Lower MMP2, FLNA, CFL1 expressions favor invasive micropapillary carcinoma prognosis over ductal carcinoma of the breast

Lower MMP2, FLNA, CFL1 expressions favor invasive micropapillary carcinoma prognosis over ductal carcinoma of the breast | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Lower MMP2, FLNA, CFL1 expressions favor invasive micropapillary carcinoma prognosis over ductal carcinoma of the breast Yidi Wang, Jingyi Zhang, Ying Wang, Yu Liu, Bohui Shi, Xiaoqian Li, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4538838/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose: The prognosis of invasive micropapillary carcinoma (IMPC) relative to invasive ductal carcinoma (IDC) of breast is contentious, despite its recognized aggressive clinical manifestations. This retrospective study aimed to explore the prognosis and underlying molecular mechanisms of IMPC. Methods: We compared IMPC and IDC patients survival outcomes after PSM using the SEER database and constructed a nomogram for predicting IMPC overall survival (OS). IMPC-specific gene expression profiles were explored using microarray data from the GEO database. The top 10 genes in the PPI network with the highest degrees of connectivity were defined as hub genes and three of them were selected for validation by immunohistochemistry. Results: IMPC patients had a better prognosis than IDC patients for both OS and BCSS. Multivariate analysis revealed that age, marital status, TN stage, ER status, and chemotherapy were independent prognostic factors for IMPC patients, which were used to construct the nomogram, with good performance in internal and external cohorts. A total of 294 DEGs were identified, with ten hub genes selected. MMP2, FLNA and CFL1, which are known to be associated with poor prognosis in breast cancer patients, were expressed at lower levels in IMPC patients than in IDC patients, indicating favorable outcomes in IMPC. Conclusions: IMPC patients had a better prognosis than IDC patients, which may due to the lower expression of pro-oncogenic genes in IMPC, but the underlying mechanism needs further investigation. Invasive micropapillary carcinoma Prognosis Nomogram Hub genes Breast cancer Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Invasive micropapillary carcinoma (IMPC) is a rare and aggressive form of invasive breast cancer, accounting for 1.0-8.4% of cases[ 1 – 3 ]. Characterized by tumor cells that form morule-like clusters without fibrovascular cores, these clusters are found within empty spaces in the stroma[ 4 ]. Fisher firstly described IMPC in 1980[ 4 ], and it was formally named as such in 1993[ 5 ]; Later, in 2003, it was recognized as a distinct histological subtype of breast cancer in the World Health Organization (WHO) Classification of Breast Tumors[ 6 ]. IMPC exhibits a higher likelihood of local recurrence, lymph vascular invasion surrounding the tumor, and axillary lymph node invasion compared to invasive ductal carcinoma (IDC)[ 7 – 10 ]. However, it is unclear whether its behavior translates into a poor prognosis[ 11 ]. Therefore, it is important to understand the prognosis of IMPC patients and use a specific prognostic evaluation system to carry out targeted clinical interventions[ 12 ]. Historically, many genes have been recognized as potential prognostic markers for forecasting the outcomes of patients with IMPC[ 13 , 14 ]. However, it remains controversial whether poor molecular expression and clinicopathologic features contribute to a poor prognosis in IMPC patients. Recently, advancements in bioinformatics help to elucidate the mechanisms of IMPC development and progression and discover new prognostic markers for it. In our research, we integrated clinical analysis with bioinformatics to explore the prognosis of IMPC and to identify and validate potential biomarkers. We aimed to understand the difference in prognosis between IMPC and IDC breast cancers and to identify new viable therapeutic targets and strategies for IMPC patients. Material and Methods Patients We used SEER*Stat version 8.4.1.2 to generate a case-listing file. Female patients diagnosed with IDC-NOS or IMPC from 2004 to 2015 were included. The specific inclusion criteria were as follows: (1) pathologically confirmed IDC-NOS (ICD-O-3 8500/3) or IMPC (ICD-O-3 8507/3); (2) no distant metastasis at diagnosis; (3) unilateral breast cancer; and (4) complete demographic information. The exclusion criteria were as follows: (1) diagnosed only at the time of death or autopsy; (2) incomplete surgical, grade, T, N, M, ER, or PR data; (3) had more than one primary carcinoma; and (4) lacked cause of death. We also collected data from an independent Chinese cohort at our center, including 204 female patients diagnosed with IMPC at the First Affiliated Hospital of Xi'an Jiaotong University from May 2016 to August 2022. Twenty-four IMPC patients and IDC patients matched for TNM stage and molecular subtype were subjected to immunohistochemistry. The use of the Chinese cohort was approved by the Ethics Committee of the First Affiliated Hospital of Xi'an Jiaotong University. Factors The parameters included age, race, marital status, tumor laterality, location, histological grade, AJCC stage, TN stage, estrogen receptor (ER) status, progesterone receptor (PR) status, type of surgery, radiotherapy, chemotherapy, number of lymph nodes removed, survival status, and cancer-specific survival (CSS) status. According to the World Health Organization (WHO) standard grading system, histological grades are classified into four levels: well differentiated (grade I), moderately differentiated (grade II), poorly differentiated (grade III), and undifferentiated (grade IV). Meanwhile, the AJCC-TNM staging system, 7th edition, is applied to classify the AJCC stage, invasion depth, lymph node involvement, and distant metastasis extent. PSM Propensity Score Matching (PSM) is a technique used to ensure comparability in retrospective studies by selecting experimental and control cases with similar characteristics, known as matching variables[ 15 ]. We used a ratio of 1:1 for nearest neighbor matching to adjust for baseline characteristics and reduce the effect of selection bias. Endpoint definition Overall Survival (OS) is measured from the date of diagnosis until death from any cause or the final follow-up. The SEER database defines CSS as the time from breast cancer diagnosis to death specifically from breast cancer, excluding other causes of death. The endpoints of this study were overall survival (OS) chiefly and breast cancer-specific survival (BCSS) secondly. Construction of the nomogram To construct an effective OS nomogram for the prognosis of IMPC patients, we used the data of 782 IMPC patients screened in the SEER database as the training cohort to predict the individual OS outcomes at 3 years, 5 years, and 10 years. Independent prognostic factors were determined through multivariate Cox regression analysis, and a corresponding nomogram was constructed for the SEER training cohort. Validation of the nomogram Identification and calibration were performed using training and external validation cohorts (an independent Chinese cohort from our center) to evaluate the prognostic performance of the nomogram. The C-index measured its discrimination, with higher values indicating better prognostic accuracy. The ROC curve validated the performance of the nomogram, while calibration compared its predicted probabilities against actual results. Identification of DEGs The gene expression profile dataset GSE66418 was obtained from the GEO database, encompassing 124 samples: 73 from IMPC and 51 from matched invasive carcinoma of no special type (ICNST). The dataset was based on the Affymetrix Human Genome U133 Plus 2.0 Array (GPL6801 platform) and was submitted by Gruel et al.[ 14 ]. The “limma” R package was used to identify differentially expressed genes between IMPC and ICNST. Excel was used to remove duplicate and invalid genes. Genes within the cutoff criteria of an adjusted p-value 1 were considered DEGs. Functional enrichment analysis To obtain a better understanding of the functionality of the DEGs in the modules most relevant to IMPC, DEGs were subjected to the Database for Annotation, Visualization, and Integrated Discovery (DAVID) ( https://david.ncifcrf.gov/home.jsp/ ) to interpret the GO functions and enriched KEGG pathways[ 16 ]. An adjusted p-value < 0.05 and q-value < 0.05 were set as the cutoff criteria. Enriched pathways were visualized using the R packages "tidyr" and "ggplot2". GSEA of all the genes in the dataset was performed to explore their enrichment in hallmark pathways. The hallmark pathways with significant enrichment results were identified based on |NES|>1, NOM p-value < 0.05, and FDR q-value < 0.25. Construction of the PPI Network STRING (version 11.5) was used to evaluate the potential associations of protein‒protein interactions (PPIs) with DEGs[ 17 ]. Only interactions with an experimentally validated composite score ≥ 0.4 were considered significant. The PPI network was constructed and visualized using Cytoscape software 3.9.1[ 18 ]. Definitions of Hub Genes and Efficacy Evaluation Based on the information from the STRING protein query and the results of the degree analysis of the PPI network with the cytoHubba plug-in in Cytoscape, we selected the top 10 most dysregulated genes as the hub genes. The genes were plotted with the "pROC" software package, and the area under the curve (AUC) was calculated to evaluate the ability of the selected genes to distinguish between IMPC patients and controls. We analyzed the mRNA‒protein correlations of the hub genes in the LinkedOmics database to facilitate the validation process for immunohistochemistry. Immunohistochemistry (IHC) For IHC analysis, the following primary antibodies were used: anti-MMP2 (1:1000; Servicebio, China), anti-Filamin A (1:150; Abcam, UK), and anti-Cofilin-1 (1:2000; Servicebio, China). The tissue specimens were deparaffinized, rehydrated, and processed in Tris-EDTA (pH 9.0) for 8 minutes with medium heat, 8-minute intervals and medium and low heat for 7 minutes for antigen retrieval. The slides were incubated in blocking reagent (Servicebio, China), incubated with primary antibody overnight at 4°C, washed with PBS (pH 7.4), and then incubated with secondary antibody (Servicebio, China) for 50 minutes at room temperature. IHC images were acquired microscopically using a 20× objective. The positive area and percent intensity were used to score the IHC staining. Two pathologists who worked independently assigned all of the ratings. The following is the staining intensity record: 0 for no staining, 1 for weak staining, 2 for medium staining, and 3 for strong staining. The percentages of positive cells were as follows: 0 for 75% positive area. Multiplying the intensity score by the proportional score yielded the final IHC score. "Low" indicates < 4 points, and "High" indicates ≥ 4 points. Statistical analysis To examine the clinicopathological traits of every group, chi-square and Wilcoxon rank-sum tests were run. Log-rank test was used to evaluate group differences and Kaplan-Meier and Gray techniques were used for survival analysis. Variables from the SEER cohort that showed a p-value < 0.05 in univariate Cox regression were chosen for multivariate analysis using forward stepwise regression. Using the “rms” R package, the nomogram model was created based on the findings of the multivariate Cox regression risk model. All the statistical analyses were performed using IBM SPSS version 18.0 and the statistical software package R4.2.2. The data for all the experiments were analyzed using GraphPad Prism software 8.0. A paired t-test was used for two-group comparisons. All the analyses were performed using R4.2.2 software. The analysis p values were bilateral, with a value of < 0.05 considered to indicate statistical significance. Results Clinicopathological characteristics Figure 1 displays our study’s design. A total of 782 patients with IMPC and 194,306 with IDC who met the inclusion and exclusion criteria from the SEER database were included in our study. The demographic, clinicopathological, and treatment characteristics of the two cohorts are summarized and compared in Table 1. The median age of the IDC patients was 57 years, whereas that of the IMPC patients was 58 years. Compared with IDC patients, IMPC patients were older, had a larger tumor size (T3), more lymph node involvement (N3), more advanced AJCC stage and greater grade. Furthermore, IMPC had a higher correlation with ER-positive and PR-positive tumor frequencies in comparison to IDC. In addition, differences in ethnicity and tumor localization between the two groups were statistically significant. Patients in the IMPC group got chemotherapy at a considerably higher rate than those in the IDC group. A total of 28.8% of patients in the IMPC group had at least 10 lymph nodes removed, which was significantly greater than the 21.4% in the IDC group (P < 0.001). PSM was utilized to reweight the patient population in each group due to the observable group differences between the IMPC and IDC groups. After a number of distributional disparities were removed, more similar IDC and IMPC groups were produced, and the characteristics of the patients after PSM are shown in Table 1. Table1 Comparison of clinical characteristics between IMPC and IDC. Before PSM After PSM Risk factors IMPC(%) IDC-NOS(%) P value IMPC(%) IDC-NOS(%) P value （n=782） (n=194306) （n=782） (n=782) Age 0.003 0.761 <60 419(53.6) 114138(58.7) 419（53.6） 426（54.5） ≥60 363（46.4） 80168（41.3） 363（46.4） 356（45.5） Race 0.043 0.270 Black 92(11.8) 19190(9.9) 92（11.8） 74（9.5） White 594(76.0) 154612(79.6) 594（76.0） 618（79.0） Other 96(12.2) 20504(10.5) 96（12.2） 90（11.5） Marital status 0.388 0.767 Married 534(68.3) 136651(70.4) 534（68.3） 544（69.6） Single 145(18.5) 32689(16.8) 145（18.5） 134（17.1） Divorced 103(13.2) 24966(12.8) 103（13.2） 104（13.3） Laterality 0.436 0.649 Left 385(49.2) 98373(50.6) 385（49.2） 395(50.5) Right 397(50.8) 95933(49.4) 397（50.8） 387(49.5) Location 0.001 0.698 