Radiogenomic analysis of the correlation between clinical, ultrasound characteristics and immune-related genes in breast cancer

Radiogenomic analysis of the correlation between clinical, ultrasound characteristics and immune-related genes in breast cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Radiogenomic analysis of the correlation between clinical, ultrasound characteristics and immune-related genes in breast cancer Tingyao Dou, Yaodong Chen, Lunhang Liu, Yaochen Zhang, Wanru Pei, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5318112/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 May, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Background: Breast cancer is the most commonly diagnosed cancer worldwide. Breast cancer screening, early diagnosis, and standardized treatment can effectively reduce the mortality of patients. Breast color Doppler ultrasound plays a significant role in the non-invasive screening and diagnosis of breast cancer. The application of immunotherapy for breast cancer can significantly prolong the overall survival rates of patients with advanced breast cancer, which is an important research area of breast cancer treatment. This study analyzed the correlation between the clinical and ultrasound characteristics of breast cancer and immune-related genes. Methods: First, differential expression of immune-related genes were obtained using the GEO and IMMPORT database. Then, differentially expressed immune-related genes related to the overall survival of breast cancer were obtained using the GEPIA and Kaplan-Meier plotter platforms. Additionally, clinical, ultrasound characteristics and pathological specimens of breast cancer patients’ tumors were collected. Transcriptome sequencing and immunohistochemical staining were performed on the tumor specimens to obtain gene expression. Results: CXCL2, MIA, NR3C2, PTX3, S100B, SAA1, SAA1, and CXCL9 genes were correlated with each other and with clinical and ultrasonic characteristics. The high expression of MIA was related to the positive expression of PR in breast cancer. The low expression of NR3C2 was correlated with the clinical characteristics of tumor size≥20mm, later stage, Her-2 positive, Ki-67≥20%. NR3C2 was negatively correlated with the value of PKI and AUC in contrast-enhanced ultrasound parameters, and positively correlated with the value of AT and TTP. The expression of the PTX3 gene was also negatively correlated with the value of PKI and E max of shear wave elastography. SAA2 was related to the presence or absence of burrs on the edge of the tumor characterized by ultrasound. The expression of the CXCL9 gene was associated with the age of onset and tumor stage. Conclusions: In this study, 8 differentially expressed immune-related genes related to the overall survival rate of breast cancer were screened, which can be further studied in the subsequent immunotherapy of breast cancer. Some clinical and ultrasonic characteristics of breast cancer were significantly correlated with immune-related genes, such as NR3C2, SAA2, and CXCL9. Further analysis of these genes provides new ideas for the diagnosis and treatment of breast cancer. Biological sciences/Cancer Biological sciences/Genetics Biological sciences/Immunology Health sciences/Medical research Health sciences/Oncology Breast cancer Breast ultrasound Immune gene Immunotherapy Radiogenomics Figures Figure 1 Figure 2 Figure 3 Figure 4 Background According to the American Cancer Society, breast cancer is still the highest incidence of cancer among women in the world in 2024, accounting for 32% of the total incidence of female cancer. Still, the mortality rate of breast cancer only accounts for 15% of all cancers, ranking second [ 1 ] . This is due to the variety of treatment methods for breast cancer. Breast cancer treatment includes not only traditional surgical resection, radiotherapy, and chemotherapy, but also endocrine therapy, targeted therapy, and immunotherapy, which bring hope to breast cancer patients [ 2 ] . It is well known that the survival of patients with early breast cancer is much better than that of late and advanced breast cancer. Therefore, breast cancer screening for all women can increase the early detection rate and diagnosis rate of breast cancer to reduce the mortality rate of breast cancer. In China, the most routine examination of the breast is breast ultrasound. By using advanced and clear two-dimensional ultrasound equipment, experienced ultrasound doctors can observe the presence or absence of breast hyperplasia, mammary duct dilatation, and breast nodules by performing breast tomography [ 3 ] . Ultrasound can accurately detect small nodules that doctors cannot accurately examine. The possibility of malignant breast nodules is judged by whether the shape of the nodules is regular, whether the aspect ratio is greater than 1, whether the posterior echo is attenuated, whether there is a rich blood flow signal, echo pattern, and so on. Now, more advanced breast ultrasound instruments are being developed, such as Mindray Nuewa R9 Pro ultrasound diagnostic instrument, which can accurately detect the micro blood flow of breast nodules (including blood flow index, perforator vessels, etc.) and hardness, which is more helpful for ultrasound doctors to judge the nature of breast nodules. At present, studies have been made to use various ultrasonic characteristics of breast nodules to predict molecular type, stage, gene expression, and even tissue hypoxia in breast cancer [ 4 – 6 ] . Therefore, some ultrasound characteristics have good predictive value for breast cancer, but more in-depth research still needs to be widely used in clinics in the future. So far, the treatment of breast cancer has come to the era of precision therapy. The emergence and clinical application of immunotherapy such as PD-1/PD-L1 and CTLA-4 inhibitors [ 7 ] have benefited many cancer patients and brought hope to breast cancer immunotherapy. The mechanism of immunotherapy is to enhance the anti-tumor activity of CD8 + T cells by presenting tumor-associated antigens to T lymphocytes through dendritic cells in patients, thereby inhibiting the development of breast cancer [ 8 ] . TIM-3, TIGIT, and VISTA are relatively clear immune-related genes