Reversal of endocrine resistance via N6AMT1-NEDD4L pathway-mediated p110α degradation

Reversal of endocrine resistance via N6AMT1-NEDD4L pathway-mediated p110α degradation | 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 Article Reversal of endocrine resistance via N6AMT1-NEDD4L pathway-mediated p110α degradation Yukun Cui, Likeng Ji, Jiongyu Chen, Li-Fang He, Fan Zhang, Zihao Deng, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4738749/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Dec, 2024 Read the published version in Oncogene → Version 1 posted 9 You are reading this latest preprint version Abstract Approximately 70% of breast cancer (BC) cases are luminal-type (estrogen receptor-positive, ER+), suitable for endocrine therapy with tamoxifen as the most commonly used drug. However, about 30% of these patients develop tamoxifen resistance due to various mechanisms, primarily involving PI3K pathway activation through mutations or unknown pathways. Here, we discover, via bioinformatics analysis and clinical samples, that N6 adenine–specific DNA methyltransferase 1 (N6AMT1) is highly expressed in luminal breast cancer but downregulated in tamoxifen-resistant (TamR) BC cells. ChIP-qPCR and luciferase reporter assays showed that FOXA1 binds to the N6AMT1 and enhances transcription. In TamR models, FOXA1 and N6AMT1 are downregulated, increasing p110α protein levels (but not mRNA), phospho-AKT levels, and tamoxifen resistance. In vivo, N6AMT1 overexpression enhanced tamoxifen sensitivity, while knockdown reduced it; this sensitivity could be restored with the p110α inhibitor A66. Clinically, decreased N6AMT1 expression correlates with poor prognosis in luminal BC patients. In TamR BC organoids, combining tamoxifen with A66 further reduced growth compared to either treatment alone. Mechanistically, increased p110α levels result from inhibited degradation by E3 ubiquitin ligase NEDD4L. These findings suggest N6AMT1 as a potential luminal breast cancer biomarker and highlight the FOXA1-N6AMT1-NEDDL4-p110α pathway as a therapeutic target to sensitize cells to tamoxifen. Biological sciences/Cancer/Breast cancer Biological sciences/Cancer/Cancer therapy/Cancer therapeutic resistance Breast cancer Tamoxifen resistance N6AMT1 PI3K/AKT Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 INTRODUCTION Breast cancer has become the preeminent malignancy, accounting for the highest mortality rate among women globally 1 . Approximately 70% of breast cancer cases are categorized as luminal-type (ER+), rendering them amenable to endocrine therapy 2 . Among the foremost treatments is tamoxifen (Tam), a first-line endocrine therapy and a discerning modulator of estrogen receptors. Despite initial favorable responses to Tamoxifen treatment, approximately 30% of ER + patients acquire resistance to the therapy, culminating in local recurrence and distant metastases. 3 . Delineation of the molecular mechanisms of Tam resistance can be expected to provide promising therapeutic targets to overcome Tam resistance in breast cancer. Our previous study found elevated levels of N6AMT1 in luminal BC cell lines 4 , prompting us to explore its potential role and significance in tamoxifen resistance within luminal BC. As a histone lysine methyltransferase, N6AMT1 plays a paramount role in gene transcription, modifying Lys12 of histone H4 5 . Furthermore, acting as a glutamine methyltransferase, N6AMT1 regulates eukaryotic translation by modifying Gln185 of eRF1 6,7 . Although a previous study has proposed that N6AMT1 is a methyltransferase for 6mA in the human genome, and 6mA is significantly enriched in exon-coding regions 8 , recent research has debunked this notion by demonstrating the absence of DNA binding capability and DNA MTase activity in N6AMT1 7,9 . Notably, N6AMT1 exhibits an oncogenic function in various tumor types, including glioblastoma 10 , colorectal cancer 11 , and non-small cell lung cancer 12 . Additionally, it has been established that N6AMT1 regulates olaparib resistance in triple-negative breast cancer 13 . However, the connections between N6AMT1 and tamoxifen resistance are still largely unexplored. Tamoxifen resistance is characterized by intricate mechanisms, with excessive activation of the PI3K/AKT pathway emerging as a prominent contributing factor 14–16 . Within this pathway, the catalytic unit of PI3K, known as p110α and encoded by PIK3CA , plays a pivotal role. PIK3CA mutations are detected in roughly 30% of ER + breast cancers, triggering hyperactivation of the PI3K/AKT pathway 17 . However, the presence of PIK3CA mutations alone fails to explain tamoxifen resistance in patients lacking such mutations. It is worth mentioning that previous studies on tamoxifen resistance have predominantly focused on PIK3CA mutations rather than the expression of p110α itself. However, mechanisms underlying tamoxifen resistance in the absence of p110α mutation remain largely unknown. In this study, we explored the role of N6AMT1 in BC tamoxifen resistance. We demonstrated that N6AMT1 was a potential biomarker of luminal BC and transcriptionally regulated by FOXA1. Notably, suppressing N6AMT1 resulted in the inhibition of NEDD4L-mediated degradation of p110α, consequently activating the PI3K/AKT signaling pathway and ultimately leading to tamoxifen resistance. It advances our understanding on the functions of N6AMT1, thus providing potential therapeutic targets for tackling tamoxifen resistance. MATERIALS AND METHODS Cells Human breast cancer MCF-7, T47D, SKBR3, HCC-1937, MDA-MB-231, MDA-MB-468 and BT-549 cell lines were acquired from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China) and cultured according to the recommended guidelines. Tamoxifen-resistant MCF-7 (MCF-7 TamR ) and T47D (T47D TamR ) cells were generated after being exposed to 5 µM 4-hydroxytamoxifen (Tam, Cat#H6278, Sigma-Aldrich) for over six months, followed by continuous culture in 0.1 µM Tam to maintain tamoxifen resistance. The authenticity of each cell line was confirmed, and they were tested for mycoplasma contamination. Clinical specimen collection This study enrolled 153 primary luminal breast cancer patients who received tamoxifen treatment following surgery at the Cancer Hospital of Shantou University Medical College (SUMCCH), China, between 2012–2017. The characteristics of patients are summarized in the Supplementary Table S1 . Surgical specimens were collected and processed using standard formalin fixation and paraffin embedding methods. Molecular subtypes were identified using immunohistochemistry (IHC), with fluorescence in situ hybridization (FISH) used to confirm HER2 status in cases with intermediate positive IHC results. To assess N6AMT1 expression, another 90 breast cancer specimens from various subtypes were also collected. Informed consent was obtained from all patients and we had access to their complete clinicopathological and follow-up data. All experiments were conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of SUMCCH (No. 2022-022 ). Immunohistochemistry (IHC) staining The tissue slides were heated at 60°C for 30 minutes, deparaffinized in xylene and rehydrated using gradient ethanol. Antigen retrieval was performed using 0.01 M citrate buffer (pH 6.0) for 30 minutes after blocking endogenous peroxidase activity with 3% hydrogen peroxide for 10 minutes. The slides were then blocked with 5% BSA before being incubated with primary antibodies overnight at 4°C. Following this, the slides were incubated with HRP-conjugated secondary antibodies for 30 minutes at room temperature and stained using the diaminobenzidine (DAB) substrate (Biosharp, China). Hematoxylin was used as a counterstain. For observation, five random fields were selected under a light microscope at 100× or 400× magnification. The percentage of positive staining was graded from 0 to 10, with 0 indicating no staining and 10 indicating 100% of cells being positively stained 18 . The staining intensity was scored as 1+, 2+, or 3 + for weak, moderate, or strong staining, respectively. The percentage (P) and intensity (I) of positive cells were multiplied to obtain a numerical score (S = P×I). Hematoxylin-eosin (H&E) staining was performed for morphological examination. Two independent pathologists evaluated the results. Low and high expression were defined as scores of < 10 and ≥ 10, respectively. The antibodies used are shown in Supplementary Table S2 . 6mA-IP-qPCR Genomic DNA was sonicated to 200–500 bp and subsequently denatured at 95 ℃ for 5 mins. After pre-clearing with magnetic beads (MCE), 1 mg of the fragmented DNA was incubated with 20 µg of anti-6mA antibody or normal rabbit IgG in IP buffer (1 mM sodium phosphate buffer, pH 7.0, 0.14 M NaCl, 0.05% Triton X-100) for 2 h at 4 ℃. Antibody-bound DNA was collected by incubating with 50 µL of magnetic beads overnight at 4°C on a rotating wheel, followed by four washes with IP buffer. The DNA was then recovered in 200 µL of digestion buffer (50 mM Tris-HCl, pH 8.0, 10 mM EDTA, pH 8.0, 0.5% SDS, 40 µg proteinase K) and incubated for 2 h at 56°C with intermittent mixing by vortexing. Recovered DNA was purified using a QIAquick PCR Purification Kit (QIAGEN) and quantified by qPCR. The ratio of exon-coding regions of NEDD4L and PIK3CA in 6mA-IP group to input was normalized to IgG group and then evaluated to determine the 6mA abundance. Primers were designed according to [G/C]AGG[C/T], the most significantly associated motif with 6mA modification 8 . The antibodies and primers used are shown in Supplementary Table S2 -3. Chromatin immunoprecipitation (ChIP) assay ChIP assays were conducted using a ChIP assay kit (Beyotime, China) in accordance with the manufacturer's guidelines. For cell collection, 2×10 7 cells were collected by centrifugation and washed twice with phosphate-buffered saline (PBS). DNA was cross-linked using 1% formaldehyde at room temperature. The precipitate was washed and then treated with lysis buffer to extract the nuclear content. The fragmented DNA was subsequently subjected to immunoprecipitation with either specific or nonspecific antibodies. Finally, the immunoprecipitated DNA was evaluated relative to control IgG by qPCR. The antibodies and primers used are shown in Supplementary Table S2 -3. Xenograft studies Four-week-old female SCID Beige mice were procured from Beijing Vital River Laboratories Animal Technology to generate xenograft models of N6AMT1-overexpressing (MCF-7 TamR -oeN6AMT1 and MCF-7 TamR -VC) or N6AMT1 stable knockdown (MCF-7-shNC and MCF-7-shN6AMT1) breast cancer cells. The mice were implanted with β-estradiol pellets (0.72 mg/pellet, 60-day release, Innovative Research, USA) in the back of their necks. After two days, 5×10 6 corresponding cells resuspended in a 100 µL 1:1 mixture of PBS (Gibco BRL) and Matrigel (BD Biosciences, USA) were subcutaneously injected into the mammary glands of the mice. Once tumors reached roughly 200 mm 3 , the mice were randomly divided into four groups based on their cell types and treatment. For the N6AMT1-overexressing xenograft models, two groups received intraperitoneal injections of 100µL of 1 mg/kg Tam in corn oil (Sigma-Aldrich) QD for five days a week, while the other two groups received vehicle control injections. For the N6AMT1 stable knockdown xenograft models, one group received intraperitoneal injections of 1 mg/kg Tam for monotherapy, another group received 100 mg/kg A66 for monotherapy, a third group received combination therapy of Tam and A66, and a fourth group received vehicle control injections. Tumor size was monitored every five days and volume was determined using the formula: Volume = 0.5*(length*width*width). After 6-week treatment, mice were sacrificed and tumors were harvested. All animal experiments adhered to the ARRIVE guidelines and were conducted in accordance with the National Research Council's Guide for the Care and Use of Laboratory Animals. The protocols were approved by the Laboratory Animal Ethics Committee of Shantou University Medical College. (No. 2022-077 ). Patient-derived organoid (PDO) culture The Ethics Committee of SUMCCH approved this research (No. 2022-022 ), and both patients provided informed consent. Tissue preparation and organoid culture were performed as described previously 19 with minor modification. PDOs were seeded in 50 µL Matrigel (Cat#3536-001-02, R&D Systems) into 24-well plates (4 drops per 50 µL Matrigel). Breast cancer organoid medium (BOM, purchased from PreceDo Pharmaceuticals, Hefei, China, Cat# PRS-BCM-3D) was added and replaced every four days. After 2–3 weeks of culture, the number of breast cancer organoids were counted and passaged. Database analysis Breast Cancer Gene-Expression Miner v4.9 (bc-GenExMiner v4.9) and cBioPortal databases were employed to assess the expression levels of N6AMT1 in various subtypes of breast cancer, as well as their correlation with ER, FOXA1 and NEDD4L. Additionally, ChIPBase and PROMO were utilized to analyze potential transcription factors of N6AMT1 . The JASPAR database was employed to identify potential FOXA1 binding sites in the N6AMT1 promoter. The ChIP-Atlas database was employed to identify binding peaks of FOXA1 in the promoter regions of the N6AMT1 loci. Statistical analysis Each experiment conducted in this study was repeated at least three times. Student’s t-tests were used to analyze comparisons between two groups, while one-way or two-way analysis of variance (ANOVA) followed by Dunnett’s test was employed to analyze comparisons among multiple groups. The two-sided Student t test was used to determine correlations between two groups. The two-sided Spearman test was used to determine correlations between the expression of two proteins. Survival curves were generated using the Kaplan-Meier method and a log-rank test was employed for comparison. Univariate and multivariate Cox proportional hazard regression models were used to assess survival risk, employing a forward stepwise procedure. The association between N6AMT1/p110α expression and clinical characteristics was analyzed using Chi-square tests. Fisher's exact test was employed when more than one-fifth of the cells had an expected frequency of less than 5. Measurable data are presented as means ± standard deviation (SD). A two-sided p -value of less than 0.05 was considered to indicate statistical significance. Significance levels are represented by asterisks (ns, non-significant; *, p < 0.05; **, p < 0.01; ***, p < 0.001). RESULTS N6AMT1 is highly expressed in luminal breast cancer We previously reported that N6AMT1 is elevated in luminal-type BC cell lines 4 . Given this, we sought to explore its potential role in tamoxifen sensitivity in luminal BC. Initially, we used western blot analysis to examine N6AMT1 expression across various BC cell lines and found pronounced expression in luminal-type (ER+) BC cell lines, specifically in MCF-7 and T47D cells (Fig. 1 A). Using the bc-GenExMiner and cBioPortal databases, we not only confirmed the high mRNA (Fig. 1 B-E) and protein (Fig. 1 F) expression levels of N6AMT1 in luminal BC, but also discerned a direct correlation with ESR1 (ER) mRNA expression (Fig. 1 G). We further analyzed the correlation between N6AMT1 and ER from TCGA BC samples based on the status of ER and progesterone receptor (PR). We found that in ER + patients, N6AMT1 was positively correlated with ER expression, regardless of PR status (Fig. 1 H-I). Conversely, in ER- patients, no significant correlation was observed between N6AMT1 and ER expression, regardless of PR status (Fig. S1 A-B). Using the Single Cell Portal ( https://singlecell.broadinstitute.org/single_cell ), we visualized the mRNA expression of N6AMT1 in epithelial cells from different BC subtypes 20 , revealing higher N6AMT1 expression in luminal A subtype epithelial cells. Additionally, we assessed 90 BC samples from SUMCCH cases, divided equally among luminal, HER2-overexpressing, and triple-negative types. Through immunohistochemistry (IHC), we observed a notable upregulation of N6AMT1 protein in the luminal BC samples (Fig. 1 K-L). In conclusion, our collective findings strongly suggest N6AMT1 as a potential biomarker for luminal BC, paving the way for further research into its clinical roles. N6AMT1 is transcriptionally regulated by FOXA1 We observed a significant decrease in N6AMT1 mRNA and protein expression levels in tamoxifen-resistant (TamR) cells compared to the parental (tamoxifen-sensitive, TamS) cells (Fig. 1 M-N). Additionally, N6AMT1 downregulation was evident in patients post neo-adjuvant tamoxifen treatment (GSE147271) and in Tam-treated normal human mammary epithelial cells (GSE106891) (Fig. 1 O). To elucidate the upstream regulator of N6AMT1 expression in luminal BC, we mined the ChIPBase and PROMO databases for potential transcription factors (TFs) associated with N6AMT1. A significant overlap was found for three factors—C/EBPβ, FOXA1, and AR—among the 49 TFs from ChIPBase and 43 TFs from PROMO (Fig. 2 A). When examining the expression of these potential regulators, only FOXA1 mRNA levels displayed a notable reduction in TamR cells compared to TamS cells (Fig. 2 B). A corresponding downregulation of FOXA1 protein expression in TamR cells was confirmed via western blotting (Fig. 2 C). Transient FOXA1 knockdown in MCF-7 and T47D cells led to decreased N6AMT1 mRNA and protein levels (Fig. 2 D). The JASPAR database identified two possible FOXA1 binding sites within the N6AMT1 promoter region, located 1 kb upstream of its transcription start site (TSS) (Fig. 2 E). ChIP-qPCR assays confirmed FOXA1's stronger binding affinity to the second putative site compared to the first, with both MCF-7 and T47D cells showing significant differences compared to the normal IgG control (Fig. 2 F-G), indicating direct binding between FOXA1 and the N6AMT1 promoter. We then cloned the N6AMT1 promoter and mutated the two potential FOXA1 binding sites to generate pGL3-N6-Mut1 or pGL3-N6-Mut2 luciferase reporter genes (Fig. 2 H). The luciferase assay showed that transient knockdown of FOXA1 (si-FOXA1) led to a reduction in luciferase expression for both pGL3-N6-WT and pGL3-N6-Mut1, while the inhibitory effect of si-FOXA1 was not significant for pGL3-N6-Mut2 (Fig. 2 I). Similarly, the F-Luc/R-luc ratios of pGL3-N6-WT and pGL3-N6-Mut1 were higher in TamS cells than in TamR cells, whereas this effect was reduced for pGL3-N6-Mut2 (Fig. 2 J). Furthermore, using the bc-GenExMiner database, we noted a robust positive correlation between FOXA1 and N6AMT1 mRNA expression in ER + breast cancer patients rather than in ER- patients (Fig. 2 K). We also analyzed the ChIP-seq results of FOXA1 protein in the ChIP-Atlas database and found several binding peaks of FOXA1 in the promoter regions of the N6AMT1 loci (Fig. 2 L). Collectively, our data underscores the potential role of FOXA1 in the transcriptional regulation of N6AMT1 in luminal