Central portion 45(5.8) 9665(5.0) 45(5.8) 52(6.7) Upper-inner quadrant 135(17.2) 27596(14.2) 135(17.2) 126(16.1) Lower-inner quadrant 64(8.2) 12739(6.6) 64(8.2) 59(7.5) Upper-outer quadrant 256(32.7) 77580(39.9) 256(32.7) 267(34.1) Lower-outer quadrant 71(9.1) 16375(8.4) 71(9.1) 57(7.3) Other 211(27.0) 50351(25.9) 211(27.0) 221(28.3) Grade 0.003 0.746 GradeⅠ 59(7.5) 39749(20.5) 59(7.5) 65(8.3) GradeⅡ 443(56.6) 78856(40.6) 443(56.6) 424(54.2) GradeⅢ 269(34.4) 74478(38.3) 269(34.4) 286(36.6) GradeⅣ 11(1.5) 1223(0.6) 11(1.5) 7(0.9) AJCC stage <0.001 0.970 Ⅰ 308(39.4) 101079(52.0) 308(39.4) 309(39.5) Ⅱ 325(41.5) 72544(37.4) 325(41.5) 324(41.4) Ⅲ 149(19.1) 20683(10.6) 149(19.1) 149(19.1) T stage <0.001 0.621 T1 459(58.7) 125319(64.5) 459(58.7) 465(59.5) T2 253(32.4) 57741(29.7) 253(32.4) 258(33.0) T3 59(7.5) 7866(4.1) 59(7.5) 48(6.1) T4 11(1.4) 3380(1.7) 11(1.4) 11(1.4) N stage <0.001 0.876 N0 404(51.7) 133504(68.7) 404(51.7) 403(51.5) N1 249(31.8) 45194(23.3) 249(31.8) 245(31.4) N2 76(9.7) 10481(5.4) 76(9.7) 79(10.1) N3 53(6.8) 5127(2.6) 53(6.8) 55(7.0) ER <0.001 0.932 Negative 76(9.7) 41276(21.2) 76(9.7) 74(9.5) Positive 706(90.3) 153030(78.8) 706(90.3) 708 (90.5) PR <0.001 0.484 Negative 148(18.9) 60416(31.1) 148(18.9) 160(20.5) Positive 634(81.1) 133890(68.9) 634(81.1) 622(79.5) Radiation therapy 0.139 0.916 None 277(35.4) 73821(38.0) 277(35.4) 280(35.8) Yes 505(64.6) 120485(62.0) 505(64.6) 502(64.2) Chemotherapy 0.001 0.577 None or unknown 357(45.7) 99773(51.3) 357(45.7) 369(47.2) Yes 425(54.3) 94533(48.7) 425(54.3) 413(52.8) Breast surgery 0.126 0.736 BCS 462(59.1) 121545(62.6) 462(59.1) 462(59.1) Reconstruction 9(1.1) 1828(0.9) 9(1.1) 6(0.8) Mastectomy 311(39.8) 70933(36.5) 311(39.8) 314(40.1) Lymph node removed 10 225(28.8) 41649(21.4) 225(28.8) 222(28.4) Unknown 2(0.3) 974(0.5) 2(0.3) 1(0.1) Survival analysis based on the Kaplan‒Meier and Gray methods Following PSM, Kaplan-Meier analysis indicated that IMPC patients had a significantly better prognosis than IDC patients (P = 0.003), with 3-year, 5-year, and 10-year mortality rates of 2.6%, 4.6%, and 5.2% for IMPC, compared to 3.6%, 6.5%, and 10.0% for IDC (Fig.2A). According to the Gray method, patients with IMPC had a significantly more favorable BCSS than patients with IDC did (P = 0.004) (Fig.2B). Univariate and multivariate analyses of IMPC prognostic factors The Cox regression model was used in the training cohort (SEER cohort) to determine the factors affecting the prognosis of IMPC patients. Univariate analysis revealed that age, race, marital status, AJCC stage, T stage, N stage, ER status, PR status, radiation therapy, chemotherapy, breast surgery methods and number of lymph nodes resected were significantly associated with overall survival. Further multivariate Cox analysis revealed that age, race, marital status, T stage, N stage, ER status, and chemotherapy were independent prognostic factors (Table 2). Table2 Prognostic factors for overall survival of patients with invasive micropapillary breast cancer Risk factors Univariate analysis Multivariate analysis HR 95%CI P value HR 95%CI P value Age <60 Ref Ref ≥60 2.364 1.526-3.663 <0.001 2.416 1.440-4.052 <0.001 Race Black Ref Ref White 0.513 0.304-0.868 0.013 0.472 0.267-0.835 0.009 Other 0.445 0.199-0.991 0.047 0.366 0.151-0.884 0.025 Marital status Married Ref Ref Single 1.255 0.698-2.256 0.447 1.111 0.599-2.063 0.738 Divorced 3.693 2.321-5.875 <0.001 3.224 1.982-5.244 <0.001 Laterality - - - Left Ref Right 0.970 0.640-1.470 0.887 Location - - - Other Ref Central portion 1.073 0.414-2.779 0.884 Upper-inner quadrant 0.666 0.340-1.306 0.237 Lower-inner quadrant 0.490 0.172-1.395 0.181 Upper-outer quadrant 0.952 0.574-1.581 0.850 Lower-outer quadrant 0.862 0.394-1.888 0.711 Grade - - - GradeⅠ Ref GradeⅡ 1.828 0.659-5.069 0.246 GradeⅢ 1.966 0.696-5.553 0.202 GradeⅣ 3.957 0.884-17.717 0.072 AJCC stage Ⅰ Ref Ref Ⅱ 1.140 0.685-1.896 0.614 0.769 0.279-2.118 0.611 Ⅲ 2.525 1.494-4.267 <0.001 0.467 0.070-3.101 0.430 T stage T1 Ref Ref T2 1.541 0.970-2.449 0.067 1.665 0.806-3.439 0.168 T3 2.785 1.462-5.306 0.002 2.673 0.948-7.535 0.063 T4 10.804 3.799-30.725 <0.001 10.220 2.260-46.178 0.003 N stage N0 Ref Ref N1 1.033 0.624-1.712 0.899 1.666 0.692-4.012 0.255 N2 1.519 0.757-3.050 0.239 3.877 0.727-20.689 0.113 N3 3.566 1.988-6.397 <0.001 10.490 2.035-54.083 0.005 ER Negative Ref Ref Positive 0.308 0.189-0.503 <0.001 0.231 0.099-0.541 <0.001 PR Negative Ref Ref Positive 0.542 0.345-0.852 0.008 1.096 0.506-2.377 0.816 Radiation therapy None Ref Ref Yes 0.710 0.467-1.080 0.11 0.658 0.395-1.094 0.106 Chemotherapy None or unknown Ref Ref Yes 0.709 0.4675-1.077 0.107 0.556 0.322-0.959 0.035 Breast surgery BCS Ref Ref Reconstruction 1.790 0.245-13.068 0.566 1.326 0.166-10.598 0.790 Mastectomy 1.898 1.247-2.888 0.003 1.165 0.660-2.058 0.599 Lymph node removed 0 Ref Ref 1-10 0.318 0.136-0.744 0.008 0.473 0.195-1.150 0.099 >10 0.527 0.222-1.255 0.148 0.355 0.125-1.007 0.051 Unknown <0.001 - 0.995 <0.001 - 0.995 Construction and validation of the nomogram The independent prognostic factors identified by Cox regression (age, race, marital status, T stage, N stage, ER status, and chemotherapy treatment) were used to construct a nomogram to predict OS in IMPC patients (Supplementary Fig.1A). Since the races in the validation cohort (Chinese cohort) were all Asian, we removed this factor to construct a new nomogram (Fig.2C), and we found that it did not have a large impact on the AUC values (Supplementary Fig.1B, Fig.2D). The nomogram highlighted that the T stage most affected prognosis, with chemotherapy having the least effect. All subtypes of all variables were assigned scores (Supplementary Table 2). The nomogram model was internally and externally validated. On the one hand, after internal verification in the training cohort (SEER cohort), the C-index predicted by OS was 0.806. The area under the ROC curve (AUC) values for the 3-, 5- and 10-year survival rates were 0.843, 0.861 and 0.743, respectively, indicating that this model has excellent discriminant ability (Fig.2D). The calibration plots showed good consistency between the nomogram predictions and the actual observations in the training cohort (Fig.2E). On the other hand, external verification using data from the First Affiliated Hospital of Xi'an Jiaotong University revealed that the C-index predicted by OS in the validation cohort (Chinese cohort) was 0.959. The area under the ROC curve (AUC) values for the 3- and 5-year survival rates were 0.838 and 0.899, respectively (Fig.2F). Identification of hub genes of IMPC To further investigate the gene expression profile of IMPC, with respect to the GSE66418 cohort, which included 73 IMPC samples and 51 ICNST samples, a total of 294 DEGs, including 192 upregulated genes and 102 downregulated genes, were selected. (Supplementary Table 3). A volcano plot of the genes differentially expressed between IMPC samples and ICNST samples is shown in Figure 3A. For a more functional understanding of the underlying mechanism of the genes involved in IMPC prognosis, GSEA, GO function and KEGG pathway enrichment analyses were performed. The GSEA enrichment results showed that genes were enriched in the unfolded protein response-, myogenesis-, mTORC1 signaling-, UV-response-up- and protein secretion-related pathways (Supplementary Fig.2). The results of GO analysis indicated that the DEGs significantly participated in the formation of cell components, which included the plasma membrane, extracellular exosome, extracellular space, extracellular region, cell surface, external side of the plasma membrane, and extracellular matrix. In addition, KEGG pathway analysis was subsequently conducted, and the results showed that the DEGs were enriched mainly in focal adhesion (Fig.3B, C). The STRING database was used to construct the PPI network (Fig.3D). The top 10 hub genes, which included 3 upregulated genes (CTNNB1, EGF and PGR) and 7 downregulated genes (ERBB2, CD4, MMP2, FYN, ITGA5, FLNA and CFL1), were identified according to their highest degrees of connectivity using the cytoHubba plug-in in Cytoscape (Fig.3E). Verification of the hub genes To determine the diagnostic value of the 10 hub genes for IMPC patients, ROC curves based on the GSE66418 dataset were generated. The AUC values of CTNNB1, ERBB2, CD4, EGF, MMP2, FYN, PGR, ITGA5, FLNA, and CFL1 were 0.824, 0.719, 0.790, 0.672, 0.723, 0.790, 0.717, 0.855, 0.959, and 0.778, respectively (Supplementary Fig.3). In addition, since the method used in our center to verify the degree of expression of selected genes is immunohistochemistry, that is, the quantification and localization of the target protein, we screened hub genes with an AUC > 0.7 and then calculated their correlation coefficients between mRNAs and proteins. The results demonstrated that the Pearson correlation coefficients of CTNNB1, ERBB2, CD4, MMP2, PGR, ITGA5, FLNA, and CFL1 were 0.0888, 0.8368, 0.3305, 0.5958, 0.7113, 0.387, 0.641, and 0.5695, respectively (Supplementary Fig.4). Subsequently, we screened hub genes with an AUC> 0.70 and the Pearson correlation coefficient> 0.5 for validation, and the selected genes were ERBB2, PGR, MMP2, FLNA and CFL1. Since, in existing studies and clinical diagnoses, we know that ERBB2 and PGR expression levels cannot be used for diagnosing IMPC and reflecting its prognosis, we decided to validate the three hub genes MMP2, FLNA and CFL1. Finally, we examined the expression of MMP2, FLNA and CFL1 in 24 IMPC tissues and stage-subtype-matched IDC tissues from the First Affiliated Hospital of Xi'an Jiaotong University using immunohistochemistry. Our results showed that MMP2, FLNA and CFL1 expression was significantly lower in IMPC tissues than in matched IDC tissues (Fig.4A,B,C). Since MMP2, FLNA and CFL1 were proved to be associated with breast cancer progression, the lower expression of these protein might contribute to the better prognosis of IMPC. To further validate our hypothesis, we explore the prognosis difference between IMPC and matched IDC patients, the K-M plotter analysis showed that IMPC patients had a better progression free survival (PFS) than IDC patients, even if the difference between them was not statistically significant due small sample size(p=0.077) (Fig.4D). Discussion IMPC is a rare subtype of invasive breast cancer whose clinical manifestations are more aggressive than those of invasive ductal carcinoma, such as overt LVI and regional LNM[ 19 ]. However, whether IMPC is associated with worse survival outcomes than IDC remains controversial. According to Chen et al.'s analysis of 100 IMPC patients with a median follow-up of five years, OS was lower for IMPC than for IDC[ 7 ], which is in line with Shi's retrospective multicenter study's results[ 20 ]. Several recent studies involving OS outcomes have shown that IMPC is noninferior to IDC[ 11 , 21 – 23 ]. The treatment methods and prognoses of patients at different risk levels are different, and therefore further exploration of the clinicopathological features and prognosis of invasive micropapillary carcinoma is important while avoiding the adverse effects of overtreatment. In this study, the clinical and pathological characteristics of 194306 patients with IDC and 782 patients with IMPC were analyzed. According to earlier research, IMPC typically manifests later in life and is more likely to involve lymph nodes[ 21 , 24 ]. Our study revealed the clinical and pathological characteristics of IMPC which is in line with the results of other investigations. Similarly, Simpsonetti's study showed that IMPC patients had greater CD24 expression and lower CD44 expression than IDC patients did, which may explain the increased propensity for lymph node metastasis[ 25 ]. After matching significant differences in clinicopathologic features between IMPC and IDC patients, we demonstrated that IMPC patients had superior OS and BCSS compared to IDC patients, which is consistent with the findings of Li et al.[ 26 ]. Lymph node metastasis and large tumor size are widely recognized as adverse prognostic factors in clinical practice. However, in the IMPC cohort, poor clinical features did not translate to a poor prognosis. At present, the concept of predicting the prognosis of breast cancer patients and guiding clinical practice according to different breast cancer subtypes, histological grade and ER, PR and HER2 status has been widely accepted. The National Comprehensive Cancer Network (NCCN) guidelines (2018 edition) introduce the American Joint Committee on Cancer 8th edition breast cancer staging system to restage breast cancer and propose the concept of prognostic staging for the first time. The new prognostic staging system included tumor histological grade, HER2 status, ER status, PR status and other biomarkers. The high frequency of positive ER and PR signals in IMPC patients may explain the better prognosis. Chemotherapy, radiation therapy, and endocrine therapy are among the subsequent treatments that are often determined by the clinical characteristics, lymph node status, and primary tumor size. In clinical practice, clinicians frequently choose aggressive treatment due to its poor clinicopathologic traits. Due to their greater HR positive rate, IMPC patients are more likely than IDC patients to get endocrine therapy. These findings are also consistent with those reported in other studi, such as Tang et al.’s study[ 9 ] and Liu et al.’s[ 27 ]. Furthermore, axillary lymph node dissection is more common in IMPC patients[ 28 ]. Resection of more than ten lymph nodes in the IMPC subgroup accounted for 28.8%, which was greater than that in the IDC subgroup (21.4%) (P < 0.001). HER-2 is an important driver gene and prognostic indicator of breast cancer. The degree of HER2 expression was not found to have an impact on the prognosis of IMPC patients in previous studies[ 29 , 30 ], and the HER2 variable was not included in this study because only patients after 2010 had HER2 expression data in the SEER database. We included 382 female patients with IMPC from 2010 to 2015 in our study according to the inclusion and exclusion criteria. Univariate