in breast cancer patients [ 9 – 11 ] , but their therapeutic effects are unclear. Radiogenomics is a new research field, which studies the relationship between tumor imaging characteristics and genomic characteristics. Radiogenomics of tumors can non-invasively reveal potential biological functional characteristics closely related to imaging characteristics and reveal potential carcinogenic mechanisms. It improves the accuracy of patient treatment by improving non-invasive prediction and survival prediction at the molecular level of the tumor [ 12 ] . At present, the use of radiogenomics to explore the diagnosis and treatment of breast cancer has been widely studied [ 13 ] . However, radiogenomics mainly uses the parameters of breast nuclear magnetic resonance [ 14 , 15 ] , and only a few studies use the characteristics of breast ultrasound. In this study, we explored the correlation between clinical and ultrasonographic characteristics of breast cancer and immune-related genes (Fig. 1 ). Firstly, the Gene Expression Omnibus (GEO) database was used to screen the differentially expressed genes (DEGs) in breast cancer and adjacent normal tissues. Then the immune-related genes (IRGs) in the IMMPORT database were used to intersect the two to obtain the differentially expressed immune-related genes (DEIRGs). The The Gene Expression Profiling Interactive Analysis (GEPIA) and Kaplan-Meier (KM) plotter platforms were used to screen the DEIRGs related to the overall survival of breast cancer. At the same time, the ultrasonic characteristics and pathological specimens of breast cancer patients were collected, and the obtained pathological specimens were subjected to transcriptome sequencing and immunohistochemical staining. The expression of the screened genes was extracted from the tumor specimens of the patients in our hospital, and the clinical characteristics and ultrasonic characteristics of the patients were statistically analyzed. The conclusion of this study was to screen for DEIRGs related to the overall survival rate of breast cancer, which can be further studied in breast cancer immunotherapy. Some ultrasonic and clinical characteristics of breast cancer were significantly correlated with immune-related genes. Further analysis provides new ideas for the diagnosis and treatment of breast cancer. Methods Patients From January 2023 to December 2023, breast patients with BI-RADS ≥ 4 breast nodules were enrolled in the First Hospital of Shanxi Medical University. The ultrasound characteristics of the breast tumor were collected by experienced ultrasound doctors before the breast tumor puncture. Patients who had received breast cancer-related treatment (including breast biopsy, surgery, chemotherapy, radiotherapy, and endocrine therapy) were excluded to avoid the impact of treatment on the ultrasound characteristics of the tumor. Patients with puncture results of breast malignant tumors were included in this study. Preoperative puncture or intraoperative frozen pathological samples were obtained after obtaining the patient's informed consent. Data analysis screened differentially expressed immune-related genes GEO database contains a large number of published gene expression data sets in various countries around the world, providing free open download and application for the public [ 16 ] . The data of DEGs screened in this study were derived from the GEO database, including 8 tumor tissues and 8 adjacent normal tissues [ 17 ] . IMMPORT Shared Data is an authoritative database of immunology, providing immunological data and analysis tools that can be used to query immune genes [ 18 ] . The immune-related genes involved in this study were derived from the IMMPORT database [ 19 ] . The DEGs between breast cancer and adjacent normal tissues were determined by R package “limma”, and the criteria was |logFC|> 2, p < 0.01. The R package “dplyr” was used to find differentially expressed genes with immune-related genes. Using the gene data of breast cancer in the public database GEO and the immune gene data in the IMMPORT database for analysis and screening, some DEIRGs in breast cancer were initially obtained. GEPIA and Kaplan-Meier plotter platforms survival analysis GEPIA is a web server for cancer and normal gene expression analysis and interaction analysis. GEPIA collates the expression values of each searchable gene in different tumor samples. It can calculate the expression level of a gene in a certain tumor, analyze the relationship between genes and tumor prognosis, and co-expression between genes [ 20 ] . The KM survival curve contains gene expression data and survival information of breast cancer patients. The survival information on KM includes OS, RFS, DMFS, and PPS of people with cancer. It is a powerful tool for analyzing patient survival outcomes [ 21 ] . We obtained the relationship between the DEIRGs and the overall survival rate of breast cancer patients through GEPIA and KM, and further screened the DEIRGs that affect the survival and prognosis of breast cancer patients. Ultrasonic characteristics In this study, Mindray Nuewa R9 Pro ultrasonic diagnostic instrument (Mindray Medical, China) was used to collect and store ultrasonic data, including B-mode ultrasound, microvascular imaging, strain shear wave elastography (SWE), and contrast-enhanced ultrasound. B-mode ultrasound characteristics are qualitative analysis, including aspect ratio (> 1 and ≤ 1), with or without edge burrs, and calcification. Microvascular imaging characteristics, SWE characteristics, and contrast-enhanced ultrasound characteristics (CEUS) belong to quantitative analysis. Microvascular imaging characteristics include color Doppler flow imaging (CDFI) and ultramicro angiography (UMA). Microvascular imaging can display blood flow direction, blood flow velocity, and blood flow dispersion. The faster the blood flow velocity, the higher the sensitivity of the blood flow pixel ratio. The characteristics of CDFI and UMA include: using the built-in analysis software of the ultrasonic diagnostic instrument, manually outlining the boundary of the lesion as the region of interest (ROI), and obtaining the color pixel percentage (CPP) of the lesion, hereinafter referred to as CDFI-CPP and UMA-CPP. The characteristics