BC cells. FOXA1 likely enhances N6AMT1 transcription by directly binding to its proximal promoter site. N6AMT1 regulates tamoxifen resistance via the PI3K/AKT pathway A previous study indicated an association between reduced luminal BC biomarker expression and tamoxifen resistance 21 . In this context, the role of N6AMT1 in mediating tamoxifen resistance was explored. We stably knocked down N6AMT1 in MCF-7 and T47D cells (Fig. 3 A) and conducted a series of in vitro drug response assays. Stable knockdown of N6AMT1 conferred tamoxifen resistance to BC cells (Fig. 3 B). Conversely, overexpressing N6AMT1 in MCF-7 TamR and T47D TamR cells (Fig. 3 C) enhanced tamoxifen sensitivity compared to vector control (VC) cells (Fig. 3 D). It has been postulated that tamoxifen resistance is linked to hyperactivation of the PI3K/AKT signaling pathway 15,22 . Our western blot analysis of our TamS and TamR cells similarly indicates increased levels of p110α (the catalytic subunit of PI3K) and phosphorylated AKT at T308 (p-AKT (T308)) in TamR versus TamS cells (Fig. 3 E). Western blot analysis of N6AMT1-modulated cells revealed that N6AMT1 knockdown upregulated p110α and p-AKT (T308) expression (Fig. 3 F), whereas N6AMT1 overexpression attenuated their expression (Fig. 3 G). To ascertain the role of PI3K/AKT signaling in N6AMT1-mediated tamoxifen resistance, we treated N6AMT1-overexpressing or -knockdown cells, alongside their controls, with tamoxifen with or without the p110α inhibitor A66. Notably, A66 reinstated tamoxifen sensitivity in N6AMT1 knockdown cells (Fig. 3 H-I) and N6AMT1-overexpressing TamR cells (Fig. 3 J-K). Cell viability was also assessed by EdU assays (Fig. 3 L-M). These results collectively posit that N6AMT1 potentially modulates tamoxifen resistance via the PI3K/AKT signaling pathway. N6AMT1 regulates p110α expression alteration through NEDD4L induction The above results indicated an association between PI3K/AKT pathway activity and p110α expression. To determine the molecular mechanisms that dictate changes in p110α expression associated with N6AMT1 levels, we carried out 6mA-IP-qPCR on several cells: TamS, TamR, and those with stable knockdown and overexpression of N6AMT1. Our data showed no significant differences in 6mA modification levels in the exon-coding regions of PIK3CA (Fig. 4 A). Additionally, RT-qPCR showed no significant differences in PIK3CA mRNA levels (Fig. 4 B). These findings indicate that the changes of p110α expression associated with N6AMT1 are unrelated to 6mA modification or transcriptional regulation of PIK3CA . To further investigate the mechanism underlying N6AMT1-related p110α expression changes, we treated N6AMT1-overexpressing cells with either cycloheximide (CHX) to block protein translation, or MG-132 to inhibit proteasome activity. Western blot analysis revealed that MG-132, but not CHX, reversed the reduction of p110α expression caused by N6AMT1 (Fig. 4 C), indicating a potential role for N6AMT1 in modulating protein stability or post-translational modifications, rather than translation. To support this, we examined the half-life of p110α in N6AMT1 stable knockdown cells and control cells when treated with MG-132. Interestingly, there were no significant differences in the half-life of p110α between the two groups (Fig. 4 D). Building on prior work identifying NEDD4L as the E3 ligase for p110α 23 , we investigated its potential involvement in N6AMT1-related alteration of p110α expression. We first conducted co-immunoprecipitation (co-IP) assays, which showed that NEDD4L could interact with p110α (Fig. 4 E). N6AMT1 silencing sharply reduced the ubiquitination levels of p110α, while N6AMT1 overexpression increased the ubiquitination levels of p110α (Fig. 4 F). Subsequently, RT-qPCR revealed that mRNA levels of NEDD4L were downregulated in N6AMT1 stable knockdown cells and TamR cells, while they were upregulated in N6AMT1-overexpressing cells (Fig. 4 G). These findings were further supported by western blot analysis (Fig. 4 H-J). To explore how N6AMT1 regulates the expression of NEDD4L, we first conducted 6mA-IP-qPCR on the cells mentioned above. Our data showed no significant differences in 6mA modification levels in the exon-coding regions of NEDD4L (Fig. 4 K). Subsequently, we conducted co-IP assays in cells before and after knockdown of N6AMT1. The results indicated that knockdown of N6AMT1 led to a decrease in monomethylation levels of histone H4 (Fig. 4 L). Further, ChIP-qPCR assays demonstrated that N6AMT1 may bind to the promoter region of NEDD4L (Fig. 4 M). These findings suggest that N6AMT1 may act as a lysine methyltransferase, particularly for H4K12me1 5 , to regulate the expression of NEDD4L. Besides, we also observed a positive correlation between N6AMT1 and NEDD4L in ER + breast cancer patients from bc-GenExMiner database (Fig. 4 N). Next, we performed RT-qPCR, western blot, and drug response assays on cells with transient knockdown or overexpression of NEDD4L. Transient knockdown of NEDD4L led to upregulation of p110α and p-AKT (T308) (Fig. 4 P) without upregulating PIK3CA mRNA levels (Fig. 4 O, Fig. S1 C), resulting in decreased sensitivity of breast cancer cells to tamoxifen (Fig. 4 Q, Fig. S1 D). Conversely, overexpression of NEDD4L led to a significant downregulation of p110α and p-AKT (T308) (Fig. 4 S) without downregulation of PIK3CA mRNA levels (Fig. 4 R, Fig. S1 E), thereby increasing the sensitivity of breast cancer cells to tamoxifen (Fig. 4 T, Fig. S1 F). Cell viability was also assessed by EdU assays (Fig. 4 U-V). In summary, our results suggest that N6AMT1 decreases p110α expression through transcriptional upregulation of the p110α E3 ligase NEDD4L. N6AMT1-p110α pathway regulates tamoxifen resistance in vivo To delineate the role of N6AMT1 in modulating tamoxifen sensitivity in vivo, we utilized a xenograft BC mouse model. Specifically, MCF-7 TamR -VC cells or MCF-7 TamR -oeN6AMT1 cells were implanted into the mammary fat pads of female nude mice, followed by administration of either vehicle control or tamoxifen (Fig. 5 A). Overexpression of N6AMT1 markedly curtailed tamoxifen resistance, as evidenced by the significant differences in tumor sizes (Fig. 5 B) and quantitatively substantiated by the tumor growth curves (Fig. 5 C) and tumor weight metrics (Fig. 5 D). IHC analysis of the excised tumor samples further corroborated our findings at the molecular level. Elevated expression of NEDD4L was discerned in the oeN6AMT1 cohort compared to the control, concomitant with a noticeable downregulation of p110α and p-AKT (T308) (Fig. 5 E-F). Further, MCF-7-shNC or MCF-7-shN6AMT1 cells were used for the establishment of xenograft tumor models (Fig. 5 G). Stable knockdown of N6AMT1 markedly reduced tamoxifen sensitivity, which was reversed by the combination of tamoxifen and the p110α inhibitor A66 (Fig. 5 H) and quantitatively substantiated by the tumor growth curves (Fig. 5 I) and tumor weight metrics (Fig. 5 J). IHC analysis of the excised tumor samples further corroborated our findings at the molecular level. Downregulation of NEDD4L was discerned in the shN6AMT1 cohort, and a noticeable upregulation of p110α and p-AKT (T308) in vehicle and tam group (Fig. 5 K-L). Taken together, these results highlight the involvement of the N6AMT1-mediated pathway in tamoxifen resistance in vivo. N6AMT1-p110α pathway in clinical samples and patient-derived organoids To discern the clinical relevance of the N6AMT1-p110α pathway in luminal BC, we assessed samples from 153 primary luminal BC patients treated post-operatively with tamoxifen at SUMCCH from 2012–2017 (Tam-SUMCCH cohort). The patients were divided into two groups: the tamoxifen-sensitive group (n = 116, 75.8%) and the tamoxifen-resistant group (n = 37, 24.2%). N6AMT1 and NEDD4L expression were downregulated in the tamoxifen-resistant group (Fig. 6 A-B), while p110α was upregulated (Fig. 6 C). Notably, FOXA1 tended to show decreased expression in the tamoxifen-resistant samples, although statistical significance was not reached (Fig. 6 D). As shown in Supplementary Table S5, low expression of N6AMT1 was significantly associated with lymph node metastasis ( p < 0.001), distant metastasis ( p = 0.037), while high expression of p110α was significantly associated with lymph node metastasis ( p < 0.001). Spearman’s correlation analysis highlighted a robust positive association between N6AMT1 and NEDD4L and a negative correlation between N6AMT1 and p110α (Fig. 6 E-F). Additionally, a positive correlation was noted between FOXA1 and N6AMT1 (Fig. 6 G). Kaplan-Meier analysis revealed that low N6AMT1 expression was associated with poorer recurrence-free survival (RFS) (Fig. 6 H). Moreover, high p110α expression was associated with poorer RFS (Fig. 6 I), while the expression of FOXA1 and NEDD4L showed no correlation with RFS (Fig. S1 G-H). Univariate and multivariate Cox proportional regression analyses identified independent factors for patients’ RFS. Lymph node metastasis (N1: HR = 2.584, 95% CI 1.266–5.275, p = 0.009; N2 & N3: HR = 3.319, 95% CI 1.033–10.658, p = 0.044), distant metastasis (M1: HR = 2.668, 95% CI 1.058–6.729, p = 0.038), and high p110α expression (HR = 2.290, 95% CI 1.027–5.107, p = 0.043) were identified as independent risk factors, while high N6AMT1 expression (HR = 0.229, 95% CI 0.092–0.573, p = 0.002) was an independent protective factor (Table 1 ). Table 1 Cox proportional hazard regression analyses for recurrence-free survival Clinical characteristics Univariate analysis Multivariate analysis Hazard ratio (95%CI a ) p value Hazard ratio (95%CI a ) p value Age ≤ 50 51–60 Reference 0.799 (0.365–1.747) 0.574 Menopause No Reference Yes 0.608 (0.216–1.720) 0.349 Histology Ductal carcinoma Reference Lobular carcinoma 0.487 (0.067–3.564) 0.479 Other types 1.624 (0.498–5.298) 0.421 Differentiation I Reference II 1.426 (0.447–4.546) 0.549 III 2.609 (0.902–7.547) 0.077 Tumor invasion T1 Reference T2 1.236 (0.501–3.049) 0.645 T3 0.953 (0.269–3.377) 0.940 T4 2.456 (0.749–8.053) 0.138 Lymph node metastasis N0 Reference N1 2.872 (1.428–5.776) 0.003 2.584 (1.266–5.275) 0.009 N2 & N3 4.142 (1.350-12.712) 0.013 3.319 (1.033–10.658) 0.044 Distant metastasis M0 Reference M1 4.509 (1.877–10.835) < 0.001 2.668 (1.058–6.729) 0.038 FOXA1 expression Low Reference High 0.485 (0.251–0.935) 0.031 1.508 (0.656–3.464) 0.333 N6AMT1 expression Low Reference High 0.215 (0.106–0.437) < 0.0001 0.229 (0.092–0.573) 0.002 NEDD4L expression Low Reference High 0.531 (0.273–1.032) 0.062 p110α expression Low Reference High 3.728 (1.757–7.911) < 0.001 2.290 (1.027–5.107) 0.043 a Confidence interval. Bold values indicate statistical significance ( p < 0.05) Supplementary Tables Table S1 Clinical characteristics of 153 human luminal BC tissue samples No. Age Menopause Histology TNM Differentiation RFS (month) Outcome 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 46 46 48 47 59 44 44 46 57 55 49 50 43 44 46 43 47 48 49 50 51 46 53 53 51 49 58 57 42 46 47 48 56 48 59 43 42 57 43 46 49 48 51 55 47 42 45 44 44 52 44 47 45 53 44 45 42 46 48 47 47 48 49 46 43 45 46 56 45 45 45 43 47 47 56 43 44 43 42 56 43 47 47 49 46 57 51 51 48 53 47 46 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Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Other types Ductal carcinoma Ductal carcinoma Lobular carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Lobular carcinoma Ductal carcinoma Other types Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Other types Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Other types Ductal carcinoma Ductal carcinoma Lobular carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Lobular carcinoma Ductal carcinoma Ductal carcinoma Lobular carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Other types Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Other types Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Lobular carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Lobular carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Other types Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Other types Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Lobular carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma Ductal carcinoma T2N1M0 T2N1M0 T2N2M0 T2N2M1 T1N2M1 T4N2M0 T2N0M0 T2N1M0 T2N2M0 T2N1M0 T2N1M0 T4N0M0 T3N1M0 T4N0M0 T2N0M0 T4N1M0 T3N1M0 T1N0M0 T3N1M0 T2N1M0 T4N0M0 T2N2M1 T2N2M1 T1N0M0 T2N2M0 T2N0M0 T1N0M0 T1N0M0 T1N0M0 T3N0M0 T2N1M0 T2N1M0 T2N1M0 T2N0M0 T2N1M0 T2N0M0 T1N0M0 T1N0M0 T1N0M0 T2N1M0 T4N1M0 T3N1M0 T2N0M0 T1N0M0 T2N0M0 T2N0M0 T2N0M0 T4N0M0 T2N0M0 T3N1M0 T2N0M0 T2N0M0 T3N0M0 T3N0M0 T2N0M0 T2N0M0 T3N0M0 T2N0M0 T1N0M0 T2N0M0 T2N0M0 T1N0M0 T1N0M0 T4N0M0 T2N1M0 T2N1M0 T3N0M0 T4N0M0 T1N1M0 T2N0M0 T2N1M0 T2N0M0 T2N1M0 T2N0M0 T4N1M0 T3N1M0 T1N0M0 T2N0M0 T2N0M0 T2N1M0 T2N0M0 T1N0M1 T2N1M0 T2N0M0 T2N2M1 T2N0M0 T1N0M0 T1N0M0 T3N1M0 T2N0M0 T2N0M0 T2N1M0 T2N0M0 T2N0M0 T1N0M0 T2N1M0 T3N0M0 T3N0M0 T2N1M0 T3N1M0 T1N1M0 T2N0M0 T3N0M0 T3N1M0 T2N1M0 T2N1M0 T2N1M0 T2N1M0 T1N1M0 T2N0M0 T2N1M0 T2N1M0 T2N0M0 T2N0M0 T2N0M0 T2N0M0 T3N1M0 T2N0M0 T2N0M0 T2N0M0 T1N0M0 T2N1M0 T2N1M0 T2N0M0 T2N0M0 T4N1M0 T3N1M0 T2N0M0 T2N1M0 T2N0M0 T2N0M0 T2N0M0 T1N0M0 T1N0M0 T2N0M0 T1N0M0 T2N0M0 T2N0M0 T2N0M0 T3N1M0 T1N0M0 T2N0M0 T2N0M0 T1N0M0 T2N0M0 T1N0M0 T3N1M0 T2N0M0 T2N1M0 T2N0M0 T1N0M0 T2N1M0 T4N0M0 III III III III III III II II III II I II II III III III III II III III III III III III II II II I II I II III III III I I I II II II II III III III I I II III II III II III III III III III I I I II III III II III III II II III III I III II I I III I II III III I I III III II III I III II II III II III III II II III II II III III II III II II II III III III III III III III I I III I III II II II III I II II I II I II II II II II I III I II III II II III II II III II III I III I III III III II I 29 30 13 55 76 7 13 13 13 15 16 19 20 21 29 29 32 38 39 41 42 45 47 53 26 64 80 81 81 82 82 83 23 90 95 41 32 25 60 58 56 5 63 66 64 65 56 70 70 70 71 71 71 72 73 23 76 76 77 77 78 10 81 81 81 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Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Survival Table S2 Antibodies used in this study Antibody Cat. # Manufacturer Application & Dilution Species FOXA1 sc-101058 Santa Cruz WB (1:1000) IHC (1:500) Mouse FOXA1 ab170933 Abcam ChIP (1:50) Rabbit N6AMT1 abx005435 Abbexa WB (1:1000) IHC (1:200) ChIP (1:50) Rabbit NEDD4L 4013S Cell Signaling Technology WB (1:1000) IP (1:50) Rabbit NEDD4L ab124643 Abcam IHC (1:500) Rabbit PIK3CA 4249S Cell Signaling Technology WB (1:1000) IP (1:50) Rabbit PIK3CA NBP2-19804 Novus IHC (1:500) Rabbit t-AKT 4691S Cell Signaling Technology WB (1:1000) Rabbit p-AKT (T308) 13038S Abcam WB (1:1000) Rabbit p-AKT (T308) ab38449 Abcam IHC (1:200) Rabbit β-Actin sc-47778 Santa Cruz WB (1:1000) Mouse Ubiquitin sc-8017 Santa Cruz WB (1:1000) Mouse Histone H4 2592S Cell Signaling Technology WB (1:1000) IP (1:50) Rabbit Mono-Methyl Lysine 14679S Cell Signaling Technology WB (1:1000) Rabbit Anti-N6-methyladenosine ABE572 Sigma-Aldrich 6mA-IP (1:50) Rabbit Anti-Mouse IgG (H + L) HRP ZB-2305 ZSGB-BIO WB (1:5000) Goat Anti-Rabbit IgG (H + L) HRP ZB-5301 ZSGB-BIO WB (1:5000) Goat Normal Rabbit IgG 2729S Cell Signaling Technology ChIP (1:50) 6mA-IP (1:50) Rabbit GTVisionTM Ⅰ Detection System/Mo&Rb GK500510A Gene Tech IHC Goat Table S3 Primer sequences Name Sequence (5’-3’) Application FOXA1 Forward: GCAATACTCGCCTTACGGCT RT-qPCR Reverse: TACACACCTTGGTAGTACGCC N6AMT1 Forward: GCAGGGGAGAACTTCGCTAC RT-qPCR Reverse: CAGCGCGTTCAAAAGCAGAAA NEDD4L Forward: GACATGGAGCATGGATGGGAA RT-qPCR Reverse: GTTCGGCCTAAATTGTCCACT PIK3CA Forward: CCACGACCATCATCAGGTGAA Reverse: CCTCACGGAGGCATTCTAAAGT RT-qPCR C/EBPβ Forward: CTTCAGCCCGTACCTGGAG Reverse: GGAGAGGAAGTCGTGGTGC RT-qPCR AR Forward: ACAGGAGGAAGGAGAGGCTT Reverse: GTTGTTGTCGTGTCCAGCAC RT-qPCR β-Actin Forward: CCTCGCCTTTGCCGATCC Reverse: CGCGGCGATATCATCATCC RT-qPCR N6AMT1-site1 Forward: GTTTCATTGCGAAACAAATTTCAG Reverse: ACGCTACCCTCGTTATGTGAG ChIP-qPCR N6AMT1-site2 Forward: AGCAAATGTCAATAGTCACGGA Reverse: CCAGTGTGTTGTTGTTGGGTC ChIP-qPCR N6AMT1-blank Forward: TCACATAACGAGGGTAGCGT Reverse: ACTGCTGAATTGGGGAGGCT ChIP-qPCR PIK3CA-1 Forward: GTATCTACCATGGAGGAGAACCC Reverse: CTGGGATTGGAACAAGGTACT 6mA-IP-qPCR PIK3CA-2 Forward: TTGGCCTCCAATCAAACCTGA Reverse: AAGCACCGAACAGCAAAACC 6mA-IP-qPCR PIK3CA-3 Forward: AGAGGTTTGGCCTGCTTTTG Reverse: TGAGCTTTTCCATTGCCTCG 6mA-IP-qPCR NEDD4L-1 Forward: TGTCAACCACAACAACCGGA Reverse: CCAGACCGAGAGAGTCTCCA 6mA-IP-qPCR NEDD4L-2 Forward: TGAGGTCATGCAGTGTCACC Reverse: GTCTTTCACAGCCCGACGTA 6mA-IP-qPCR NEDD4L-3 Forward: TTGCTGGTCTGGCCGTATTT Reverse: GAACCACTGAATGACGGGGT 6mA-IP-qPCR GPER1-1 Forward: TGGATGTGACTTCCCAAGCC Reverse: ACAAAGCCGATGGGGAAGAG 6mA-IP-qPCR GPER1-2 Forward: TGCACCTTCATGTCGCTCTT Reverse: GACATCCGCGAAACAGAAGC 6mA-IP-qPCR GPER1-3 Forward: ATGACCATCCCCGACCTGTA Reverse: GAGGAAGAAGACGCTGCTGT 6mA-IP-qPCR NEDD4L-site1 Forward: ACCCAAGTAGGCACTTTGACG Reverse: CCCAGCTACCGTTTCTCTAGC ChIP-qPCR NEDD4L-site2 Forward: GAGAAACGGTAGCTGGGTGG Reverse: TGTGTCAACCTTGCAGGGAA ChIP-qPCR NEDD4L-site3 Forward: ACCATGCTGTCTGTTTTCGC Reverse: GACGAGCAGGACTTGGCTAC ChIP-qPCR NEDD4L-site4 Forward: ATGTAGCCAAGTCCTGCTCG Reverse: CCCGGTGGTCTTTTTACCGA ChIP-qPCR NEDD4L-site5 Forward: CGGTAAAAAGACCACCGGGA Reverse: GGTTTCCCTCCGCTTTAGGG ChIP-qPCR Table S4 Name Target sequence siFOXA1-1 GCGACTGGAACAGCTACTA siFOXA1-2 GCACTGCAATACTCGCCTT siFOXA1-3 CCACTCGCTGTCCTTCAAT siNEDD4L-1 CAAGAAGTCATAAGTCTCGAGTTAA siNEDD4L-2 CCACAAACAGTAACAACCATCTAAT siNEDD4L-3 CATATGCGGTCAAAGACATCTTTAA non targeting siRNA (siNC) CGTACGCGGAATACTTCGA shN6AMT1-1 GTTCACATTCAACCAGTTATT shN6AMT1-2 GTTGATCTTCTGGTGTTTAAT shN6AMT1-3 GTACATGTGCACTGATATCAA Scramble GCTCCCAGGTTATGGGAGAAG Target sequences of siRNA and shRNA Table S5 Clinicopathological correlation of N6AMT1 and p110α expression in luminal BC Clinical characteristics Cases N6AMT1 expression p value Cases p110α expression p value Low (%) High (%) Low (%) High (%) Age ≤ 50 115 44 (38.3%) 71 (61.7%) 0.480 115 46 (48.7%) 59 (51.3%) 0.325 51–60 38 17 (44.7%) 21 (55.3%) 38 22 (57.9%) 16 (42.1%) Menopause No 129 50 (38.8%) 79 (61.2%) 0.516 129 63 (48.8%) 66 (51.2%) 0.219 Yes 24 11 (45.8%) 13 (54.2%) 24 15 (62.5%) 9 (37.5%) Histology Ductal carcinoma 136 51 (37.5%) 85 (62.5%) 0.234 136 73 (53.7%) 63 (46.3%) 0.136 Lobular carcinoma 8 5 (62.5%) 3 (37.5%) 8 3 (37.5%) 5 (62.5%) Other types 9 5 (55.6%) 4 (44.4%) 9 7 (22.2%) 2 (77.8%) Differentiation I 29 13 (44.8%) 16 (55.2%) 0.832 29 13 (44.8%) 16 (55.2%) 0.758 II 54 21 (38.9%) 33 (61.1%) 54 28 (51.9%) 26 (48.1%) III 70 27 (38.6%) 43 (61.4%) 70 37 (52.9%) 33 (47.1%) T stage T1 29 14 (48.3%) 15 (51.7%) 0.129 29 16 (55.2%) 13 (44.8%) 0.123 T2 91 32 (35.2%) 59 (64.8%) 91 51 (56.0%) 40 (44.0%) T3 21 7 (33.3%) 14 (66.7%) 21 8 (38.1%) 13 (61.9%) T4 12 8 (66.7%) 4 (33.3%) 12 3 (25.0%) 9 (75.0%) N stage N0 93 24 (25.8%) 69 (74.2%) < 0.001 93 61 (65.6%) 32 (34.4%) < 0.001 N1 51 31 (60.8%) 20 (39.2%) 51 15 (29.4%) 36 (70.6%) N2 & N3 9 7 (77.8%) 2 (22.2%) 9 2 (22.2%) 7 (77.8%) M stage M0 147 56 (38.1%) 91 (61.9%) 0.037 147 77 (52.4%) 70 (47.6%) 0.112 M1 6 5 (83.3%) 1 (16.7%) 6 1 (16.7%) 5 (83.3%) Bold values indicate statistical significance ( p < 0.05) We also generated breast cancer organoids using samples obtained through percutaneous needle biopsy from two tamoxifen-resistant patients undergoing follow-up at SUMCCH. Western blot analysis of the biopsy specimens from both patients indicated that only Case 1 matched the N6AMT1-p110α axis profile from our study (N6AMT1 downregulation while p110α upregulation, Fig. S1 G). Subsequent 3D organoid drug response assays revealed significant growth suppression, in Case 1 organoids, upon combined tamoxifen and p110α inhibitor A66 treatment (Fig. 6 J-K). In contrast, Case 2, which lacked the p110α expression, showed no significant response to the combined treatment (Fig. 6 L-M). In summary, our data suggest a potential role of the N6AMT1-p110α axis in modulating tamoxifen resistance in luminal BC. DISCUSSION Reduced expression of luminal breast cancer biomarkers, including ER, FOXA1, and GATA3, has consistently correlated with tamoxifen resistance 21 . Our study elucidates a mechanism involving N6AMT1-mediated alteration of p110α expression in tamoxifen resistance. We show N6AMT1 is downregulated, in tamoxifen-resistant cells, in a manner transcriptionally regulated by FOXA1 (forkhead box A1). As a member of the forkhead box transcription factor family, FOXA1 acts as a pioneer factor, binding to silent chromatin target sites and dominantly initiating regulatory cascades 24 . Its significance is further underscored as a recognized biomarker for luminal breast cancer, where it modulates ER 25 and is pivotal in hormone responses, both during breast development and in ER-positive breast cancer scenarios 26 . Around 50% of ER binding sites overlap with FOXA1 binding sites 27 . Clinically, it has been observed that elevated FOXA1 expression in luminal A-type breast cancer patients is associated with enhanced survival rate and a more favorable response to endocrine therapies 18 . Conversely, low FOXA1 expression is linked with the maintenance of tumor cell stemness, an upsurge in IL-6 expression, and the mediation of tamoxifen resistance 28 . The FOXA1 promoter region contains estrogen response elements (EREs), suggesting that FOXA1 is a ER-regulated gene 29 . In this context, our results suggest that prolonged tamoxifen exposure could reduce FOXA1 expression by disrupting the intricate regulatory network of ER-associated genes, providing an explanation for the reduced FOXA1 levels in TamR cells. Subsequently, the observed downregulation of N6AMT1, that occurs as a result of decreased FOXA1 levels, leads to increased expression of p110α and concomitant tamoxifen resistance. Previous research has primarily focused on PIK3CA mutation status, particularly in two hotspots: exon 9 (E542K and E545K) encoding the helix domain, and exon 20 (H1047R) encoding the kinase domain 30–32 . These mutations, as evidenced by both in vitro and in vivo models, possess pronounced oncogenic potential 33–36 . While a meta-analysis has correlated them with unfavorable survival outcomes 37 , a comprehensive analysis encompassing 10,319 breast cancer patients paints a different picture, associating PIK3CA mutations with enhanced survival rates 38 . The activation of the PI3K/AKT pathway by PIK3CA mutations is a well-documented mechanism contributing to tamoxifen resistance 14 . Yet, this fails to elucidate the resistance observed in patients devoid of PIK3CA mutations. Our data posits a novel perspective: the downregulation of N6AMT1 potentially escalates the levels of p110α and p-AKT (T308), thereby attenuating tamoxifen sensitivity. Notably, our experiments indicate that N6AMT1 overexpression or stable knockdown, both in vitro and in vivo, inversely affects p110α and p-AKT (T308) levels to regulate tamoxifen sensitivity. In transgenic mouse models of breast cancer (MMTV and HER2/Neu), PIK3CA knockout inhibits tumor growth 39 . Additionally, p110α overexpression might play a role in the resistance of Snu-5 gastric xenografts to tyrosine kinase MET inhibitors 40 . Collectively, these insights suggest that the surge in p110α levels not only amplifies PI3K activity but also activates the PI3K/AKT pathway. This could explain the observed correlation between altered p110α expression and the dynamics of the PI3K/AKT pathway in tamoxifen resistance in our study. N6AMT1 primarily functions as a glutamine/lysine methyltransferase, modifying Gln185 of eRF1 to regulate translation in eukaryotes, and modifying Lys12 of histone H4 to regulate gene transcription 5,6 . Our 6mA-IP-qPCR assays showed no discernible shifts in 6mA levels within the exon-coding region of PIK3CA , irrespective of the presence or absence of tamoxifen resistance and the reduced N6AMT1 expression. Additionally, RT-qPCR showed no significant changes in PIK3CA mRNA levels, suggesting that alterations in p110α expression is not associated with 6mA modification or transcriptional regulation, but rather with protein stability and post-translational modifications. Previous research has shown that NEDD4L, an E3 ligase, enhances p110α degradation through poly-ubiquitination and proteasomal degradation 23 . NEDD4L belongs to the HECT E3 ubiquitin ligase family and interacts with substrates via its four WW domains 41 . Dysregulation of NEDD4L is observed in various cancers, and reduced levels are associated with tumor development 42–44 . In breast cancer cells, miR-106b-25 can downregulate NEDD4L, leading to increased NOTCH1 and activation of tumor-initiating cells 45 . Our study shows that N6AMT1 overexpression upregulates NEDD4L mRNA and protein levels, sensitizing breast cancer cells to tamoxifen. Conversely, N6AMT1 knockdown has the opposite effect. These findings suggest that N6AMT1 may regulate p110α via NEDD4L, highlighting the role of NEDD4L as a tumor suppressor in tamoxifen sensitivity. Although the role of N6AMT1 in the transcriptional regulation of NEDD4L remains to be fully elucidated, our 6mA-IP-qPCR assays showed no significant changes in the 6mA levels within the exon-coding regions of NEDD4L after N6AMT1 overexpression or stable knockdown, while ChIP-qPCR assays revealed that N6AMT1 may bind to the promoter region of NEDD4L and co-IP assays showed that knockdown of N6AMT1 led to a decrease in monomethylation levels of histone H4. Based on these, we propose that N6AMT1, as a KMT9, might regulate NEDD4L transcription through H4K12 monomethylation modification 5 . Unfortunately, the unavailability of the H4K12me1 antibody from the Schüle Laboratory has prevented further verification. Nevertheless, this limitation does not impact our conclusion that the alteration in p110α expression related to N6AMT1 might be dependent on NEDD4L. Furthermore, we validated the role of the N6AMT1-p110α pathway in clinical samples and patient-derived organoid (PDO) models. The PDO model accurately reproduces the clinical response by maintaining the tumor phenotype and genotype, making it suitable for preclinical drug discovery and validation 46 . A66, a potent and specific inhibitor of wild-type p110α, reduces phosphorylation of Akt on T308 47 . A synergistic effect of the p110α inhibitor A66 with tamoxifen was only observed in the PDO model of case 1, which exhibited a resistance mechanism similar to our study (downregulation of N6AMT1 and upregulation of p110α). These findings suggest that targeting p110α could be a promising approach to overcoming tamoxifen resistance associated with an N6AMT1-p110α pathway. Clinical trials have explored the use of alpelisib, a PI3Kα-selective inhibitor, in hormone receptor-positive, human epidermal growth factor receptor-2-negative advanced breast cancer with PIK3CA mutations. The SOLAR-1 trial (NCT02437318) demonstrates a significant progression-free survival (PFS) benefit when alpelisib is added to fulvestrant treatment 48 . However, clinical trials specifically focusing on patients with p110α expression alterations have not been reported. Our study provides a theoretical basis for understanding tamoxifen resistance mechanisms in patients without PIK3CA mutations. Further investigations should evaluate the therapeutic efficacy of p110α inhibition in preclinical models with varying p110α expression levels before advancing to clinical trials. This study has certain limitations. The mechanisms underlying the downregulation of FOXA1 and the regulation of NEDD4L by N6AMT1 remain unknown. Additionally, the use of cell line models (MCF-7, T47D) and a limited number of clinical samples (153) and PDOs ( 2 ) cannot fully capture the complexity and heterogeneity of luminal breast cancers. Therefore, future studies with a larger representation of tumors are needed to validate the role of the N6AMT1-p110α pathway in luminal breast cancers, especially those with p110α expression alterations associated with tamoxifen resistance. In conclusion, our study illustrates that N6AMT1 confers tamoxifen resistance by altering p110α expression in luminal breast cancer. These results suggest that the N6AMT1-p110α pathway might not only predict tamoxifen sensitivity, but also serve as a viable target for overcoming tamoxifen resistance in luminal breast cancer. Declarations COMPETING INTERESTS The authors declare no competing interests. AUTHOR CONTRIBUTIONS L. Ji and Y. Cui designed the studies, revised the manuscript, and supervised the progress throughout this study. L. Ji carried out the experiments, analyzed the data, and wrote the manuscript. J. Chen carried out the experiments and analyzed the data. L. He provided case data and clinical specimens. F. Zhang analyzed the data. Z. Deng assisted in animal and organoid experiments. J. Lin assisted in the procurement of laboratory reagents. Z. Qi assisted in analyzed the data. X. Luo participated in some of the experiments. S.L. Li provided guidance for the experiment of cell biology and revised the manuscript. A. Giuliano and X. Cui revised the manuscript. All authors reviewed and approved the final manuscript. ACKNOWLEDGEMENTS This work was supported by the National Natural Science Foundation of China (Grant No. 82272670) and the Special Fund for Science and Technology of Guangdong Province (Grant No. 210729156901814 and No. 210728156901648). DATA AVAILABILITY The authors declare that all data supporting the findings of this study are available within the article and its Supplementary Information files or from the corresponding author upon reasonable request. References Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA A Cancer J Clin 2021; 71: 209–249. Loibl S, Poortmans P, Morrow M, Denkert C, Curigliano G. Breast cancer. The Lancet 2021; 397: 1750–1769. Waks AG, Winer EP. Breast Cancer Treatment: A Review. JAMA 2019; 321: 288. Chen J, Zhuang Y, Wang P, Ning J, Liu W, Huang Y et al. Reducing N6AMT1-mediated 6mA DNA modification promotes breast tumor progression via transcriptional repressing cell cycle inhibitors. Cell Death Dis 2022; 13: 216. Metzger E, Wang S, Urban S, Willmann D, Schmidt A, Offermann A et al. 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Krug K, Jaehnig EJ, Satpathy S, Blumenberg L, Karpova A, Anurag M et al. Proteogenomic Landscape of Breast Cancer Tumorigenesis and Targeted Therapy. Cell 2020; 183: 1436–1456.e31. Additional Declarations There is NO conflict of interest to disclose. 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Lin","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Stanley","middleName":"Li","lastName":"Lin","suffix":""}],"badges":[],"createdAt":"2024-07-14 14:10:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4738749/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4738749/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41388-024-03238-3","type":"published","date":"2024-12-02T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":62656260,"identity":"fd99721e-81ca-4a7f-930b-795615394ccb","added_by":"auto","created_at":"2024-08-17 01:58:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1378751,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eN6AMT1 is highly expressed in luminal breast cancer.\u003c/strong\u003e \u003cstrong\u003eA\u003c/strong\u003e, Expression of N6AMT1 in breast cancer cell lines of different subtypes was quantified by western blotting. Representative western blot (left) and quantitative analysis (right). \u003cstrong\u003eB-E\u003c/strong\u003e, Expression of N6AMT1 mRNA in different subtypes of breast cancer from TCGA (\u003cstrong\u003eB\u003c/strong\u003e), SCAN-B (The Sweden Cancerome Analysis Network – Breast) (\u003cstrong\u003eC\u003c/strong\u003e) \u003csup\u003e49\u003c/sup\u003e, METABRIC (Molecular Taxonomy of Breast Cancer International Consortium) (\u003cstrong\u003eD\u003c/strong\u003e) \u003csup\u003e50\u003c/sup\u003e and SMC (a Korean BC cohort) (\u003cstrong\u003eE\u003c/strong\u003e) \u003csup\u003e51\u003c/sup\u003e samples were analyzed using the bc-GenExMiner and cBioPortal databases. \u003cstrong\u003eF\u003c/strong\u003e, Expression of N6AMT1 protein in different subtypes of breast cancer from CPTAC (Clinical Proteomic Tumor Analysis Consortium) \u003csup\u003e52\u003c/sup\u003e samples was analyzed using the cBioPortal database.\u003cstrong\u003e G\u003c/strong\u003e, Co-expression analysis of N6AMT1 mRNA with ESR1 mRNA in breast cancer patients from the bc-GenExMiner database.\u003cstrong\u003e H-I\u003c/strong\u003e,\u003cstrong\u003e \u003c/strong\u003eCo-expression analysis of N6AMT1 mRNA with ESR1 mRNA in ER+/PR+ (\u003cstrong\u003eH\u003c/strong\u003e) or ER+/PR- (\u003cstrong\u003eI\u003c/strong\u003e) BC patients from the bc-GenExMiner database. \u003cstrong\u003eJ\u003c/strong\u003e, UMAP visualization of cancer epithelial cells. Top row, each plot illustrates cells categorized into unique molecular subtypes. Bottom row, log-normalized expression of N6AMT1 in each subtype, visualized by color intensity. \u003cstrong\u003eK\u003c/strong\u003e, Representative IHC staining of N6AMT1 in different types of breast cancer. Scale bar = 1 mm (low magnification) and 100 μm (high magnification). \u003cstrong\u003eL\u003c/strong\u003e, Comparison of N6AMT1 expression in different types of breast cancer according to IHC score. \u003cstrong\u003eM\u003c/strong\u003e, Expression of N6AMT1 in TamR and TamS cells was quantified by RT-qPCR. \u003cstrong\u003eN\u003c/strong\u003e, Expression of N6AMT1 in TamR and TamS cells was quantified western blot. Representative western blot (left) and quantitative analysis (right). \u003cstrong\u003eO\u003c/strong\u003e, N6AMT1 mRNA levels in patients before and after neo-adjuvant tamoxifen (Tam) treatment (left, GSE147271). N6AMT1 mRNA levels in normal human mammary epithelial cells before and after Tam treatment (right, GSE10689). \u0026nbsp;*, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05; **, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01; ***, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figures1.png","url":"https://assets-eu.researchsquare.com/files/rs-4738749/v1/beec4bb537494e2cb7ea40d0.png"},{"id":62656265,"identity":"0d6848c8-0059-43fd-a116-73c6651a7331","added_by":"auto","created_at":"2024-08-17 01:58:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":512942,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eN6AMT1 is transcriptionally regulated by FOXA1.\u003c/strong\u003e \u003cstrong\u003eA\u003c/strong\u003e, Venn diagram shows the overlap of transcription factors for N6AMT1 predicted by PROMO and ChIPBase. \u003cstrong\u003eB\u003c/strong\u003e, The mRNA expression of potential transcription factors of \u003cem\u003eN6AMT1\u003c/em\u003ein TamR and TamS cells was verified by RT-qPCR. \u003cstrong\u003eC\u003c/strong\u003e, Protein expression of FOXA1 was determined by western blotting. \u003cstrong\u003eD\u003c/strong\u003e, FOXA1 mRNA (left) and protein expression (right) were determined 24 h and 72 h, respectively, after transfection with FOXA1 siRNAs. \u003cstrong\u003eE\u003c/strong\u003e, FOXA1 binding motif and sequences as predicted by the JASPAR database. \u003cstrong\u003eF\u003c/strong\u003e, Binding between FOXA1 and the \u003cem\u003eN6AMT1 \u003c/em\u003epromoter at potential binding site 1 and 2 was determined by ChIP-qPCR, with site BLK used as a negative control. \u003cstrong\u003eG\u003c/strong\u003e, Agarose electrophoresis of \u003cem\u003eN6AMT1\u003c/em\u003epromoter qPCR products following ChIP-qPCR. \u003cstrong\u003eH\u003c/strong\u003e, Schematic representation of mutation in the \u003cem\u003eN6AMT1\u003c/em\u003e promoter for investigation the role of FOXA1 on N6AMT1 expression. \u003cstrong\u003eI\u003c/strong\u003e, The ratio of reporter plasmid activity to pRL-TK activity (F-Luc/R-Luc) was determined 48 h after cells were separately co-transfected with pGL3-N6-Luc (WT, Mut1 or Mut2) and pRL-TK plasmids, along with either si-NC or si-FOXA1-1. \u003cstrong\u003eJ\u003c/strong\u003e, The F-Luc/R-Luc ratios of pGL3-N6-WT-Luc, pGL3-N6-Mut1-Luc, or pGL3-N6-Mut2-Luc and pRL-TK plasmid were determined in TamS and TamR cells. \u003cstrong\u003eK\u003c/strong\u003e, Co-expression analysis of the FOXA1 mRNA with N6AMT1 mRNA in ER+ (left) and ER- (right) breast cancer patients from the bc-GenExMiner database. \u003cstrong\u003eL\u003c/strong\u003e, ChIP-seq analysis revealed FOXA1 binding peaks on the promoter regions of \u003cem\u003eN6AMT1\u003c/em\u003e according to the ChIP-Atlas database. ns, non-significant; **, \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.01; ***, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figures2.png","url":"https://assets-eu.researchsquare.com/files/rs-4738749/v1/88b4ef7a5acb92863afa85b0.png"},{"id":62656263,"identity":"38d41ab4-3f55-44f1-8ffd-fb3ff16f4af3","added_by":"auto","created_at":"2024-08-17 01:58:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":774806,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eN6AMT1 regulates tamoxifen resistance via the PI3K/AKT pathway.