and multivariate Cox analyses revealed that HER2 expression was not an independent factor for the prognosis of IMPC patients (Supplementary Table 1); therefore, we broadened the study period to include more patients in the training set. In this study, we identified 294 DEGs between IMPC and ICNST samples by analyzing microarray data from the GEO database. The enrichment of these DEGs revealed that the identification of core pathways and hub genes may lead to new insights into the prognosis of IMPC patients. GO functional enrichment analysis revealed that the DEGs were enriched mainly in cell components, which suggested that interactions with the extracellular environment can be triggered during IMPC. KEGG enrichment analysis revealed that focal adhesion plays significant roles in IMPC, where several components are involved in the structural link between membrane receptors and the actin cytoskeleton, and other signaling events ultimately lead to reorganization of the actin cytoskeleton, which is a prerequisite for changes in cell shape and motility and in gene expression[ 31 ]. Cell migration is the primary factor that influences the regulation of cytoskeletal activity. Protrusion, adhesion, contraction, and retraction from active cells are mediated in space and time by the dynamic actin cytoskeleton. Aggressive phenotypes in cancer cells are conferred by variation in their acting skeleton, such as EMT. Notably, the GSEA results showed that the enrichment pathways were the most common markers involved in IMPC, and it is generally accepted that the above processes are closely related to the development and progression of tumors. Importantly, the top 10 hub genes were identified in IMPC, including 3 up-regulated genes and 7 down-regulated genes. Among them, CTNNB1 is the hub gene with the highest degree. It encodes β-Catenin, a key biomarker of the Wnt/β-Catenin signaling pathway that regulates epithelial–mesenchymal transition (EMT) in cancer cells. In the absence of WNT ligands, β-catenin is involved in cell adhesion, acting as a bridge between E-cadherin and cytoskeleton-associated actin to form adherens junctions between adjacent cells[ 32 , 33 ]. Additionally, it is involved mainly in cell adhesion and gene transcription regulation and plays important roles in cell permeability, polarity, and migration. This may explain why IMPC patients have a greater rate of lymph node metastasis. Furthermore, to test the expressions of selected genes, we detected the protein levels of three genes in IMPC and IDC samples from our center, as IDC is included within the ICNST. Compared with those in IDC, the protein expression levels of MMP2, FLNA and CFL1 in IMPC were significantly lower. At the same time, in comparing the difference in survival prognosis between the two groups of these 24 pairs of samples, IMPC showed a trend of better prognosis. The reason why the difference was not statistically significant may be because of the small sample size, and we speculate that increasing the sample size may work better. MMP2 is a member of the matrix metalloproteinase family and plays a key role in extracellular matrix remodeling. MMP2 is able to degrade type IV collagen in the basement membrane, providing conditions for invasion and metastasis of cancer cells, and can also specifically participate in tumor-associated angiogenesis through the release of proangiogenic factors[ 34 ]. Its high expression is associated with disease progression and reduced survival in patients with various cancers, such as breast cancer, oral cancer, prostate cancer, lung cancer, head and neck cancer, and colorectal cancer. Filamin A (FLNA) is an actin cross-linking protein that interacts with a variety of binding proteins and plays an important role in cell migration, differentiation, proliferation, and survival[ 35 , 36 ]; moreover, FLNA has been identified as a marker for cancer[ 37 , 38 ]. It interacts with signaling molecules in the cytoplasm and can promote tumor invasion and metastasis by affecting cell migration and adhesion[ 39 ]. Some studies have shown that the expression level of FLNA in invasive breast cancer tissues increases with decreasing differentiation. Cofilin 1 (CFL1) is a subtype of actin-depolymerizing factor (ADF)/cofilin family protein that plays a crucial role in tissue development, internal environment homeostasis, and diseases[ 40 , 41 ]. As a key regulator of actin dynamics, CFL1 is involved in a variety of cellular activities, including apoptosis, cell motility, and cytokinesis. Recent studies have shown that cofilin-1 is highly expressed in tumor cells, such as those of colorectal cancer, pancreatic cancer, endometrial cancer and hepatocellular carcinoma; cofilin-1 promotes the occurrence, migration and invasion of tumors and is closely related to poor tumor prognosis. Overall, high expression of these three genes is associated with poor prognosis, which also indirectly explains the better clinical prognosis of IMPC. This study has several limitations. First, our study was retrospective, and selection bias and recall bias may have influenced our findings. Second, the external validation cohort included data from only a single center and had a relatively small sample size. In the future, multicenter clinical trials with larger sample sizes and different ethnic groups are needed to evaluate the diagnostic performance of this prognostic model. Third, the data used for the nomogram were obtained from the SEER database. These data do not include specific information, such as information about LVI, BRCA1/2 mutations, or Oncotype DX recurrence scores. Finally, the mechanism of action of the hub genes we screened in IMPC is unclear, further research and additional clinicopathological data are needed. Overall, our present study focused on IMPC, a rare subtype of breast cancer with a significantly better prognosis than IDC, and this study indirectly explained this phenomenon at the molecular level for the first time. The established nomogram accurately predicts the outcomes of IMPC patients and provides a reference for informed decision-making in clinical practice. Moreover, the biological functions and regulatory mechanisms of hub genes in IMPC development need to be further studied. Declarations Acknowledgments This study was funded by the Innovation Capability Support Project of Shaanxi Province (NO. 2023KJXX－032). Funding This work was supported by the Innovation Capability Support Project of Shaanxi Province (NO. 2023KJXX－032). Competing Interests The authors have no relevant financial or non-financial interests to disclose. Author Contributions J.H. and H.Z. designed and supervised the study; Y.W., J.Z., and Y.W. analyzed and interpreted the data (e.g., statistical analysis, biostatistics, computational analysis); Y.W. and Y.L. performed the immunohistochemistry experiments; B.S. provided breast cancer patients and clinical data; X.L. arranged the data from our center; Y.W. written the manuscript, ; J.H. and H.Z. reviewed and revisioned of the manuscript. All authors read and approved the final manuscript. Data Availability The datasets analysed during the current study are available in the online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm.nih.gov/, GSE66418, and Surveillance, Epidemiology, and End Results (SEER) Program (https://seer.cancer.gov) SEER*Stat Database. The other relevant data are available from the authors upon reasonable request. Ethics approval The study was performed in accordance with the Declaration of Helsinki and was approved by the Ethical Review Committee of the First Affiliated Hospital of Xi'an Jiaotong University (No. XJTU1AF2024LSYY-094). Consent to participate As the study was retrospective, the ethics committee exempted the informed consent of the individual participants. Consent to publish Not applicable. References Nassar, H., et al., Clinicopathologic analysis of invasive micropapillary differentiation in breast carcinoma. Mod Pathol, 2001. 14 (9): p. 836-41. Kuroda, H., et al., Clinical and pathologic features of invasive micropapillary carcinoma. Breast Cancer, 2004. 11 (2): p. 169-74. Ide, Y., et al., Clinicopathological significance of invasive micropapillary carcinoma component in invasive breast carcinoma. Pathology International, 2011. 61 (12): p. 731-736. Fisher, E.R., et al., Pathologic findings from the National Surgical Adjuvant Breast Project (protocol no. 4). VI. 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Wahba, Propensity Score-Matching Methods for Nonexperimental Causal Studies. The Review of Economics and Statistics, 2002. 84 (1): p. 151-161. Ashburner, M., et al., Gene Ontology: tool for the unification of biology. Nature Genetics, 2000. 25 (1): p. 25-29. Szklarczyk, D., et al., STRING v10: protein–protein interaction networks, integrated over the tree of life. Nucleic Acids Research, 2015. 43 (D1): p. D447-D452. Shannon, P., et al., Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks. Genome Research, 2003. 13 (11): p. 2498-2504. Luna-Moré, S., et al., Estrogen and Progesterone Receptors, C-ERBB-2, p53, and BCL-2 in Thirty-three Invasive Micropapillary Breast Carcinomas. Pathology - Research and Practice, 1996. 192 (1): p. 27-32. Coleman, W.B., et al., Clinico-Pathological Features and Prognosis of Invasive Micropapillary Carcinoma Compared to Invasive Ductal Carcinoma: A Population-Based Study from China. PLoS ONE, 2014. 9 (6). 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Li, D., et al., A Competing Nomogram to Predict Survival Outcomes in Invasive Micropapillary Breast Cancer. Journal of Cancer, 2019. 10 (27): p. 6801-6812. Metze, K., et al., Similar Prognoses for Invasive Micropapillary Breast Carcinoma and Pure Invasive Ductal Carcinoma: A Retrospectively Matched Cohort Study in China. PLoS ONE, 2014. 9 (9). Mahe, E., M. Farag, and O. Boutross-Tadross, Invasive micropapillary breast carcinoma: a retrospective study of classification by pathological parameters. Malays J Pathol, 2013. 35 (2): p. 133-8. Hao, S., et al., Invasive micropapillary carcinoma of the breast had no difference in prognosis compared with invasive ductal carcinoma: a propensity-matched analysis. Scientific Reports, 2019. 9 (1). Lewis, G.D., et al., Prognosis of lymphotropic invasive micropapillary breast carcinoma analyzed by using data from the National Cancer Database. Cancer Communications, 2019. 39 (1): p. 1-9. Burridge, K., Focal adhesions: a personal perspective on a half century of progress. The FEBS Journal, 2017. 284 (20): p. 3355-3361. Lv, D., et al., Emerging Regulatory Mechanisms Involved in Liver Cancer Stem Cell Properties in Hepatocellular Carcinoma. Frontiers in Cell and Developmental Biology, 2021. 9 . Huber, A.H. and W.I. Weis, The structure of the beta-catenin/E-cadherin complex and the molecular basis of diverse ligand recognition by beta-catenin. Cell, 2001. 105 (3): p. 391-402. Reddy, R.A., M. Sai Varshini, and R.S. Kumar, Matrix Metalloproteinase-2 (MMP-2): As an Essential Factor in Cancer Progression. Recent Pat Anticancer Drug Discov, 2023. Feng, Y. and C.A. Walsh, The many faces of filamin: a versatile molecular scaffold for cell motility and signalling. Nat Cell Biol, 2004. 6 (11): p. 1034-8. Popowicz, G.M., et al., Filamins: promiscuous organizers of the cytoskeleton. Trends in Biochemical Sciences, 2006. 31 (7): p. 411-419. Shao, Q.Q., et al., Filamin A: Insights into its Exact Role in Cancers. Pathol Oncol Res, 2016. 22 (2): p. 245-52. Yue, J., et al., Filamin-A as a marker and target for DNA damage based cancer therapy. DNA Repair, 2012. 11 (2): p. 192-200. Savoy, R.M. and P.M. Ghosh, The dual role of filamin A in cancer: can't live with (too much of) it, can't live without it. Endocrine-Related Cancer, 2013. 20 (6): p. R341-R356. Chugh, P., et al., Actin cortex architecture regulates cell surface tension. Nature Cell Biology, 2017. 19 (6): p. 689-697. Hotulainen, P., et al., Actin-depolymerizing factor and cofilin-1 play overlapping roles in promoting rapid F-actin depolymerization in mammalian nonmuscle cells. Mol Biol Cell, 2005. 16 (2): p. 649-64. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigure1.tif Supplementary Fig. 1. (A) Nomogram predicting the 3-year, 5-year and 10-year OS for invasive micropapillary carcinoma patients. (included Race) (B)Discriminatory accuracy for predicting OS assessed by ROC analysis calculating AUC. (included Race) SupplementaryFigure2.tif Supplementary Fig. 2. GSEA analysis of genes in GSE66418 SupplementaryFigure3.tif Supplementary Fig. 3. ROC curve of the top 10 hub genes (A)CTNNB1 (B)ERBB2 (C)CD4 (D)EGF (E)MMP2 (F)FYN (G)PGR (H)ITGA5(I)FLNA (J)CFL1 SupplementaryFigure4.tif Supplementary Fig. 4. Correlation of mRNA and protein in 8 hub genes (A)CTNNB1 (B)ERBB2 (C) CD4 (D)MMP2 (E)PGR (F)ITGA5 (G)FLNA (H)CFL1 SupplementaryTable1.pdf Supplementary Table 1. Prognostic factors for overall survival of patients with invasive micropapillary breast cancer (included HER2) SupplementaryTable2.pdf Supplementary Table 2. Point assignment and prognostic score in the nomogram (IMPC SEER Cohort) SupplementaryTable3.pdf Supplementary Table 3. DEGs between IMPC and ICNST in GSE66418 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4538838","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":313857394,"identity":"093bf42e-04b0-4a5a-9c06-1a2707e1d1f9","order_by":0,"name":"Yidi Wang","email":"","orcid":"","institution":"The First Affiliated Hospital of Xi'an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Yidi","middleName":"","lastName":"Wang","suffix":""},{"id":313857395,"identity":"ca14896f-62f3-4680-a4ac-e9e44b3b03dd","order_by":1,"name":"Jingyi Zhang","email":"","orcid":"","institution":"The First Affiliated Hospital of Xi'an 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19:00:29","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":178140,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction and verification of the nomogram in the training and validation cohort (\u003cstrong\u003eA\u003c/strong\u003e) Overall survival (OS) and (\u003cstrong\u003eB\u003c/strong\u003e) breast cancer specific survival (BCSS) of IMPC patients and IDC patients after PSM (\u003cstrong\u003eC\u003c/strong\u003e)Nomogram predicting the 3-year, 5-year and 10-year OS for invasive micropapillary carcinoma patients (\u003cstrong\u003eD\u003c/strong\u003e)Discriminatory accuracy for predicting OS assessed by ROC analysis calculating AUC in the training cohort (\u003cstrong\u003eE\u003c/strong\u003e)The calibration curves for predicting patient 3-year, 5-year and 10-year OS (\u003cstrong\u003eF\u003c/strong\u003e)Discriminatory accuracy for predicting OS assessed by ROC analysis calculating AUC in the validation cohort\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4538838/v1/8f5c07da9cae4411e5a607a1.jpeg"},{"id":59215864,"identity":"9df5c17f-4401-415f-92a7-344d7b0826ad","added_by":"auto","created_at":"2024-06-27 19:00:30","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":334827,"visible":true,"origin":"","legend":"\u003cp\u003eBioinformatics analysis of GSE66418 (\u003cstrong\u003eA\u003c/strong\u003e)The volcano plot shows the genes expressed significantly differentially between IMPC and ICNST: red indicates the upregulated genes, and blue indicates the downregulated genes (\u003cstrong\u003eB\u003c/strong\u003e)(\u003cstrong\u003eC\u003c/strong\u003e)GO and KEGG pathways enrichment analyses of differentially expressed genes (DEGs) between IMPC and ICNST (\u003cstrong\u003eD\u003c/strong\u003e)Protein–protein interaction network of proteins encoded by differentially expressed genes between IMPC and ICNST: Red nodes represent upregulated genes, blue nodes represent downregulated genes (\u003cstrong\u003eE\u003c/strong\u003e)Top 10 hub genes with the highest degrees of connectivity\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4538838/v1/7e07e55e390a8b687433b944.jpeg"},{"id":59215862,"identity":"f70e36e1-e9c7-4ab6-9cb0-2cbdf0b50731","added_by":"auto","created_at":"2024-06-27 19:00:30","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":913692,"visible":true,"origin":"","legend":"\u003cp\u003eHub genes expression in IMPC and IDC validated by clinical samples. Representative images and quantification of IHC staining with (\u003cstrong\u003eA\u003c/strong\u003e) MMP2, (\u003cstrong\u003eB\u003c/strong\u003e) FLNA and (\u003cstrong\u003eC\u003c/strong\u003e) CFL1 Magnification: 20x (\u003cstrong\u003eD\u003c/strong\u003e) Profression-free survival of 24 IMPC and matched IDC patients from our center\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4538838/v1/ad59101697f71dbd1a31ea76.jpeg"},{"id":59217321,"identity":"729b9014-6de7-4d67-97bf-a6a0e954437b","added_by":"auto","created_at":"2024-06-27 19:16:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2557798,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4538838/v1/66af6d14-fdf1-45aa-a197-bdeb4cdd42a4.pdf"},{"id":59215863,"identity":"a256553a-b832-4314-9792-c74b89d527d5","added_by":"auto","created_at":"2024-06-27 19:00:30","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2558380,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Fig. 1. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Nomogram predicting the 3-year, 5-year and 10-year OS for invasive micropapillary carcinoma patients. (included Race) (\u003cstrong\u003eB\u003c/strong\u003e)Discriminatory accuracy for predicting OS assessed by ROC analysis calculating AUC. (included Race)\u003c/p\u003e","description":"","filename":"SupplementaryFigure1.tif","url":"https://assets-eu.researchsquare.com/files/rs-4538838/v1/55cb0caeef166d5d7c61485d.tif"},{"id":59215867,"identity":"7d631889-08b7-4283-a1b6-ab98a8f30d5f","added_by":"auto","created_at":"2024-06-27 19:00:31","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":6393000,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Fig. 2.\u003c/strong\u003e GSEA analysis of genes in GSE66418\u003c/p\u003e","description":"","filename":"SupplementaryFigure2.tif","url":"https://assets-eu.researchsquare.com/files/rs-4538838/v1/103254e2f2325c503ce8d5ec.tif"},{"id":59216655,"identity":"7e49bbda-0e52-48b0-ba15-59b491cea80c","added_by":"auto","created_at":"2024-06-27 19:08:30","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":3276512,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Fig. 3. \u003c/strong\u003eROC curve of the top 10 hub genes (\u003cstrong\u003eA\u003c/strong\u003e)CTNNB1 (\u003cstrong\u003eB\u003c/strong\u003e)ERBB2 (\u003cstrong\u003eC\u003c/strong\u003e)CD4 (\u003cstrong\u003eD\u003c/strong\u003e)EGF (\u003cstrong\u003eE\u003c/strong\u003e)MMP2 (\u003cstrong\u003eF\u003c/strong\u003e)FYN (\u003cstrong\u003eG\u003c/strong\u003e)PGR (\u003cstrong\u003eH\u003c/strong\u003e)ITGA5(\u003cstrong\u003eI\u003c/strong\u003e)FLNA (\u003cstrong\u003eJ\u003c/strong\u003e)CFL1\u003c/p\u003e","description":"","filename":"SupplementaryFigure3.tif","url":"https://assets-eu.researchsquare.com/files/rs-4538838/v1/ad0b68c029625bb18c2f24b9.tif"},{"id":59215869,"identity":"4fd9ed1d-2a6a-4db6-abc4-69c6d6d4a918","added_by":"auto","created_at":"2024-06-27 19:00:31","extension":"tif","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":3742812,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Fig. 4.\u003c/strong\u003e Correlation of mRNA and protein in 8 hub genes (\u003cstrong\u003eA\u003c/strong\u003e)CTNNB1 (\u003cstrong\u003eB\u003c/strong\u003e)ERBB2 (\u003cstrong\u003eC\u003c/strong\u003e) CD4 (\u003cstrong\u003eD\u003c/strong\u003e)MMP2 (\u003cstrong\u003eE\u003c/strong\u003e)PGR (\u003cstrong\u003eF\u003c/strong\u003e)ITGA5 (\u003cstrong\u003eG\u003c/strong\u003e)FLNA (\u003cstrong\u003eH\u003c/strong\u003e)CFL1\u003c/p\u003e","description":"","filename":"SupplementaryFigure4.tif","url":"https://assets-eu.researchsquare.com/files/rs-4538838/v1/079dfa29eccb270c836a4d37.tif"},{"id":59215866,"identity":"ce9f3e57-28b4-4132-ac6b-bf9cc02d9112","added_by":"auto","created_at":"2024-06-27 19:00:31","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":62775,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 1. \u003c/strong\u003ePrognostic factors for overall survival of patients with invasive micropapillary breast cancer (included HER2)\u003c/p\u003e","description":"","filename":"SupplementaryTable1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4538838/v1/7584a86bc195be0886c3c3b3.pdf"},{"id":59215870,"identity":"81048696-90b7-47e4-9c04-c1e6600853a2","added_by":"auto","created_at":"2024-06-27 19:00:31","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":50047,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 2. \u003c/strong\u003ePoint assignment and prognostic score in the nomogram (IMPC SEER Cohort)\u003c/p\u003e","description":"","filename":"SupplementaryTable2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4538838/v1/87f77bf42c948611d1230a34.pdf"},{"id":59215872,"identity":"58165f8f-ef2c-4764-9143-22eaada070d3","added_by":"auto","created_at":"2024-06-27 19:00:32","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":100550,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 3. \u003c/strong\u003eDEGs between IMPC and ICNST in GSE66418\u003c/p\u003e","description":"","filename":"SupplementaryTable3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4538838/v1/02236e538f198e36a72d26f9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Lower MMP2, FLNA, CFL1 expressions favor invasive micropapillary carcinoma prognosis over ductal carcinoma of the breast","fulltext":[{"header":"Introduction","content":"\u003cp\u003eInvasive micropapillary carcinoma (IMPC) is a rare and aggressive form of invasive breast cancer, accounting for 1.0-8.4% of cases[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Characterized by tumor cells that form morule-like clusters without fibrovascular cores, these clusters are found within empty spaces in the stroma[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Fisher firstly described IMPC in 1980[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], and it was formally named as such in 1993[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]; Later, in 2003, it was recognized as a distinct histological subtype of breast cancer in the World Health Organization (WHO) Classification of Breast Tumors[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. IMPC exhibits a higher likelihood of local recurrence, lymph vascular invasion surrounding the tumor, and axillary lymph node invasion compared to invasive ductal carcinoma (IDC)[\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, it is unclear whether its behavior translates into a poor prognosis[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Therefore, it is important to understand the prognosis of IMPC patients and use a specific prognostic evaluation system to carry out targeted clinical interventions[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHistorically, many genes have been recognized as potential prognostic markers for forecasting the outcomes of patients with IMPC[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, it remains controversial whether poor molecular expression and clinicopathologic features contribute to a poor prognosis in IMPC patients. Recently, advancements in bioinformatics help to elucidate the mechanisms of IMPC development and progression and discover new prognostic markers for it.\u003c/p\u003e \u003cp\u003eIn our research, we integrated clinical analysis with bioinformatics to explore the prognosis of IMPC and to identify and validate potential biomarkers. We aimed to understand the difference in prognosis between IMPC and IDC breast cancers and to identify new viable therapeutic targets and strategies for IMPC patients.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003eWe used SEER*Stat version 8.4.1.2 to generate a case-listing file. Female patients diagnosed with IDC-NOS or IMPC from 2004 to 2015 were included. The specific inclusion criteria were as follows: (1) pathologically confirmed IDC-NOS (ICD-O-3 8500/3) or IMPC (ICD-O-3 8507/3); (2) no distant metastasis at diagnosis; (3) unilateral breast cancer; and (4) complete demographic information. The exclusion criteria were as follows: (1) diagnosed only at the time of death or autopsy; (2) incomplete surgical, grade, T, N, M, ER, or PR data; (3) had more than one primary carcinoma; and (4) lacked cause of death. We also collected data from an independent Chinese cohort at our center, including 204 female patients diagnosed with IMPC at the First Affiliated Hospital of Xi'an Jiaotong University from May 2016 to August 2022. Twenty-four IMPC patients and IDC patients matched for TNM stage and molecular subtype were subjected to immunohistochemistry. The use of the Chinese cohort was approved by the Ethics Committee of the First Affiliated Hospital of Xi'an Jiaotong University.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eFactors\u003c/h2\u003e \u003cp\u003eThe parameters included age, race, marital status, tumor laterality, location, histological grade, AJCC stage, TN stage, estrogen receptor (ER) status, progesterone receptor (PR) status, type of surgery, radiotherapy, chemotherapy, number of lymph nodes removed, survival status, and cancer-specific survival (CSS) status. According to the World Health Organization (WHO) standard grading system, histological grades are classified into four levels: well differentiated (grade I), moderately differentiated (grade II), poorly differentiated (grade III), and undifferentiated (grade IV). Meanwhile, the AJCC-TNM staging system, 7th edition, is applied to classify the AJCC stage, invasion depth, lymph node involvement, and distant metastasis extent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003ePSM\u003c/h2\u003e \u003cp\u003ePropensity Score Matching (PSM) is a technique used to ensure comparability in retrospective studies by selecting experimental and control cases with similar characteristics, known as matching variables[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. We used a ratio of 1:1 for nearest neighbor matching to adjust for baseline characteristics and reduce the effect of selection bias.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eEndpoint definition\u003c/h2\u003e \u003cp\u003eOverall Survival (OS) is measured from the date of diagnosis until death from any cause or the final follow-up. The SEER database defines CSS as the time from breast cancer diagnosis to death specifically from breast cancer, excluding other causes of death. The endpoints of this study were overall survival (OS) chiefly and breast cancer-specific survival (BCSS) secondly.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of the nomogram\u003c/h2\u003e \u003cp\u003eTo construct an effective OS nomogram for the prognosis of IMPC patients, we used the data of 782 IMPC patients screened in the SEER database as the training cohort to predict the individual OS outcomes at 3 years, 5 years, and 10 years. Independent prognostic factors were determined through multivariate Cox regression analysis, and a corresponding nomogram was constructed for the SEER training cohort.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eValidation of the nomogram\u003c/h2\u003e \u003cp\u003eIdentification and calibration were performed using training and external validation cohorts (an independent Chinese cohort from our center) to evaluate the prognostic performance of the nomogram. The C-index measured its discrimination, with higher values indicating better prognostic accuracy. The ROC curve validated the performance of the nomogram, while calibration compared its predicted probabilities against actual results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of DEGs\u003c/h2\u003e \u003cp\u003eThe gene expression profile dataset GSE66418 was obtained from the GEO database, encompassing 124 samples: 73 from IMPC and 51 from matched invasive carcinoma of no special type (ICNST). The dataset was based on the Affymetrix Human Genome U133 Plus 2.0 Array (GPL6801 platform) and was submitted by Gruel et al.