Expression profile analysis and the role of miRNA in breast adenocarcinoma

Expression profile analysis and the role of miRNA in breast adenocarcinoma | 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 Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Expression profile analysis and the role of miRNA in breast adenocarcinoma Ming-Yang Zhang, Yi-Min Huang, Xiang Lv, Xingxia Yang, Si-Jia Shen, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4147896/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 To search for hub microRNAs (miRNAs) that might serve as biomarkers for breast cancer (BC), we conducted out comprehensive analysis of data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and whole transcriptome profiling (WT). For overall sample analysis, we found 3 differently expressed miRNA in BC tissues compared to para-carcinoma tissues (PT). Subtype analysis showed that 19, 36 and 19 miRNAs were respectively specific differently expressed in early-stage breast cancer (EBC), advanced stage breast cancer (ABC) and Triple-negative breast cancer (TNBC) compared to PT. Multivariate Cox regression analysis showed that hsa-miR-342-3p and hsa-miR-7705 were independent prognostic factors for overall BC and EBC, respectively. And we found hsa-miR-181b-5p, hsa-miR-3200-3p and hsa-miR-4789-3p were all independent prognostic factors for ABC. Moreover, Kaplan-Meier survival analysis showed that hsa-miR-160b-5p significantly affected the survival of patients in ABC. GSEA demonstrated that tumor related KEGG items (such as cell cycle, ERBB signaling pathway, Wnt signaling pathway, etc.) were differentially enriched in BC. The results of qPCR showed that the expression status of hsa-miR-342-3p, hsa-miR-7705 hsa-miR-160b-5p and hsa-miR-3200-3p were consistent with the results of comprehensive analysis. Finally, this study revealed hsa-miR-342-3p, hsa-miR-7705, hsa-miR-160b-5p and hsa-miR-3200-3p can be used as prognostic biomarkers for BC. Breast adenocarcinoma miRNA TCGA GEO whole transcriptome profiling Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction In 2020, the number of newly-diagnosed breast cancer (BC) cases worldwide was 2.26 million [ 1 ]. It has overtaken lung cancer as the most commonly-diagnosed cancer type for the first time. The global burden of BC is increasing [ 2 ]. Despite great advances in the research and treatment of BC, it remains an unresolved health problem and represents a major focus of biomedical research [ 3 ]. MicroRNAs (miRNAs) are small non-coding RNAs of about 21–22 nucleotide (nt) that regulate gene expression by recognizing homologous sequences and interfering with transcriptional, translational, or epigenetic processes [ 4 ]. Which regulate gene expression at the post-transcriptional stage by inhibiting messenger RNA (mRNA) translation or promoting mRNA degradation [ 5 ]. For a balanced physiological environment, the expression of miRNA needs to be properly regulated. As these small molecules can affect almost all cellular activities, including cell cycle checkpoints, cell proliferation and apoptosis, and have a wide range of target genes. The dysregulation of miRNA expression is associated with various cancers by acting as tumor suppressors and oncogenes [ 6 – 10 ]. Circulating miRNAs also serve as potential biomarkers for diagnosis and prognosis of various cancers and other known diseases and syndromes [ 11 – 13 ]. Therefore, these miRNA biomarkers that predict disease diagnosis and prognosis may become therapeutic targets for some diseases. For example, studies in recent decades have revealed that miRNAs are potentially reliable prognostic markers for breast cancer progression and overall survival, and can even be used to monitor patient progression and inform treatment [ 14 ]. In this study, we aim to identify miRNAs as independent prognostic factors of BC by comprehensive analysis of the miRNA expression data obtained from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and whole transcriptome profiling (WT), with the hope of providing more guidance for the diagnosis and prognosis of breast adenocarcinoma. Methods and methods Collection of data on miRNAs from TCGA, GEO and WT The miRNAs expression data of BC were downloaded from TCGA ( http://portal.gdc.cancer.gov/ ). WT was conducted by the biotech company GENEWIZ (Suzhou, China, www.genewiz.com.cn ). Data were organized, extracted and annotated via Practical Extraction and Report Language 5.28 (Perl). The standardization of miRNAs and mRNAs were realized by R software (version 4.1.3). Data of GSE57897 were analyzed using GEOR2 tool. Screen of different expression miRNAs A total of 1104 tumor samples and 104 para-carcinoma samples from TCGA, 422 tumor samples and 31 para-carcinoma samples from GSE57897 and 14 tumor samples and 9 para-carcinoma samples from WT were included in this study. miRNAs of all samples from TCGA and WT were used to screen differentially expressed miRNAs by R language "limma" and "edgeR" software package [ 15 , 16 ]. Data of GSE57897 were analyzed using GEOR2 tool. The screening criteria for differentially expressed miRNAs were set as |log fold change (FC)| > |0.8|, and a relative P -value < 0.05. R language "gplots" software package was utilized to produce heat map and volcano plot [ 17 ]. Jvenn was used to screen co-different expression miRNAs among TCGA, GEO and WT [ 18 ]. Moreover, subgroup analysis was conducted in this study, since BC is a heterogeneous malignant disease. And Jveen was used to screen out special different expression miRNAs in early-stage breast cancer (EBC), advanced stage breast cancer (ABC) or Triple-negative breast cancer (TNBC). Identify independent prognosis prediction miRNAs The over survival (OS) of differentially expressed miRNAs (DEMs) were analyzed by Kaplan-Meier analysis using R language "survival" software package [ 19 ]. Subsequently, Multivariate cox regressions analysis was also performed using R language "survival" software package to identify independent prognosis prediction miRNAs. The "forestplot" package of R language was used to draw forest map for hazard ratios (HRs) [ 20 ]. Gene sets enrichment analysis (GSEA) Data of standardized miRNAs and mRNAs were merged which was combined and used for carry out GSEA via GSEA 4.3.2 [ 21 ]. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were identified and R language "ggplot2" software package was utilized to merge GO and KEGG pathways figures [ 22 ]. Real‑time polymerase chain reaction (qPCR) BC samples and para-carcinoma samples were collected in Jiaxing Maternity and Child Health Care Hospital. The 1st -miRNA cDNA was synthesized via miRNA First Strand cDNA Synthesis (Tailing Reaction) Kit (Sangon Biotech, Shanghai, China). The expression level of the miRNA was quantified by qPCR using SYBR Green Premix (YEASEN, Shanghai, China) on the ABI Prism® 7500 Sequence Detection System (Applied Biosystems, China). U6 was served as the internal control for qPCR normalization, and the relative gene levels were calculated by 2 −ΔΔCT method. The primer sequences of these miRNAs were listed in Tables S1. Statistical Analysis Statistical analysis of qPCR results was performed using GraphPad Prism v8.0 (GraphPad Software). The Shapiro-Wilk test was conducted to check for a normal distribution of the qPCR data. The data which were normally distributed were analyzed by Student’s t-test. And the data which were abnormally distributed were analyzed by Wilcoxon’s rank sum test. A P value < 0.05 was considered statistically significant. Results Characteristic of data from TCGA, GEO and WT The overall study flow scheme was depicted in Fig. 1 . In this study, data of 1104 BC patients were gathered from TCGA for analysis, which included the expression data of 2223 miRNAs. And data of 14 BC patients whose samples were used to conduct whole transcriptome profiling. The clinicopathological characteristics (age, race, survival status, survival time, gender, pathological type, and TNM stage) of the patients were displayed in Tables S2 and S3. Unfortunately, we failed to obtain the details of patients from GSE57897. Screen of different expression miRNAs Overall sample analysis The screening results showed that there were 421 up-regulated miRNAs and 198 down-regulated miRNAs of BC tissues from TCGA (Fig. 2 A). And 64 up-regulated miRNAs and 73 down-regulated miRNAs of BC tissues from GSE57897 were identified (Fig. 2 B). Moreover, 117 up-regulated miRNAs and 159 down-regulated miRNAs of BC tissues were screened from WT data (Fig. 2 C). Venn diagram results showed that there were 2 co-upregulated miRNAs (hsa-miR-342-3p and hsa-miR-425-5p, Table 1 ) and 1 co-downregulated miRNAs (hsa-miR-4701-5p, Table 1 ) in BC tissues among TCGA, GSE57897 and WT (Fig. 2 D). Table 1 Different expression miRNAs in overall sample analysis. ID TCGA BC vs PT GEO BC vs PT WT BC vs PT LogFC P value LogFC P value LogFC P value hsa-miR-342-3p 1.551893 2.43E-31 1.128 4.66E-11 0.9559 0.039166 hsa-miR-425-5p 0.944353 2E-14 0.97 1.2E-10 2.191864 1.44E-05 hsa-miR-4701-5p -1.15569 6.96E-09 -1.045 2.07E-19 -2.71885 0.048955 BC, breast cancer; PT, para-carcinoma tissues. Subtype analysis As BC is a heterogeneous malignant disease, data from TCGA and WT were used for further subgroup analysis to screen out special DEMs in EBC, ABC or TNBC. Compared with PT in TCGA, 426 miRNAs were higher expression and 182 miRNAs were lower expression in EBC tissues (Fig. 3 A, G). And 414 miRNAs were higher expression and 199 miRNAs were lower expression in ABC tissues (Fig. 3 B, H). Compared with EBC in TCGA, 21 miRNAs were higher expression and 17 miRNAs were lower expression in ABC tissues (Fig. 3 C, I). Compared with PT in WT, 109 miRNAs were higher expression and 59 miRNAs were lower expression in EBC tissues (Fig. 3 D, G). And 106 miRNAs were higher expression and 80 miRNAs were lower expression in ABC tissues (Fig. 3 E, H). Moreover, compared with EBC in WT, 25 miRNAs were higher expression and 13 miRNAs were lower expression in ABC tissues (Fig. 3 F, I). Finally, our results showed that 13 and 17 miRNAs were respectively specific higher expression in EBC and ABC through comprehensive analysis of data from TCGA and WT (Fig. 3 J) (Tables 2 and 3 ). Compared with PT, 6 and 19 miRNAs were respectively specific lower expression in EBC and ABC (Fig. 3 J) (Tables 4 and 5 ). Table 2 Special upregulated miRNAs in EBC. ID TCGA EBC vs PT WT EBC vs PT LogFC P value LogFC P value hsa-miR-15a-5p 0.936999 1.46E-27 1.124094 0.022207 hsa-miR-190b-5p 3.521921 1.69E-45 1.916965 0.025522 hsa-miR-191-5p 1.367259 2.22E-36 1.050876 0.031633 hsa-miR-2114-5p 2.193752 1.52E-13 3.605533 0.048527 hsa-miR-301b-5p 2.884384 1.85E-25 2.527786 0.020858 hsa-miR-3156-3p 3.241717 2.18E-10 5.584326 0.046212 hsa-miR-3194-5p 1.099299 2.05E-05 1.886259 0.043308 hsa-miR-33b-3p 1.784616 3.19E-16 1.718871 0.048216 hsa-miR-342-3p 1.566281 1.42E-32 1.219814 0.018391 hsa-miR-449a 5.535369 1.88E-26 3.613272 4.6E-06 hsa-miR-493-5p 1.294733 6.43E-25 1.209437 0.035677 hsa-miR-7705 2.88908 2.34E-45 2.626203 0.018283 hsa-miR-92b-3p 1.576487 3.17E-33 1.457292 0.007472 EBC, early-stage breast cancer; PT, para-carcinoma tissues. Table 3 Special upregulated miRNA in ABC. ID TCGA ABC vs PT WT ABC vs PT LogFC P value LogFC P value hsa-miR-106b-5p 1.259236 3.29E-39 1.321641 0.010428 hsa-miR-1269b 5.270462 4.04E-16 6.603516 0.016474 hsa-miR-1301-3p 2.067576 4.2E-61 1.547168 0.017491 hsa-miR-181b-5p 1.236281 1.11E-28 1.708271 0.001532 hsa-miR-196a-5p 3.19388 2.48E-41 1.514487 0.007847 hsa-miR-3176 2.135932 4.69E-14 3.471851 0.03316 hsa-miR-3200-3p 1.595043 4.15E-15 1.506522 0.020521 hsa-miR-3619-3p 1.683338 3.1E-12 