\u003c/strong\u003e \u003cstrong\u003eA\u003c/strong\u003e The mRNA and protein expression of N6AMT1 was determined in cells transfected with N6AMT1-shRNAs or shRNA negative control (shNC). \u003cstrong\u003eB\u003c/strong\u003e, CCK-8 assays to evaluate tamoxifen sensitivity in N6AMT1 stable knockdown cells and control cells. \u003cstrong\u003eC\u003c/strong\u003e, The mRNA and protein expression of N6AMT1 were determined in -overexpressing and vector control cells. \u003cstrong\u003eD\u003c/strong\u003e, CCK-8 assays to evaluate tamoxifen sensitivity in N6AMT1-overexpressing and vector control cells. \u003cstrong\u003eE-G\u003c/strong\u003e, Protein expression of p110α, p-AKT (T308) and N6AMT1 were quantified by western blotting in corresponding cells. \u003cstrong\u003eH-K\u003c/strong\u003e, CCK-8 assays to evaluate tamoxifen sensitivity in N6AMT1 stable knockdown cells, N6AMT1-overexpressingTamR cells, and their corresponding control cells upon treatment with tamoxifen alone or in combination with p110α inhibitor A66. \u003cstrong\u003eL-M\u003c/strong\u003e, Cell viability was measured by EdU assays after N6AMT1 knockdown or overexpression. DAPI staining indicated living cells, and EdU staining indicated cells that underwent DNA replication. Scale bar = 100 μm. ns, non-significant; ***, \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figures3.png","url":"https://assets-eu.researchsquare.com/files/rs-4738749/v1/7d9a7b43ab9c12a5c585376a.png"},{"id":62657069,"identity":"2f993d94-093b-410b-864f-36dbe3c4dc39","added_by":"auto","created_at":"2024-08-17 02:06:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":956803,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eN6AMT1 regulates p110α expression alteration through NEDD4L induction.\u003c/strong\u003e \u003cstrong\u003eA\u003c/strong\u003e, 6mA abundance of \u003cem\u003ePIK3CA \u003c/em\u003egene exon-coding regions was determined by 6mA-IP-qPCR in the indicated cells. \u003cstrong\u003eB\u003c/strong\u003e, The mRNA expression of PIK3CA was characterized in the indicated cells. \u003cstrong\u003eC\u003c/strong\u003e, N6AMT1-overexpressing and vector control cells were treated with CHX (10 μg/ml) or MG-132 (5 μM) for 6 h, and the expression of p110α was characterized by western blotting. Representative western blot (left) and quantitative analysis (right). \u003cstrong\u003eD\u003c/strong\u003e, Cells were treated with 5 μM MG-132 for the indicated times, and the expression of p110α was determined by western blotting. Representative western blot (left) and quantitative analysis (right). \u003cstrong\u003eE\u003c/strong\u003e, Co-IP assays identified the interaction between NEDD4L and p110α. \u003cstrong\u003eF\u003c/strong\u003e, After transient knockdown or overexpression of NEDD4L, cells were treated with MG-132 for 6 h. Ubiquitination levels of p110α were then determined by immunoprecipitation-immunoblotting. \u003cstrong\u003eG\u003c/strong\u003e, The mRNA expression of NEDD4Lin N6AMT1 stable knockdown, TamR and corresponding control cells was quantified by RT-qPCR. \u003cstrong\u003eH-J\u003c/strong\u003e, Protein expression of NEDD4L in N6AMT1 stable knockdown, TamR, N6AMT1-overexpressing and corresponding control cells was determined by western blotting. \u003cstrong\u003eK\u003c/strong\u003e, 6mA abundance of \u003cem\u003eNEDD4L \u003c/em\u003egene exon-coding regions was determined by 6mA-IP-qPCR in the indicated cells. \u003cstrong\u003eL\u003c/strong\u003e, Co-IP assays showed that knockdown of N6AMT1 led to a decrease in the monomethylation levels of histone H4. \u003cstrong\u003eM\u003c/strong\u003e, ChIP assays showing that N6AMT1 binds to the promoter region of \u003cem\u003eNEDD4L\u003c/em\u003e. \u003cstrong\u003eN\u003c/strong\u003e, Co-expression analysis of N6AMT1 mRNA with NEDD4L mRNA in ER+ breast cancer patients from the bc-GenExMiner database. \u003cstrong\u003eO\u003c/strong\u003e, The mRNA expression of NEDD4L in NEDD4L transient knockdown cells and corresponding control cells was quantified by RT-qPCR. \u003cstrong\u003eP\u003c/strong\u003e, Protein expression of NEDD4L, p110α and p-AKT (T308) was quantified by western blotting. \u003cstrong\u003eQ\u003c/strong\u003e, CCK-8 assays to evaluate tamoxifen sensitivity in NEDD4L transient knockdown cells and corresponding control cells. \u003cstrong\u003eR\u003c/strong\u003e, The mRNA expression of NEDD4L in NEDD4L-overexpressing and corresponding control cells was determined by RT-qPCR. \u003cstrong\u003eS\u003c/strong\u003e, Protein expression of NEDD4L, p110α and p-AKT (T308) was checked by western blotting. \u003cstrong\u003eT\u003c/strong\u003e, CCK-8 assays to evaluate tamoxifen sensitivity in NEDD4L-overexpressing and corresponding control cells. \u003cstrong\u003eU-V\u003c/strong\u003e, Cell viability was measured by EdU assays. DAPI staining indicates living cells, and EdU staining indicates cells that had replicated their DNA. Scale bar = 100 μm. ns, non-significant; *, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05; ***, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figures4.png","url":"https://assets-eu.researchsquare.com/files/rs-4738749/v1/2941111a05f83707dfbaceb6.png"},{"id":62657070,"identity":"35346616-33f3-4036-85e7-5a0acf5d8a8f","added_by":"auto","created_at":"2024-08-17 02:06:05","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2880653,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eN6AMT1 overexpression inhibits tamoxifen resistance in vivo.\u003c/strong\u003e \u003cstrong\u003eA\u003c/strong\u003e, Schematic representation of the N6AMT1-overexpressing xenograft BC mouse model and treatment regimen. \u003cstrong\u003eB\u003c/strong\u003e, Representative images of the xenograft tumors showed that N6AMT1 overexpression increased tumor sensitivity to tamoxifen in vivo. \u003cstrong\u003eC-D\u003c/strong\u003e, Growth curves (\u003cstrong\u003eC\u003c/strong\u003e) and weight (\u003cstrong\u003eD\u003c/strong\u003e) of subcutaneous tumors in each group were analyzed. \u003cstrong\u003eE\u003c/strong\u003e, Representative IHC images of N6AMT1, NEDD4L, p110α and p-AKT (T308) in subcutaneous tumors. Scale bar = 100 μm (high magnification). \u003cstrong\u003eF\u003c/strong\u003e, Expression of p110α and p-AKT (T308) were compared according to IHC score. \u003cstrong\u003eG\u003c/strong\u003e, Schematic representation of the N6AMT1 stable knockdown xenograft BC mouse model and treatment regimen. \u003cstrong\u003eH\u003c/strong\u003e, Representative images of the xenograft tumors showed that the combination of tamoxifen and the p110α inhibitor A66 inhibited tumor growth compared to tamoxifen or A66 monotherapy in vivo. \u003cstrong\u003eI-J\u003c/strong\u003e, Growth curves (\u003cstrong\u003eI\u003c/strong\u003e) and weight (\u003cstrong\u003eJ\u003c/strong\u003e) of subcutaneous tumors in each group were analyzed. \u003cstrong\u003eK\u003c/strong\u003e, Representative IHC images of N6AMT1, NEDD4L, p110α and p-AKT (T308) in subcutaneous tumors. Scale bar = 100 μm (high magnification). \u003cstrong\u003eL\u003c/strong\u003e, Expression of p-AKT (T308) in different groups were compared according to IHC score. *, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05; ***, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figures5.png","url":"https://assets-eu.researchsquare.com/files/rs-4738749/v1/d1db0bcbd9f1263c8aa97b1f.png"},{"id":62657071,"identity":"1de5c690-c581-4bcb-93ab-ce19b77b64ee","added_by":"auto","created_at":"2024-08-17 02:06:05","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2282977,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eN6AMT1-p110α pathway in clinical samples and patient-derived organoids.\u003c/strong\u003e \u003cstrong\u003eA-C\u003c/strong\u003e, Representative IHC images of N6AMT1, NEDD4L and p110α in tamoxifen-sensitive patients and tamoxifen-resistant patients of the Tam-SUMCCH cohort. Scale bar = 1 mm (low magnification) and 100 μm (high magnification). \u003cstrong\u003eD\u003c/strong\u003e, Expression of FOXA1, N6AMT1, NEDD4L and p110α in the Tam-SUMCCH cohort\u003cstrong\u003e \u003c/strong\u003ewere compared according to the IHC score.\u003cstrong\u003e E-G\u003c/strong\u003e,\u003cstrong\u003e \u003c/strong\u003eCorrelation between N6AMT1 and NEDD4L expression, N6AMT1 and p110α expression, and FOXA1 and N6AMT1 expression in the Tam-SUMCCH cohort. \u003cstrong\u003eH-I\u003c/strong\u003e, Kaplan-Meier analysis of the recurrence-free survival of luminal breast cancer patients in the Tam-SUMCCH cohort with high or low N6AMT1/p110α expression. \u003cstrong\u003eJ\u003c/strong\u003e,\u003cstrong\u003e L\u003c/strong\u003e, Schematic diagram of the establishment of patient-derived organoids (PDOs) from tamoxifen-resistant breast cancer tissues in the Tam-SUMCCH cohort (up), and 3D-organoid drug response assays of patient-derived organoids treated with the corresponding agent (down). Case 1 (\u003cstrong\u003eJ\u003c/strong\u003e), chest wall metastases. Case 2 (\u003cstrong\u003eL\u003c/strong\u003e), liver metastases. Red arrows indicate the location of the metastases. \u003cstrong\u003eK\u003c/strong\u003e, \u003cstrong\u003eM\u003c/strong\u003e, Calcein-AM and PI can effectively mark live or dead cells in patient-derived tumor organoids from Case 1 (\u003cstrong\u003eK\u003c/strong\u003e) and Case 2 (\u003cstrong\u003eM\u003c/strong\u003e) after different treatments. Images obtained by continuous screening of the same well from multiple layers were used to make a stacked image. Scale bar = 50 μm. ns, non-significant; *, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05; ***, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figures6.png","url":"https://assets-eu.researchsquare.com/files/rs-4738749/v1/2b46b515c788b5b067e0a3f8.png"},{"id":70442574,"identity":"56ed9cd2-d2cc-4d4e-8983-0e11a71c5dea","added_by":"auto","created_at":"2024-12-03 08:10:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":10901247,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4738749/v1/a2e32c31-47ea-485d-84bc-df1619567e58.pdf"},{"id":62656262,"identity":"0b4acdfc-635b-4e4c-af52-b8ef9a99f34e","added_by":"auto","created_at":"2024-08-17 01:58:05","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":492058,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryfigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-4738749/v1/b90cd7d2bf122095d55533e9.docx"},{"id":62656267,"identity":"561001f3-7adb-41e8-814f-a952bc304ef7","added_by":"auto","created_at":"2024-08-17 01:58:05","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":28165,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymaterialsandmethods.docx","url":"https://assets-eu.researchsquare.com/files/rs-4738749/v1/995b3a818c45e95c7a2cebfb.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.","formattedTitle":"Reversal of endocrine resistance via N6AMT1-NEDD4L pathway-mediated p110α degradation","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eBreast cancer has become the preeminent malignancy, accounting for the highest mortality rate among women globally \u003csup\u003e1\u003c/sup\u003e. Approximately 70% of breast cancer cases are categorized as luminal-type (ER+), rendering them amenable to endocrine therapy \u003csup\u003e2\u003c/sup\u003e. Among the foremost treatments is tamoxifen (Tam), a first-line endocrine therapy and a discerning modulator of estrogen receptors. Despite initial favorable responses to Tamoxifen treatment, approximately 30% of ER\u0026thinsp;+\u0026thinsp;patients acquire resistance to the therapy, culminating in local recurrence and distant metastases. \u003csup\u003e3\u003c/sup\u003e. Delineation of the molecular mechanisms of Tam resistance can be expected to provide promising therapeutic targets to overcome Tam resistance in breast cancer.\u003c/p\u003e \u003cp\u003eOur previous study found elevated levels of N6AMT1 in luminal BC cell lines \u003csup\u003e4\u003c/sup\u003e, prompting us to explore its potential role and significance in tamoxifen resistance within luminal BC. As a histone lysine methyltransferase, N6AMT1 plays a paramount role in gene transcription, modifying Lys12 of histone H4 \u003csup\u003e5\u003c/sup\u003e. Furthermore, acting as a glutamine methyltransferase, N6AMT1 regulates eukaryotic translation by modifying Gln185 of eRF1 \u003csup\u003e6,7\u003c/sup\u003e. Although a previous study has proposed that N6AMT1 is a methyltransferase for 6mA in the human genome, and 6mA is significantly enriched in exon-coding regions \u003csup\u003e8\u003c/sup\u003e, recent research has debunked this notion by demonstrating the absence of DNA binding capability and DNA MTase activity in N6AMT1 \u003csup\u003e7,9\u003c/sup\u003e. Notably, N6AMT1 exhibits an oncogenic function in various tumor types, including glioblastoma \u003csup\u003e10\u003c/sup\u003e, colorectal cancer \u003csup\u003e11\u003c/sup\u003e, and non-small cell lung cancer \u003csup\u003e12\u003c/sup\u003e. Additionally, it has been established that N6AMT1 regulates olaparib resistance in triple-negative breast cancer \u003csup\u003e13\u003c/sup\u003e. However, the connections between N6AMT1 and tamoxifen resistance are still largely unexplored.\u003c/p\u003e \u003cp\u003eTamoxifen resistance is characterized by intricate mechanisms, with excessive activation of the PI3K/AKT pathway emerging as a prominent contributing factor \u003csup\u003e14\u0026ndash;16\u003c/sup\u003e. Within this pathway, the catalytic unit of PI3K, known as p110α and encoded by \u003cem\u003ePIK3CA\u003c/em\u003e, plays a pivotal role. \u003cem\u003ePIK3CA\u003c/em\u003e mutations are detected in roughly 30% of ER\u0026thinsp;+\u0026thinsp;breast cancers, triggering hyperactivation of the PI3K/AKT pathway \u003csup\u003e17\u003c/sup\u003e. However, the presence of \u003cem\u003ePIK3CA\u003c/em\u003e mutations alone fails to explain tamoxifen resistance in patients lacking such mutations. It is worth mentioning that previous studies on tamoxifen resistance have predominantly focused on \u003cem\u003ePIK3CA\u003c/em\u003e mutations rather than the expression of p110α itself. However, mechanisms underlying tamoxifen resistance in the absence of p110α mutation remain largely unknown.\u003c/p\u003e \u003cp\u003eIn this study, we explored the role of N6AMT1 in BC tamoxifen resistance. We demonstrated that N6AMT1 was a potential biomarker of luminal BC and transcriptionally regulated by FOXA1. Notably, suppressing N6AMT1 resulted in the inhibition of NEDD4L-mediated degradation of p110α, consequently activating the PI3K/AKT signaling pathway and ultimately leading to tamoxifen resistance. It advances our understanding on the functions of N6AMT1, thus providing potential therapeutic targets for tackling tamoxifen resistance.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCells\u003c/h2\u003e \u003cp\u003eHuman breast cancer MCF-7, T47D, SKBR3, HCC-1937, MDA-MB-231, MDA-MB-468 and BT-549 cell lines were acquired from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China) and cultured according to the recommended guidelines. Tamoxifen-resistant MCF-7 (MCF-7\u003csup\u003eTamR\u003c/sup\u003e) and T47D (T47D\u003csup\u003eTamR\u003c/sup\u003e) cells were generated after being exposed to 5 \u0026micro;M 4-hydroxytamoxifen (Tam, Cat#H6278, Sigma-Aldrich) for over six months, followed by continuous culture in 0.1 \u0026micro;M Tam to maintain tamoxifen resistance. The authenticity of each cell line was confirmed, and they were tested for mycoplasma contamination.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eClinical specimen collection\u003c/h2\u003e \u003cp\u003eThis study enrolled 153 primary luminal breast cancer patients who received tamoxifen treatment following surgery at the Cancer Hospital of Shantou University Medical College (SUMCCH), China, between 2012\u0026ndash;2017. The characteristics of patients are summarized in the Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Surgical specimens were collected and processed using standard formalin fixation and paraffin embedding methods. Molecular subtypes were identified using immunohistochemistry (IHC), with fluorescence in situ hybridization (FISH) used to confirm HER2 status in cases with intermediate positive IHC results. To assess N6AMT1 expression, another 90 breast cancer specimens from various subtypes were also collected. Informed consent was obtained from all patients and we had access to their complete clinicopathological and follow-up data. All experiments were conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of SUMCCH (No. \u003cb\u003e2022-022\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eImmunohistochemistry (IHC) staining\u003c/h2\u003e \u003cp\u003eThe tissue slides were heated at 60\u0026deg;C for 30 minutes, deparaffinized in xylene and rehydrated using gradient ethanol. Antigen retrieval was performed using 0.01 M citrate buffer (pH 6.0) for 30 minutes after blocking endogenous peroxidase activity with 3% hydrogen peroxide for 10 minutes. The slides were then blocked with 5% BSA before being incubated with primary antibodies overnight at 4\u0026deg;C. Following this, the slides were incubated with HRP-conjugated secondary antibodies for 30 minutes at room temperature and stained using the diaminobenzidine (DAB) substrate (Biosharp, China). Hematoxylin was used as a counterstain. For observation, five random fields were selected under a light microscope at 100\u0026times; or 400\u0026times; magnification. The percentage of positive staining was graded from 0 to 10, with 0 indicating no staining and 10 indicating 100% of cells being positively stained \u003csup\u003e18\u003c/sup\u003e. The staining intensity was scored as 1+, 2+, or 3\u0026thinsp;+\u0026thinsp;for weak, moderate, or strong staining, respectively. The percentage (P) and intensity (I) of positive cells were multiplied to obtain a numerical score (S\u0026thinsp;=\u0026thinsp;P\u0026times;I). Hematoxylin-eosin (H\u0026amp;E) staining was performed for morphological examination. Two independent pathologists evaluated the results. Low and high expression were defined as scores of \u0026lt;\u0026thinsp;10 and \u0026ge;\u0026thinsp;10, respectively. The antibodies used are shown in Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e6mA-IP-qPCR\u003c/h2\u003e \u003cp\u003eGenomic DNA was sonicated to 200\u0026ndash;500 bp and subsequently denatured at 95 ℃ for 5 mins. After pre-clearing with magnetic beads (MCE), 1 mg of the fragmented DNA was incubated with 20 \u0026micro;g of anti-6mA antibody or normal rabbit IgG in IP buffer (1 mM sodium phosphate buffer, pH 7.0, 0.14 M NaCl, 0.05% Triton X-100) for 2 h at 4 ℃. Antibody-bound DNA was collected by incubating with 50 \u0026micro;L of magnetic beads overnight at 4\u0026deg;C on a rotating wheel, followed by four washes with IP buffer. The DNA was then recovered in 200 \u0026micro;L of digestion buffer (50 mM Tris-HCl, pH 8.0, 10 mM EDTA, pH 8.0, 0.5% SDS, 40 \u0026micro;g proteinase K) and incubated for 2 h at 56\u0026deg;C with intermittent mixing by vortexing. Recovered DNA was purified using a QIAquick PCR Purification Kit (QIAGEN) and quantified by qPCR. The ratio of exon-coding regions of \u003cem\u003eNEDD4L\u003c/em\u003e and \u003cem\u003ePIK3CA\u003c/em\u003e in 6mA-IP group to input was normalized to IgG group and then evaluated to determine the 6mA abundance. Primers were designed according to [G/C]AGG[C/T], the most significantly associated motif with 6mA modification \u003csup\u003e8\u003c/sup\u003e. The antibodies and primers used are shown in Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e-3.