparaffinized with xylene twice, for 10 minutes each, then immersed sequentially in 100%, 95%, 80%, and 75% ethanol and DEPC-H\u003csub\u003e2\u003c/sub\u003eO once, for 5 minutes each time. After treatment with 0.2N HCl at room temperature for 5 minutes, the specimens were washed thrice with PBS (phosphate buffered saline) for 5 minutes each time. These were incubated for 20 minutes in proteinase K (40 \u0026micro;g/ml), washed three times with PBS containing 0.2% glycine for 5 minutes each, and fixed with 4% paraformaldehyde for 10 minutes. They were washed twice with PBS for 5 minutes each time, incubated with acetic anhydride and triethanolamine solutions at room temperature for 10 minutes, and washed twice with PBS for 5 minutes each time. The specimens underwent pre-hybridization at 37\u0026deg;C for 2 hours and hybridization at 37\u0026deg;C overnight. The specimens were washed with 5\u0026times;SSC twice for 20 minutes each time, 50% deionized formamide/ 2\u0026times;SSC three times at 37\u0026deg;C for 20 minutes each time, and TBST five times for 5 minutes each time. Blocking was done for 1 hour at room temperature. The specimens were incubated with anti-digoxigenin primary antibody (1:1000) at 4\u0026deg;C overnight and washed with TBST four times for 10 minutes each time. They were then washed twice with BCIP/NBT staining buffer for 10 minutes each time and stained with BCIP/NBT in the dark for 4\u0026ndash;48 hours. The reaction was stopped by washing twice with TE (pH 8.0), and the residual liquid was rinsed with water. The slides were dehydrated, mounted, observed under microscopy, and photographed.\u003c/p\u003e \u003cp\u003e \u003cb\u003eInterpretation of in situ hybridization results\u003c/b\u003e \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAn ordinary optical microscope was used, and five different fields of view were randomly selected for observation at high magnification (10\u0026times;10). The percentage of positive cells among the total number of cells was calculated and scored (0 points, no positive cells; 1 point, 1\u0026ndash;25%; 2 points, 26\u0026ndash;50%; 3 points, 51\u0026ndash;75%; 4 points, \u0026gt; 75%). The staining intensity was also observed (0 points, no staining; 1 point, light yellow; 2 points, light brown; 3 points, brown). The scores for the percentage of positive cells and staining intensity were multiplied to define the relative expression of miR-206 in tumor tissue.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eTCGA database analysis of hsa-miR-206 signaling pathway in breast cancer\u003c/h2\u003e \u003cp\u003eThe online tool on the TCGA database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioinfo.life.hust.edu.cn/miR_path/index.html\u003c/span\u003e\u003cspan address=\"http://bioinfo.life.hust.edu.cn/miR_path/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used for analysis to obtain the action pathways of hsa-miR-206 in breast cancer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eSTRING network analysis\u003c/h2\u003e \u003cp\u003eThe STRING version 11.0 database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/cgi/input.pl\u003c/span\u003e\u003cspan address=\"https://string-db.org/cgi/input.pl\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is a platform for analyzing predictable protein-protein regulatory networks. This database predicted the upstream and downstream regulation of proteins targeted by hsa-miR-206 and regulatory relationships between proteins. The search conditions were: ① Protein name; ② organism: homo sapiens; ③ click \u0026ldquo;analysis.\u0026rdquo;\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData analysis was performed using SPSS 21.0. A paired t-test was used to compare between-groups of paired samples. One-way analysis of variance (ANOVA) was used to analyze the relationship between hsa-miR-206 expression and the clinicopathological stages of breast cancer, as well as between hsa-miR-206 expression and prognosis. Survival analysis was conducted using the Kaplan\u0026ndash;Meier model. Statistical significance was set at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eHsa-miR-206 expression in breast cancer\u003c/h2\u003e \u003cp\u003eThe dbMEMC 2.0 database revealed that hsa-miR-206 expression was significantly lower in breast cancer tissue compared to normal breast tissue ( \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.78E-4, FC = -1.41). Further analysis of different molecular subtypes of breast cancer indicated significantly lower hsa-miR-206 expression in ER-positive (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.44E-4, FC = -1.39), luminal B (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.70E-3, FC = -1.48), HER2+ (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.97E-3, FC = -1.73) and basal-like (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9.45E-4, FC = -1.50) breast cancer compared to normal breast tissues (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003emiR-206 expression of breast cancer and paracancerous tissues in the dbMEM 2.0 database\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExperiment ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCancer Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCancer Subtype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDesign\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003elogFC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAveExpr\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eT value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eadj Pvalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eStatus\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEXP00034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ebreast cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ecancer vs normal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-3.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.78e-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.29e-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDOWN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEXP00035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ebreast cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eER positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ecancer vs normal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-3.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.44e-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.65e-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDOWN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEXP00036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ebreast cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eER negative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ecancer vs normal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-3.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.30e-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.58e-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDOWN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEXP00038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ebreast cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLuminal B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ecancer vs normal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-3.