3.1462 0.034498 hsa-miR-429 2.612503 4.68E-58 1.033966 0.042356 hsa-miR-4662a-5p 0.854002 5.08E-06 1.778002 0.013401 hsa-miR-4789-3p 0.88428 0.001659 2.94055 0.019853 hsa-miR-5008-3p 1.281618 8.81E-07 4.299928 0.048551 hsa-miR-6516-3p 0.841089 0.001133 3.678157 0.016588 hsa-miR-767-5p 4.995602 6.71E-19 6.31534 0.002387 hsa-miR-934 1.721749 2.72E-05 3.514104 0.001366 hsa-miR-93-5p 1.087921 4.78E-27 1.600565 0.002583 hsa-miR-9-5p 1.722351 1.44E-12 3.597604 0.00038 ABC, advanced stage breast cancer; PT, para-carcinoma tissues. Table 4 Special downregulated miRNAs in EBC. ID TCGA EBC vs PT WT EBC vs PT LogFC P value LogFC P value hsa-miR-1468-5p -0.94911 8.14E-15 -1.4296 0.026404 hsa-miR-206 -6.14414 4.89E-63 -4.61526 0.002985 hsa-miR-296-5p -1.0766 3.86E-16 -1.39376 0.019486 hsa-miR-4286 -1.1209 2.08E-10 -11.2947 0.001904 hsa-miR-4701-5p -1.13146 4.23E-08 -4.82009 0.021193 hsa-miR-6507-3p -1.10523 8.03E-07 -2.83533 0.022771 EBC, early-stage breast cancer; PT, para-carcinoma tissues. Table 5 Special downregulated miRNAs in ABC. ID TCGA ABC vs PT WT ABC vs PT LogFC P value LogFC P value hsa-miR-1247-5p -1.60938 7.71E-23 -1.8958 0.006124 hsa-miR-125b-5p -2.00387 1.9E-85 -1.58392 0.007594 hsa-miR-1262 -1.23544 2.18E-14 -3.52842 0.005475 hsa-miR-126-3p -0.84322 1.71E-19 -1.15464 0.026154 hsa-miR-127-3p -0.93738 1.54E-15 -1.18521 0.046982 hsa-miR-129-5p -1.27005 1.5E-10 -1.99406 0.012142 hsa-miR-130a-3p -0.90275 3.8E-21 -1.45547 0.011239 hsa-miR-133a-3p -6.7795 9.44E-97 -1.44129 0.023451 hsa-miR-1-3p -6.33193 2.5E-88 -1.64772 0.006923 hsa-miR-144-3p -2.05266 8.79E-21 -1.68826 0.010785 hsa-miR-376c-3p -1.16706 1.71E-22 -1.36516 0.022259 hsa-miR-381-3p -1.33801 3.15E-28 -1.33127 0.031196 hsa-miR-432-5p -1.21805 4.29E-19 -1.62148 0.021124 hsa-miR-4524a-5p -1.15875 8.03E-05 -1.76239 0.02543 hsa-miR-548y -1.21436 0.000131 -6.31297 0.008672 hsa-miR-585-3p -1.70029 1.44E-19 -2.82184 0.004104 hsa-miR-605-5p -1.36152 4.58E-18 -6.56927 8.57E-05 hsa-miR-655-3p -0.97177 4.55E-16 -1.21377 0.049563 hsa-miR-675-3p -1.28388 4.68E-18 -1.39966 0.041653 ABC, advanced stage breast cancer; PT, para-carcinoma tissues. Compared with PT in TCGA, 347 miRNAs were higher expression and 206 miRNAs were lower expression in non-triple-negative breast cancer (NTNBC) tissues (Fig. 4 A, G). And 528 miRNAs were higher expression and 198 miRNAs were lower expression in TNBC tissues (Fig. 4 B, H). Compared with NTNBC in TCGA, 212 miRNAs were higher expression and 86 miRNAs were lower expression in TNBC tissues (Fig. 4 C, I). Compared with PT in WT, 110 miRNAs were higher expression and 148 miRNAs were lower expression in NTNBC tissues (Fig. 4 D, G). And 87 miRNAs were higher expression and 37 miRNAs were lower expression in TNBC tissues (Fig. 4 E, H). Moreover, compared with NTNBC in WT, 64 miRNAs were higher expression and 12 miRNAs were lower expression in TNBC tissues (Fig. 4 F, I). Finally, our results showed that 16 miRNAs were specific higher expression in TNBC through comprehensive analysis of data from TCGA and WT (Fig. 4 J, Table 6 ). And 3 miRNAs were specific lower expression in TNBC compared to PT (Fig. 4 J, Table 7 ). Table 6 Specific upregulated miRNAs in TNBC. ID TCGA TNBC vs PT WT TNBC vs PT LogFC P value LogFC P value hsa-miR-106b-5p 1.786603 4.53E-68 1.694648 0.006159 hsa-miR-135b-5p 4.056819 2.21E-60 2.046605 0.010829 hsa-miR-148a-3p 1.028366 4.52E-17 1.332135 0.028009 hsa-miR-188-3p 1.698355 2.82E-18 2.179056 0.042837 hsa-miR-18a-5p 2.671302 3.98E-66 2.229379 0.001271 hsa-miR-196a-5p 2.224518 2.92E-28 1.754737 0.011458 hsa-miR-196b-5p 0.863346 2.42E-06 1.577776 0.039434 hsa-miR-3200-3p 2.20244 6.48E-30 2.061783 0.004785 hsa-miR-323b-3p 0.960278 6.73E-05 2.148181 0.024215 hsa-miR-455-5p 1.495093 2.25E-26 2.737403 0.000619 hsa-miR-4677-3p 1.580144 8.67E-46 1.399928 0.03661 hsa-miR-4789-3p 1.250728 2.33E-05 3.466033 0.028991 hsa-miR-577 3.563552 2.14E-30 2.722326 0.003022 hsa-miR-588 0.884739 0.003055 4.701366 0.014861 hsa-miR-93-5p 1.545965 9.13E-44 2.204972 0.00029 hsa-miR-9-5p 3.041252 3.86E-35 4.82452 7.24E-07 TNBC, triple-negative breast cancer; PT, para-carcinoma tissues. Table 7 Specific downregulated miRNAs in TNBC. ID TCGA TNBC vs PT WT TNBC vs PT LogFC P value LogFC P value hsa-miR-4683 -1.08729 1.68E-05 -6.04299 0.020632 hsa-miR-489-3p -0.86129 0.001966 -2.24825 0.027471 hsa-miR-653-3p -1.00008 4.72E-06 -2.02443 0.039885 TNBC, triple-negative breast cancer; PT, para-carcinoma tissues. Identify independent prognosis prediction miRNAs Kaplan-Meier survival analysis To ascertain whether these miRNAs influence the survival of BC, Kaplan-Meier survival analysis was further performed. The results showed that there was a statistically significant differences in the survival time between the high and low expression groups of hsa-miR-342-3p ( P = 0.0122) in all BC tissues (Fig. 5 A). hsa-miR-7705 ( P = 0.0492) played an important role in EBC via Kaplan-Meier survival analysis (Fig. 5 B). Moreover, Kaplan-Meier survival analysis showed that hsa-miR-106b-5p ( P = 0.0212), hsa-miR-181b-5p ( P = 0.0494), hsa-miR-3200-3p ( P = 0.0246) and hsa-miR-4789-3p ( P = 0.0282) significantly influence the survival of ABC patients (Fig. 5 C - F). hsa-miR-135b-5p ( P = 0.0441) and hsa-miR-577 ( P = 0.0471) were found to play key roles in TNBC through Kaplan-Meier survival analysis (Fig. 5 G, H). Multivariate cox regressions analysis Subsequently, independent prognosis prediction miRNAs were identified by Multivariate cox regressions analysis and the results showed that hsa-miR-342-3p (HR = 0.39; 95% CI 0.22–0.69; P = 0.0013) and hsa-miR-7705 (HR = 2.37; 95% CI 1.17–4.82; P = 0.0169) were independent prognostic factors for overall BC and EBC, respectively (Fig. 6 A, B). Furthermore, hsa-miR-181b-5p (HR = 0.17; 95% CI 0.03–0.84; P = 0.0294), hsa-miR-3200-3p (HR = 5.78; 95% CI 2.26–14.77; P = 0.0003) and hsa-miR-4789-3p (HR = 27.66; 95% CI 2.02–378.35; P = 0.0129) were found to be independent prognostic factors for ABC (Fig. 6 C). Unfortunately, hsa-miR-135b-5p (HR = 0.62; 95% CI 0.17–2.30; P = 0.4783) and hsa-miR-577 (HR = 0.94; 95% CI 0.33–2.69; P = 0.9149) were not independent prognostic factors for TNBC (Fig. 6 D). Gene sets enrichment analysis (GSEA) GSEA was conducted to explore the biological relevance of 8 miRNAs using KEGG database. KEGG items cell cycle, DNA replication, ERBB signaling pathway and Wnt signaling pathway differentially enriched in low hsa-miR-342-3p expression phenotype (Fig. 7 A). Base excision repair and homologous recombination signaling pathway differentially enriched in high hsa-miR-7705 expression phenotype (Fig. 7 B). KEGG items TGF-β and Wnt signaling pathways differentially enriched in low hsa-miR-106b-5p and hsa-miR-181b-5p expression phenotypes (Fig. 7 C, D). Moreover, KEGG items cell cycle and DNA replication were enriched in hsa-miR-3200-3p and hsa-miR-4789-3p high expression phenotype (Fig. 7 E, F). KEGG items renin angiotensin system and natural killer cell mediated cytotoxicity were enriched in in hsa-miR-135b-5p low and high expression phenotype, respectively (Fig. 7 G). Immune related KEGG items antigen processing and presentation, B cell receptor and T cell receptor signaling pathways were respectively enriched in in hsa-miR-577 high expression phenotype (Fig. 7 H). Clinical sample validation To validated the expression of miRNA in BC, 20 of EBC and 20 of ABC patients were recruited, including 23 NTNBC and 11 NTBC. The results of qPCR showed that hsa-miR-342-3p ( P < 0.0001) was significantly higher in BC than PT (Fig. 8 A). hsa-miR-7705 ( P = 0.024) was specific higher expression in EBC vs PT (Fig. 8 B, C). Meanwhile, hsa-miR-160b-5p ( P < 0.0001) and hsa-miR-3200-3p ( P = 0.0319) were specific higher expression in ABC vs PT (Fig. 8 D, E, H, I). hsa-miR-181b-5p was higher expression in EBC ( P < 0.0001) and ABC ( P < 0.0001) than PT (Fig. 8 F, G). The expression of hsa-miR-4789-3p was not significant difference between ABC and PT ( P = 0.245) (Fig. 8 K). Our results also demonstrated that hsa-miR-135b-5p was increased in NTNBC ( P = 0.0097) and TNBC ( P = 0.007) (Fig. 8 L, M). And there was no significant difference in the expression of hsa-miR-577 between TNBC and PT ( P = 0.083) (Fig. 8 O). Discussion In some developing countries, the incidence and mortality rate of breast adenocarcinoma are increasing [ 23 ]. Early detection and diagnosis of the disease can reduce mortality, especially in some Asia-Pacific countries where breast cancer mortality rate is two-fold higher than in Western countries and more frequently diagnosed in advanced stages. Moreover, new non-invasive diagnostic biomarkers with high sensitivity and specificity are needed for early breast adenocarcinoma detection [ 24 ]. The purpose of this study was to find out the independent prognostic factors of breast adenocarcinoma and to provide a basis for the diagnosis and prognosis evaluation of BC. In this study, by performing bioinformatical analysis on patient data obtained from TCGA, GEO and WT, we confirmed that hsa-miR-342-3p and hsa-miR-106-5p are important prognostic factors for BC. In recent years, an increasing number of studies have shown that miRNAs may play the role of oncogene or tumor suppressor genes in the pathogenesis of cancer [ 25 ]. There were numerous studies focusing on the effects of differential miRNA expression on various cancer types. Previous studies showed that loss of function of hsa-miR-342-3p promoted the development of BC [ 26 ]. Which are consistent with our results that overexpression of hsa-miR-342-3p significantly prolonged survival time of patients with breast cancer. GSEA indicated that hsa-miR-342-3p may suppress the progression of BC via inhibiting cancer- related ERBB and Wnt signaling pathway. Sang et al reported that the expression of hsa-miR-7705 was increased in BC vs PT, which was similar with our results [ 27 ]. Although our comprehensive analysis results showed that hsa-miR-7705 was special increased in EBC compared with PT and was a poor independent prognosis factor for EBC, our validation results indicated that the expression of hsa-miR-7705 was higher in EBC vs PT. BRCA mutation could lead to homologous recombination repair deficiency and Poly-ADP ribose polymerase inhibitors could cause synthetic lethality in BRCA mutation cells [ 28 ]. We previously reported that PARPi can improved PFS of patients with BRCA -mutated breast cancer [ 29 ]. However, PARPi resistance occurred in some BRCA mutation cancers, epigenetic regulation may contribute to DNA repair pathways [ 30 ]. GSEA showed that hsa-miR-7705 improved the progression of EBC via activating base excision repair and homologous recombination signaling pathway, which may improve PARPi resistance in breast cancer. Some studies showed hsa-miR-106b-5p play crucial roles in the progression of BC [ 31 , 32 ]. Zhou et al reported that miR-106b-5p promoted cisplatin resistance in TNBC by down-regulating GDF11 [ 31 ]. Meanwhile, Farré et al demonstrated that hsa-miR-106b-5p was up-regulated in Basal subtypes, which were more aggressive than others [ 32 ]. And they did not find that the stage affected the expression of hsa-miR-106b-5p. This result is different from our results of comprehensive analysis. We found that hsa-miR-106b-5p was special up-regulated in ABC and it was a good prognosis factor for ABC patients. GSEA indicated that multiple cancer-related pathway items differently enriched in miR-106b-5p low expression phenotype. hsa-miR-106b-5p may suppress the progression of ABC via inhibiting key molecules of these pathways. Studies showed that hsa-miR-3200-3p plays a key role in the development of various tumors, such as glioma, cervical