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eChromatin immunoprecipitation (ChIP) assay\u003c/h2\u003e \u003cp\u003eChIP assays were conducted using a ChIP assay kit (Beyotime, China) in accordance with the manufacturer's guidelines. For cell collection, 2\u0026times;10\u003csup\u003e7\u003c/sup\u003e cells were collected by centrifugation and washed twice with phosphate-buffered saline (PBS). DNA was cross-linked using 1% formaldehyde at room temperature. The precipitate was washed and then treated with lysis buffer to extract the nuclear content. The fragmented DNA was subsequently subjected to immunoprecipitation with either specific or nonspecific antibodies. Finally, the immunoprecipitated DNA was evaluated relative to control IgG by qPCR. The antibodies and primers used are shown in Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e-3.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eXenograft studies\u003c/h2\u003e \u003cp\u003eFour-week-old female SCID Beige mice were procured from Beijing Vital River Laboratories Animal Technology to generate xenograft models of N6AMT1-overexpressing (MCF-7\u003csup\u003eTamR\u003c/sup\u003e-oeN6AMT1 and MCF-7\u003csup\u003eTamR\u003c/sup\u003e-VC) or N6AMT1 stable knockdown (MCF-7-shNC and MCF-7-shN6AMT1) breast cancer cells. The mice were implanted with β-estradiol pellets (0.72 mg/pellet, 60-day release, Innovative Research, USA) in the back of their necks. After two days, 5\u0026times;10\u003csup\u003e6\u003c/sup\u003e corresponding cells resuspended in a 100 \u0026micro;L 1:1 mixture of PBS (Gibco BRL) and Matrigel (BD Biosciences, USA) were subcutaneously injected into the mammary glands of the mice. Once tumors reached roughly 200 mm\u003csup\u003e3\u003c/sup\u003e, the mice were randomly divided into four groups based on their cell types and treatment. For the N6AMT1-overexressing xenograft models, two groups received intraperitoneal injections of 100\u0026micro;L of 1 mg/kg Tam in corn oil (Sigma-Aldrich) QD for five days a week, while the other two groups received vehicle control injections. For the N6AMT1 stable knockdown xenograft models, one group received intraperitoneal injections of 1 mg/kg Tam for monotherapy, another group received 100 mg/kg A66 for monotherapy, a third group received combination therapy of Tam and A66, and a fourth group received vehicle control injections. Tumor size was monitored every five days and volume was determined using the formula: Volume\u0026thinsp;=\u0026thinsp;0.5*(length*width*width). After 6-week treatment, mice were sacrificed and tumors were harvested. All animal experiments adhered to the ARRIVE guidelines and were conducted in accordance with the National Research Council's Guide for the Care and Use of Laboratory Animals. The protocols were approved by the Laboratory Animal Ethics Committee of Shantou University Medical College. (No. \u003cb\u003e2022-077\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePatient-derived organoid (PDO) culture\u003c/h2\u003e \u003cp\u003eThe Ethics Committee of SUMCCH approved this research (No. \u003cb\u003e2022-022\u003c/b\u003e), and both patients provided informed consent. Tissue preparation and organoid culture were performed as described previously \u003csup\u003e19\u003c/sup\u003e with minor modification. PDOs were seeded in 50 \u0026micro;L Matrigel (Cat#3536-001-02, R\u0026amp;D Systems) into 24-well plates (4 drops per 50 \u0026micro;L Matrigel). Breast cancer organoid medium (BOM, purchased from PreceDo Pharmaceuticals, Hefei, China, Cat# PRS-BCM-3D) was added and replaced every four days. After 2\u0026ndash;3 weeks of culture, the number of breast cancer organoids were counted and passaged.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eDatabase analysis\u003c/h2\u003e \u003cp\u003eBreast Cancer Gene-Expression Miner v4.9 (bc-GenExMiner v4.9) and cBioPortal databases were employed to assess the expression levels of N6AMT1 in various subtypes of breast cancer, as well as their correlation with ER, FOXA1 and NEDD4L. Additionally, ChIPBase and PROMO were utilized to analyze potential transcription factors of \u003cem\u003eN6AMT1\u003c/em\u003e. The JASPAR database was employed to identify potential FOXA1 binding sites in the \u003cem\u003eN6AMT1\u003c/em\u003e promoter. The ChIP-Atlas database was employed to identify binding peaks of FOXA1 in the promoter regions of the \u003cem\u003eN6AMT1\u003c/em\u003e loci.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eEach experiment conducted in this study was repeated at least three times. Student\u0026rsquo;s t-tests were used to analyze comparisons between two groups, while one-way or two-way analysis of variance (ANOVA) followed by Dunnett\u0026rsquo;s test was employed to analyze comparisons among multiple groups. The two-sided Student t test was used to determine correlations between two groups. The two-sided Spearman test was used to determine correlations between the expression of two proteins. Survival curves were generated using the Kaplan-Meier method and a log-rank test was employed for comparison. Univariate and multivariate Cox proportional hazard regression models were used to assess survival risk, employing a forward stepwise procedure. The association between N6AMT1/p110α expression and clinical characteristics was analyzed using Chi-square tests. Fisher's exact test was employed when more than one-fifth of the cells had an expected frequency of less than 5. Measurable data are presented as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). A two-sided \u003cem\u003ep\u003c/em\u003e-value of less than 0.05 was considered to indicate statistical significance. Significance levels are represented by asterisks (ns, non-significant; *, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ***, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eN6AMT1 is highly expressed in luminal breast cancer\u003c/h2\u003e \u003cp\u003eWe previously reported that N6AMT1 is elevated in luminal-type BC cell lines \u003csup\u003e4\u003c/sup\u003e. Given this, we sought to explore its potential role in tamoxifen sensitivity in luminal BC. Initially, we used western blot analysis to examine N6AMT1 expression across various BC cell lines and found pronounced expression in luminal-type (ER+) BC cell lines, specifically in MCF-7 and T47D cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Using the bc-GenExMiner and cBioPortal databases, we not only confirmed the high mRNA (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB-E) and protein (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF) expression levels of N6AMT1 in luminal BC, but also discerned a direct correlation with ESR1 (ER) mRNA expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG). We further analyzed the correlation between N6AMT1 and ER from TCGA BC samples based on the status of ER and progesterone receptor (PR). We found that in ER\u0026thinsp;+\u0026thinsp;patients, N6AMT1 was positively correlated with ER expression, regardless of PR status (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eH-I). Conversely, in ER- patients, no significant correlation was observed between N6AMT1 and ER expression, regardless of PR status (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA-B). Using the Single Cell Portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://singlecell.broadinstitute.org/single_cell\u003c/span\u003e\u003cspan address=\"https://singlecell.broadinstitute.org/single_cell\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), we visualized the mRNA expression of N6AMT1 in epithelial cells from different BC subtypes \u003csup\u003e20\u003c/sup\u003e, revealing higher N6AMT1 expression in luminal A subtype epithelial cells. Additionally, we assessed 90 BC samples from SUMCCH cases, divided equally among luminal, HER2-overexpressing, and triple-negative types. Through immunohistochemistry (IHC), we observed a notable upregulation of N6AMT1 protein in the luminal BC samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eK-L). In conclusion, our collective findings strongly suggest N6AMT1 as a potential biomarker for luminal BC, paving the way for further research into its clinical roles.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eN6AMT1 is transcriptionally regulated by FOXA1\u003c/h2\u003e \u003cp\u003eWe observed a significant decrease in N6AMT1 mRNA and protein expression levels in tamoxifen-resistant (TamR) cells compared to the parental (tamoxifen-sensitive, TamS) cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eM-N). Additionally, N6AMT1 downregulation was evident in patients post neo-adjuvant tamoxifen treatment (GSE147271) and in Tam-treated normal human mammary epithelial cells (GSE106891) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eO). To elucidate the upstream regulator of N6AMT1 expression in luminal BC, we mined the ChIPBase and PROMO databases for potential transcription factors (TFs) associated with N6AMT1. A significant overlap was found for three factors\u0026mdash;C/EBPβ, FOXA1, and AR\u0026mdash;among the 49 TFs from ChIPBase and 43 TFs from PROMO (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). When examining the expression of these potential regulators, only FOXA1 mRNA levels displayed a notable reduction in TamR cells compared to TamS cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). A corresponding downregulation of FOXA1 protein expression in TamR cells was confirmed via western blotting (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Transient FOXA1 knockdown in MCF-7 and T47D cells led to decreased N6AMT1 mRNA and protein levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe JASPAR database identified two possible FOXA1 binding sites within the N6AMT1 promoter region, located 1 kb upstream of its transcription start site (TSS) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). ChIP-qPCR assays confirmed FOXA1's stronger binding affinity to the second putative site compared to the first, with both MCF-7 and T47D cells showing significant differences compared to the normal IgG control (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF-G), indicating direct binding between FOXA1 and the N6AMT1 promoter. We then cloned the N6AMT1 promoter and mutated the two potential FOXA1 binding sites to generate pGL3-N6-Mut1 or pGL3-N6-Mut2 luciferase reporter genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH). The luciferase assay showed that transient knockdown of FOXA1 (si-FOXA1) led to a reduction in luciferase expression for both pGL3-N6-WT and pGL3-N6-Mut1, while the inhibitory effect of si-FOXA1 was not significant for pGL3-N6-Mut2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eI). Similarly, the F-Luc/R-luc ratios of pGL3-N6-WT and pGL3-N6-Mut1 were higher in TamS cells than in TamR cells, whereas this effect was reduced for pGL3-N6-Mut2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eJ). Furthermore, using the bc-GenExMiner database, we noted a robust positive correlation between FOXA1 and N6AMT1 mRNA expression in ER\u0026thinsp;+\u0026thinsp;breast cancer patients rather than in ER- patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eK). We also analyzed the ChIP-seq results of FOXA1 protein in the ChIP-Atlas database and found several binding peaks of FOXA1 in the promoter regions of the \u003cem\u003eN6AMT1\u003c/em\u003e loci (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eL). Collectively, our data underscores the potential role of FOXA1 in the transcriptional regulation of N6AMT1 in luminal BC cells. FOXA1 likely enhances N6AMT1 transcription by directly binding to its proximal promoter site.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eN6AMT1 regulates tamoxifen resistance via the PI3K/AKT pathway\u003c/h2\u003e \u003cp\u003eA previous study indicated an association between reduced luminal BC biomarker expression and tamoxifen resistance \u003csup\u003e21\u003c/sup\u003e. In this context, the role of N6AMT1 in mediating tamoxifen resistance was explored. We stably knocked down N6AMT1 in MCF-7 and T47D cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA) and conducted a series of in vitro drug response assays. Stable knockdown of N6AMT1 conferred tamoxifen resistance to BC cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Conversely, overexpressing N6AMT1 in MCF-7\u003csup\u003eTamR\u003c/sup\u003e and T47D\u003csup\u003eTamR\u003c/sup\u003e cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC) enhanced tamoxifen sensitivity compared to vector control (VC) cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). It has been postulated that tamoxifen resistance is linked to hyperactivation of the PI3K/AKT signaling pathway \u003csup\u003e15,22\u003c/sup\u003e. Our western blot analysis of our TamS and TamR cells similarly indicates increased levels of p110α (the catalytic subunit of PI3K) and phosphorylated AKT at T308 (p-AKT (T308)) in TamR versus TamS cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). Western blot analysis of N6AMT1-modulated cells revealed that N6AMT1 knockdown upregulated p110α and p-AKT (T308) expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF), whereas N6AMT1 overexpression attenuated their expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). To ascertain the role of PI3K/AKT signaling in N6AMT1-mediated tamoxifen resistance, we treated N6AMT1-overexpressing or -knockdown cells, alongside their controls, with tamoxifen with or without the p110α inhibitor A66. Notably, A66 reinstated tamoxifen sensitivity in N6AMT1 knockdown cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH-I) and N6AMT1-overexpressing TamR cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eJ-K). Cell viability was also assessed by EdU assays (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eL-M). These results collectively posit that N6AMT1 potentially modulates tamoxifen resistance via the PI3K/AKT signaling pathway.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eN6AMT1 regulates p110α expression alteration through NEDD4L induction\u003c/h2\u003e \u003cp\u003eThe above results indicated an association between PI3K/AKT pathway activity and p110α expression. To determine the molecular mechanisms that dictate changes in p110α expression associated with N6AMT1 levels, we carried out 6mA-IP-qPCR on several cells: TamS, TamR, and those with stable knockdown and overexpression of N6AMT1. Our data showed no significant differences in 6mA modification levels in the exon-coding regions of \u003cem\u003ePIK3CA\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Additionally, RT-qPCR showed no significant differences in PIK3CA mRNA levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). These findings indicate that the changes of p110α expression associated with N6AMT1 are unrelated to 6mA modification or transcriptional regulation of \u003cem\u003ePIK3CA\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further investigate the mechanism underlying N6AMT1-related p110α expression changes, we treated N6AMT1-overexpressing cells with either cycloheximide (CHX) to block protein translation, or MG-132 to inhibit proteasome activity. Western blot analysis revealed that MG-132, but not CHX, reversed the reduction of p110α expression caused by N6AMT1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC), indicating a potential role for N6AMT1 in modulating protein stability or post-translational modifications, rather than translation. To support this, we examined the half-life of p110α in N6AMT1 stable knockdown cells and control cells when treated with MG-132. Interestingly, there were no significant differences in the half-life of p110α between the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eBuilding on prior work identifying NEDD4L as the E3 ligase for p110α \u003csup\u003e23\u003c/sup\u003e, we investigated its potential involvement in N6AMT1-related alteration of p110α expression. We first conducted co-immunoprecipitation (co-IP) assays, which showed that NEDD4L could interact with p110α (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). N6AMT1 silencing sharply reduced the ubiquitination levels of p110α, while N6AMT1 overexpression increased the ubiquitination levels of p110α (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). Subsequently, RT-qPCR revealed that mRNA levels of NEDD4L were downregulated in N6AMT1 stable knockdown cells and TamR cells, while they were upregulated in N6AMT1-overexpressing cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG). These findings were further supported by western blot analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH-J). To explore how N6AMT1 regulates the expression of NEDD4L, we first conducted 6mA-IP-qPCR on the cells mentioned above. Our data showed no significant differences in 6mA modification levels in the exon-coding regions of \u003cem\u003eNEDD4L\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eK). Subsequently, we conducted co-IP assays in cells before and after knockdown of N6AMT1. The results indicated that knockdown of N6AMT1 led to a decrease in monomethylation levels of histone H4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eL). Further, ChIP-qPCR assays demonstrated that N6AMT1 may bind to the promoter region of NEDD4L (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eM). These findings suggest that N6AMT1 may act as a lysine methyltransferase, particularly for H4K12me1 \u003csup\u003e5\u003c/sup\u003e, to regulate the expression of NEDD4L. Besides, we also observed a positive correlation between N6AMT1 and NEDD4L in ER\u0026thinsp;+\u0026thinsp;breast cancer patients from bc-GenExMiner database (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eN).\u003c/p\u003e \u003cp\u003eNext, we performed RT-qPCR, western blot, and drug response assays on cells with transient knockdown or overexpression of NEDD4L. Transient knockdown of NEDD4L led to upregulation of p110α and p-AKT (T308) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eP) without upregulating PIK3CA mRNA levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eO, Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eC), resulting in decreased sensitivity of breast cancer cells to tamoxifen (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eQ, Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eD). Conversely, overexpression of NEDD4L led to a significant downregulation of p110α and p-AKT (T308) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eS) without downregulation of PIK3CA mRNA levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eR, Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eE), thereby increasing the sensitivity of breast cancer cells to tamoxifen (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eT, Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eF). Cell viability was also assessed by EdU assays (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eU-V). In summary, our results suggest that N6AMT1 decreases p110α expression through transcriptional upregulation of the p110α E3 ligase NEDD4L.