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.70e-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.46e-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDOWN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEXP00039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ebreast cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHER2+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ecancer vs normal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-3.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.97e-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.99e-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDOWN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEXP00040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ebreast cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBasal-like\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ecancer vs normal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-3.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.45e-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.29e-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDOWN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eHsa-miR-206 expression of breast cancer in the TCGA database\u003c/h2\u003e \u003cp\u003eThe TCGA-BRCA includes 1,227 breast cancer specimens and 104 normal breast tissues, which were used to analyze the expression of hsa-miR-206 in breast cancer and paracancerous tissues. A normality test indicated that the sample was not normally distributed (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), so the Mann\u0026ndash;Whitney U test was performed. The results showed that hsa-miR-206 expression was lower in the breast cancer group compared to the normal breast tissue group, and the difference was statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eRelationship of hsa-miR-206 with TCGA and METABRIC breast cancer prognosis\u003c/h2\u003e \u003cp\u003eThe relationship between hsa-miR-206 and breast cancer prognosis was analyzed online using the Kaplan\u0026ndash;Meier Plotter with data from the METABRIC database. The results of the analysis showed that in the METABRIC database (n\u0026thinsp;=\u0026thinsp;1,262), patients with low hsa-miR-206 expression had a longer median overall survival (OS) than those with high expression (89 months vs. 77.52 months, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02). Further analysis of the molecular subtypes showed no significant difference in median OS between patients with low and high hsa-miR-206 expressions (Luminal A, n\u0026thinsp;=\u0026thinsp;546, 250.06 months vs.189.3 months, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.28) (shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eHsa-miR-206 expression in breast cancer TMA\u003c/h2\u003e \u003cp\u003eIn situ hybridization assay results showed that miR-206 was expressed in the cytoplasm. Under microscopy, the products of positive expression showed a granular or patchy distribution, appearing yellow, light brown, or brown depending on staining intensity (shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eComparison of hsa-miR-206 expression in breast cancer and paracancerous tissues\u003c/h2\u003e \u003cp\u003eIn situ hybridization results indicated that hsa-miR-206 expression was positive in 25 of the 80 breast cancer tissue specimens, with a positive expression rate of 31.25%. In contrast, hsa-miR-206 expression was positive in 55 of the 80 paracancerous tissue specimens, with a positive expression rate of 68.75%. Thus, the positive expression rate of hsa-miR-206 was significantly lower in breast cancer tissues compared to paracancerous breast tissues, with a statistically significant difference (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003emiR-206 expression in breast cancer and normal paracancerous tissues\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTissue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003emiR\u0026minus;206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003epositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreast cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25(31.25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55(68.75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal breast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55(68.75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25(31.25%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eHsa-miR-206 expression in cancerous and paracancerous tissues in breast cancer subtypes\u003c/h2\u003e \u003cp\u003eAmong the 80 specimens of breast cancer tissues, the relationship of hsa-miR-206 expression with clinical molecular subtypes was as follows: In the luminal A subtype, the expression of hsa-miR-206 in ER-positive human breast cancer tissues was significantly lower than that of paracancerous tissues, with a positive expression rate of 21.74% in cancerous tissues compared to 73.91% in paracancerous tissues (\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;12.545, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000). In the HER2\u0026thinsp;+\u0026thinsp;subtype, the positive expression rate of hsa-miR-206 in cancerous tissues was 28%, while in paracancerous tissues, it was 72% (\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;9.68, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002). Pearson\u0026rsquo;s \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e test revealed a statistically significant difference between the cancerous and paracancerous groups in the luminal A and HER2\u0026thinsp;+\u0026thinsp;molecular subtypes (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003emiR-206 expression in breast cancer and normal paracancerous tissues in different molecular subtypes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMolecular subtypes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eBreast cancer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eBreast normal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003epositive\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003epositive\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLumina A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(21.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18(78.26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17(73.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8(26.09%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12.545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLumina B Her\u0026minus;2(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(46.15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7(53.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9(69.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4(30.77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLumina B Her\u0026minus;2(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(36.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12(63.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11(57.