of SWE include Young's modulus E mean , E max , E min , and E sd [ 22 ] . The E mean value reflects the average Young's modulus of the whole area of the breast mass; the E max value reflects the Young's modulus value of the hardest part of the breast mass; the E min value is the minimum value of Young's modulus of SWE. E sd value reflects the degree of variation of Young's modulus in different regions of breast mass. CEUS include peak intensity (PKI), area under the curve (AUC), contrast agent development time (AT), and time to peak (TTP), which are used to indicate the blood supply of local lesions [ 23 ] . Whole transcriptome sequencing The expression of DEIRGs related to breast cancer survival was analyzed by whole transcriptome sequencing of frozen pathological samples obtained by puncture or surgery in 38 breast cancer patients included in the study. Following the manufacturer’s instructions, TRlzol® Reagent was used to extract total RNA. High-quality RNA was used to construct the sequencing library. RNA Purification Kit was used to purify nucleic acid. The kit used in this experiment used Illumina® Stranded Total RNA Prep, Ligation with Ribo-Zero Plus, and Microbiome for library preparation. They were sequenced by using the NovaSeq Reagent Kit. Immunohistochemical staining Immunohistochemical staining was performed on the paraffin sections of the selected genes with high expression in breast cancer to analyze the expression of immune-related genes related to the overall survival rate of breast cancer. The pathological sections used for immunohistochemical analysis were all from the First Hospital of Shanxi Medical University. Breast cancer patients included in this study were screened again, and patients who underwent neoadjuvant therapy and did not undergo surgical treatment were excluded. Immunohistochemical staining was performed on 22 cases of breast cancer and adjacent tissues. Firstly, paraffin-embedded slides were dewaxed and rehydrated with xylene and graded series of ethanol (100%, 95%, 80%, 75%). Then the citrate repair solution was boiled at the high temperature (180℃) in a pressure cooker for 2 min and cooled under flowing water to repair the antigen on the slice. Then the peroxidase blocker was added dropwise at room temperature in the dark for 15 minutes. Then incubated with anti-CXCL9 (Zhongshan Jinqiao, China) primary antibody (1:100) in a wet chamber at 37°C for 1.5 hours. The slides were incubated with the secondary antibody ( Zhongshan Jinqiao, China) for 30 minutes at room temperature. Washed with PBS 3 times between each step, 5 mins each time. For the color reaction, the slide was incubated with the DAB solution. Subsequently, the slides were counter-stained with hematoxylin, dehydrated with graded alcohol series, and covered with neutral balsam. Statistical analysis SPSS 27.0 and R language (version 4.2.1) were used for statistical analysis and mapping. The patient's age and tumor size were expressed as mean ± standard deviation (x ± s), and menopause, stage, and classification were expressed as number and percentage (%).The expression of gene was expressed as median and quartiles (M(Q1, Q2)). For the hypothesis test of classification characteristics (such as staging, with or without edge burrs, calcification, etc.), non-parametric T-test was used for data characteristics. Two independent samples were tested by the Mann-Whitney U test, Kolmogorov-Smirnov, or Moses extreme reaction test according to different data characteristics. The Kruskal-Wallis test was used for multiple independent samples. Spearman correlation analysis was used to test the hypothesis of continuous variables (such as gene expression and characteristic parameters of ultrasound). Sanger Box biomedical big data analysis platform was used to analyze the correlation between differentially expressed genes and draw heat maps [ 24 ] . P < 0.05 was considered statistically significant. Results Clinical characteristics of patients This study included 38 patients with breast cancer. The clinical characteristics of the patients (including age, menopausal status, mean maximum diameter of the tumor, and molecular typing ) are shown in Table 1 . The average size of breast cancer is 23.42 mm (range 10.4-43.3mm). These patients are female, and paraffin pathology results are invasive ductal carcinoma. Table 1 Clinical characteristics of patients Clinical characteristics Number Frequency Age(years) 52.82 ± 9.83 ≥ 60 23 60.53% <60 15 39.47% Menopause Yes 20 52.6% No 18 47.4% Tumor size(mm) 23.42 ± 8.51 <20mm 16 42.1% ≥ 20mm 22 57.9% Positive lymph nodes Yes 20 52.6% No 18 47.4% TNM Stage ⅠA 10 26.3% ⅡA 13 34.2% ⅡB 15 39.5% Immunohistochemical results LuminalA 7 18.4% LuminalB 14 36.8% Her-2 +(HR+) 7 18.4% Her-2 +(HR-) 7 18.4% Triple-Negative 3 7.9% ER positive 28 73.7% PR positive 23 60.5% HER-2 positive 14 36.8% Ki-67 ≥ 20% 26 68.4% Ki-67<20% 12 31.6% Data are displayed as mean ± standard deviation (x ± s) ER estrogen receptor, PR progesterone receptor, HER-2 human epidermal growth factor receptor 2 DEGs and IRGs in breast cancer Gene expression data of 8 cases of breast cancer and 8 cases of adjacent normal tissues were downloaded from the GEO database. A total of 174 differentially expressed genes were screened out (Fig. 2 a). Compared with normal adjacent tissues, 111 differentially expressed genes were lowly expressed in breast cancer, and 63 differentially expressed genes were highly expressed in breast cancer (Fig. 2 b). Using the data in the IMMPORT database, 31 DEIRGs were screened (Fig. 2 c). The specific name, modified P value, logFC, and up or down-regulation of 31 genes are listed in Table 2 . Among them, 20 genes were lowly expressed in breast cancer, and 11 genes were highly expressed. Table 2 DEIRGs in breast cancer and their correlation with overall survival Gene adj.P.Val logFC change OS(logrank)GEPIA OS(logrank)KM BMP8A 0.008947 2.1 up 0.79 0.0094 CCL28 0.002098 -2.88 down 0.12 0.11 CCR8 0.007918 2.29 up 0.73 0.13 CD70 0.002701 2.12 up 0.16 0.21 CXCL10 0.003527 3.51 up 0.61 0.0061 CXCL11 0.008336 3.47 up 0.51 0.042 CXCL2 0.002391 -3.82 down 0.0039 0.042 CXCL9 0.005165 3.86 up 0.0049 0.0011 DEFB1 0.002404 -3.6 down 0.31 0.00064 DMBT1 0.00961 -2.14 down 0.22 0.0086 ESM1 0.008056 2.94 