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eN6AMT1-p110α pathway regulates tamoxifen resistance in vivo\u003c/h2\u003e \u003cp\u003eTo delineate the role of N6AMT1 in modulating tamoxifen sensitivity in vivo, we utilized a xenograft BC mouse model. Specifically, MCF-7\u003csup\u003eTamR\u003c/sup\u003e-VC cells or MCF-7\u003csup\u003eTamR\u003c/sup\u003e-oeN6AMT1 cells were implanted into the mammary fat pads of female nude mice, followed by administration of either vehicle control or tamoxifen (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Overexpression of N6AMT1 markedly curtailed tamoxifen resistance, as evidenced by the significant differences in tumor sizes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB) and quantitatively substantiated by the tumor growth curves (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC) and tumor weight metrics (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). IHC analysis of the excised tumor samples further corroborated our findings at the molecular level. Elevated expression of NEDD4L was discerned in the oeN6AMT1 cohort compared to the control, concomitant with a noticeable downregulation of p110α and p-AKT (T308) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE-F). Further, MCF-7-shNC or MCF-7-shN6AMT1 cells were used for the establishment of xenograft tumor models (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG). Stable knockdown of N6AMT1 markedly reduced tamoxifen sensitivity, which was reversed by the combination of tamoxifen and the p110α inhibitor A66 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eH) and quantitatively substantiated by the tumor growth curves (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eI) and tumor weight metrics (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eJ). IHC analysis of the excised tumor samples further corroborated our findings at the molecular level. Downregulation of NEDD4L was discerned in the shN6AMT1 cohort, and a noticeable upregulation of p110α and p-AKT (T308) in vehicle and tam group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eK-L). Taken together, these results highlight the involvement of the N6AMT1-mediated pathway in tamoxifen resistance in vivo.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eN6AMT1-p110α pathway in clinical samples and patient-derived organoids\u003c/h2\u003e \u003cp\u003eTo discern the clinical relevance of the N6AMT1-p110α pathway in luminal BC, we assessed samples from 153 primary luminal BC patients treated post-operatively with tamoxifen at SUMCCH from 2012\u0026ndash;2017 (Tam-SUMCCH cohort). The patients were divided into two groups: the tamoxifen-sensitive group (n\u0026thinsp;=\u0026thinsp;116, 75.8%) and the tamoxifen-resistant group (n\u0026thinsp;=\u0026thinsp;37, 24.2%). N6AMT1 and NEDD4L expression were downregulated in the tamoxifen-resistant group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-B), while p110α was upregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Notably, FOXA1 tended to show decreased expression in the tamoxifen-resistant samples, although statistical significance was not reached (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs shown in Supplementary Table S5, low expression of N6AMT1 was significantly associated with lymph node metastasis (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u003c/b\u003e\u0026thinsp;0.001), distant metastasis (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.037), while high expression of p110α was significantly associated with lymph node metastasis (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Spearman\u0026rsquo;s correlation analysis highlighted a robust positive association between N6AMT1 and NEDD4L and a negative correlation between N6AMT1 and p110α (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE-F). Additionally, a positive correlation was noted between FOXA1 and N6AMT1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG). Kaplan-Meier analysis revealed that low N6AMT1 expression was associated with poorer recurrence-free survival (RFS) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eH). Moreover, high p110α expression was associated with poorer RFS (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eI), while the expression of FOXA1 and NEDD4L showed no correlation with RFS (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eG-H). Univariate and multivariate Cox proportional regression analyses identified independent factors for patients\u0026rsquo; RFS. Lymph node metastasis (N1: HR\u0026thinsp;=\u0026thinsp;2.584, 95% CI 1.266\u0026ndash;5.275, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009; N2 \u0026amp; N3: HR\u0026thinsp;=\u0026thinsp;3.319, 95% CI 1.033\u0026ndash;10.658, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.044), distant metastasis (M1: HR\u0026thinsp;=\u0026thinsp;2.668, 95% CI 1.058\u0026ndash;6.729, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.038), and high p110α expression (HR\u0026thinsp;=\u0026thinsp;2.290, 95% CI 1.027\u0026ndash;5.107, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.043) were identified as independent risk factors, while high N6AMT1 expression (HR\u0026thinsp;=\u0026thinsp;0.229, 95% CI 0.092\u0026ndash;0.573, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002) was an independent protective factor (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCox proportional hazard regression analyses for recurrence-free survival\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eClinical characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHazard ratio (95%CI\u003csup\u003ea\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHazard ratio (95%CI\u003csup\u003ea\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;50\u003c/p\u003e \u003cp\u003e51\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003cp\u003e0.799 (0.365\u0026ndash;1.747)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMenopause\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.608 (0.216\u0026ndash;1.720)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistology\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLobular carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.487 (0.067\u0026ndash;3.564)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther types\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.624 (0.498\u0026ndash;5.298)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDifferentiation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.426 (0.447\u0026ndash;4.546)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.609 (0.902\u0026ndash;7.547)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumor invasion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.236 (0.501\u0026ndash;3.049)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.953 (0.269\u0026ndash;3.377)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.456 (0.749\u0026ndash;8.053)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLymph node metastasis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.872 (1.428\u0026ndash;5.776)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.584 (1.266\u0026ndash;5.275)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN2 \u0026amp; N3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.142 (1.350-12.712)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.013\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.319 (1.033\u0026ndash;10.658)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.044\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDistant metastasis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.509 (1.877\u0026ndash;10.835)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.668 (1.058\u0026ndash;6.729)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.038\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFOXA1 expression\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.485 (0.251\u0026ndash;0.935)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.031\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.508 (0.656\u0026ndash;3.464)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eN6AMT1 expression\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.215 (0.106\u0026ndash;0.437)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.229 (0.092\u0026ndash;0.573)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNEDD4L expression\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.531 (0.273\u0026ndash;1.032)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ep110α expression\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.728 (1.757\u0026ndash;7.911)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.290 (1.027\u0026ndash;5.107)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.043\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003ea\u003c/sup\u003eConfidence interval.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eBold values indicate statistical significance (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eSupplementary Tables\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable S1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical characteristics of 153 human luminal BC tissue samples\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMenopause\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHistology\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTNM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDifferentiation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRFS (month)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e5\u003c/p\u003e \u003cp\u003e6\u003c/p\u003e \u003cp\u003e7\u003c/p\u003e \u003cp\u003e8\u003c/p\u003e \u003cp\u003e9\u003c/p\u003e \u003cp\u003e10\u003c/p\u003e \u003cp\u003e11\u003c/p\u003e \u003cp\u003e12\u003c/p\u003e \u003cp\u003e13\u003c/p\u003e \u003cp\u003e14\u003c/p\u003e \u003cp\u003e15\u003c/p\u003e \u003cp\u003e16\u003c/p\u003e \u003cp\u003e17\u003c/p\u003e \u003cp\u003e18\u003c/p\u003e \u003cp\u003e19\u003c/p\u003e \u003cp\u003e20\u003c/p\u003e \u003cp\u003e21\u003c/p\u003e \u003cp\u003e22\u003c/p\u003e \u003cp\u003e23\u003c/p\u003e \u003cp\u003e24\u003c/p\u003e \u003cp\u003e25\u003c/p\u003e \u003cp\u003e26\u003c/p\u003e \u003cp\u003e27\u003c/p\u003e \u003cp\u003e28\u003c/p\u003e \u003cp\u003e29\u003c/p\u003e \u003cp\u003e30\u003c/p\u003e \u003cp\u003e31\u003c/p\u003e \u003cp\u003e32\u003c/p\u003e \u003cp\u003e33\u003c/p\u003e \u003cp\u003e34\u003c/p\u003e \u003cp\u003e35\u003c/p\u003e \u003cp\u003e36\u003c/p\u003e \u003cp\u003e37\u003c/p\u003e \u003cp\u003e38\u003c/p\u003e \u003cp\u003e39\u003c/p\u003e \u003cp\u003e40\u003c/p\u003e \u003cp\u003e41\u003c/p\u003e \u003cp\u003e42\u003c/p\u003e \u003cp\u003e43\u003c/p\u003e \u003cp\u003e44\u003c/p\u003e \u003cp\u003e45\u003c/p\u003e \u003cp\u003e46\u003c/p\u003e \u003cp\u003e47\u003c/p\u003e \u003cp\u003e48\u003c/p\u003e \u003cp\u003e49\u003c/p\u003e \u003cp\u003e50\u003c/p\u003e \u003cp\u003e51\u003c/p\u003e \u003cp\u003e52\u003c/p\u003e \u003cp\u003e53\u003c/p\u003e \u003cp\u003e54\u003c/p\u003e \u003cp\u003e55\u003c/p\u003e \u003cp\u003e56\u003c/p\u003e \u003cp\u003e57\u003c/p\u003e \u003cp\u003e58\u003c/p\u003e \u003cp\u003e59\u003c/p\u003e \u003cp\u003e60\u003c/p\u003e \u003cp\u003e61\u003c/p\u003e \u003cp\u003e62\u003c/p\u003e \u003cp\u003e63\u003c/p\u003e \u003cp\u003e64\u003c/p\u003e \u003cp\u003e65\u003c/p\u003e \u003cp\u003e66\u003c/p\u003e \u003cp\u003e67\u003c/p\u003e \u003cp\u003e68\u003c/p\u003e \u003cp\u003e69\u003c/p\u003e \u003cp\u003e70\u003c/p\u003e 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\u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eOther types\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eOther types\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eLobular carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eLobular carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eOther types\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eOther types\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eOther types\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eLobular carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eLobular carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eLobular carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eOther types\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eOther types\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eLobular carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eLobular carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eOther types\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eOther types\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eLobular carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eT2N1M0\u003c/p\u003e \u003cp\u003eT2N1M0\u003c/p\u003e \u003cp\u003eT2N2M0\u003c/p\u003e \u003cp\u003eT2N2M1\u003c/p\u003e \u003cp\u003eT1N2M1\u003c/p\u003e \u003cp\u003eT4N2M0\u003c/p\u003e \u003cp\u003eT2N0M0\u003c/p\u003e \u003cp\u003eT2N1M0\u003c/p\u003e \u003cp\u003eT2N2M0\u003c/p\u003e \u003cp\u003eT2N1M0\u003c/p\u003e \u003cp\u003eT2N1M0\u003c/p\u003e \u003cp\u003eT4N0M0\u003c/p\u003e \u003cp\u003eT3N1M0\u003c/p\u003e \u003cp\u003eT4N0M0\u003c/p\u003e \u003cp\u003eT2N0M0\u003c/p\u003e \u003cp\u003eT4N1M0\u003c/p\u003e \u003cp\u003eT3N1M0\u003c/p\u003e \u003cp\u003eT1N0M0\u003c/p\u003e \u003cp\u003eT3N1M0\u003c/p\u003e \u003cp\u003eT2N1M0\u003c/p\u003e \u003cp\u003eT4N0M0\u003c/p\u003e \u003cp\u003eT2N2M1\u003c/p\u003e \u003cp\u003eT2N2M1\u003c/p\u003e \u003cp\u003eT1N0M0\u003c/p\u003e \u003cp\u003eT2N2M0\u003c/p\u003e \u003cp\u003eT2N0M0\u003c/p\u003e \u003cp\u003eT1N0M0\u003c/p\u003e \u003cp\u003eT1N0M0\u003c/p\u003e \u003cp\u003eT1N0M0\u003c/p\u003e \u003cp\u003eT3N0M0\u003c/p\u003e \u003cp\u003eT2N1M0\u003c/p\u003e \u003cp\u003eT2N1M0\u003c/p\u003e \u003cp\u003eT2N1M0\u003c/p\u003e \u003cp\u003eT2N0M0\u003c/p\u003e \u003cp\u003eT2N1M0\u003c/p\u003e \u003cp\u003eT2N0M0\u003c/p\u003e \u003cp\u003eT1N0M0\u003c/p\u003e \u003cp\u003eT1N0M0\u003c/p\u003e \u003cp\u003eT1N0M0\u003c/p\u003e \u003cp\u003eT2N1M0\u003c/p\u003e \u003cp\u003eT4N1M0\u003c/p\u003e \u003cp\u003eT3N1M0\u003c/p\u003e \u003cp\u003eT2N0M0\u003c/p\u003e \u003cp\u003eT1N0M0\u003c/p\u003e \u003cp\u003eT2N0M0\u003c/p\u003e \u003cp\u003eT2N0M0\u003c/p\u003e \u003cp\u003eT2N0M0\u003c/p\u003e \u003cp\u003eT4N0M0\u003c/p\u003e \u003cp\u003eT2N0M0\u003c/p\u003e \u003cp\u003eT3N1M0\u003c/p\u003e \u003cp\u003eT2N0M0\u003c/p\u003e \u003cp\u003eT2N0M0\u003c/p\u003e \u003cp\u003eT3N0M0\u003c/p\u003e \u003cp\u003eT3N0M0\u003c/p\u003e \u003cp\u003eT2N0M0\u003c/p\u003e \u003cp\u003eT2N0M0\u003c/p\u003e \u003cp\u003eT3N0M0\u003c/p\u003e \u003cp\u003eT2N0M0\u003c/p\u003e \u003cp\u003eT1N0M0\u003c/p\u003e \u003cp\u003eT2N0M0\u003c/p\u003e \u003cp\u003eT2N0M0\u003c/p\u003e \u003cp\u003eT1N0M0\u003c/p\u003e \u003cp\u003eT1N0M0\u003c/p\u003e \u003cp\u003eT4N0M0\u003c/p\u003e \u003cp\u003eT2N1M0\u003c/p\u003e \u003cp\u003eT2N1M0\u003c/p\u003e \u003cp\u003eT3N0M0\u003c/p\u003e \u003cp\u003eT4N0M0\u003c/p\u003e \u003cp\u003eT1N1M0\u003c/p\u003e \u003cp\u003eT2N0M0\u003c/p\u003e \u003cp\u003eT2N1M0\u003c/p\u003e \u003cp\u003eT2N0M0\u003c/p\u003e \u003cp\u003eT2N1M0\u003c/p\u003e \u003cp\u003eT2N0M0\u003c/p\u003e \u003cp\u003eT4N1M0\u003c/p\u003e \u003cp\u003eT3N1M0\u003c/p\u003e \u003cp\u003eT1N0M0\u003c/p\u003e \u003cp\u003eT2N0M0\u003c/p\u003e \u003cp\u003eT2N0M0\u003c/p\u003e \u003cp\u003eT2N1M0\u003c/p\u003e \u003cp\u003eT2N0M0\u003c/p\u003e \u003cp\u003eT1N0M1\u003c/p\u003e \u003cp\u003eT2N1M0\u003c/p\u003e \u003cp\u003eT2N0M0\u003c/p\u003e \u003cp\u003eT2N2M1\u003c/p\u003e \u003cp\u003eT2N0M0\u003c/p\u003e \u003cp\u003eT1N0M0\u003c/p\u003e \u003cp\u003eT1N0M0\u003c/p\u003e \u003cp\u003eT3N1M0\u003c/p\u003e \u003cp\u003eT2N0M0\u003c/p\u003e \u003cp\u003eT2N0M0\u003c/p\u003e \u003cp\u003eT2N1M0\u003c/p\u003e \u003cp\u003eT2N0M0\u003c/p\u003e \u003cp\u003eT2N0M0\u003c/p\u003e \u003cp\u003eT1N0M0\u003c/p\u003e \u003cp\u003eT2N1M0\u003c/p\u003e \u003cp\u003eT3N0M0\u003c/p\u003e \u003cp\u003eT3N0M0\u003c/p\u003e \u003cp\u003eT2N1M0\u003c/p\u003e \u003cp\u003eT3N1M0\u003c/p\u003e \u003cp\u003eT1N1M0\u003c/p\u003e \u003cp\u003eT2N0M0\u003c/p\u003e \u003cp\u003eT3N0M0\u003c/p\u003e \u003cp\u003eT3N1M0\u003c/p\u003e \u003cp\u003eT2N1M0\u003c/p\u003e \u003cp\u003eT2N1M0\u003c/p\u003e \u003cp\u003eT2N1M0\u003c/p\u003e \u003cp\u003eT2N1M0\u003c/p\u003e \u003cp\u003eT1N1M0\u003c/p\u003e \u003cp\u003eT2N0M0\u003c/p\u003e \u003cp\u003eT2N1M0\u003c/p\u003e \u003cp\u003eT2N1M0\u003c/p\u003e \u003cp\u003eT2N0M0\u003c/p\u003e \u003cp\u003eT2N0M0\u003c/p\u003e \u003cp\u003eT2N0M0\u003c/p\u003e \u003cp\u003eT2N0M0\u003c/p\u003e \u003cp\u003eT3N1M0\u003c/p\u003e \u003cp\u003eT2N0M0\u003c/p\u003e \u003cp\u003eT2N0M0\u003c/p\u003e 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\u003cp\u003e65\u003c/p\u003e \u003cp\u003e69\u003c/p\u003e \u003cp\u003e67\u003c/p\u003e \u003cp\u003e72\u003c/p\u003e \u003cp\u003e75\u003c/p\u003e \u003cp\u003e77\u003c/p\u003e \u003cp\u003e81\u003c/p\u003e \u003cp\u003e83\u003c/p\u003e \u003cp\u003e83\u003c/p\u003e \u003cp\u003e85\u003c/p\u003e \u003cp\u003e85\u003c/p\u003e \u003cp\u003e90\u003c/p\u003e \u003cp\u003e90\u003c/p\u003e \u003cp\u003e91\u003c/p\u003e \u003cp\u003e93\u003c/p\u003e \u003cp\u003e94\u003c/p\u003e \u003cp\u003e98\u003c/p\u003e \u003cp\u003e101\u003c/p\u003e \u003cp\u003e102\u003c/p\u003e \u003cp\u003e106\u003c/p\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDeath\u003c/p\u003e \u003cp\u003eDeath\u003c/p\u003e \u003cp\u003eDeath\u003c/p\u003e \u003cp\u003eDeath\u003c/p\u003e \u003cp\u003eDeath\u003c/p\u003e \u003cp\u003eDeath\u003c/p\u003e \u003cp\u003eDeath\u003c/p\u003e \u003cp\u003eDeath\u003c/p\u003e \u003cp\u003eDeath\u003c/p\u003e \u003cp\u003eDeath\u003c/p\u003e \u003cp\u003eDeath\u003c/p\u003e \u003cp\u003eDeath\u003c/p\u003e \u003cp\u003eDeath\u003c/p\u003e \u003cp\u003eDeath\u003c/p\u003e \u003cp\u003eDeath\u003c/p\u003e \u003cp\u003eDeath\u003c/p\u003e \u003cp\u003eDeath\u003c/p\u003e \u003cp\u003eDeath\u003c/p\u003e \u003cp\u003eDeath\u003c/p\u003e \u003cp\u003eDeath\u003c/p\u003e \u003cp\u003eDeath\u003c/p\u003e \u003cp\u003eDeath\u003c/p\u003e \u003cp\u003eDeath\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable S2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAntibodies used in this study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntibody\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCat. #\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eManufacturer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApplication \u0026amp; Dilution\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFOXA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003esc-101058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSanta Cruz\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWB (1:1000)\u003c/p\u003e \u003cp\u003eIHC (1:500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMouse\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFOXA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eab170933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAbcam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChIP (1:50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRabbit\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN6AMT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eabx005435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAbbexa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWB (1:1000)\u003c/p\u003e \u003cp\u003eIHC (1:200)\u003c/p\u003e \u003cp\u003eChIP (1:50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRabbit\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNEDD4L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4013S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCell Signaling Technology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWB (1:1000)\u003c/p\u003e \u003cp\u003eIP (1:50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRabbit\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNEDD4L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eab124643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAbcam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIHC (1:500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRabbit\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIK3CA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4249S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCell Signaling Technology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWB (1:1000)\u003c/p\u003e \u003cp\u003eIP (1:50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRabbit\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIK3CA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNBP2-19804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNovus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIHC (1:500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRabbit\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003et-AKT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4691S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCell Signaling Technology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWB (1:1000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRabbit\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep-AKT (T308)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13038S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAbcam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWB (1:1000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRabbit\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep-AKT (T308)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eab38449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAbcam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIHC (1:200)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRabbit\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ-Actin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003esc-47778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSanta Cruz\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWB (1:1000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMouse\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUbiquitin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003esc-8017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSanta Cruz\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWB (1:1000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMouse\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistone H4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2592S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCell Signaling Technology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWB (1:1000)\u003c/p\u003e \u003cp\u003eIP (1:50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRabbit\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMono-Methyl Lysine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14679S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCell Signaling Technology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWB (1:1000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRabbit\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-N6-methyladenosine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eABE572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSigma-Aldrich\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6mA-IP (1:50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRabbit\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-Mouse IgG (H\u0026thinsp;+\u0026thinsp;L) HRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZB-2305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eZSGB-BIO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWB (1:5000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGoat\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-Rabbit IgG (H\u0026thinsp;+\u0026thinsp;L) HRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZB-5301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eZSGB-BIO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWB (1:5000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGoat\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal Rabbit IgG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2729S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCell Signaling Technology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChIP (1:50)\u003c/p\u003e \u003cp\u003e6mA-IP (1:50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRabbit\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGTVisionTM Ⅰ Detection System/Mo\u0026amp;Rb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGK500510A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGene Tech\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIHC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGoat\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable S3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrimer sequences\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eName\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSequence (5\u0026rsquo;-3\u0026rsquo;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eApplication\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFOXA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eForward: GCAATACTCGCCTTACGGCT RT-qPCR\u003c/p\u003e \u003cp\u003eReverse: TACACACCTTGGTAGTACGCC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN6AMT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eForward: GCAGGGGAGAACTTCGCTAC RT-qPCR\u003c/p\u003e \u003cp\u003eReverse: CAGCGCGTTCAAAAGCAGAAA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNEDD4L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eForward: GACATGGAGCATGGATGGGAA RT-qPCR\u003c/p\u003e \u003cp\u003eReverse: GTTCGGCCTAAATTGTCCACT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIK3CA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward: CCACGACCATCATCAGGTGAA\u003c/p\u003e \u003cp\u003eReverse: CCTCACGGAGGCATTCTAAAGT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRT-qPCR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC/EBPβ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward: CTTCAGCCCGTACCTGGAG\u003c/p\u003e \u003cp\u003eReverse: GGAGAGGAAGTCGTGGTGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRT-qPCR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward: ACAGGAGGAAGGAGAGGCTT\u003c/p\u003e \u003cp\u003eReverse: GTTGTTGTCGTGTCCAGCAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRT-qPCR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ-Actin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward: CCTCGCCTTTGCCGATCC\u003c/p\u003e \u003cp\u003eReverse: CGCGGCGATATCATCATCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRT-qPCR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN6AMT1-site1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward: GTTTCATTGCGAAACAAATTTCAG\u003c/p\u003e \u003cp\u003eReverse: ACGCTACCCTCGTTATGTGAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChIP-qPCR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN6AMT1-site2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward: AGCAAATGTCAATAGTCACGGA\u003c/p\u003e \u003cp\u003eReverse: CCAGTGTGTTGTTGTTGGGTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChIP-qPCR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN6AMT1-blank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward: TCACATAACGAGGGTAGCGT\u003c/p\u003e \u003cp\u003eReverse: ACTGCTGAATTGGGGAGGCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChIP-qPCR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIK3CA-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward: GTATCTACCATGGAGGAGAACCC\u003c/p\u003e \u003cp\u003eReverse: CTGGGATTGGAACAAGGTACT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6mA-IP-qPCR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIK3CA-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward: TTGGCCTCCAATCAAACCTGA\u003c/p\u003e \u003cp\u003eReverse: AAGCACCGAACAGCAAAACC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6mA-IP-qPCR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIK3CA-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward: AGAGGTTTGGCCTGCTTTTG\u003c/p\u003e \u003cp\u003eReverse: TGAGCTTTTCCATTGCCTCG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6mA-IP-qPCR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNEDD4L-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward: TGTCAACCACAACAACCGGA\u003c/p\u003e \u003cp\u003eReverse: CCAGACCGAGAGAGTCTCCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6mA-IP-qPCR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNEDD4L-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward: TGAGGTCATGCAGTGTCACC\u003c/p\u003e \u003cp\u003eReverse: GTCTTTCACAGCCCGACGTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6mA-IP-qPCR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNEDD4L-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward: TTGCTGGTCTGGCCGTATTT\u003c/p\u003e \u003cp\u003eReverse: GAACCACTGAATGACGGGGT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6mA-IP-qPCR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGPER1-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward: TGGATGTGACTTCCCAAGCC\u003c/p\u003e \u003cp\u003eReverse: ACAAAGCCGATGGGGAAGAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6mA-IP-qPCR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGPER1-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward: TGCACCTTCATGTCGCTCTT\u003c/p\u003e \u003cp\u003eReverse: GACATCCGCGAAACAGAAGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6mA-IP-qPCR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGPER1-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward: ATGACCATCCCCGACCTGTA\u003c/p\u003e \u003cp\u003eReverse: GAGGAAGAAGACGCTGCTGT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6mA-IP-qPCR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNEDD4L-site1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward: ACCCAAGTAGGCACTTTGACG\u003c/p\u003e \u003cp\u003eReverse: CCCAGCTACCGTTTCTCTAGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChIP-qPCR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNEDD4L-site2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward: GAGAAACGGTAGCTGGGTGG\u003c/p\u003e \u003cp\u003eReverse: TGTGTCAACCTTGCAGGGAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChIP-qPCR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNEDD4L-site3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward: ACCATGCTGTCTGTTTTCGC\u003c/p\u003e \u003cp\u003eReverse: GACGAGCAGGACTTGGCTAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChIP-qPCR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNEDD4L-site4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward: ATGTAGCCAAGTCCTGCTCG\u003c/p\u003e \u003cp\u003eReverse: CCCGGTGGTCTTTTTACCGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChIP-qPCR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNEDD4L-site5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward: CGGTAAAAAGACCACCGGGA\u003c/p\u003e \u003cp\u003eReverse: GGTTTCCCTCCGCTTTAGGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChIP-qPCR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable S4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e\u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eName\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTarget sequence\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esiFOXA1-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCGACTGGAACAGCTACTA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esiFOXA1-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCACTGCAATACTCGCCTT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esiFOXA1-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCACTCGCTGTCCTTCAAT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esiNEDD4L-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCAAGAAGTCATAAGTCTCGAGTTAA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esiNEDD4L-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCACAAACAGTAACAACCATCTAAT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esiNEDD4L-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCATATGCGGTCAAAGACATCTTTAA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enon targeting siRNA (siNC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCGTACGCGGAATACTTCGA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eshN6AMT1-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGTTCACATTCAACCAGTTATT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eshN6AMT1-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGTTGATCTTCTGGTGTTTAAT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eshN6AMT1-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGTACATGTGCACTGATATCAA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScramble\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCTCCCAGGTTATGGGAGAAG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cb\u003eTarget sequences of siRNA and shRNA\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable S5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinicopathological correlation of N6AMT1 and p110α expression in luminal BC\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eClinical characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eN6AMT1 expression\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003ep110α expression\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLow (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHigh (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44 (38.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71 (61.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e46 (48.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e59 (51.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.325\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e51\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17 (44.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21 (55.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e22 (57.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e16 (42.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMenopause\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50 (38.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e79 (61.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e63 (48.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e66 (51.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.219\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11 (45.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13 (54.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15 (62.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9 (37.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistology\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuctal carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51 (37.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e85 (62.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e73 (53.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e63 (46.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLobular carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (62.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3 (37.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3 (37.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5 (62.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther types\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (55.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4 (44.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7 (22.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2 (77.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDifferentiation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13 (44.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16 (55.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13 (44.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e16 (55.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.758\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21 (38.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33 (61.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e28 (51.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e26 (48.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27 (38.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43 (61.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e37 (52.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e33 (47.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eT stage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14 (48.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15 (51.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16 (55.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e13 (44.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32 (35.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59 (64.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e51 (56.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e40 (44.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14 (66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8 (38.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e13 (61.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9 (75.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eN stage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24 (25.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e69 (74.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e61 (65.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e32 (34.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31 (60.