cancer and non-small cell lung cancer [ 33 – 35 ]. There are few researches reported that if it can influence the progression of BC. Our results indicated that hsa-miR-3200-3p significantly affected survival of ABC patients and was a bad independent prognosis factor. hsa-miR-3200-3p can improve the progression of ABC via promoting cell cycle of cancer cells. Of course, there are some shortcomings in this study. Firstly, the survival data only obtained from TCGA database, which makes survival analysis unverifiable. Secondly, no immunohistochemical methods were used for analysis and discussion. Last but not least, the sample size was smaller in whole transcriptome profiling analysis. In conclusion, hsa-miR-342-3p and hsa-miR-7705 were respectively independent prognostic factors for overall BC and EBC. Moreover, hsa-miR-160b-5p played a key role in ABC. Finally, hsa-miR-3200-3p was independent prognostic factor for ABC. This study further supported that the miRNAs with distinctive expression pattern in breast adenocarcinoma from normal tissues/cells may be exploited as indicators for disease progression or choice of treatments, or therapeutic targets. Declarations Acknowledgements Not Applicable. Funding This work was supported by Natural Science Foundation of Zhejiang Province (LQ21H1600400) and Jiaxing Science and Technology Bureau (LGF20H090020). Availability of data and materials Datasets of this study are available through the corresponding author on reasonable request. Authors’ contribution The first two authors contributed equally to this manuscript. They planned and designed the experiment and carried out the study. Xiang Lv collected the data and wrote the main manuscript. Xingxia Yang helped in analyzing data, Sijia Shen designed this study and reviewed the manuscript, Jianguo Wang and Juanying Zhu recruited the patient and collected the data. All authors have read and approved the manuscript. Ethics approval and consent to participate This study was approved by Ethics Committee of Jiaxing Maternal and Child Health Care Hospital (2021(YL)-115) and written informed consent from all participants. 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Proc Natl Acad Sci U S A 102:15545–15550 Wickham H (2016) ggplot2: Elegant graphics for data analysis. Springer-, New York DeSantis CE, Bray F, Ferlay J, Lortet-Tieulent J, Anderson BO, Jemal A (2015) International variation in female breast cancer incidence and mortality rates. Cancer Epidemiol Biomarkers Prev 24:1495–1506 Imani S, Zhang X, Hosseinifard H, Fu S, Fu J (2017) The diagnostic role of microRNA-34a in breast cancer: a systematic review and meta-analysis. Oncotarget 8:23177–23187 Wang Y, Zhang X, Chao Z, Kung HF, Lin MC, Dress A, Wardle F, Jiang BH, Lai L (2017) MiR-34a modulates ErbB2 in breast cancer. Cell Biol Int 41:93–101 Romero-Cordoba SL, Rodriguez-Cuevas S, Bautista-Pina V, Maffuz-Aziz A, D’Ippolito E, Cosentino G, Baroni S, Iorio MV, Hidalgo-Miranda A (2018) Loss of function of miR-342-3p results in MCT1 over-expression and contributes to oncogenic metabolic reprogramming in triple negative breast cancer. Sci Rep 8:12252 Sang M, Li A, Wang X, Chen C, Liu K, Bai L, Wu M, Liu F, Sang M (2020) Identification of three miRNAs signature as a prognostic biomarker in breast cancer using bioinformatics analysis. Transl Cancer Res 9:1884–1893 Murai J, Pommier Y (2023) BRCAness, homologous recombination deficiencies, and synthetic lethality. Cancer Res 83:1173–1174 Zhang M, Yu X, Wang J, Li Y, Cao L (2021) Efficacy and safety of poly (ADP-ribose) polymerase inhibitors therapy for BRCA-mutated breast cancer: A systematic review and meta-analysis. J Cancer Res Ther 17:1672–1678 Fugger K, Hewitt G, West SC, Boulton SJ (2021) Tackling PARP inhibitor resistance. Trends Cancer 7:1102–1118 Zhou Q, Hu Q (2023) Oncogenic miR-106b-5p promotes cisplatin resistance in triple-negative breast cancer by targeting GDF11. Histol Histopathol 18668 Farré PL, Duca RB, Massillo C, Dalton GN, Graña KD, Gardner K, Lacunza E, De Siervi A (2021) MiR-106b-5p: A master regulator of potential biomarkers for breast cancer aggressiveness and prognosis. Int J Mol Sci 22 Wang H, Zeng Z, Yi R, Luo J, Chen J, Lou J (2022) MicroRNA-3200-3p targeting CAMK2A modulates the proliferation and metastasis of glioma in vitro. Bioengineered 13:7785–7797 Cho O, Kim DW, Cheong JY (2021) Plasma exosomal miRNA levels after radiotherapy are associated with early progression and metastasis of cervical cancer: A pilot study. J Clin Med 10 Hui K, Dong C, Hu C, Li J, Yan D, Jiang X (2024) VEGFR affects miR-3200-3p-mediated regulatory T cell senescence in tumour-derived exosomes in non-small cell lung cancer. Funct Integr Genomics 24:31 Additional Declarations No competing interests reported. Supplementary Files SupplementaryTables.docx 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 Our Team In Review Editorial Policies Advisory Board 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-4147896","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":283906515,"identity":"6ad0f509-0df9-4702-a340-82912b1ce377","order_by":0,"name":"Ming-Yang Zhang","email":"","orcid":"","institution":"Jiaxing Women and Children's Hospital, Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ming-Yang","middleName":"","lastName":"Zhang","suffix":""},{"id":283906516,"identity":"1bfe68a1-72c2-4539-b628-56849742ad41","order_by":1,"name":"Yi-Min Huang","email":"","orcid":"","institution":"Jiaxing Women and Children's Hospital, Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yi-Min","middleName":"","lastName":"Huang","suffix":""},{"id":283906517,"identity":"7ee8d28a-41d1-4643-9777-7c7e9ec8196e","order_by":2,"name":"Xiang Lv","email":"","orcid":"","institution":"Jiaxing Women and Children's Hospital, Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiang","middleName":"","lastName":"Lv","suffix":""},{"id":283906518,"identity":"0c41029d-3f70-4b81-b497-d9913aef9855","order_by":3,"name":"Xingxia Yang","email":"","orcid":"","institution":"Jiaxing Women and Children's Hospital, Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xingxia","middleName":"","lastName":"Yang","suffix":""},{"id":283906519,"identity":"c1f05412-f991-4e1a-b24a-ba552bd35211","order_by":4,"name":"Si-Jia Shen","email":"","orcid":"","institution":"Zhejiang Chinese Medical University","correspondingAuthor":false,"prefix":"","firstName":"Si-Jia","middleName":"","lastName":"Shen","suffix":""},{"id":283906520,"identity":"f20ccc77-5638-4e7d-9cd7-c7e820226610","order_by":5,"name":"Jian-Guo Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYBACxgbGZ1Am84EDHyqI0sJsBmWyJR6ccYYoe+BaeIwP87YQo2FGMttjnj+H7Q3On/lwgLeBQZ5f7AABh/UcZjfm4UlL3HAjd8MByR0MhjNnJxDQ0t5/TJpHwibB4AbvhgOGZxgSDG4T0tLMzCbNYyABctiDA4ltxGhpbwZqSbBh3HAgh+HAQaK0AP1iOOdAWuLMG2kGBxvOSBD2iyEwxB68AYYY3/nDjz//qbCR55cmpKWBgYGJB8hQOADmS+BXDgLyIMf9ADEaCCseBaNgFIyCEQoAtkZJfGSq5W4AAAAASUVORK5CYII=","orcid":"","institution":"Jiangnan University","correspondingAuthor":true,"prefix":"","firstName":"Jian-Guo","middleName":"","lastName":"Wang","suffix":""},{"id":283906521,"identity":"9a6bf53c-ca0f-421b-989f-d56d42e2aeb7","order_by":6,"name":"Juan-Yin Zhu","email":"","orcid":"","institution":"Jiaxing Women and Children's Hospital, Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Juan-Yin","middleName":"","lastName":"Zhu","suffix":""}],"badges":[],"createdAt":"2024-03-22 07:35:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4147896/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4147896/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53757469,"identity":"4f816024-bf60-49d4-962c-51e84cc64baf","added_by":"auto","created_at":"2024-03-29 19:07:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":132342,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe overall study flow scheme.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4147896/v1/e314f8c5dda0a9539de65a0d.png"},{"id":53758607,"identity":"8e588f72-2736-46c1-8b32-259037e4e768","added_by":"auto","created_at":"2024-03-29 19:15:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":47629,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferently expressed miRNAs between total BC and PT.\u003c/strong\u003e (A) The volcano plot of miRNAs expression data from TCGA. (B) The volcano plot of miRNAs expression data from GSE57897. (C) The volcano plot of miRNAs expression data from WT. (D) Intersection of differently expressed miRNAs among TCGA, GSE57897 and WT. BC, breast cancer; PT, para-carcinoma tissues. Red dots represent up-regulated miRNAs in breast cancer, blue dots represent down-regulated miRNAs in breast cancer, rey dots represent those miRNAs which are not differential expression. \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, logFC \u0026gt; |0.8|, are judged to be statistically significant.\u003c/p\u003e","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4147896/v1/13708d98609926e8feeaa8e7.png"},{"id":53757472,"identity":"e05bb1ec-67e1-4e91-b0e2-1a4d4058e20b","added_by":"auto","created_at":"2024-03-29 19:07:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":136819,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferently expressed miRNAs among EBC, ABC and PT.\u003c/strong\u003e (A) Differently expressed miRNAs between EBC and PT from TCGA. (B) Differently expressed miRNAs between ABC and PT from TCGA. (C) Differently expressed miRNAs between ABC and EBC from TCGA. (D) Differently expressed miRNAs between EBC and PT from WT. (E) Differently expressed miRNAs between ABC and PT from WT. (F) Differently expressed miRNAs between ABC and EBC from WT. (G) Intersection of differently expressed miRNAs between EBC and PT from TCGA and WT. (H) Intersection of differently expressed miRNAs between ABC and PT from TCGA and WT. (I) Intersection of differently expressed miRNAs between ABC and EBC from TCGA and WT. (J) Intersection of differently expressed miRNAs among ABC, EBC and PT from TCGA and WT. EBC, early-stage breast cancer; PT, para-carcinoma tissues; ABC, advanced stage breast cancer. Red dots represent up-regulated miRNAs in breast cancer, blue dots represent down-regulated miRNAs in breast cancer, grey dots represent those miRNAs which are not differential expression. \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, logFC \u0026gt; |0.8|, are judged to be statistically significant.\u003c/p\u003e","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4147896/v1/9a2c16d9be2412725c7bb3e0.png"},{"id":53758609,"identity":"b16f9175-b8bf-452a-9369-740931b6061e","added_by":"auto","created_at":"2024-03-29 19:15:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":139354,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferently expressed miRNAs among TNBC, NTNBC and PT.\u003c/strong\u003e (A) Differently expressed miRNAs between NTNBC and PT from TCGA. (B) Differently expressed miRNA between TNBC and PT from TCGA. (C) Differently expressed miRNAs between TNBC and NTNBC from TCGA. (D) Differently expressed miRNAs between NTNBC and PT from WT. (E) Differently expressed miRNA between TNBC and PT from WT. (F) Differently expressed miRNAs between TNBC and NTNBC from WT. (G) Intersection of differently expressed miRNAs between NTNBC and PT from TCGA and WT. (H) Intersection of differently expressed miRNAs between TNBC and PT from TCGA and WT. (I) Intersection of differently expressed miRNAs between NTNBC and TNBC from TCGA and WT. (J) Intersection of differently expressed miRNAs among NTNBC, TNBC and PT from TCGA and WT. TNBC, triple-negative breast cancer; NTNBC, non-triple-negative breast cancer; PT, para-carcinoma tissues. Red dots represent up-regulated miRNAs in breast cancer, blue dots represent down-regulated miRNAs in breast cancer, grey dots represent those miRNAs which are not differential expression. \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, logFC \u0026gt; |0.8|, are judged to be statistically significant.