up 0.22 0.0001 FAM3D 0.008056 -3.09 down 0.053 0.0049 FGF2 0.008452 -2.23 down 0.24 0.0024 FOS 0.009983 -2.39 down 0.14 0.000021 IL21R 0.001299 2.39 up 0.96 0.0005 IL22RA1 0.006937 -2.02 down 0.053 0.077 LIFR 0.006145 -2.26 down 0.22 0.00034 MIA 0.006937 -3.94 down 0.0014 0.0003 MSR1 0.00178 2.35 up 0.37 0.027 NPPA 0.006557 -2.87 down 0.097 0.11 NR3C2 0.006422 -2.01 down 0.027 0.036 OLR1 0.005477 3.26 up 0.31 0.18 PENK 0.002763 -4.08 down 0.21 0.0019 PF4V1 0.005165 -2.61 down Not found 0.11 PTN 0.001125 -3.42 down 0.1 0.19 PTX3 0.002854 -2.94 down 0.037 0.026 S100B 0.005223 -2.93 down 0.031 0.0012 SAA1 0.004015 -3.41 down 0.0068 0.024 SAA2 0.00178 -3.48 down 0.048 0.0042 TNFRSF9 0.00178 2.78 up 0.98 0.000035 TSLP 0.001353 -2.26 down 0.096 0.29 The expression of DEIRGs and their relationship with overall survival The expression of these 31 genes and their correlation with overall survival were obtained in GEPIA and Kaplan-Meier plotter platforms. A total of 8 genes were associated with overall survival (P < 0.05) in both database platforms (Table 2 ), of which 7 genes were lowly expressed in breast cancer and 1 gene was lowly expressed, which was consistent with previous results. The curves of these 8 genes in the two databases with the overall survival of patients are shown in Fig. 3 a and b. The results showed that the overall survival of patients with high expression of these 8 genes was higher. The specific expression of these 8 immune-related genes in breast cancer was obtained in GEPIA (Fig. 3 c) and the relationship between different breast cancer stages (Fig. 3 d). The expression of CXCL2, S100B, and SAA1 in different stages of breast cancer was statistically different. We extracted the expression of these 8 genes in the transcriptome sequencing database for subsequent analysis. Correlation between DEIRGs and clinical characteristics of breast cancer Table 3 shows the relationship between 8 immune-related genes and different clinical characteristics. The age-related genes were S100B and CXCL9. Low expression of S100B ( median, 0.62vs0.72, P = 0.035 ) and high expression of CXCL9 ( median, 26.31vs4.59, P = 0.004 ) were found in patients aged ≥ 60 years. The NR3C2 gene was associated with tumor size. Patients with tumor size ≥ 20 mm usually had low expression of NR3C2 ( median, 0.95 vs 2.63, P = 0.001 ). The genes related to breast cancer stages were NR3C2, S100B, and CXCL9. As the clinical stage of the patients became later, the expression of NR3C2 gradually decreased ( median, 2.46vs1.97vs0.93, P = 0.034 ), the expression of S100B increased first and then decreased ( median, 0.76vs1.24vs0.52, P = 0.048 ), and the expression of CXCL9 also increased first and then decreased ( median, 5.55vs40.79vs22.31, P = 0.042 ). The expression of the MIA gene was related to the expression of PR in immunohistochemistry, and the expression of the MIA gene was high in PR-positive patients ( median, 2.72vs0.65, P = 0.024 ). Patients with low expression of NR3C2 tended to have positive Her-2 expression ( median, 1.94 vs 1.16, P = 0.016 ) and Ki-67 expression ≥ 20% ( median, 2.73 vs 1.16, P = 0.009 ). These 8 immune genes were not significantly different from whether the patient was menopausal, whether there was lymph node metastasis, and whether the ER expression was positive ( P > 0.05 ). Table 3 Correlation between DEIRGs and clinical characteristics Clinicopathological characteristics CXCL2 MIA NR3C2 PTX3 S100B SAA1 SAA2 CXCL9 Age ≥ 60 0.45(0.26,0.98) 1.32(0.38,6.63) 2.19(0.86,3.56) 0.16(0.10,0.51) 0.62(0.17,1.86) 11.31(5.58,47.26) 0.89(0.24,2.84) 4.59(1.49,12.09) <60 0.79(0.53,1.28) 1.25(0.49,4.64) 1.32(0.86,2.47) 0.18(0.10, 0.37) 0.72(0.40,1.15) 14.79(8.36,31.87) 1.13(0.49,2.22) 26.31(8.88,61.00) P value 0.101 0.46 0.442 0.757 0.035 0.782 0.546 0.004 Menopause Yes 0.70(0.36,1.21) 1.02(0.38,5.45) 1.73(0.70,3.11) 0.19(0.09,0.38) 0.66(0.23,1.75) 12.96(6.69,31.25) 1.00(0.38,1.96) 12.51(2.83,44.51) No 0.72(0.50,1.16) 1.59(0.56,6.42) 1.16(0.88,2.19) 0.15(0.10,0.44) 0.72(0.42,0.98) 15.41(8.40,34.75) 1.60(0.45,2.35) 26.31(7.56,52.32) P value 0.633 0.553 0.874 0.965 0.942 0.613 0.613 0.317 Tumor size ≥ 20mm 0.62(0.44,0.82) 0.88(0.33,3.65) 0.95(0.62,1.65) 0.15(0.08,0.30) 0.64(0.34,0.89) 13.14(7.87,23.69) 0.98(0.50,1.99) 33.92(9.06,58.64) <20mm 1.15(0.36,2.02) 2.56(0.61,8.87) 2.63(1.52,4.02) 0.33(0.11,0.45) 0.92(0.39,2.02) 24.01(7.30,44.72) 1.60(0.38,3.73) 6.92(3.92,16.73) P value 0.171 0.234 0.001 0.162 0.122 0.356 0.529 0.084 Positive lymph nodes Yes 0.79(0.47,1.28) 1.10(0.43,3.59) 1.49(0.86,2.72) 0.28(0.11,0.43) 0.64(0.34,1.08) 14.11(6.69,24.26) 0.89(0.49,2.05) 14.14(4.17,61.00) No 0.66(0.33,1.09) 1.50(0.55,6.98) 1.48(0.88,3.13) 0.15(0.06,0.33) 0.74(0.41,1.84) 16.69(8.29,40.57) 1.66(0.34,2.43) 15.60(4.66,41.81) P value 0.478 0.409 0.696 0.206 0.361 0.633 0.553 0.828 TNM Stage ⅠA 0.60(0.31,1.36) 3.61(0.66,7.47) 2.46(1.33,4.55) 0.17(0.09,0.39) 0.76(0.49,1.84) 24.01(6.91,40.57) 1.70(0.40,2.60) 5.55(2.26,14.59) ⅡA 0.89(0.49,2.04) 0.89(0.35,9.10) 1.97(0.92,2.63) 0.20(0.10,0.45) 1.24(0.39,2.05) 11.72(7.56,60.91) 1.09(0.27,4.46) 40.79(6.76,111.49) ⅡB 0.59(0.45,0.80) 0.86(0.36,3.47) 0.93(0.57,1.53) 0.15(0.09,0.34) 0.52(0.21,0.73) 14.04(6.98,23.41) 0.91(0.55,1.66) 22.31(3.80,57.46) P value 0.247 0.302 0.034 0.828 0.048 0.776 0.729 0.042 ER Positive 0.72(0.42,1.10) 1.47(0.43,4.64) 1.25(0.86,3.11) 0.17(0.10,0.38) 0.73(0.42,1.22) 14.11(8.20,31.87) 1.07(0.42,1.72) 14.14(5.05,44.51) Negative 0.70(0.34,1.29) 1.00(0.45,9.07) 1.75(1.14,2.54) 0.21(0.07,0.48) 0.40(0.18,2.00) 13.02(6.70,54.58) 2.11(0.37,4.27) 13.94(3.84,52.32) P value 0.858 0.708 0.683 0.832 0.503 0.987 0.272 0.883 PR Positive 0.75(0.41,1.64) 2.72(0.85,5.37) 1.41(0.86,3.15) 0.18(0.11,0.420 0.74(0.52,1.60) 19.74(8.43,33.20) 1.14(0.47,1.76) 13.58(4.90,40.79) Negative 0.62(0.35,1.03) 0.65(0.36,6.58) 1.51(0.86,2.46) 0.15(0.08,0.38) 0.44(0.20,0.89) 9.24(5.90,24.54) 0.87(0.37,2.40) 22.31(3.92,57.46) P value 0.535 0.024 0.768 0.836 0.162 0.153 0.906 0.658 Her-2 Positive 0.61(0.34,0.90) 1.00(0.45,3.94) 1.16(0.86,1.63) 0.15(0.11,0.31) 0.72(0.42,0.89) 13.14(6.98,36.50) 1.70(0.50,2.71) 17.95(5.45,59.83) Negative 0.79(0.43,1.83) 2.53(0.34,5.89) 1.94(0.88,3.67) 0.23(0.09,0.43) 0.68(0.23,1.75) 14.86(8.23,31.97) 1.07(0.50,2.71) 14.14(2.83,42.76) P value 0.26 0.777 0.016 