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20 (39.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15 (29.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e36 (70.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN2 \u0026amp; N3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7 (77.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2 (22.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2 (22.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7 (77.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eM stage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56 (38.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e91 (61.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e0.037\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e77 (52.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e70 (47.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (83.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5 (83.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eBold values indicate statistical significance (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe also generated breast cancer organoids using samples obtained through percutaneous needle biopsy from two tamoxifen-resistant patients undergoing follow-up at SUMCCH. Western blot analysis of the biopsy specimens from both patients indicated that only Case 1 matched the N6AMT1-p110α axis profile from our study (N6AMT1 downregulation while p110α upregulation, Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eG). Subsequent 3D organoid drug response assays revealed significant growth suppression, in Case 1 organoids, upon combined tamoxifen and p110α inhibitor A66 treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eJ-K). In contrast, Case 2, which lacked the p110α expression, showed no significant response to the combined treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eL-M). In summary, our data suggest a potential role of the N6AMT1-p110α axis in modulating tamoxifen resistance in luminal BC.\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eReduced expression of luminal breast cancer biomarkers, including ER, FOXA1, and GATA3, has consistently correlated with tamoxifen resistance \u003csup\u003e21\u003c/sup\u003e. Our study elucidates a mechanism involving N6AMT1-mediated alteration of p110α expression in tamoxifen resistance. We show N6AMT1 is downregulated, in tamoxifen-resistant cells, in a manner transcriptionally regulated by FOXA1 (forkhead box A1). As a member of the forkhead box transcription factor family, FOXA1 acts as a pioneer factor, binding to silent chromatin target sites and dominantly initiating regulatory cascades \u003csup\u003e24\u003c/sup\u003e. Its significance is further underscored as a recognized biomarker for luminal breast cancer, where it modulates ER \u003csup\u003e25\u003c/sup\u003e and is pivotal in hormone responses, both during breast development and in ER-positive breast cancer scenarios \u003csup\u003e26\u003c/sup\u003e. Around 50% of ER binding sites overlap with FOXA1 binding sites \u003csup\u003e27\u003c/sup\u003e. Clinically, it has been observed that elevated FOXA1 expression in luminal A-type breast cancer patients is associated with enhanced survival rate and a more favorable response to endocrine therapies \u003csup\u003e18\u003c/sup\u003e. Conversely, low FOXA1 expression is linked with the maintenance of tumor cell stemness, an upsurge in IL-6 expression, and the mediation of tamoxifen resistance \u003csup\u003e28\u003c/sup\u003e. The FOXA1 promoter region contains estrogen response elements (EREs), suggesting that FOXA1 is a ER-regulated gene \u003csup\u003e29\u003c/sup\u003e. In this context, our results suggest that prolonged tamoxifen exposure could reduce FOXA1 expression by disrupting the intricate regulatory network of ER-associated genes, providing an explanation for the reduced FOXA1 levels in TamR cells. Subsequently, the observed downregulation of N6AMT1, that occurs as a result of decreased FOXA1 levels, leads to increased expression of p110α and concomitant tamoxifen resistance.\u003c/p\u003e \u003cp\u003ePrevious research has primarily focused on \u003cem\u003ePIK3CA\u003c/em\u003e mutation status, particularly in two hotspots: exon 9 (E542K and E545K) encoding the helix domain, and exon 20 (H1047R) encoding the kinase domain \u003csup\u003e30\u0026ndash;32\u003c/sup\u003e. These mutations, as evidenced by both in vitro and in vivo models, possess pronounced oncogenic potential \u003csup\u003e33\u0026ndash;36\u003c/sup\u003e. While a meta-analysis has correlated them with unfavorable survival outcomes \u003csup\u003e37\u003c/sup\u003e, a comprehensive analysis encompassing 10,319 breast cancer patients paints a different picture, associating \u003cem\u003ePIK3CA\u003c/em\u003e mutations with enhanced survival rates \u003csup\u003e38\u003c/sup\u003e. The activation of the PI3K/AKT pathway by \u003cem\u003ePIK3CA\u003c/em\u003e mutations is a well-documented mechanism contributing to tamoxifen resistance \u003csup\u003e14\u003c/sup\u003e. Yet, this fails to elucidate the resistance observed in patients devoid of \u003cem\u003ePIK3CA\u003c/em\u003e mutations. Our data posits a novel perspective: the downregulation of N6AMT1 potentially escalates the levels of p110α and p-AKT (T308), thereby attenuating tamoxifen sensitivity. Notably, our experiments indicate that N6AMT1 overexpression or stable knockdown, both in vitro and in vivo, inversely affects p110α and p-AKT (T308) levels to regulate tamoxifen sensitivity. In transgenic mouse models of breast cancer (MMTV and HER2/Neu), \u003cem\u003ePIK3CA\u003c/em\u003e knockout inhibits tumor growth \u003csup\u003e39\u003c/sup\u003e. Additionally, p110α overexpression might play a role in the resistance of Snu-5 gastric xenografts to tyrosine kinase MET inhibitors \u003csup\u003e40\u003c/sup\u003e. Collectively, these insights suggest that the surge in p110α levels not only amplifies PI3K activity but also activates the PI3K/AKT pathway. This could explain the observed correlation between altered p110α expression and the dynamics of the PI3K/AKT pathway in tamoxifen resistance in our study.\u003c/p\u003e \u003cp\u003eN6AMT1 primarily functions as a glutamine/lysine methyltransferase, modifying Gln185 of eRF1 to regulate translation in eukaryotes, and modifying Lys12 of histone H4 to regulate gene transcription \u003csup\u003e5,6\u003c/sup\u003e. Our 6mA-IP-qPCR assays showed no discernible shifts in 6mA levels within the exon-coding region of \u003cem\u003ePIK3CA\u003c/em\u003e, irrespective of the presence or absence of tamoxifen resistance and the reduced N6AMT1 expression. Additionally, RT-qPCR showed no significant changes in PIK3CA mRNA levels, suggesting that alterations in p110α expression is not associated with 6mA modification or transcriptional regulation, but rather with protein stability and post-translational modifications.\u003c/p\u003e \u003cp\u003ePrevious research has shown that NEDD4L, an E3 ligase, enhances p110α degradation through poly-ubiquitination and proteasomal degradation \u003csup\u003e23\u003c/sup\u003e. NEDD4L belongs to the HECT E3 ubiquitin ligase family and interacts with substrates via its four WW domains \u003csup\u003e41\u003c/sup\u003e. Dysregulation of NEDD4L is observed in various cancers, and reduced levels are associated with tumor development \u003csup\u003e42\u0026ndash;44\u003c/sup\u003e. In breast cancer cells, miR-106b-25 can downregulate NEDD4L, leading to increased NOTCH1 and activation of tumor-initiating cells \u003csup\u003e45\u003c/sup\u003e. Our study shows that N6AMT1 overexpression upregulates NEDD4L mRNA and protein levels, sensitizing breast cancer cells to tamoxifen. Conversely, N6AMT1 knockdown has the opposite effect. These findings suggest that N6AMT1 may regulate p110α via NEDD4L, highlighting the role of NEDD4L as a tumor suppressor in tamoxifen sensitivity. Although the role of N6AMT1 in the transcriptional regulation of NEDD4L remains to be fully elucidated, our 6mA-IP-qPCR assays showed no significant changes in the 6mA levels within the exon-coding regions of \u003cem\u003eNEDD4L\u003c/em\u003e after N6AMT1 overexpression or stable knockdown, while ChIP-qPCR assays revealed that N6AMT1 may bind to the promoter region of \u003cem\u003eNEDD4L\u003c/em\u003e and co-IP assays showed that knockdown of N6AMT1 led to a decrease in monomethylation levels of histone H4. Based on these, we propose that N6AMT1, as a KMT9, might regulate NEDD4L transcription through H4K12 monomethylation modification \u003csup\u003e5\u003c/sup\u003e. Unfortunately, the unavailability of the H4K12me1 antibody from the Sch\u0026uuml;le Laboratory has prevented further verification. Nevertheless, this limitation does not impact our conclusion that the alteration in p110α expression related to N6AMT1 might be dependent on NEDD4L.\u003c/p\u003e \u003cp\u003eFurthermore, we validated the role of the N6AMT1-p110α pathway in clinical samples and patient-derived organoid (PDO) models. The PDO model accurately reproduces the clinical response by maintaining the tumor phenotype and genotype, making it suitable for preclinical drug discovery and validation \u003csup\u003e46\u003c/sup\u003e. A66, a potent and specific inhibitor of wild-type p110α, reduces phosphorylation of Akt on T308 \u003csup\u003e47\u003c/sup\u003e. A synergistic effect of the p110α inhibitor A66 with tamoxifen was only observed in the PDO model of case 1, which exhibited a resistance mechanism similar to our study (downregulation of N6AMT1 and upregulation of p110α). These findings suggest that targeting p110α could be a promising approach to overcoming tamoxifen resistance associated with an N6AMT1-p110α pathway. Clinical trials have explored the use of alpelisib, a PI3Kα-selective inhibitor, in hormone receptor-positive, human epidermal growth factor receptor-2-negative advanced breast cancer with \u003cem\u003ePIK3CA\u003c/em\u003e mutations. The SOLAR-1 trial (NCT02437318) demonstrates a significant progression-free survival (PFS) benefit when alpelisib is added to fulvestrant treatment \u003csup\u003e48\u003c/sup\u003e. However, clinical trials specifically focusing on patients with p110α expression alterations have not been reported. Our study provides a theoretical basis for understanding tamoxifen resistance mechanisms in patients without \u003cem\u003ePIK3CA\u003c/em\u003e mutations. Further investigations should evaluate the therapeutic efficacy of p110α inhibition in preclinical models with varying p110α expression levels before advancing to clinical trials.\u003c/p\u003e \u003cp\u003eThis study has certain limitations. The mechanisms underlying the downregulation of FOXA1 and the regulation of NEDD4L by N6AMT1 remain unknown. Additionally, the use of cell line models (MCF-7, T47D) and a limited number of clinical samples (153) and PDOs (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) cannot fully capture the complexity and heterogeneity of luminal breast cancers. Therefore, future studies with a larger representation of tumors are needed to validate the role of the N6AMT1-p110α pathway in luminal breast cancers, especially those with p110α expression alterations associated with tamoxifen resistance.\u003c/p\u003e \u003cp\u003eIn conclusion, our study illustrates that N6AMT1 confers tamoxifen resistance by altering p110α expression in luminal breast cancer. These results suggest that the N6AMT1-p110α pathway might not only predict tamoxifen sensitivity, but also serve as a viable target for overcoming tamoxifen resistance in luminal breast cancer.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCOMPETING INTERESTS\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAUTHOR CONTRIBUTIONS\u003c/h2\u003e \u003cp\u003e \u003cb\u003eL. Ji\u003c/b\u003e and \u003cb\u003eY. Cui\u003c/b\u003e designed the studies, revised the manuscript, and supervised the progress throughout this study. \u003cb\u003eL. Ji\u003c/b\u003e carried out the experiments, analyzed the data, and wrote the manuscript. \u003cb\u003eJ. Chen\u003c/b\u003e carried out the experiments and analyzed the data. \u003cb\u003eL. He\u003c/b\u003e provided case data and clinical specimens. \u003cb\u003eF. Zhang\u003c/b\u003e analyzed the data. \u003cb\u003eZ. Deng\u003c/b\u003e assisted in animal and organoid experiments. \u003cb\u003eJ. Lin\u003c/b\u003e assisted in the procurement of laboratory reagents. \u003cb\u003eZ. Qi\u003c/b\u003e assisted in analyzed the data. \u003cb\u003eX. Luo\u003c/b\u003e participated in some of the experiments. \u003cb\u003eS.L. Li\u003c/b\u003e provided guidance for the experiment of cell biology and revised the manuscript. \u003cb\u003eA. Giuliano\u003c/b\u003e and \u003cb\u003eX. Cui\u003c/b\u003e revised the manuscript. All authors reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eACKNOWLEDGEMENTS\u003c/h2\u003e \u003cp\u003eThis work was supported by the National Natural Science Foundation of China (Grant No. 82272670) and the Special Fund for Science and Technology of Guangdong Province (Grant No. 210729156901814 and No. 210728156901648).\u003c/p\u003e\u003ch2\u003eDATA AVAILABILITY\u003c/h2\u003e \u003cp\u003eThe authors declare that all data supporting the findings of this study are available within the article and its Supplementary Information files or from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A \u003cem\u003eet al.\u003c/em\u003e Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. \u003cem\u003eCA A Cancer J Clin\u003c/em\u003e 2021; 71: 209\u0026ndash;249.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLoibl S, Poortmans P, Morrow M, Denkert C, Curigliano G. Breast cancer. The Lancet 2021; 397: 1750\u0026ndash;1769.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWaks AG, Winer EP. Breast Cancer Treatment: A Review. JAMA 2019; 321: 288.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen J, Zhuang Y, Wang P, Ning J, Liu W, Huang Y \u003cem\u003eet al.\u003c/em\u003e Reducing N6AMT1-mediated 6mA DNA modification promotes breast tumor progression via transcriptional repressing cell cycle inhibitors. Cell Death Dis 2022; 13: 216.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMetzger E, Wang S, Urban S, Willmann D, Schmidt A, Offermann A \u003cem\u003eet al.\u003c/em\u003e KMT9 monomethylates histone H4 lysine 12 and controls proliferation of prostate cancer cells. Nat Struct Mol Biol 2019; 26: 361\u0026ndash;371.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFigaro S, Scrima N, Buckingham RH, Heurgu\u0026eacute;-Hamard V. 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Cell 2020; 183: 1436\u0026ndash;1456.e31.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"oncogene","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"onc","sideBox":"Learn more about [Oncogene](http://www.nature.com/onc/)","snPcode":"41388","submissionUrl":"https://mts-onc.nature.com/cgi-bin/main.plex","title":"Oncogene","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Breast cancer, Tamoxifen resistance, N6AMT1, PI3K/AKT","lastPublishedDoi":"10.21203/rs.3.rs-4738749/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4738749/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eApproximately 70% of breast cancer (BC) cases are luminal-type (estrogen receptor-positive, ER+), suitable for endocrine therapy with tamoxifen as the most commonly used drug. However, about 30% of these patients develop tamoxifen resistance due to various mechanisms, primarily involving PI3K pathway activation through mutations or unknown pathways. Here, we discover, via bioinformatics analysis and clinical samples, that N6 adenine\u0026ndash;specific DNA methyltransferase 1 (N6AMT1) is highly expressed in luminal breast cancer but downregulated in tamoxifen-resistant (TamR) BC cells. ChIP-qPCR and luciferase reporter assays showed that FOXA1 binds to the N6AMT1 and enhances transcription. In TamR models, FOXA1 and N6AMT1 are downregulated, increasing p110α protein levels (but not mRNA), phospho-AKT levels, and tamoxifen resistance. In vivo, N6AMT1 overexpression enhanced tamoxifen sensitivity, while knockdown reduced it; this sensitivity could be restored with the p110α inhibitor A66. Clinically, decreased N6AMT1 expression correlates with poor prognosis in luminal BC patients. In TamR BC organoids, combining tamoxifen with A66 further reduced growth compared to either treatment alone. Mechanistically, increased p110α levels result from inhibited degradation by E3 ubiquitin ligase NEDD4L. These findings suggest N6AMT1 as a potential luminal breast cancer biomarker and highlight the FOXA1-N6AMT1-NEDDL4-p110α pathway as a therapeutic target to sensitize cells to tamoxifen.\u003c/p\u003e","manuscriptTitle":"Reversal of endocrine resistance via N6AMT1-NEDD4L pathway-mediated p110α degradation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-17 01:58:00","doi":"10.21203/rs.3.rs-4738749/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2024-08-29T16:07:17+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2024-08-15T19:21:15+00:00","index":2,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2024-07-28T12:53:57+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-07-23T19:24:09+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-07-18T04:50:36+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2024-07-18T04:22:19+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-15T09:53:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-14T14:08:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Oncogene","date":"2024-07-14T14:07:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"oncogene","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"onc","sideBox":"Learn more about [Oncogene](http://www.nature.com/onc/)","snPcode":"41388","submissionUrl":"https://mts-onc.nature.com/cgi-bin/main.plex","title":"Oncogene","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d8b88259-7a53-4076-8021-ade12a4c5465","owner":[],"postedDate":"August 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":34766869,"name":"Biological sciences/Cancer/Breast cancer"},{"id":34766870,"name":"Biological sciences/Cancer/Cancer therapy/Cancer therapeutic resistance"}],"tags":[],"updatedAt":"2024-12-03T08:10:21+00:00","versionOfRecord":{"articleIdentity":"rs-4738749","link":"https://doi.org/10.1038/s41388-024-03238-3","journal":{"identity":"oncogene","isVorOnly":false,"title":"Oncogene"},"publishedOn":"2024-12-02 05:00:00","publishedOnDateReadable":"December 2nd, 2024"},"versionCreatedAt":"2024-08-17 01:58:00","video":"","vorDoi":"10.1038/s41388-024-03238-3","vorDoiUrl":"https://doi.org/10.1038/s41388-024-03238-3","workflowStages":[]},"version":"v1","identity":"rs-4738749","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4738749","identity":"rs-4738749","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}