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8(42.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHer\u0026minus;2(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18(72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18(72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7(28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eRelationship between hsa-miR-206 expression and clinicopathological features of patients with non-triple-negative breast cancer\u003c/h2\u003e \u003cp\u003eRelationship between hsa-miR-206 expression and patient-related clinicopathological data in invasive ductal carcinoma was examined using in situ hybridization. The results revealed that breast cancer patients aged\u0026thinsp;\u0026lt;\u0026thinsp;50 years showed a significantly lower positive expression rate for hsa-miR-206 compared to patients aged\u0026thinsp;\u0026ge;\u0026thinsp;50 years (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.019). Additionally, the expression level of hsa-miR-206 was significantly associated with histological grade (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.048) and Ki-67 expression (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, it was not significantly associated with tumor size or the status of sex hormone receptors, such as ER, PR, and HER2 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRelationship between hsa-miR-206 expression and clinicopathological features of patients with non-triple-negative breast cancer\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeature\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePositive expression rate/%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative expression rate/%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (31.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55 (68.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. Age at onset/ years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.019\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (18.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31 (81.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (42.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (57.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2. Tumor size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.297\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (24.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (75.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u0026ndash;5cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 (62.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3. Location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.673\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight breast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (33.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (66.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft breast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (28.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27 (71.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4. Clinical stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.364\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (24.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (75.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIIA-B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIIIA-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (42.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (57.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5. Histological grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.048\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u0026ndash;II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (23.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (76.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u0026ndash;III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (44.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (55.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6. Differentiation grade T stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.432\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (27.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (72.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (62.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7. Differentiation grade N stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.391\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36 (75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (33.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (66.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (62.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8. Lymph node metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (25.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35 (74.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (39.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (60.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8. Ki-67 expression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.046\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;14%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (42.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (57.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;14%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (21.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (78.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10. p53 expression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.247\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow expression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (38.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (61.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh expression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (26.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34 (73.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11. Molecular subtype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.438\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLuminal A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (21.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (78.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLuminal B Her2(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (46.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (53.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLuminal B Her2(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (36.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (63.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHer2(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eRelationship between hsa-miR-206 expression and prognosis of patients with non-triple-negative breast cancer\u003c/h2\u003e \u003cp\u003ePostoperative cumulative survival rates of the hsa-miR-206 high and low expression groups were 56.0% and 81.8%, respectively. Compared to the hsa-miR-206 high expression group, the median OS of the low expression group was 2 months longer (83 months vs. 85 months, \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;5.586, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018). The 5-year OS of the hsa-miR-206 high and low expression groups were 66.4% and 85.68%, respectively.