\u003c/p\u003e","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4147896/v1/b4d4b2b3fa51d166326f4e22.png"},{"id":53757473,"identity":"893fabcd-cea2-4abc-b58f-56dd32ad47a1","added_by":"auto","created_at":"2024-03-29 19:07:44","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":272828,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier survival curves of BC patients with differential miRNA expression levels. \u003c/strong\u003e(A) The expression level of hsa-miR-342-3p was a key prognostic factor of total BC. (B) The expression level of hsa-miR-7705 was a key prognostic factor of EBC. (C - F) The expression level of hsa-miR-106b-5p, hsa-miR-181b-5p, hsa-miR-3200-3p and hsa-miR-4789-3p were key prognostic factors of ABC. (G) and (H) The expression level of hsa-miR-135b-5p and hsa-miR-577 were key prognostic factors of TNBC. BC, breast cancer; EBC, early-stage breast cancer; ABC, advanced stage breast cancer; TNBC, triple-negative breast cancer.\u003c/p\u003e","description":"","filename":"OnlineFigure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4147896/v1/3f84cbb86612cd0b865b9262.png"},{"id":53757476,"identity":"39593673-e89d-4db0-9b24-473e575077e4","added_by":"auto","created_at":"2024-03-29 19:07:44","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":317359,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe forest plots for Multivariate cox regressions analysis. \u003c/strong\u003e(A) Risk factors for all BC. (B) Risk factors for EBC. (C) Risk factors for ABC. (D) Risk factors for TNBC. BC, breast cancer; EBC, early-stage breast cancer; ABC, advanced stage breast cancer; TNBC, triple-negative breast cancer.\u003c/p\u003e","description":"","filename":"OnlineFigure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4147896/v1/be1a637c0a6697e8ed930a9e.png"},{"id":53758608,"identity":"ca0d6b3e-ef0b-4e10-abfd-88f0c55f3e36","added_by":"auto","created_at":"2024-03-29 19:15:47","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":155359,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGSEA of key prognostic factors of BC. \u003c/strong\u003e(A) GSEA of hsa-miR-342-3p. (B) GSEA hsa-miR-7705. (C) GSEA of hsa-miR-106b-5p. (D) GSEA of hsa-miR-181b-5p. (E) GSEA of hsa-miR-3200-3p. (F) GSEA of hsa-miR-4789-3p. (G) GSEA of hsa-miR-135b-5p. (H) GSEA of hsa-miR-577.\u003c/p\u003e","description":"","filename":"OnlineFigure7.png","url":"https://assets-eu.researchsquare.com/files/rs-4147896/v1/a0c4c50a05de2aa5ed1b3a62.png"},{"id":53757477,"identity":"88558ebe-97b1-4839-8f20-82011fcfe593","added_by":"auto","created_at":"2024-03-29 19:07:44","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":175144,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eClinical sample validation of differently expressed miRNAs.\u003c/strong\u003e (A) The relative expression of hsa-miR-342-3p between all BC and PT. (B) The relative expression of hsa-miR-7705 between EBC and PT. (C) The relative expression of hsa-miR-7705 between ABC and PT. (D) The relative expression of hsa-miR-106b-5p between EBC and PT. (E) The relative expression of hsa-miR-106b-5p between ABC and PT. (F) The relative expression of hsa-miR-181b-5p between EBC and PT. (G) The relative expression of hsa-miR-181b-5p between ABC and PT. (H) The relative expression of hsa-miR-3200-3p between EBC and PT. (I) The relative expression of hsa-miR-3200-3p between ABC and PT. (J) The relative expression of hsa-miR-4789-3p between EBC and PT. (K) The relative expression of hsa-miR-4789-3p between ABC and PT. (L) The relative expression of hsa-miR-135b-5p between NTNBC and PT. (M) The relative expression of hsa-miR-135b-5p between TNBC and PT. (N) The relative expression of hsa-miR-577 between NTNBC and PT. (O) The relative expression of hsa-miR-577 between TNBC and PT. BC, breast cancer; PT, para-carcinoma tissues; EBC, early-stage breast cancer; ABC, advanced stage breast cancer; NTNBC, non-triple-negative breast cancer; TNBC, triple-negative breast cancer.\u003c/p\u003e","description":"","filename":"OnlineFigure8.png","url":"https://assets-eu.researchsquare.com/files/rs-4147896/v1/a73df918d61db632189188c6.png"},{"id":53759461,"identity":"cc231a41-3fa8-4192-8a21-4bde89a26347","added_by":"auto","created_at":"2024-03-29 19:31:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2634847,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4147896/v1/b01a14f9-03c0-4672-8487-ace9cc86e338.pdf"},{"id":53757471,"identity":"239e59e6-cf48-446e-8c60-28972b7e3ec6","added_by":"auto","created_at":"2024-03-29 19:07:44","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":24761,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-4147896/v1/b8ecd411a09c7adf2fab2104.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Expression profile analysis and the role of miRNA in breast adenocarcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn 2020, the number of newly-diagnosed breast cancer (BC) cases worldwide was 2.26\u0026nbsp;million [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It has overtaken lung cancer as the most commonly-diagnosed cancer type for the first time. The global burden of BC is increasing [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Despite great advances in the research and treatment of BC, it remains an unresolved health problem and represents a major focus of biomedical research [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMicroRNAs (miRNAs) are small non-coding RNAs of about 21\u0026ndash;22 nucleotide (nt) that regulate gene expression by recognizing homologous sequences and interfering with transcriptional, translational, or epigenetic processes [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Which regulate gene expression at the post-transcriptional stage by inhibiting messenger RNA (mRNA) translation or promoting mRNA degradation [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. For a balanced physiological environment, the expression of miRNA needs to be properly regulated. As these small molecules can affect almost all cellular activities, including cell cycle checkpoints, cell proliferation and apoptosis, and have a wide range of target genes. The dysregulation of miRNA expression is associated with various cancers by acting as tumor suppressors and oncogenes [\u003cspan additionalcitationids=\"CR7 CR8 CR9\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Circulating miRNAs also serve as potential biomarkers for diagnosis and prognosis of various cancers and other known diseases and syndromes [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Therefore, these miRNA biomarkers that predict disease diagnosis and prognosis may become therapeutic targets for some diseases. For example, studies in recent decades have revealed that miRNAs are potentially reliable prognostic markers for breast cancer progression and overall survival, and can even be used to monitor patient progression and inform treatment [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, we aim to identify miRNAs as independent prognostic factors of BC by comprehensive analysis of the miRNA expression data obtained from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and whole transcriptome profiling (WT), with the hope of providing more guidance for the diagnosis and prognosis of breast adenocarcinoma.\u003c/p\u003e"},{"header":"Methods and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCollection of data on miRNAs from TCGA, GEO and WT\u003c/h2\u003e \u003cp\u003eThe miRNAs expression data of BC were downloaded from TCGA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"http://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). WT was conducted by the biotech company GENEWIZ (Suzhou, China, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\nwww.genewiz.com.cn\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.genewiz.com.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Data were organized, extracted and annotated via Practical Extraction and Report Language 5.28 (Perl). The standardization of miRNAs and mRNAs were realized by R software (version 4.1.3). Data of GSE57897 were analyzed using GEOR2 tool.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eScreen of different expression miRNAs\u003c/h2\u003e \u003cp\u003eA total of 1104 tumor samples and 104 para-carcinoma samples from TCGA, 422 tumor samples and 31 para-carcinoma samples from GSE57897 and 14 tumor samples and 9 para-carcinoma samples from WT were included in this study. miRNAs of all samples from TCGA and WT were used to screen differentially expressed miRNAs by R language \"limma\" and \"edgeR\" software package [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Data of GSE57897 were analyzed using GEOR2 tool. The screening criteria for differentially expressed miRNAs were set as |log fold change (FC)| \u0026gt; |0.8|, and a relative \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. R language \"gplots\" software package was utilized to produce heat map and volcano plot [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Jvenn was used to screen co-different expression miRNAs among TCGA, GEO and WT [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Moreover, subgroup analysis was conducted in this study, since BC is a heterogeneous malignant disease. And Jveen was used to screen out special different expression miRNAs in early-stage breast cancer (EBC), advanced stage breast cancer (ABC) or Triple-negative breast cancer (TNBC).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eIdentify independent prognosis prediction miRNAs\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe over survival (OS) of differentially expressed miRNAs (DEMs) were analyzed by Kaplan-Meier analysis using R language \"survival\" software package [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Subsequently, Multivariate cox regressions analysis was also performed using R language \"survival\" software package to identify independent prognosis prediction miRNAs. The \"forestplot\" package of R language was used to draw forest map for hazard ratios (HRs) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eGene sets enrichment analysis (GSEA)\u003c/h2\u003e \u003cp\u003eData of standardized miRNAs and mRNAs were merged which was combined and used for carry out GSEA via GSEA 4.3.2 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were identified and R language \"ggplot2\" software package was utilized to merge GO and KEGG pathways figures [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eReal‑time polymerase chain reaction (qPCR)\u003c/h2\u003e \u003cp\u003e BC samples and para-carcinoma samples were collected in Jiaxing Maternity and Child Health Care Hospital. The 1st -miRNA cDNA was synthesized via miRNA First Strand cDNA Synthesis (Tailing Reaction) Kit (Sangon Biotech, Shanghai, China). The expression level of the miRNA was quantified by qPCR using SYBR Green Premix (YEASEN, Shanghai, China) on the ABI Prism\u0026reg; 7500 Sequence Detection System (Applied Biosystems, China). U6 was served as the internal control for qPCR normalization, and the relative gene levels were calculated by 2\u003csup\u003e\u0026minus;ΔΔCT\u003c/sup\u003e method. The primer sequences of these miRNAs were listed in Tables S1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis of qPCR results was performed using GraphPad Prism v8.0 (GraphPad Software). The Shapiro-Wilk test was conducted to check for a normal distribution of the qPCR data. The data which were normally distributed were analyzed by Student\u0026rsquo;s t-test. And the data which were abnormally distributed were analyzed by Wilcoxon\u0026rsquo;s rank sum test. A P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eCharacteristic of data from TCGA, GEO and WT\u003c/h2\u003e\n \u003cp\u003eThe overall study flow scheme was depicted in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. In this study, data of 1104 BC patients were gathered from TCGA for analysis, which included the expression data of 2223 miRNAs. And data of 14 BC patients whose samples were used to conduct whole transcriptome profiling. The clinicopathological characteristics (age, race, survival status, survival time, gender, pathological type, and TNM stage) of the patients were displayed in Tables S2 and S3. Unfortunately, we failed to obtain the details of patients from GSE57897.