0.54 0.94 0.777 0.463 0.427 Ki-67 ≥ 20% 0.66(0.44,0.96) 0.83(0.33,3.96) 1.16(0.71,2.11) 0.16(0.10,0.35) 0.69(0.33,1.07) 12.27(6.70,24.86) 0.96(0.35,1.92) 23.42(4.93,52.32) <20% 0.93(0.35,1.96) 3.60(1.31,9.46) 2.73(1.17,4.80) 0.26(0.07,0.45) 0.89(0.55,1.78) 25.84(9.88,39.18) 1.60(0.58,3.88) 9.84(3.10,32.79) P value 0.447 0.053 0.009 0.631 0.312 0.076 0.137 0.243 Data refers to the numbers of subject included medians and quartiles (M (Q1, Q2)) for continuous variables P values indicate comparisons between two groups using the Mann-Whitney U test, Kolmogorov-Smirnov, or Moses extreme reaction test and multiple independent samples using the Kruskal–Wallis test ER estrogen receptor, PR progesterone receptor, HER-2 human epidermal growth factor receptor 2 Correlation between DEIRGs and ultrasound characteristics of breast cancer The correlation between 8 immune-related genes and ultrasound characteristics in clinically collected breast cancer patients is shown in Table 4 and Table 5 . In Table 4 , we divided the patients into two groups according to the characteristics of B-mode ultrasound: whether the aspect ratio of the tumor is greater than or equal to 1, whether there are burrs at the edge of the tumor, and whether there is calcification inside the tumor, and analyzed whether the immune-related genes were differentially expressed in the two groups. In Table 5 , the correlation of two continuous variables between the expression of immune genes and the characteristics of microvascular ultrasound, SWE, and contrast-enhanced ultrasound was analyzed. Table 4 Correlation between immune-related genes and B-mode ultrasound characteristics Ultrasound characteristics CXCL2 MIA NR3C2 PTX3 S100B SAA1 SAA2 CXCL9 Burrs Yes 0.69(0.41,1.19) 1.16(0.53,4.33) 1.41(0.86,3.08) 0.15(0.08,0.40) 0.66(0.32,1.09) 14.04(7.56,30.67) 1.05(0.38,1.75) 14.06(5.08,42.11) No 0.89(0.42,1.50) 6.77(0.31,10.54) 1.99(1.11,2.46) 0.35(0.16,0.44) 1.80(0.67,2.72) 25.81(8.11,89.07) 2.03(1.22,10.64) 50.61(2.73,319.57) P value 0.769 0.449 0.802 0.252 0.056 0.331 0.04 0.501 Aspect ratio >1 0.63(0.41,0.92) 1.78(0.70,6.67) 1.09(0.79,2.46) 0.15(0.10,0.36) 0.74(0.41,1.38) 14.04(7.57,35.84) 1.09(0.48,1.95) 24.53(4.41,54.03) ≤ 1 1.10(0.46,1.77) 0.66(0.33,3.50) 1.97(1.19,3.92) 0.20(0.09,0.45) 0.62(0.27,1.60) 16.79(7.70,30.67) 1.13(0.37,2.49) 10.77(4.54,30.75) P value 0.234 0.199 0.104 0.627 0.523 0.976 0.605 0.345 Calcification Yes 0.63(0.32,1.09) 1.34(0.23,5.76) 0.98(0.79,2.63) 0.20(0.11,0.46) 0.72(0.32,1.38) 9.45(5.50,20.04) 0.87(0.32,2.14) 11.44(3.69,47.74) No 0.75(0.47,1.46) 1.16(0.55,5.71) 1.53(1.03,3.08) 0.16(0.07,0.38) 0.73(0.43,1.52) 22.21(8.38,35.84) 1.54(0.52,2.18) 17.62(5.54,52.80) P value 0.467 0.728 0.337 0.581 0.622 0.128 0.243 0.45 Data refers to the numbers of subject included medians and quartiles (M (Q1, Q2)) for continuous variables P values indicate comparisons between two groups using the Mann-Whitney U test, Kolmogorov-Smirnov, or Moses extreme reaction test The SAA2 gene was associated with the presence of burrs at the edge of the tumor, and the SAA2 expression was low in tumors with burrs at the edge (median, 1.05 vs 2.03, P = 0.04). Whether the aspect ratio of the tumor was greater than 1 and whether there was calcification inside the tumor were not correlated with these 8 immune-related genes (P > 0.05). In the linear correlation analysis between SWE and immune genes, PTX3 had a linear correlation with E max (r=-0.346, P = 0.033), and the two were negatively correlated. In CEUS, PKI values were negatively correlated with the expression levels of NR3C2 (r=-0.450, P = 0.005) and PTX3 (r=-0.347, P = 0.033). NR3C2 expression was also negatively correlated with AUC (r=-0.390, P = 0.015), and positively correlated with AT (r = 0.407, P = 0.011) and TTP (r = 0.384, P = 0.017). There was no linear correlation between the expression of these 8 immune-related genes and the remaining ultrasound parameters(r0.05). Table 5 Correlation between DEIRGs and microvascular ultrasound, SWE, and contrast-enhanced ultrasound characteristics Ultrasound characteristics CXCL2 MIA NR3C2 PTX3 S100B SAA1 SAA2 CXCL9 CDFI-CPP r -0.113 0.145 -0.052 -0.037 0.033 0.238 0.243 -0.143 P value 0.500 0.384 0.757 0.826 0.845 0.150 0.142 0.391 UMA-CPP r -0.229 -0.040 0.002 -0.221 -0.006 0.077 0.141 0.166 P value 0.166 0.811 0.991 0.182 0.969 0.644 0.399 0.320 E mean r 0.025 0.151 -0.140 -0.197 0.059 0.045 -0.040 0.195 P value 0.882 0.365 0.403 0.235 0.724 0.787 0.809 0.241 E max r -0.240 0.007 -0.311 -0.346 -0.075 -0.177 -0.229 0.217 P value 0.146 0.967 0.057 0.033 0.654 0.288 0.167 0.191 E min r 0.104 -0.090 0.048 -0.132 -0.082 -0.031 -0.007 0.107 P value 0.533 0.589 0.776 0.429 0.625 0.855 0.965 0.522 E sd r -0.124 0.068 -0.242 0.296 -0.008 -0.128 -0.186 0.220 P value 0.460 0.684 0.144 0.071 0.964 0.445 0.263 0.185 Pkl r -0.141 -0.083 -0.450 -0.347 -0.217 -0.133 -0.083 0.256 P value 0.399 0.619 0.005 0.033 0.191 0.427 0.621 0.121 AUC r -0.063 -0.007 -0.390 -0.300 -0.184 -0.075 -0.033 0.213 P value 0.709 0.968 0.015 0.067 0.270 0.656 0.842 0.198 AT r 0.019 0.072 0.407 0.235 0.071 0.120 0.099 -0.147 P value 0.908 0.669 0.011 0.155 0.873 0.474 0.555 0.377 TTP r 0.010 0.032 0.384 0.221 0.012 0.126 0.139 -0.190 P value 0.954 0.850 0.017 0.183 0.943 451.000 0.404 0.252 P values indicate comparisons between two groups using Spearman correlation analysis r indicate correlation coefficient between the two groups The correlation between DEIRGs and expression products Since some ultrasound and clinical characteristics are related to a variety of immune-related genes, we performed correlation analysis on the expression levels of these 8 immune-related genes and plotted heat maps (Fig. 4 a). The results showed that the expression levels of CXCL2, MIA, S100 B, SAA1, and SAA2 were correlated with each other. The expression of CXCL2 was also related to NR3C2 and PTX3. The expression of NR3C2 was related to PTX3 and CXCL2. The expression of CXCL9 was related to S100 B and SAA2. We analyzed the correlation between genes by network diagram and found that there was a correlation between S100B and CXCL9 related to breast cancer stage, and there was a correlation between NR3C2 and PTX3 related to PKI (Fig. 4 b). Based on previous studies, we found that CXCL9 was significantly