\u003c/p\u003e \u003cp\u003eIn luminal A breast cancer, the median OS of the hsa-miR-206 high expression group was 10.5 months longer than that of the low expression group (115 months vs. 104.5 months, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.598).\u003c/p\u003e \u003cp\u003eConversely, in luminal B breast cancer, the median OS of the hsa-miR-206 low expression group was 30 months longer than that of the high expression group (84 months vs. 54 months, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), while the 5-year OS of the hsa-miR-206 high and low expression groups were 74.95% and 75.35%, respectively.\u003c/p\u003e \u003cp\u003eIn HER2\u0026thinsp;+\u0026thinsp;breast cancer, the median OS of the hsa-miR-206 low expression group was 1 month longer than that of the high expression group (82 months vs. 81 months, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.845) (shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003ePathway analysis of hsa-miR-206 expression in breast cancer combined with TCGA data\u003c/h2\u003e \u003cp\u003eThe online tool on the TCGA database was used to analyze the significant regulatory pathways of hsa-miR-206 in breast cancer. It was mainly enriched in the roles of BRCA1, BRCA2, and ATR in tumor susceptibility, cell cycle, G2/M checkpoint, blockade of neurotransmitter release by botulinum toxin, CARM1, and ER regulation, regulation of cell cycle progression by PLK3, CDC25C, and CHEK1 regulatory pathway response to DNA damage, and so on. The target genes of hsa-miR-206 that intersected with the pathways above included: BRCA1, BRCA2, CHEK1, CDC25C, STX1A, SNAP25, CCND1, ESR1, CXCR3, CXCR4, SLC25A22, and WT1 (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSignificant regulation of 17 pathways by hsa-miR-206 in breast invasive carcinoma Interactions of hsa-miR-206 intersecting genes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathway alias\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003epathway name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePvalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTargets exp.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eATRBRC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRole of BRCA1, BRCA2 and ATR in Cancer Susceptibility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBRCA1, BRCA2, CHEK1,\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCell Cycle: G2/M Checkpoint\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBRCA1, CDC25C, CHEK1,\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBOTULIN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBlockade of Neurotransmitter Relase by Botulinum Toxin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSTX1A, SNAP25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCARM-ER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCARM1 and Regulation of the Estrogen Receptor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBRCA1, CCND1, ESR1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLK3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegulation of cell cycle progression by Plk3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCDC25C, CHEK1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDC25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecdc25 and chk1 Regulatory Pathway in response to DNA damage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCDC25C, CHEK1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRB Tumor Suppressor/Checkpoint Signaling in response to DNA damage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCDC25C, CHEK1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eATM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eATM Signaling Pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBRCA1, CHEK1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelective expression of chemokine receptors during T-cell polarization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCXCR3, CXCR4,\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHIV-1 defeats host-mediated resistance by CEM15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCXCR4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRHODOPSIN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVisual Signal Transduction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSLC25A22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePELP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePelp1 Modulation of Estrogen Receptor Activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eESR1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverview of telomerase protein component gene hTert Transcriptional Regulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWT1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBARD1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBRCA1-dependent Ub-ligase activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBRCA1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSonic Hedgehog (SHH) Receptor Ptc1 Regulates cell cycle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCDC25C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eActivation of Src by Protein-tyrosine phosphatase alpha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCDC25C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBTG2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBTG family proteins and cell cycle regulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCCND1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe interactions between the intersecting genes of hsa-miR-206 target genes and the pathways above, along with their related signaling pathways, were analyzed. Among which, the interaction scores for STX1A and SNAP25, CDC25 and CHEK1, BRCA2 and BRCA1, ESR1 and BRCA1, CCND1 and ESR1, and CHEK1 and BRCA1 were all 0.99. Based on the biological functions and KEGG enrichment, these intersecting genes were mainly involved in the proliferation of breast cancer, cell cycle, and homologous recombination (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Hence, these genes played a crucial role in breast cancer.