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eScreen of different expression miRNAs\u003c/h2\u003e\n \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\n \u003ch2\u003eOverall sample analysis\u003c/h2\u003e\n \u003cp\u003eThe screening results showed that there were 421 up-regulated miRNAs and 198 down-regulated miRNAs of BC tissues from TCGA (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA). And 64 up-regulated miRNAs and 73 down-regulated miRNAs of BC tissues from GSE57897 were identified (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB). Moreover, 117 up-regulated miRNAs and 159 down-regulated miRNAs of BC tissues were screened from WT data (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC). Venn diagram results showed that there were 2 co-upregulated miRNAs (hsa-miR-342-3p and hsa-miR-425-5p, Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) and 1 co-downregulated miRNAs (hsa-miR-4701-5p, Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) in BC tissues among TCGA, GSE57897 and WT (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDifferent expression miRNAs in overall sample analysis.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTCGA BC vs PT\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eGEO BC vs PT\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eWT BC vs PT\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLogFC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLogFC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLogFC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-342-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.551893\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.43E-31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.66E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.9559\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.039166\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-425-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.944353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.191864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.44E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-4701-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.15569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.96E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.07E-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.71885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.048955\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eBC, breast cancer; PT, para-carcinoma tissues.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eSubtype analysis\u003c/h2\u003e\n \u003cp\u003eAs BC is a heterogeneous malignant disease, data from TCGA and WT were used for further subgroup analysis to screen out special DEMs in EBC, ABC or TNBC.\u003c/p\u003e\n \u003cp\u003eCompared with PT in TCGA, 426 miRNAs were higher expression and 182 miRNAs were lower expression in EBC tissues (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA, G). And 414 miRNAs were higher expression and 199 miRNAs were lower expression in ABC tissues (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB, H). Compared with EBC in TCGA, 21 miRNAs were higher expression and 17 miRNAs were lower expression in ABC tissues (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC, I). Compared with PT in WT, 109 miRNAs were higher expression and 59 miRNAs were lower expression in EBC tissues (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eD, G). And 106 miRNAs were higher expression and 80 miRNAs were lower expression in ABC tissues (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eE, H). Moreover, compared with EBC in WT, 25 miRNAs were higher expression and 13 miRNAs were lower expression in ABC tissues (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eF, I). Finally, our results showed that 13 and 17 miRNAs were respectively specific higher expression in EBC and ABC through comprehensive analysis of data from TCGA and WT (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eJ) (Tables \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Compared with PT, 6 and 19 miRNAs were respectively specific lower expression in EBC and ABC (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eJ) (Tables 4 and \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSpecial upregulated miRNAs in EBC.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTCGA EBC vs PT\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eWT EBC vs PT\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLogFC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLogFC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-15a-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.936999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.46E-27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.124094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.022207\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-190b-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.521921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.69E-45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.916965\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.025522\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-191-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.367259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.22E-36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.050876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.031633\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-2114-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.193752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.52E-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.605533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.048527\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-301b-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.884384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.85E-25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.527786\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.020858\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-3156-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.241717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.18E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.584326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.046212\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-3194-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.099299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.05E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.886259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.043308\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-33b-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.784616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.19E-16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.718871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.048216\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-342-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.566281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.42E-32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.219814\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.018391\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-449a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.535369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.88E-26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.613272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.6E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-493-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.294733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.43E-25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.209437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.035677\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-7705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.88908\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.34E-45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.626203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.018283\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-92b-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.576487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.17E-33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.457292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007472\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eEBC, early-stage breast cancer; PT, para-carcinoma tissues.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSpecial upregulated miRNA in ABC.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003eID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eTCGA ABC vs PT\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eWT ABC vs PT\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLogFC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLogFC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ehsa-miR-106b-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.259236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3.29E-39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.321641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010428\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ehsa-miR-1269b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e5.270462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.04E-16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6.603516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.016474\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ehsa-miR-1301-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2.067576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.2E-61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.547168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.017491\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ehsa-miR-181b-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.236281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.11E-28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.708271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001532\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ehsa-miR-196a-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3.19388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2.48E-41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.514487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007847\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ehsa-miR-3176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2.135932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.69E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