[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The \u0026ldquo;limma\u0026rdquo; R package was used to identify differentially expressed genes between IMPC and ICNST. Excel was used to remove duplicate and invalid genes. Genes within the cutoff criteria of an adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and a |log2 (Fold-change)| \u0026gt; 1 were considered DEGs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eFunctional enrichment analysis\u003c/h2\u003e \u003cp\u003eTo obtain a better understanding of the functionality of the DEGs in the modules most relevant to IMPC, DEGs were subjected to the Database for Annotation, Visualization, and Integrated Discovery (DAVID) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://david.ncifcrf.gov/home.jsp/\u003c/span\u003e\u003cspan address=\"https://david.ncifcrf.gov/home.jsp/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to interpret the GO functions and enriched KEGG pathways[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. An adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and q-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were set as the cutoff criteria. Enriched pathways were visualized using the R packages \"tidyr\" and \"ggplot2\". GSEA of all the genes in the dataset was performed to explore their enrichment in hallmark pathways. The hallmark pathways with significant enrichment results were identified based on |NES|\u0026gt;1, NOM p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and FDR q-value\u0026thinsp;\u0026lt;\u0026thinsp;0.25.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of the PPI Network\u003c/h2\u003e \u003cp\u003eSTRING (version 11.5) was used to evaluate the potential associations of protein‒protein interactions (PPIs) with DEGs[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Only interactions with an experimentally validated composite score\u0026thinsp;\u0026ge;\u0026thinsp;0.4 were considered significant. The PPI network was constructed and visualized using Cytoscape software 3.9.1[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDefinitions of Hub Genes and Efficacy Evaluation\u003c/h2\u003e \u003cp\u003eBased on the information from the STRING protein query and the results of the degree analysis of the PPI network with the cytoHubba plug-in in Cytoscape, we selected the top 10 most dysregulated genes as the hub genes. The genes were plotted with the \"pROC\" software package, and the area under the curve (AUC) was calculated to evaluate the ability of the selected genes to distinguish between IMPC patients and controls. We analyzed the mRNA‒protein correlations of the hub genes in the LinkedOmics database to facilitate the validation process for immunohistochemistry.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eImmunohistochemistry (IHC)\u003c/h2\u003e \u003cp\u003eFor IHC analysis, the following primary antibodies were used: anti-MMP2 (1:1000; Servicebio, China), anti-Filamin A (1:150; Abcam, UK), and anti-Cofilin-1 (1:2000; Servicebio, China). The tissue specimens were deparaffinized, rehydrated, and processed in Tris-EDTA (pH 9.0) for 8 minutes with medium heat, 8-minute intervals and medium and low heat for 7 minutes for antigen retrieval. The slides were incubated in blocking reagent (Servicebio, China), incubated with primary antibody overnight at 4\u0026deg;C, washed with PBS (pH 7.4), and then incubated with secondary antibody (Servicebio, China) for 50 minutes at room temperature. IHC images were acquired microscopically using a 20\u0026times; objective. The positive area and percent intensity were used to score the IHC staining. Two pathologists who worked independently assigned all of the ratings. The following is the staining intensity record: 0 for no staining, 1 for weak staining, 2 for medium staining, and 3 for strong staining. The percentages of positive cells were as follows: 0 for \u0026lt;\u0026thinsp;5% positive area, 1 for 5%-25% positive area, 2 for 26%-50% positive area, 3 for 51%-75% positive area, and 4 for \u0026gt;\u0026thinsp;75% positive area. Multiplying the intensity score by the proportional score yielded the final IHC score. \"Low\" indicates\u0026thinsp;\u0026lt;\u0026thinsp;4 points, and \"High\" indicates\u0026thinsp;\u0026ge;\u0026thinsp;4 points.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eTo examine the clinicopathological traits of every group, chi-square and Wilcoxon rank-sum tests were run. Log-rank test was used to evaluate group differences and Kaplan-Meier and Gray techniques were used for survival analysis. Variables from the SEER cohort that showed a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in univariate Cox regression were chosen for multivariate analysis using forward stepwise regression. Using the \u0026ldquo;rms\u0026rdquo; R package, the nomogram model was created based on the findings of the multivariate Cox regression risk model. All the statistical analyses were performed using IBM SPSS version 18.0 and the statistical software package R4.2.2.\u003c/p\u003e \u003cp\u003eThe data for all the experiments were analyzed using GraphPad Prism software 8.0. A paired t-test was used for two-group comparisons. All the analyses were performed using R4.2.2 software. The analysis p values were bilateral, with a value of \u0026lt;\u0026thinsp;0.05 considered to indicate statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eClinicopathological characteristics\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFigure 1 displays our study\u0026rsquo;s design. A total of 782 patients with IMPC and 194,306 with IDC who met the inclusion and exclusion criteria from the SEER database were included in our study. The demographic, clinicopathological, and treatment characteristics of the two cohorts are summarized and compared in Table 1. The median age of the IDC patients was 57 years, whereas that of the IMPC patients was 58 years. Compared with IDC patients, IMPC patients were older, had a larger tumor size (T3), more lymph node involvement (N3), more advanced AJCC stage and greater grade. Furthermore, IMPC had a higher correlation with ER-positive and PR-positive tumor frequencies in comparison to IDC. In addition, differences in ethnicity and tumor localization between the two groups were statistically significant. Patients in the IMPC group got chemotherapy at a considerably higher rate than those in the IDC group. A total of 28.8% of patients in the IMPC group had at least 10 lymph nodes removed, which was significantly greater than the 21.4% in the IDC group (P \u0026lt; 0.001). PSM was utilized to reweight the patient population in each group due to the observable group differences between the IMPC and IDC groups. After a number of distributional disparities were removed, more similar IDC and IMPC groups were produced, and the characteristics of the patients after PSM are shown in Table 1.\u003c/p\u003e\n\u003cp\u003eTable1 Comparison of clinical characteristics between IMPC and IDC.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"586\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003eBefore PSM \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; After PSM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eRisk factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003eIMPC(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003eIDC-NOS(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003eIMPC(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003eIDC-NOS(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.184265010351968%\" valign=\"top\"\u003e\n \u003cp\u003e（n=782）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.11801242236025%\" valign=\"top\"\u003e\n \u003cp\u003e(n=194306)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.977225672877847%\" valign=\"top\"\u003e\n \u003cp\u003e（n=782）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.25465838509317%\" valign=\"top\"\u003e\n \u003cp\u003e(n=782)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.664596273291925%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e0.761\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e419(53.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e114138(58.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e419（53.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e426（54.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026ge;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e363（46.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e80168（41.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e363（46.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e356（45.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e0.270\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e92(11.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e19190(9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e92（11.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e74（9.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e594(76.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e154612(79.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e594（76.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e618（79.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e96(12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e20504(10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e96（12.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e90（11.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e0.388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e0.767\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e534(68.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e136651(70.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e534（68.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e544（69.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e145(18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e32689(16.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e145（18.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e134（17.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003eDivorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e103(13.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e24966(12.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e103（13.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e104（13.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003eLaterality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e0.436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e0.649\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003eLeft\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e385(49.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e98373(50.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e385（49.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e395(50.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003eRight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e397(50.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e95933(49.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e397（50.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e387(49.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003eLocation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e0.698\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003eCentral portion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e45(5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e9665(5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e45(5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e52(6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003eUpper-inner quadrant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e135(17.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e27596(14.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e135(17.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e126(16.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003eLower-inner quadrant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e64(8.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e12739(6.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e64(8.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e59(7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003eUpper-outer quadrant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e256(32.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e77580(39.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e256(32.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e267(34.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003eLower-outer quadrant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e71(9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e16375(8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e71(9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e57(7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e211(27.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e50351(25.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e211(27.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e221(28.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003eGrade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e0.746\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003eGradeⅠ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e59(7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e39749(20.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e59(7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e65(8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003eGradeⅡ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e443(56.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e78856(40.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e443(56.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e424(54.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003eGradeⅢ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e269(34.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e74478(38.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e269(34.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e286(36.