correlated with age and stage in clinical characteristics of breast cancer, and it was highly expressed in breast cancer. Therefore, we performed anti-CXCL9 immunohistochemical staining on paraffin sections of patients to analyze their gene expression products. The results of immunohistochemistry showed that the expression of CXCL9 in breast cancer tissues was stronger than that in adjacent breast tissues (Fig. 4 c). We randomly selected three fields of vision under a 400X microscope to count brown-positive cells on the slides of cancer tissues. The results showed that the gene sequencing amount (GSA) of CXCL9 was significantly correlated with the number of positive cells (NPCs) obtained by immunohistochemical staining (P < 0.001). A correlation scatter plot and linear equation is shown in Fig. 4 d. Discussion Our study found that the expression of immune-related genes was related to the clinical and ultrasonic characteristics of breast cancer patients. In the correlation analysis with clinical characteristics, NR3C2 expression was associated with tumor size, stage, Her-2, and Ki-67 expression, MIA was associated with PR expression, and S100B and CXCL9 were associated with age and stage of patients. This suggests that the expression of these genes can affect the stage and molecular typing of patients, thus affecting the survival and prognosis of patients. In the correlation analysis with ultrasound characteristics, SAA2 was related to the presence or absence of burrs at the edge of the tumor. NR3C2 was correlated with CEU parameters, including PKI, AUC, AT, and TTP. PTX3 was also associated with E max and PKI (P < 0.05). These parameters have predictive value for the expression of immune genes in patients and can predict tumor prognosis and develop more accurate treatment plans. Therefore, when the peak intensity and the area under the curve are larger, the contrast agent development time and time to peak are shorter, and the expression of the NR3C2 gene is lower. The lower the expression of NR3C2, the larger the tumor size, the later the stage, Her-2 tends to be positive, and the higher the expression of Ki-67. It prompts us to predict the expression of immune gene NR3C2 by CEUS, and then predict the stage and type of tumor. We can also detect the expression of NR3C2 in advanced or Her-2 positive or high Ki-67 expression breast cancer, to guide the follow-up treatment of breast cancer patients. In future studies, we can explore a new therapeutic approach to increase NR3C2 gene expression. Previously, many studies have used radiogenomics to screen differentially expressed genes in breast cancer by using breast ultrasound or breast nuclear magnetic resonance [ 14 , 25 ] . The innovation of our study is to analyze the correlation between immune-related genes and clinical and ultrasound characteristics of tumors and to further screen immune-related genes. This is helpful to explore the guiding value of ultrasound characteristics in the diagnosis and treatment of breast cancer. Our study shows that PKI, AUC, AT, and TTP can predict the expression of NR3C2 and can be used to guide subsequent treatment. The expression products of immune-related genes, such as chemokines, can promote tumor angiogenesis [ 26 ] . These new tumor vessels showed higher peaks and areas under the curve on breast ultrasound, faster contrast agent development time and peak time, so we believe that contrast-enhanced ultrasound parameters can be used to select immunotherapy. Breast cancer is a highly heterogeneous malignant tumor, but the treatment of breast cancer now mainly depends on the results of immunohistochemistry. Breast ultrasound can focus on the overall situation of the tumor. Our study predicted the expression of immune genes through macroscopic ultrasound characteristics to guide the follow-up treatment of breast cancer. For example, the positive expression of Her-2 is associated with low expression of NR3C2. Therefore, in the treatment of Her-2 positive breast cancer, in addition to conventional chemotherapy and targeted therapy, we can use a new treatment method to promote the expression of NR3C2 to improve the survival rate of patients. Our study not only screened IRGs that can predict the prognosis of patients, but also explored the ultrasonic characteristics associated with the expression of these genes. This is conducive to the diagnosis of breast cancer, the choice of treatment methods, and the prediction of patient survival in the future, to formulate accurate individualized monitoring and treatment plans for patients. Endothelial cells are the basic components of blood vessels. Endothelial cell proliferation is conducive to the formation of new blood vessels, so it is a good target for predicting angiogenesis. Dysfunction of neovascularization is associated with cancer, inflammation, and immune disorders [ 27 ] . It has been reported that the interaction between breast cancer cells and tumor endothelial cells can induce tumor angiogenesis and regulate the immune response in the tumor microenvironment, such as promoting the secretion of immune regulatory factors [ 28 ] . A large number of studies have shown that infiltrating immune cell subsets in the tumor microenvironment can regulate tumor angiogenesis and remodeling [ 29 ] . Our study found that the blood flow characteristics observed by CEU may predict the expression of immune genes in tumors. Abnormal expression of immune genes can also affect tumor angiogenesis, which is manifested in changes in CEUS, which is consistent with previous research results. Therefore, we found that there was a correlation between CEUS and IRGs in breast cancer. CEUS can be used as a promising imaging biomarker to predict the prognosis and treatment strategies of tumor patients. In our study, the high expression of MIA (MIA SH3 domain containing) is associated with PR positive in breast cancer immunohistochemistry. The role of MIA in breast cancer has not been studied experimentally, but in esophageal cancer, MIA can be used as a biomarker for the prognosis of poorly differentiated esophageal cancer [ 30 ] . A large number of studies have shown that NR3C2 (Nuclear receptor subfamily 3 group C member 2) is lowly expressed in breast cancer compared with