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInteraction coefficients of intersecting genes between hsa-miR-206 target genes and pathways\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003enode1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003enode2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003enode1_external_id\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003enode2_external_id\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ecombined_score\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTX1A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSNAP25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSP00000222812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eENSP00000254976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.999\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDC25C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCHEK1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSP00000321656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eENSP00000391090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.999\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRCA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBRCA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSP00000369497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eENSP00000418960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.999\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBRCA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSP00000405330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eENSP00000418960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.999\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCND1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eESR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSP00000227507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eENSP00000405330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.997\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHEK1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBRCA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSP00000391090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eENSP00000418960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.996\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCXCR3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCXCR4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSP00000362795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eENSP00000386884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.969\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRCA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCHEK1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSP00000369497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eENSP00000391090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.963\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCND1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBRCA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSP00000227507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eENSP00000418960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.922\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCND1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBRCA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSP00000227507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eENSP00000369497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.861\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eESR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSP00000368370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eENSP00000405330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.736\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDC25C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBRCA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSP00000321656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eENSP00000418960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.735\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRCA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eESR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSP00000369497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eENSP00000405330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.723\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCND1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCXCR4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSP00000227507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eENSP00000386884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.706\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCXCR4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eESR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSP00000386884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eENSP00000405330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.702\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBRCA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSP00000368370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eENSP00000418960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.683\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCND1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCDC25C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSP00000227507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eENSP00000321656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.638\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCND1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCHEK1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSP00000227507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eENSP00000391090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.593\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDC25C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBRCA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSP00000321656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eENSP00000369497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.479\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBRCA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSP00000368370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eENSP00000369497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.469\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHEK1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eESR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSP00000391090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eENSP00000405330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.455\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCND1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSP00000227507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eENSP00000368370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.446\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eDifferential expression of hsa-miR-206 target genes in breast cancer\u003c/h2\u003e \u003cp\u003eCompared to paracancerous tissues, breast cancer tissues exhibited higher expressions of BRCA1, BRCA2, CHEK1, SNAP25, STX1A, CCND1, ESR1, CXCR3, CXCR4, SLC25A22, and WT1, and the differences were statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); SNAP25 was lower than that of paracancerous tissues, and the difference was statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation analysis of hsa-miR-206-related target genes\u003c/h2\u003e \u003cp\u003eBy combining the results from Sections 2.9, 2.10, and 2.11, we found through correlation analysis positive correlations between STX1A and SNAP25, CDC25C and CHEK1, BRCA2 and BRCA1, ESR1 and BRCA1, CCND1 and ESR1, and CHEK1 and BRCA1. Notably, the strongest positive correlations were between CDC25C and CHEK1, and CCND1 and ESR1(shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003emiRNAs (miR-206s) are a series of endogenous non-coding RNAs that inhibit the expression of protein-coding genes at transcriptional and post-transcriptional levels by binding