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003eGradeⅣ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e11(1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e1223(0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e11(1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e7(0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003eAJCC stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e0.970\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003eⅠ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e308(39.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e101079(52.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e308(39.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e309(39.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003eⅡ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e325(41.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e72544(37.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e325(41.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e324(41.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003eⅢ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e149(19.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e20683(10.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e149(19.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e149(19.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003eT stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e0.621\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e459(58.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e125319(64.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e459(58.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e465(59.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e253(32.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e57741(29.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e253(32.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e258(33.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e59(7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e7866(4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e59(7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e48(6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003eT4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e11(1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e3380(1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e11(1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e11(1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003eN stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e0.876\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003eN0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e404(51.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e133504(68.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e404(51.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e403(51.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003eN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e249(31.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e45194(23.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e249(31.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e245(31.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003eN2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e76(9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e10481(5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e76(9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e79(10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003eN3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e53(6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e5127(2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e53(6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e55(7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003eER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e0.932\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e76(9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e41276(21.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e76(9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e74(9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e706(90.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e153030(78.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e706(90.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e708 (90.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003ePR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e0.484\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e148(18.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e60416(31.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e148(18.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e160(20.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e634(81.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e133890(68.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e634(81.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e622(79.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003eRadiation therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e0.916\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e277(35.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e73821(38.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e277(35.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e280(35.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e505(64.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e120485(62.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e505(64.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e502(64.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003eChemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e0.577\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003eNone or unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e357(45.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e99773(51.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e357(45.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e369(47.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e425(54.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e94533(48.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e425(54.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e413(52.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003eBreast surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e0.736\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003eBCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e462(59.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e121545(62.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e462(59.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e462(59.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003eReconstruction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e9(1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e1828(0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e9(1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e6(0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003eMastectomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e311(39.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e70933(36.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e311(39.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e314(40.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003eLymph node removed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e0.941\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e26(3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e5860(3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e26(3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e25(3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003e1-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e529(67.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e145823(75.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e529(67.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e534(68.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e225(28.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e41649(21.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e225(28.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e222(28.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.717206132879046%\" valign=\"top\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.139693356047701%\" valign=\"top\"\u003e\n \u003cp\u003e2(0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.37649063032368%\" valign=\"top\"\u003e\n \u003cp\u003e974(0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.710391822827939%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.969335604770016%\" valign=\"top\"\u003e\n \u003cp\u003e2(0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.843270868824531%\" valign=\"top\"\u003e\n \u003cp\u003e1(0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eSurvival analysis based on the Kaplan‒Meier and Gray methods\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFollowing PSM, Kaplan-Meier analysis indicated that IMPC patients had a significantly better prognosis than IDC patients (P = 0.003), with 3-year, 5-year, and 10-year mortality rates of 2.6%, 4.6%, and 5.2% for IMPC, compared to 3.6%, 6.5%, and 10.0% for IDC (Fig.2A). According to the Gray method, patients with IMPC had a significantly more favorable BCSS than patients with IDC did (P = 0.004) (Fig.2B).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eUnivariate and multivariate analyses of IMPC prognostic factors\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe Cox regression model was used in the training cohort (SEER cohort) to determine the factors affecting the prognosis of IMPC patients. Univariate analysis revealed that age, race, marital status, AJCC stage, T stage, N stage, ER status, PR status, radiation therapy, chemotherapy, breast surgery methods and number of lymph nodes resected were significantly associated with overall survival. Further multivariate Cox analysis revealed that age, race, marital status, T stage, N stage, ER status, and chemotherapy were independent prognostic factors (Table 2).\u003c/p\u003e\n\u003cp\u003eTable2 Prognostic factors for overall survival of patients with invasive micropapillary breast cancer\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"550\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eRisk factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.81818181818182%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eUnivariate analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.27272727272727%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eMultivariate analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.780269058295964%\" valign=\"top\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.076233183856502%\" valign=\"top\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.695067264573991%\" valign=\"top\"\u003e\n \u003cp\u003eP\u0026nbsp;value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.556053811659194%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.094170403587444%\" valign=\"top\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.798206278026905%\" valign=\"top\"\u003e\n \u003cp\u003eP\u0026nbsp;value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026ge;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e2.364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e1.526-3.663\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e2.416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e1.440-4.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e0.513\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e0.304-0.868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e0.267-0.835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e0.445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e0.199-0.991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e0.151-0.884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e1.255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e0.698-2.256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e0.447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e0.599-2.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e0.738\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eDivorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e3.693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e2.321-5.875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e3.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e1.982-5.244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eLaterality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eLeft\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eRight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e0.970\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e0.640-1.470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e0.887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eLocation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eCentral portion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e1.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e0.414-2.779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e0.884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eUpper-inner quadrant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e0.666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e0.340-1.306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e0.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eLower-inner quadrant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e0.490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e0.172-1.395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eUpper-outer quadrant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e0.952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e0.574-1.581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e0.850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eLower-outer quadrant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e0.862\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e0.394-1.888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e0.711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eGrade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eGradeⅠ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eGradeⅡ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e1.828\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e0.659-5.