3.471851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03316\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ehsa-miR-3200-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.595043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.15E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.506522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.020521\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ehsa-miR-3619-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.683338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3.1E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3.1462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.034498\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ehsa-miR-429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2.612503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.68E-58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.033966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.042356\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ehsa-miR-4662a-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.854002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e5.08E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.778002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013401\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ehsa-miR-4789-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.88428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.001659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2.94055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.019853\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ehsa-miR-5008-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.281618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e8.81E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.299928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.048551\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ehsa-miR-6516-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.841089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.001133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3.678157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.016588\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ehsa-miR-767-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.995602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6.71E-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6.31534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002387\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ehsa-miR-934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.721749\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2.72E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3.514104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001366\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ehsa-miR-93-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.087921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.78E-27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.600565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002583\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ehsa-miR-9-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.722351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.44E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3.597604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00038\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003eABC, advanced stage breast cancer; PT, para-carcinoma tissues.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSpecial downregulated miRNAs in EBC.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eTCGA EBC vs PT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eWT EBC vs PT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLogFC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLogFC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-1468-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.94911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e8.14E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-1.4296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.026404\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-6.14414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.89E-63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-4.61526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.002985\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-296-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-1.0766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3.86E-16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-1.39376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.019486\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-4286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-1.1209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2.08E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-11.2947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.001904\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-4701-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-1.13146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4.23E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-4.82009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.021193\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-6507-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-1.10523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e8.03E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-2.83533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.022771\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003eEBC, early-stage breast cancer; PT, para-carcinoma tissues.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\u003cbr\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSpecial downregulated miRNAs in ABC.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTCGA ABC vs PT\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eWT ABC vs PT\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLogFC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLogFC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-1247-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.60938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.71E-23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.8958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006124\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-125b-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.00387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.9E-85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.58392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007594\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-1262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.23544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.18E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.52842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005475\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-126-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.84322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.71E-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.15464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.026154\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-127-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.93738\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.54E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.18521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.046982\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-129-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.27005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.99406\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012142\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-130a-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.90275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.8E-21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.45547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011239\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-133a-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.7795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.44E-97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.44129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.023451\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-1-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.33193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.5E-88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.64772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006923\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-144-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.05266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.79E-21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.68826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010785\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-376c-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.16706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.71E-22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.36516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.022259\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-381-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.33801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.15E-28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.33127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.031196\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-432-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.21805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.29E-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.62148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.021124\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-4524a-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.15875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.03E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.76239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02543\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-548y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.21436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.31297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008672\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-585-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.70029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.44E-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.82184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004104\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-605-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.36152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.58E-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.56927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.57E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-655-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.97177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.55E-16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.21377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.049563\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-675-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.28388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.68E-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.39966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.041653\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eABC, advanced stage breast cancer; PT, para-carcinoma tissues.