to the 3\u0026rsquo;-untranslated region (3\u0026rsquo;-UTR) of specific target mRNA molecules, leading to their degradation or translation inhibition. miR-206 has been found to play a crucial role in a variety of malignancies, such as lung, gastric, colorectal, and kidney cancers, and endometrioid adenocarcinoma, glioma, and neuroblastoma \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Sun et al. \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e demonstrated that miR-206 could bind directly to the 3\u0026rsquo;-UTR of c-Met and Bcl-2, thus affecting the proliferation, migration, and colony formation of lung cancer cells. miR-206 can suppress the proliferation and invasion of clear cell renal cell carcinoma by targeting vascular endothelial growth factor A \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Furthermore, cell cycle arrest and inhibition of epithelial-mesenchymal transition (EMT) also contribute to the miR-206-mediated suppression of tumor growth \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Its differential expression in breast cancer can either promote or inhibit cancer progression \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Moreover, miR-206 expression in breast tumors is downregulated, correlating with tumor size and pathological stage. Therefore, miR-206 has been suggested as a tumor suppressor in breast cancer \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eHowever, previous studies on breast cancer have found that the expression of hsa-miR-206 was downregulated in patients with breast cancer \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. In contrast, others have reported that it was upregulated in patients with breast cancer \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. In this study, GSE microarray analysis was first performed on the dbDEMC 2.0 database, which revealed the significantly low expression of hsa-miR-206 in breast cancer. The differential expression of hsa-miR-206 was then analyzed online on the TCGA database, and the verification results were consistent with those of the dbDEMC 2.0 database. Following this, we performed in situ hybridization and found that the positive expression rate of hsa-miR-206 among 80 specimens of breast cancer tissues was 31.25%, while that among paracancerous breast tissues was 68.75%. The difference was statistically significant, which implies that hsa-miR-206 exhibits low expression in breast cancer tissues. Further analysis of the relationship between hsa-miR-206 and different clinicopathological features revealed that hsa-miR-206 expression in breast cancer tissues was associated with age, histological grade, and Ki-67 expression, suggesting high hsa-miR-206 expression in breast cancer may promote the proliferation, invasion, and metastasis of breast cancer cells, and is associated with poor prognosis. Based on this, we speculate that hsa-miR-206 can be an important factor in the prognostic evaluation of breast cancer.\u003c/p\u003e\n\u003cp\u003eIn the analysis of breast cancer subtypes, we observed that hsa-miR-206 expression was significantly lower in ER-positive breast cancer tissues compared to paracancerous tissues. This may be because hsa-miR-206 inhibits ER\u0026alpha; expression via two binding sites in the 3\u0026rsquo;UTR \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e, while ER\u0026alpha; agonists can also inhibit hsa-miR-206 but not by progesterone or ER\u0026beta; agonists. These results suggest there is a feedback loop between hsa-miR-206 and ER\u0026alpha;. In addition, hsa-miR-206 has been shown to induce cell cycle arrest and suppress estrogen-induced proliferation and migration \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. Since aberrant ER\u0026alpha; expression is generally considered a hallmark of breast cancer, hsa-miR-206 may serve as a new candidate gene for endocrine therapy in breast cancer.\u003c/p\u003e\n\u003cp\u003eSurvival analysis revealed that the median OS of the hsa-miR-206 low expression group was 2 months longer than that of the high expression group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018). In luminal A breast cancer, the median OS of the hsa-miR-206 low expression group did not differ significantly from that of the high expression group. In luminal B breast cancer, the median OS of the hsa-miR-206 low expression group was 30 months longer (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002). In HER2\u0026thinsp;+\u0026thinsp;breast cancer, the median OS of the hsa-miR-206 low expression group was 1 month longer, but the difference was not statistically significant. These findings suggest that the differential expression of hsa-miR-206 is associated with different prognoses in patients with breast cancer with different molecular subtypes. This may have been because hsa-miR-206 can bind to multiple target genes, and these target genes can perform different functions, including as transcription factors, secretory proteins, receptors, transporters, and so on, or that the 3\u0026rsquo;-UTR of individual genes have multiple binding sites for several miRNAs. This implies that there are complex combinations in the regulation of gene expression by miRNAs. Another possibility is that miRNAs may regulate multiple signaling pathways, and the deletion or aberrant expression of these small RNAs may be closely associated with the pathogenesis of diseases \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAlthough numerous studies have been conducted on hsa-miR-206, the mechanisms underlying its regulation of breast cancer remain unclear \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. To clarify the role of hsa-miR-206 in breast cancer-related protein networks and how it regulates the malignant progression of breast cancer, we incorporated TCGA data to analyze the target genes of hsa-miR-206 in breast cancer pathways. The intersecting target genes of the various pathways were integrated, and our analysis revealed that the target genes of hsa-miR-206 included BRCA1, BRCA2, CHEK1, CDC25C, STX1A, SNAP25, CCND1, ESR1, CXCR3, CXCR4, SLC25A22, WT1, and other interactions. Comparisons were performed with paracancerous tissues for the target genes above, and the results showed that all target genes were highly expressed in breast cancer tissues, except for WT1, which was lowly expressed. The target genes of hsa-miR-206 were mainly involved in signaling pathways and biological processes, such as breast cancer, cell cycle, and homologous recombination. By performing correlation analysis, we found positive correlations between CDC25C and CHEK1 and between CCND1 and ESR1, indicating crucial roles in breast cancer pathogenesis. CDC25C, an important cell cycle regulatory protein, has shown significantly elevated expressions in various tumor tissues compared to normal tissues and is also associated with tumor invasion, recurrence, and low survival rate \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. CHEK1 is involved in cell cycle regulation and DNA damage repair \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. Recent research has shown that CHEK1 is upregulated in malignancies such as ovarian