normal tissues. It is a potential prognostic biomarker and is related to the choice of subsequent treatment options for patients [ 31 ] . Studies have shown that miR-301b-3p can target NR3C2, thereby promoting the proliferation, migration, and invasion of breast cancer cells [ 32 ] . In our study, the low expression of NR3C2 was associated with tumor size ≥ 20mm, later stage, Her-2 positive, and Ki-67 ≥ 20% in clinical characteristics. It was negatively correlated with PKI and AUC in CEUS, and positively correlated with AT and TTP, indicating that the low expression of NR3C2 was associated with more abundant tumor blood vessels, which was consistent with previous research conclusions. Therefore, we believe that the role of NR3C2 in breast cancer can be further studied and can be predicted by ultrasound characteristics. PTX3 (Pentraxin 3) is an immune-related gene. Its expression product PTX3 protein has innate immune function and angiogenesis, which can inhibit or promote angiogenesis [ 33 ] . Studies have shown that the expression level of PTX3 is associated with breast cancer staging. Overexpression of PTX3 promotes tumor invasion, proliferation, and stemness [ 34 , 35 ] . In our study, the expression of the PTX3 gene was negatively correlated with the E max , indicating that the higher the expression of the PTX3, the lower the maximum hardness of the tumor. The expression of PTX3 gene was also negatively correlated with the value of PKI in CEUs, indicating that high expression of PTX3 could inhibit vascular function. Studies have shown that high expression of S100B ( S100 calcium binding protein B ) can inhibit the metastasis of ER-negative breast cancer and is a biomarker for predicting breast cancer metastasis [ 36 ] . In our study, patients with high expression of S100B had earlier onset (< 60 years) and earlier tumor stage. In triple-negative breast cancer, SAA1 (serum amyloid A1) expression is associated with invasiveness, cancer-associated adipocyte infiltration, inflammation, lipolysis, tumor stemness, and tumor microenvironment [ 37 ] . There is no research on SAA2 (serum amyloid A2) and breast cancer, but the SAA gene expression product is synthesized by cytokines released by activated monocytes and macrophages [ 38 ] . Our study found that SAA2 was associated with the presence or absence of burrs at the edge of the tumor by ultrasound. Studies have shown that CXCL9 (C-X-C motif chemokine ligand 9) can stimulate the JAK/STAT signaling pathway and is a biomarker for the prognosis and efficacy of immunotherapy in patients with triple negative breast cancer [ 39 , 40 ] . In this study, the expression of the CXCL9 gene was also statistically different from the age of onset and tumor stage. Therefore, the expression of immune genes is related to the clinical and ultrasonic characteristics of patients. We can predict the expression of immune genes based on ultrasonic characteristics, to guide the choice of treatment strategies and predict the survival rate of patients. Our study had several limitations. First, the number of breast cancer patients included was small. Among the 38 patients included, only 3 were triple-negative breast cancer. The number was relatively small and non-parametric tests for multiple independent samples could not be performed, so some statistical results were missing. And the 38 cases of breast cancer pathology results were invasive ductal carcinoma, pathological type was single. Second, there was a selective bias. In the inclusion and exclusion criteria of our study, patients with locally advanced breast cancer with a tumor size of more than 50mm and patients with distant metastasis of breast cancer were excluded. Therefore, our results may not apply to all breast cancers, and further studies are needed to expand the number of cases. Third, there are few classifications of ultrasonic characteristics and direct correlation analysis of numerical variables. We believe that further processing and classification of these values can lead to more clear conclusions. Fourth, there was no correlation analysis between the protein of gene expression product and the clinical and ultrasonic characteristics of patients, and further research can be carried out after expanding the database. Conclusions The expression of immune-related genes was related to the clinical characteristics of breast cancer patients, such as age of onset and tumor stage. The detection of immune-related genes in patients who meet these characteristics is conducive to the development of personalized precision treatment strategies for patients. Immune-related genes can be predicted by ultrasound characteristics, such as whether there are burrs at the edge of the tumor, E max , PKI, AUC, AT, and TTP. These ultrasonic characteristics can be used to predict the expression of certain immune-related genes in patients, to predict the survival and recurrence of breast cancer patients, and to monitor the expression of genes in tumors. Therefore, the use of ultrasound characteristics to predict the expression of immune genes helps to identify potential immune-related biomarkers and develop effective treatment plans for patients. Declarations Ethics declarations This research was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee at the First Hospital of Shanxi Medical University (Approval ID: NO.KYLL-2024-034). Consent to publish All authors gave consent for publication. Competing interests The authors declare that they have no competing interests. Funding The research fund was provided by Research Project Supported by Shanxi Scholarship Council of China (2021 − 157), Open Fund from Key Laboratory of Cellular Physiology (CPOF202310), Wu Jieping Medical Fund (320.6750.2023-18-121), Shanxi natural Science Project youth project (202303021222339), The central government leads the local science and technology development fund project (YDZJSX2024D068), China Postdoctoral Science Foundation (2021M691993), Doctoral Research Project of Shanxi Medical University (XD1901), Postgraduate Practice Innovation Project in Shanxi Province (2024SJ166). Author Contribution HJ contributed to the notion of this research. TD designed the research strategy and completed the first manuscript. YC revised the manuscript. LL and YZ performed IHC and data analysis. WP, JL, YL, and YW helped perform the analysis with constructive discussions. All authors contributed to the article and approved the submitted manuscript. Acknowledgements Not applicable. Data Availability The results of this study are partially based on data available at the GEO (https://www.ncbi.nlm.nih.gov/geo/) and IMMPORT Shared Data (https://www.immport.org/home). The whole transcriptome sequencing datasets have not yet reached the stage of being made public, but are available from the corresponding author upon reasonable request. References Siegel, R. L., Giaquinto, A. N., Jemal, A. & Cancer statistics CA: A Cancer Journal for Clinicians 74, 12–49, doi: (2024). 