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eCompared with PT in TCGA, 347 miRNAs were higher expression and 206 miRNAs were lower expression in non-triple-negative breast cancer (NTNBC) tissues (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA, G). And 528 miRNAs were higher expression and 198 miRNAs were lower expression in TNBC tissues (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB, H). Compared with NTNBC in TCGA, 212 miRNAs were higher expression and 86 miRNAs were lower expression in TNBC tissues (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC, I). Compared with PT in WT, 110 miRNAs were higher expression and 148 miRNAs were lower expression in NTNBC tissues (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eD, G). And 87 miRNAs were higher expression and 37 miRNAs were lower expression in TNBC tissues (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eE, H). Moreover, compared with NTNBC in WT, 64 miRNAs were higher expression and 12 miRNAs were lower expression in TNBC tissues (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eF, I). Finally, our results showed that 16 miRNAs were specific higher expression in TNBC through comprehensive analysis of data from TCGA and WT (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eJ, Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). And 3 miRNAs were specific lower expression in TNBC compared to PT (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eJ, Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSpecific upregulated miRNAs in TNBC.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTCGA TNBC vs PT\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eWT TNBC vs PT\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLogFC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLogFC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-106b-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.786603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.53E-68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.694648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006159\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-135b-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.056819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.21E-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.046605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010829\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-148a-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.028366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.52E-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.332135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.028009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-188-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.698355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.82E-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.179056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.042837\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-18a-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.671302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.98E-66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.229379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001271\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-196a-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.224518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.92E-28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.754737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011458\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-196b-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.863346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.42E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.577776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.039434\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-3200-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.20244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.48E-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.061783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004785\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-323b-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.960278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.73E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.148181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.024215\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-455-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.495093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.25E-26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.737403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000619\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-4677-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.580144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.67E-46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.399928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03661\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-4789-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.250728\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.33E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.466033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.028991\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.563552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.14E-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.722326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.884739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.701366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014861\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-93-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.545965\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.13E-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.204972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-9-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.041252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.86E-35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.82452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.24E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eTNBC, triple-negative breast cancer; PT, para-carcinoma tissues.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSpecific downregulated miRNAs in TNBC.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTCGA TNBC vs PT\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eWT TNBC vs PT\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLogFC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLogFC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-4683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.08729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.68E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.04299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.020632\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-489-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.86129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.24825\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.027471\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa-miR-653-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.00008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.72E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.02443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.039885\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eTNBC, triple-negative breast cancer; PT, para-carcinoma tissues.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eIdentify independent prognosis prediction miRNAs\u003c/h2\u003e\n \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\n \u003ch2\u003eKaplan-Meier survival analysis\u003c/h2\u003e\n \u003cp\u003eTo ascertain whether these miRNAs influence the survival of BC, Kaplan-Meier survival analysis was further performed. The results showed that there was a statistically significant differences in the survival time between the high and low expression groups of hsa-miR-342-3p (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0122) in all BC tissues (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA). hsa-miR-7705 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0492) played an important role in EBC via Kaplan-Meier survival analysis (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB). Moreover, Kaplan-Meier survival analysis showed that hsa-miR-106b-5p (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0212), hsa-miR-181b-5p (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0494), hsa-miR-3200-3p (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0246) and hsa-miR-4789-3p (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0282) significantly influence the survival of ABC patients (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eC - F). hsa-miR-135b-5p (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0441) and hsa-miR-577 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0471) were found to play key roles in TNBC through Kaplan-Meier survival analysis (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eG, H).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eMultivariate cox regressions analysis\u003c/h2\u003e\n \u003cp\u003eSubsequently, independent prognosis prediction miRNAs were identified by Multivariate cox regressions analysis and the results showed that hsa-miR-342-3p (HR\u0026thinsp;=\u0026thinsp;0.39; 95% CI 0.22\u0026ndash;0.69; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0013) and hsa-miR-7705 (HR\u0026thinsp;=\u0026thinsp;2.37; 95% CI 1.17\u0026ndash;4.82; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0169) were independent prognostic factors for overall BC and EBC, respectively (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA, B). Furthermore, hsa-miR-181b-5p (HR\u0026thinsp;=\u0026thinsp;0.17; 95% CI 0.03\u0026ndash;0.84; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0294), hsa-miR-3200-3p (HR\u0026thinsp;=\u0026thinsp;5.78; 95% CI 2.26\u0026ndash;14.77; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0003) and hsa-miR-4789-3p (HR\u0026thinsp;=\u0026thinsp;27.66; 95% CI 2.02\u0026ndash;378.35; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0129) were found to be independent prognostic factors for ABC (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eC). Unfortunately, hsa-miR-135b-5p (HR\u0026thinsp;=\u0026thinsp;0.62; 95% CI 0.17\u0026ndash;2.30; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.4783) and hsa-miR-577 (HR\u0026thinsp;=\u0026thinsp;0.94; 95% CI 0.33\u0026ndash;2.69; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.9149) were not independent prognostic factors for TNBC (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eD).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eGene sets enrichment analysis (GSEA)\u003c/h2\u003e\n \u003cp\u003eGSEA was conducted to explore the biological relevance of 8 miRNAs using KEGG database. KEGG items cell cycle, DNA replication, ERBB signaling pathway and Wnt signaling pathway differentially enriched in low hsa-miR-342-3p expression phenotype (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eA). Base excision repair and homologous recombination signaling pathway differentially enriched in high hsa-miR-7705 expression phenotype (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eB). KEGG items TGF-\u0026beta; and Wnt signaling pathways differentially enriched in low hsa-miR-106b-5p and hsa-miR-181b-5p expression phenotypes (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eC, D). Moreover, KEGG items cell cycle and DNA replication were enriched in hsa-miR-3200-3p and hsa-miR-4789-3p high expression phenotype (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eE, F). KEGG items renin angiotensin system and natural killer cell mediated cytotoxicity were enriched in in hsa-miR-135b-5p low and high expression phenotype, respectively (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eG). Immune related KEGG items antigen processing and presentation, B cell receptor and T cell receptor signaling pathways were respectively enriched in in hsa-miR-577 high expression phenotype (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eH).