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e0.246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eGradeⅢ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e1.966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e0.696-5.553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e0.202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eGradeⅣ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e3.957\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e0.884-17.717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eAJCC stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eⅠ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eⅡ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e1.140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e0.685-1.896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e0.614\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.769\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e0.279-2.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e0.611\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eⅢ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e2.525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e1.494-4.267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e0.070-3.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e0.430\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eT stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e1.541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e0.970-2.449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e0.806-3.439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e0.168\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e2.785\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e1.462-5.306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e2.673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e0.948-7.535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eT4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e10.804\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e3.799-30.725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e10.220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e2.260-46.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eN stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eN0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e1.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e0.624-1.712\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e0.899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e0.692-4.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e0.255\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eN2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e1.519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e0.757-3.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e0.239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e3.877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e0.727-20.689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eN3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e3.566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e1.988-6.397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e10.490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e2.035-54.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e0.308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e0.189-0.503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e0.099-0.541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003ePR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e0.542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e0.345-0.852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e0.506-2.377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e0.816\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eRadiation therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e0.710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e0.467-1.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.658\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e0.395-1.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eChemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eNone or unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e0.709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e0.4675-1.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e0.322-0.959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eBreast surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eBCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eReconstruction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e1.790\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e0.245-13.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e0.566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e0.166-10.598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e0.790\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eMastectomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e1.898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e1.247-2.888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e0.660-2.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e0.599\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eLymph node removed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003e1-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e0.318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e0.136-0.744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e0.195-1.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e0.527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e0.222-1.255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e0.125-1.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.90909090909091%\" valign=\"top\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.363636363636363%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.09090909090909%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e0.995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.181818181818182%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e0.995\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eConstruction and validation of the nomogram\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe independent prognostic factors identified by Cox regression (age, race, marital status, T stage, N stage, ER status, and chemotherapy treatment) were used to construct a nomogram to predict OS in IMPC patients (Supplementary Fig.1A). Since the races in the validation cohort (Chinese cohort) were all Asian, we removed this factor to construct a new nomogram (Fig.2C), and we found that it did not have a large impact on the AUC values (Supplementary Fig.1B, Fig.2D). The nomogram highlighted that the T stage most affected prognosis, with chemotherapy having the least effect. All subtypes of all variables were assigned scores (Supplementary Table 2).\u003c/p\u003e\n\u003cp\u003eThe nomogram model was internally and externally validated. On the one hand, after internal verification in the training cohort (SEER cohort), the C-index predicted by OS was 0.806. The area under the ROC curve (AUC) values for the 3-, 5- and 10-year survival rates were 0.843, 0.861 and 0.743, respectively, indicating that this model has excellent discriminant ability (Fig.2D). The calibration plots showed good consistency between the nomogram predictions and the actual observations in the training cohort (Fig.2E). On the other hand, external verification using data from the First Affiliated Hospital of Xi\u0026apos;an Jiaotong University revealed that the C-index predicted by OS in the validation cohort (Chinese cohort) was 0.959. The area under the ROC curve (AUC) values for the 3- and 5-year survival rates were 0.838 and 0.899, respectively (Fig.2F).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eIdentification of hub genes of IMPC\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo further investigate the gene expression profile of IMPC, with respect to the GSE66418 cohort, which included 73 IMPC samples and 51 ICNST samples, a total of 294 DEGs, including 192 upregulated genes and 102 downregulated genes, were selected. (Supplementary Table 3). A volcano plot of the genes differentially expressed between IMPC samples and ICNST samples is shown in Figure 3A.\u003c/p\u003e\n\u003cp\u003eFor a more functional understanding of the underlying mechanism of the genes involved in IMPC prognosis, GSEA, GO function and KEGG pathway enrichment analyses were performed. The GSEA enrichment results showed that genes were enriched in the unfolded protein response-, myogenesis-, mTORC1 signaling-, UV-response-up- and protein secretion-related pathways (Supplementary Fig.2). The results of GO analysis indicated that the DEGs significantly participated in the formation of cell components, which included the plasma membrane, extracellular exosome, extracellular space, extracellular region, cell surface, external side of the plasma membrane, and extracellular matrix. In addition, KEGG pathway analysis was subsequently conducted, and the results showed that the DEGs were enriched mainly in focal adhesion (Fig.3B, C).\u003c/p\u003e\n\u003cp\u003eThe STRING database was used to construct the PPI network (Fig.3D). The top 10 hub genes, which included 3 upregulated genes (CTNNB1, EGF and PGR) and 7 downregulated genes (ERBB2, CD4, MMP2, FYN, ITGA5, FLNA and CFL1), were identified according to their highest degrees of connectivity using the cytoHubba plug-in in Cytoscape (Fig.3E).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eVerification of the hub genes\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo determine the diagnostic value of the 10 hub genes for IMPC patients, ROC curves based on the GSE66418 dataset were generated. The AUC values of CTNNB1, ERBB2, CD4, EGF, MMP2, FYN, PGR, ITGA5, FLNA, and CFL1 were 0.824, 0.719, 0.790, 0.672, 0.723, 0.790, 0.717, 0.855, 0.959, and 0.778, respectively (Supplementary Fig.3). In addition, since the method used in our center to verify the degree of expression of selected genes is immunohistochemistry, that is, the quantification and localization of the target protein, we screened hub genes with an AUC \u0026gt; 0.7 and then calculated their correlation coefficients between mRNAs and proteins. The results demonstrated that the Pearson correlation coefficients of CTNNB1, ERBB2, CD4, MMP2, PGR, ITGA5, FLNA, and CFL1 were 0.0888, 0.8368, 0.3305, 0.5958, 0.7113, 0.387, 0.641, and 0.5695, respectively (Supplementary Fig.4). Subsequently, we screened hub genes with an AUC\u0026gt; 0.70 and the Pearson correlation coefficient\u0026gt; 0.5 for validation, and the selected genes were ERBB2, PGR, MMP2, FLNA and CFL1. Since, in existing studies and clinical diagnoses, we know that ERBB2 and PGR expression levels cannot be used for diagnosing IMPC and reflecting its prognosis, we decided to validate the three hub genes MMP2, FLNA and CFL1.\u003c/p\u003e\n\u003cp\u003eFinally, we examined the expression of MMP2, FLNA and CFL1 in 24 IMPC tissues and stage-subtype-matched IDC tissues from the First Affiliated Hospital of Xi\u0026apos;an Jiaotong University using immunohistochemistry. Our results showed that MMP2, FLNA and CFL1 expression was significantly lower in IMPC tissues than in matched IDC tissues (Fig.4A,B,C). Since MMP2, FLNA and CFL1 were proved to be associated with breast cancer progression, the lower expression of these protein might contribute to the better prognosis of IMPC. To further validate our hypothesis, we explore the prognosis difference between IMPC and matched IDC patients, the K-M plotter analysis showed that IMPC patients had a better progression free survival (PFS) than IDC patients, even if the difference between them was not statistically significant due small sample size(p=0.077) (Fig.4D).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIMPC is a rare subtype of invasive breast cancer whose clinical manifestations are more aggressive than those of invasive ductal carcinoma, such as overt LVI and regional LNM[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, whether IMPC is associated with worse survival outcomes than IDC remains controversial. According to Chen et al.'s analysis of 100 IMPC patients with a median follow-up of five years, OS was lower for IMPC than for IDC[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], which is in line with Shi's retrospective multicenter study's results[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Several recent studies involving OS outcomes have shown that IMPC is noninferior to IDC[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The treatment methods and prognoses of patients at different risk levels are different, and therefore further exploration of the clinicopathological features and prognosis of invasive micropapillary carcinoma is important while avoiding the adverse effects of overtreatment.\u003c/p\u003e \u003cp\u003eIn this study, the clinical and pathological characteristics of 194306 patients with IDC and 782 patients with IMPC were analyzed. According to earlier research, IMPC typically manifests later in life and is more likely to involve lymph nodes[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Our study revealed the clinical and pathological characteristics of IMPC which is in line with the results of other investigations. Similarly, Simpsonetti's study showed that IMPC patients had greater CD24 expression and lower CD44 expression than IDC patients did, which may explain the increased propensity for lymph node metastasis[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. After matching significant differences in clinicopathologic features between IMPC and IDC patients, we demonstrated that IMPC patients had superior OS and BCSS compared to IDC patients, which is consistent with the findings of Li et al.