cancer \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e and breast cancer \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e and acts as an oncogene that can promote the malignant progression of tumors. Some researchers have proposed that the greater activation of cell cycle checkpoint proteins (e.g., cyclin-dependent kinases, CDKs) may be related to increased stem cell activity in triple-negative breast cancer \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e, as the accumulation of mutations in the p53 tumor suppressor gene and BRCA1 gene may trigger the ATM/ATR-CHK1/2-CDC25 pathway \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/sup\u003e. Interactions between the ER (ESR1) and cyclin D1 (CCND1) pathways can also be found. ER generally targets CCND1 transcriptionally to promote the formation of CCND1-CDK4/6 complexes, activate the transcription factor E2F, and play a role in promoting tumor cell proliferation \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/sup\u003e. Research ideas are abundant concerning regulating breast cancer development by hsa-miR-206 expression. Therefore, further in-depth investigations are needed on the specific mechanisms involved.\u003c/p\u003e\n\u003cp\u003eIn conclusion, by conducting bioinformatics analysis using the TCGA database and TCGA online software, combined with clinical in situ hybridization, this study demonstrated the low expression of hsa-miR-206 in the cancerous tissues of patients with non-triple-negative breast cancer and its higher expression in paracancerous tissues. Furthermore, patients with breast cancer with low hsa-miR-206 expression showed good survival and prognosis. Therefore, hsa-miR-206 is a promising candidate as a novel tumor marker for the prognostic evaluation of non-triple-negative breast cancer. However, the limited sample size of this study should be further expanded to verify this claim.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003cstrong\u003ecknowledgement\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThanks are due to the laboratory colleagues for their great help in technology.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatement of Ethics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll patient samples were collected with their or their family members\u0026apos; informed consent, and written informed consent was provided prior to enrollment, which was approved by the Ethics Committee of the Third Affiliated Hospital of Jinzhou Medical University (approval No. KX2020016), in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study was supported by the Youth Project of the Education Department of Liaoning Province (Project No. JYTQN2020021) and the Natural Science Foundation of Liaoning Province (Fund No. 2022-BS-316).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions \u0026nbsp;\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThroughout the study, HDN and CSX conceived and designed the study. HDN and WYD wrote the manuscript. Experiments were conducted on GZH. CSX, TQ and GZH were analyzed. WYD and GZH create charts and graphs. HDN and CSX oversaw the study. HDN and CSX confirm the authenticity of all raw data. All authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient consent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKatsura C, Ogunmwonyi I, Kankam HK, \u003cem\u003eet al\u003c/em\u003e. Breast cancer: presentation, investigation and management. Br J Hosp Med (Lond). 2022 Feb 2;83(2):1-7.\u003c/li\u003e\n\u003cli\u003eA. Mcguire, J.A.L. Brown, C. 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Clin Cancer Res, 2005, 11(16):6075-86.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"hsa-miR-206, breast cancer, prognostic biomarker, non-triple-negative, microRNA expression","lastPublishedDoi":"10.21203/rs.3.rs-4507297/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4507297/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study aims to analyze the expression and prognostic value of hsa-miR-206 in non-triple-negative breast cancer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe expression of has-miR-206 in breast cancer and normal breast tissues was analyzed using the dbDEMC 2.0 database. The TCGA dataset was used to verify hsa-miR-206 expression and analyze its role in breast cancer pathways. In situ hybridization was conducted on tissue microarrays comprising 80 breast cancer specimens and corresponding paracancerous tissues. The relationship between hsa-miR-206 expression and the clinicopathological features of patients with non-triple-negative breast cancer was assessed. Patients were divided into high and low-expression groups based on hsa-miR-206 expression levels, and survival curves were plotted. Online TCGA data analysis was performed to determine intersecting genes and action pathways of hsa-miR-206, with further STRING network analysis to explore possible mechanisms involving hsa-miR-206-related intersecting genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dbDEMC 2.0 and TCGA database and in situ hybridization assay confirmed significantly lower hsa-miR-206 expression in breast cancer tissues compared to paracancerous tissues. In the luminal A subtype, hsa-miR-206 expression was markedly lower in ER-positive human breast cancer tissues than in paracancerous tissues. In the HER2+ subtype, the positive expression rate of hsa-miR-206 in cancerous tissues was 28%, while that in paracancerous tissues was 72%. Patients under 50 years old showed significantly lower positive expression rates. Additionally, hsa-miR-206 expression level correlated significantly with histological grade and Ki-67 expression but not with tumor size or sex hormone receptor status. Kaplan–Meier Plotter analysis of the TCGA and METABRIC databases indicated that patients with low hsa-miR-206 expression had longer overall survival (OS). Subtype-specific analysis showed varying OS benefits: longer OS in luminal A and B breast cancer with low hsa-miR-206 and a slight increase in OS in HER2+ breast cancer. Target genes regulated by hsa-miR-206 were linked to cell cycle and estrogen signaling pathways.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDownregulation of hsa-miR-206 expression in breast cancer may prolong patient OS. Hsa-miR-206 plays distinct roles across breast cancer subtypes, potentially through different target genes affecting cell cycle and estrogen signaling, which underscore its prognostic implications.\u003c/p\u003e","manuscriptTitle":"Expression and prognostic value of hsa-miR-206 in non-triple-negative breast cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-17 16:40:36","doi":"10.21203/rs.3.rs-4507297/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f0456e99-3b02-4aa0-b178-91c6bec0c9ea","owner":[],"postedDate":"June 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-17T12:38:16+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-17 16:40:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4507297","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4507297","identity":"rs-4507297","version":["v1"]},"buildId":"pf3fE39SIOqb-0xH_OWvX","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}