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003eClinical sample validation\u003c/h2\u003e\n \u003cp\u003eTo validated the expression of miRNA in BC, 20 of EBC and 20 of ABC patients were recruited, including 23 NTNBC and 11 NTBC. The results of qPCR showed that hsa-miR-342-3p (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) was significantly higher in BC than PT (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eA). hsa-miR-7705 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024) was specific higher expression in EBC vs PT (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eB, C). Meanwhile, hsa-miR-160b-5p (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and hsa-miR-3200-3p (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0319) were specific higher expression in ABC vs PT (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eD, E, H, I). hsa-miR-181b-5p was higher expression in EBC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and ABC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) than PT (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eF, G). The expression of hsa-miR-4789-3p was not significant difference between ABC and PT (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.245) (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eK). Our results also demonstrated that hsa-miR-135b-5p was increased in NTNBC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0097) and TNBC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007) (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eL, M). And there was no significant difference in the expression of hsa-miR-577 between TNBC and PT (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.083) (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eO).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn some developing countries, the incidence and mortality rate of breast adenocarcinoma are increasing [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Early detection and diagnosis of the disease can reduce mortality, especially in some Asia-Pacific countries where breast cancer mortality rate is two-fold higher than in Western countries and more frequently diagnosed in advanced stages. Moreover, new non-invasive diagnostic biomarkers with high sensitivity and specificity are needed for early breast adenocarcinoma detection [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The purpose of this study was to find out the independent prognostic factors of breast adenocarcinoma and to provide a basis for the diagnosis and prognosis evaluation of BC. In this study, by performing bioinformatical analysis on patient data obtained from TCGA, GEO and WT, we confirmed that hsa-miR-342-3p and hsa-miR-106-5p are important prognostic factors for BC.\u003c/p\u003e \u003cp\u003eIn recent years, an increasing number of studies have shown that miRNAs may play the role of oncogene or tumor suppressor genes in the pathogenesis of cancer [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. There were numerous studies focusing on the effects of differential miRNA expression on various cancer types. Previous studies showed that loss of function of hsa-miR-342-3p promoted the development of BC [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Which are consistent with our results that overexpression of hsa-miR-342-3p significantly prolonged survival time of patients with breast cancer. GSEA indicated that hsa-miR-342-3p may suppress the progression of BC via inhibiting cancer- related ERBB and Wnt signaling pathway.\u003c/p\u003e \u003cp\u003eSang et al reported that the expression of hsa-miR-7705 was increased in BC vs PT, which was similar with our results [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Although our comprehensive analysis results showed that hsa-miR-7705 was special increased in EBC compared with PT and was a poor independent prognosis factor for EBC, our validation results indicated that the expression of hsa-miR-7705 was higher in EBC vs PT. BRCA mutation could lead to homologous recombination repair deficiency and Poly-ADP ribose polymerase inhibitors could cause synthetic lethality in \u003cem\u003eBRCA\u003c/em\u003e mutation cells [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. We previously reported that PARPi can improved PFS of patients with \u003cem\u003eBRCA\u003c/em\u003e-mutated breast cancer [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. However, PARPi resistance occurred in some BRCA mutation cancers, epigenetic regulation may contribute to DNA repair pathways [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. GSEA showed that hsa-miR-7705 improved the progression of EBC via activating base excision repair and homologous recombination signaling pathway, which may improve PARPi resistance in breast cancer.\u003c/p\u003e \u003cp\u003eSome studies showed hsa-miR-106b-5p play crucial roles in the progression of BC [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Zhou et al reported that miR-106b-5p promoted cisplatin resistance in TNBC by down-regulating GDF11 [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Meanwhile, Farr\u0026eacute; et al demonstrated that hsa-miR-106b-5p was up-regulated in Basal subtypes, which were more aggressive than others [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. And they did not find that the stage affected the expression of hsa-miR-106b-5p. This result is different from our results of comprehensive analysis. We found that hsa-miR-106b-5p was special up-regulated in ABC and it was a good prognosis factor for ABC patients. GSEA indicated that multiple cancer-related pathway items differently enriched in miR-106b-5p low expression phenotype. hsa-miR-106b-5p may suppress the progression of ABC via inhibiting key molecules of these pathways.\u003c/p\u003e \u003cp\u003eStudies showed that hsa-miR-3200-3p plays a key role in the development of various tumors, such as glioma, cervical cancer and non-small cell lung cancer [\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. There are few researches reported that if it can influence the progression of BC. Our results indicated that hsa-miR-3200-3p significantly affected survival of ABC patients and was a bad independent prognosis factor. hsa-miR-3200-3p can improve the progression of ABC via promoting cell cycle of cancer cells.\u003c/p\u003e \u003cp\u003eOf course, there are some shortcomings in this study. Firstly, the survival data only obtained from TCGA database, which makes survival analysis unverifiable. Secondly, no immunohistochemical methods were used for analysis and discussion. Last but not least, the sample size was smaller in whole transcriptome profiling analysis.\u003c/p\u003e \u003cp\u003eIn conclusion, hsa-miR-342-3p and hsa-miR-7705 were respectively independent prognostic factors for overall BC and EBC. Moreover, hsa-miR-160b-5p played a key role in ABC. Finally, hsa-miR-3200-3p was independent prognostic factor for ABC. This study further supported that the miRNAs with distinctive expression pattern in breast adenocarcinoma from normal tissues/cells may be exploited as indicators for disease progression or choice of treatments, or therapeutic targets.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Natural Science Foundation of Zhejiang Province (LQ21H1600400) and Jiaxing Science and Technology Bureau (LGF20H090020).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDatasets of this study are available through the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe first two authors contributed equally to this manuscript.\u0026nbsp;They planned and designed the experiment and carried out the study. Xiang Lv collected the data and wrote the main manuscript. Xingxia Yang helped in analyzing data, Sijia Shen designed this study and reviewed the manuscript, Jianguo Wang and Juanying Zhu recruited the patient and collected the data. All authors have read and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by Ethics Committee of Jiaxing Maternal and Child Health Care Hospital (2021(YL)-115) and written informed consent from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient consent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe publication of this study was consent by patient.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWilkinson L, Gathani T (2022) Understanding breast cancer as a global health concern. 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Funct Integr Genomics 24:31\u003c/span\u003e\u003c/li\u003e\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":"Breast adenocarcinoma, miRNA, TCGA, GEO, whole transcriptome profiling","lastPublishedDoi":"10.21203/rs.3.rs-4147896/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4147896/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo search for hub microRNAs (miRNAs) that might serve as biomarkers for breast cancer (BC), we conducted out comprehensive analysis of data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and whole transcriptome profiling (WT). For overall sample analysis, we found 3 differently expressed miRNA in BC tissues compared to para-carcinoma tissues (PT). Subtype analysis showed that 19, 36 and 19 miRNAs were respectively specific differently expressed in early-stage breast cancer (EBC), advanced stage breast cancer (ABC) and Triple-negative breast cancer (TNBC) compared to PT. Multivariate Cox regression analysis showed that hsa-miR-342-3p and hsa-miR-7705 were independent prognostic factors for overall BC and EBC, respectively. And we found hsa-miR-181b-5p, hsa-miR-3200-3p and hsa-miR-4789-3p were all independent prognostic factors for ABC. Moreover, Kaplan-Meier survival analysis showed that hsa-miR-160b-5p significantly affected the survival of patients in ABC. GSEA demonstrated that tumor related KEGG items (such as cell cycle, ERBB signaling pathway, Wnt signaling pathway, etc.) were differentially enriched in BC. The results of qPCR showed that the expression status of hsa-miR-342-3p, hsa-miR-7705 hsa-miR-160b-5p and hsa-miR-3200-3p were consistent with the results of comprehensive analysis. Finally, this study revealed hsa-miR-342-3p, hsa-miR-7705, hsa-miR-160b-5p and hsa-miR-3200-3p can be used as prognostic biomarkers for BC.\u003c/p\u003e","manuscriptTitle":"Expression profile analysis and the role of miRNA in breast adenocarcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-29 19:07:39","doi":"10.21203/rs.3.rs-4147896/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":"42da8f18-84ee-448b-bc59-f05396bebf73","owner":[],"postedDate":"March 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-29T19:07:41+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-29 19:07:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4147896","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4147896","identity":"rs-4147896","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}