[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Lymph node metastasis and large tumor size are widely recognized as adverse prognostic factors in clinical practice. However, in the IMPC cohort, poor clinical features did not translate to a poor prognosis. At present, the concept of predicting the prognosis of breast cancer patients and guiding clinical practice according to different breast cancer subtypes, histological grade and ER, PR and HER2 status has been widely accepted. The National Comprehensive Cancer Network (NCCN) guidelines (2018 edition) introduce the American Joint Committee on Cancer 8th edition breast cancer staging system to restage breast cancer and propose the concept of prognostic staging for the first time. The new prognostic staging system included tumor histological grade, HER2 status, ER status, PR status and other biomarkers. The high frequency of positive ER and PR signals in IMPC patients may explain the better prognosis. Chemotherapy, radiation therapy, and endocrine therapy are among the subsequent treatments that are often determined by the clinical characteristics, lymph node status, and primary tumor size. In clinical practice, clinicians frequently choose aggressive treatment due to its poor clinicopathologic traits. Due to their greater HR positive rate, IMPC patients are more likely than IDC patients to get endocrine therapy. These findings are also consistent with those reported in other studi, such as Tang et al.\u0026rsquo;s study[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and Liu et al.\u0026rsquo;s[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Furthermore, axillary lymph node dissection is more common in IMPC patients[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Resection of more than ten lymph nodes in the IMPC subgroup accounted for 28.8%, which was greater than that in the IDC subgroup (21.4%) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eHER-2 is an important driver gene and prognostic indicator of breast cancer. The degree of HER2 expression was not found to have an impact on the prognosis of IMPC patients in previous studies[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], and the HER2 variable was not included in this study because only patients after 2010 had HER2 expression data in the SEER database. We included 382 female patients with IMPC from 2010 to 2015 in our study according to the inclusion and exclusion criteria. Univariate and multivariate Cox analyses revealed that HER2 expression was not an independent factor for the prognosis of IMPC patients (Supplementary Table\u0026nbsp;1); therefore, we broadened the study period to include more patients in the training set.\u003c/p\u003e \u003cp\u003eIn this study, we identified 294 DEGs between IMPC and ICNST samples by analyzing microarray data from the GEO database. The enrichment of these DEGs revealed that the identification of core pathways and hub genes may lead to new insights into the prognosis of IMPC patients. GO functional enrichment analysis revealed that the DEGs were enriched mainly in cell components, which suggested that interactions with the extracellular environment can be triggered during IMPC. KEGG enrichment analysis revealed that focal adhesion plays significant roles in IMPC, where several components are involved in the structural link between membrane receptors and the actin cytoskeleton, and other signaling events ultimately lead to reorganization of the actin cytoskeleton, which is a prerequisite for changes in cell shape and motility and in gene expression[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Cell migration is the primary factor that influences the regulation of cytoskeletal activity. Protrusion, adhesion, contraction, and retraction from active cells are mediated in space and time by the dynamic actin cytoskeleton. Aggressive phenotypes in cancer cells are conferred by variation in their acting skeleton, such as EMT. Notably, the GSEA results showed that the enrichment pathways were the most common markers involved in IMPC, and it is generally accepted that the above processes are closely related to the development and progression of tumors.\u003c/p\u003e \u003cp\u003eImportantly, the top 10 hub genes were identified in IMPC, including 3 up-regulated genes and 7 down-regulated genes. Among them, CTNNB1 is the hub gene with the highest degree. It encodes β-Catenin, a key biomarker of the Wnt/β-Catenin signaling pathway that regulates epithelial\u0026ndash;mesenchymal transition (EMT) in cancer cells. In the absence of WNT ligands, β-catenin is involved in cell adhesion, acting as a bridge between E-cadherin and cytoskeleton-associated actin to form adherens junctions between adjacent cells[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Additionally, it is involved mainly in cell adhesion and gene transcription regulation and plays important roles in cell permeability, polarity, and migration. This may explain why IMPC patients have a greater rate of lymph node metastasis.\u003c/p\u003e \u003cp\u003eFurthermore, to test the expressions of selected genes, we detected the protein levels of three genes in IMPC and IDC samples from our center, as IDC is included within the ICNST. Compared with those in IDC, the protein expression levels of MMP2, FLNA and CFL1 in IMPC were significantly lower. At the same time, in comparing the difference in survival prognosis between the two groups of these 24 pairs of samples, IMPC showed a trend of better prognosis. The reason why the difference was not statistically significant may be because of the small sample size, and we speculate that increasing the sample size may work better.\u003c/p\u003e \u003cp\u003eMMP2 is a member of the matrix metalloproteinase family and plays a key role in extracellular matrix remodeling. MMP2 is able to degrade type IV collagen in the basement membrane, providing conditions for invasion and metastasis of cancer cells, and can also specifically participate in tumor-associated angiogenesis through the release of proangiogenic factors[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Its high expression is associated with disease progression and reduced survival in patients with various cancers, such as breast cancer, oral cancer, prostate cancer, lung cancer, head and neck cancer, and colorectal cancer. Filamin A (FLNA) is an actin cross-linking protein that interacts with a variety of binding proteins and plays an important role in cell migration, differentiation, proliferation, and survival[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]; moreover, FLNA has been identified as a marker for cancer[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. It interacts with signaling molecules in the cytoplasm and can promote tumor invasion and metastasis by affecting cell migration and adhesion[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Some studies have shown that the expression level of FLNA in invasive breast cancer tissues increases with decreasing differentiation. Cofilin 1 (CFL1) is a subtype of actin-depolymerizing factor (ADF)/cofilin family protein that plays a crucial role in tissue development, internal environment homeostasis, and diseases[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. As a key regulator of actin dynamics, CFL1 is involved in a variety of cellular activities, including apoptosis, cell motility, and cytokinesis. Recent studies have shown that cofilin-1 is highly expressed in tumor cells, such as those of colorectal cancer, pancreatic cancer, endometrial cancer and hepatocellular carcinoma; cofilin-1 promotes the occurrence, migration and invasion of tumors and is closely related to poor tumor prognosis. Overall, high expression of these three genes is associated with poor prognosis, which also indirectly explains the better clinical prognosis of IMPC.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, our study was retrospective, and selection bias and recall bias may have influenced our findings. Second, the external validation cohort included data from only a single center and had a relatively small sample size. In the future, multicenter clinical trials with larger sample sizes and different ethnic groups are needed to evaluate the diagnostic performance of this prognostic model. Third, the data used for the nomogram were obtained from the SEER database. These data do not include specific information, such as information about LVI, BRCA1/2 mutations, or Oncotype DX recurrence scores. Finally, the mechanism of action of the hub genes we screened in IMPC is unclear, further research and additional clinicopathological data are needed.\u003c/p\u003e \u003cp\u003eOverall, our present study focused on IMPC, a rare subtype of breast cancer with a significantly better prognosis than IDC, and this study indirectly explained this phenomenon at the molecular level for the first time. The established nomogram accurately predicts the outcomes of IMPC patients and provides a reference for informed decision-making in clinical practice. Moreover, the biological functions and regulatory mechanisms of hub genes in IMPC development need to be further studied.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eThis study was funded by the Innovation Capability Support Project of Shaanxi Province (NO. 2023KJXX－032).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Innovation Capability Support Project of Shaanxi Province (NO. 2023KJXX－032).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting Interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthor Contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eJ.H. and H.Z. designed and supervised the study; Y.W., J.Z., and Y.W. analyzed and interpreted the data (e.g., statistical analysis, biostatistics, computational analysis); Y.W. and Y.L. performed the immunohistochemistry experiments; B.S. provided breast cancer patients and clinical data; \u0026nbsp;X.L. arranged the data from our center; Y.W. written the manuscript, ; J.H. and H.Z. reviewed and revisioned of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData Availability\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analysed during the current study are available in the online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm.nih.gov/, GSE66418, and Surveillance, Epidemiology, and End Results (SEER) Program (https://seer.cancer.gov) SEER*Stat Database. The other relevant data are available from the authors upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEthics approval\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe study was performed in accordance with the Declaration of Helsinki and was approved by the Ethical Review Committee of the First Affiliated Hospital of Xi\u0026apos;an Jiaotong University (No. XJTU1AF2024LSYY-094).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent to participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAs the study was retrospective, the ethics committee exempted the informed consent of the individual participants.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent to publish\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eNassar, H., et al., \u003cem\u003eClinicopathologic analysis of invasive micropapillary differentiation in breast carcinoma.\u003c/em\u003e Mod Pathol, 2001. \u003cstrong\u003e14\u003c/strong\u003e(9): p. 836-41.\u003c/li\u003e\n\u003cli\u003eKuroda, H., et al., \u003cem\u003eClinical and pathologic features of invasive micropapillary carcinoma.\u003c/em\u003e Breast Cancer, 2004. \u003cstrong\u003e11\u003c/strong\u003e(2): p. 169-74.\u003c/li\u003e\n\u003cli\u003eIde, Y., et al., \u003cem\u003eClinicopathological significance of invasive micropapillary carcinoma component in invasive breast carcinoma.\u003c/em\u003e Pathology International, 2011. \u003cstrong\u003e61\u003c/strong\u003e(12): p. 731-736.\u003c/li\u003e\n\u003cli\u003eFisher, E.R., et al., \u003cem\u003ePathologic findings from the National Surgical Adjuvant Breast Project (protocol no. 4). 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This retrospective study aimed to explore the prognosis and underlying molecular mechanisms of IMPC.\u003c/p\u003e\n\u003cp\u003eMethods: We compared IMPC and IDC patients survival outcomes after PSM using the SEER database and constructed a nomogram for predicting IMPC overall survival (OS). IMPC-specific gene expression profiles were explored using microarray data from the GEO database. The top 10 genes in the PPI network with the highest degrees of connectivity were defined as hub genes and three of them were selected for validation by immunohistochemistry.\u003c/p\u003e\n\u003cp\u003eResults: IMPC patients had a better prognosis than IDC patients for both OS and BCSS. Multivariate analysis revealed that age, marital status, TN stage, ER status, and chemotherapy were independent prognostic factors for IMPC patients, which were used to construct the nomogram, with good performance in internal and external cohorts. A total of 294 DEGs were identified, with ten hub genes selected. MMP2, FLNA and CFL1, which are known to be associated with poor prognosis in breast cancer patients, were expressed at lower levels in IMPC patients than in IDC patients, indicating favorable outcomes in IMPC.\u003c/p\u003e\n\u003cp\u003eConclusions: IMPC patients had a better prognosis than IDC patients, which may due to the lower expression of pro-oncogenic genes in IMPC, but the underlying mechanism needs further investigation.\u003c/p\u003e","manuscriptTitle":"Lower MMP2, FLNA, CFL1 expressions favor invasive micropapillary carcinoma prognosis over ductal carcinoma of the breast","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-27 19:00:23","doi":"10.21203/rs.3.rs-4538838/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d97b9f34-2a5a-4926-8556-4ab06b62465f","owner":[],"postedDate":"June 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-27T19:00:26+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-27 19:00:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4538838","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4538838","identity":"rs-4538838","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}