10.3322/caac.21820 (2024). Maughan, K. L. & Ham, P. Lutterbie Ma Fau - Ham, P. S. S. Treatment of breast cancer. Guo, R., Lu, G., Qin, B. & Fei, B. Ultrasound Imaging Technologies for Breast Cancer Detection and Management: A Review. Ultrasound. Med. Biol. 44 , 37–70. 10.1016/j.ultrasmedbio.2017.09.012 (2018). Cui, H. et al. Radiogenomic analysis of prediction HER2 status in breast cancer by linking ultrasound radiomic feature module with biological functions. J. Translational Med. 21 10.1186/s12967-022-03840-7 (2023). Bene, I. et al. Radiomic Signatures Derived from Hybrid Contrast-Enhanced Ultrasound Images (CEUS) for the Assessment of Histological Characteristics of Breast Cancer: A Pilot Study. Cancers . 14 , 3905. 10.3390/cancers14163905 (2022). Yoo, J. et al. Tumor stiffness measured by shear wave elastography correlates with tumor hypoxia as well as histologic biomarkers in breast cancer. Cancer Imaging . 20 10.1186/s40644-020-00362-7 (2020). Willsmore, Z. N. et al. Combined anti-PD‐1 and anti‐CTLA‐4 checkpoint blockade: Treatment of melanoma and immune mechanisms of action. Eur. J. Immunol. 51 , 544–556. 10.1002/eji.202048747 (2021). Nencioni, A. et al. The use of dendritic cells in cancer immunotherapy. Crit. Rev. Oncol. Hematol. 65 , 191–199. 10.1016/j.critrevonc.2007.10.002 (2008). Zong, L. et al. Expression of the immune checkpoint VISTA in breast cancer. Cancer Immunol. Immunother. 69 , 1437–1446. 10.1007/s00262-020-02554-3 (2020). Tang, L. et al. Expression and Clinical Significance of TIGIT in Primary Breast Cancer. Int. J. Gen. Med. Volume . 16 , 2405–2417. 10.2147/ijgm.s407725 (2023). de Pulido, M. TIM-3 Regulates CD103 + Dendritic Cell Function and Response to Chemotherapy in Breast Cancer. Cancer Cell. 33 , 60–74e66. 10.1016/j.ccell.2017.11.019 (2018). Story, M. D., Durante, M. & Radiogenomics Med. Phys. 45 , doi: 10.1002/mp.13064 (2018). Grimm, L. J. & Mazurowski, M. A. Breast Cancer Radiogenomics: Current Status and Future Directions. Acad. Radiol. 27 , 39–46. 10.1016/j.acra.2019.09.012 (2020). Bismeijer, T. et al. Radiogenomic Analysis of Breast Cancer by Linking MRI Phenotypes with Tumor Gene Expression. Radiology . 296 , 277–287. 10.1148/radiol.2020191453 (2020). Yeh, A. C. et al. Radiogenomics of breast cancer using dynamic contrast enhanced MRI and gene expression profiling. Cancer Imaging . 19 10.1186/s40644-019-0233-5 (2019). Edgar, R. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 30 , 207–210. 10.1093/nar/30.1.207 (2002). Barrett, T. et al. NCBI GEO: archive for functional genomics data sets—update. Nucleic Acids Res. 41 , D991–D995. 10.1093/nar/gks1193 (2012). Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43 , e47–e47. 10.1093/nar/gkv007 (2015). Bhattacharya, S. et al. ImmPort, toward repurposing of open access immunological assay data for translational and clinical research. Sci. Data . 5 10.1038/sdata.2018.15 (2018). Tang, Z. et al. GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res. 45 , W98–W102. 10.1093/nar/gkx247 (2017). Kishore, J., Goel, M. & Khanna, P. Understanding survival analysis: Kaplan-Meier estimate. Int. J. Ayurveda Res. 1 , 274. 10.4103/0974-7788.76794 (2010). Shi, X. Q., Li, J. L., Wan, W. B. & Huang, Y. A. Set of Shear Wave Elastography Quantitative Parameters Combined with Ultrasound BI-RADS to Assess Benign and Malignant Breast Lesions. Ultrasound. Med. Biol. 41 , 960–966. 10.1016/j.ultrasmedbio.2014.11.014 (2015). Kristiansen, M. U., Martiniussen, M. A. & Larsen, A. S. F. Contrast-enhanced ultrasound of breast tumors: an initial experience. Acta Radiol. Open. 11 , 205846012210974. 10.1177/20584601221097458 (2022). Shen, W. et al. Sangerbox: A comprehensive, interaction-friendly clinical bioinformatics analysis platform. iMeta 1, e36, doi: (2022). 10.1002/imt2.36 Park, A. Y. et al. Radiogenomic Analysis of Breast Cancer by Using B-Mode and Vascular US and RNA Sequencing. Radiology . 295 , 24–34. 10.1148/radiol.2020191368 (2020). Strieter, R. M. et al. Cancer CXC chemokine networks and tumour angiogenesis. Eur. J. Cancer . 42 , 768–778. 10.1016/j.ejca.2006.01.006 (2006). Naito, H., Iba, T. & Takakura, N. Mechanisms of new blood-vessel formation and proliferative heterogeneity of endothelial cells. Int. Immunol. 32 , 295–305. 10.1093/intimm/dxaa008 (2020). Terceiro, L. E. L. et al. The Breast Tumor Microenvironment: A Key Player in Metastatic Spread. Cancers . 13 , 4798. 10.3390/cancers13194798 (2021). Stockmann, C., Schadendorf, D., Klose, R. & Helfrich, I. The Impact of the Immune System on Tumor: Angiogenesis and Vascular Remodeling. Front. Oncol. 4 10.3389/fonc.2014.00069 (2014). You, J. et al. TNFSF15 and MIA Variant Associated with Immunotherapy and Prognostic Evaluation in Esophageal Cancer. Journal of Oncology 1–12, doi: (2023). 10.1155/2023/1248024 (2023). Lu, J., Hu, F., Zhou, Y. & Guo, X. NR3C2-Related Transcriptome Profile and Clinical Outcome in Invasive Breast Carcinoma. BioMed Research International 1–13, doi: (2021). 10.1155/2021/9025481 (2021). Fan, Y. et al. miR-301b-3p Regulates Breast Cancer Cell Proliferation, Migration, and Invasion by Targeting NR3C2. J. Oncol. 2021 , 1–9. 10.1155/2021/8810517 (2021). Giacomini, A., Ghedini, G. C., Presta, M. & Ronca, R. Long pentraxin 3: A novel multifaceted player in cancer. Biochim. et Biophys. Acta (BBA) - Reviews Cancer . 1869 , 53–63. 10.1016/j.bbcan.2017.11.004 (2018). Giacomini, A. et al. The PTX3/TLR4 autocrine loop as a novel therapeutic target in triple negative breast cancer. Experimental Hematol. Oncol. 12 10.1186/s40164-023-00441-y (2023). Wu, J., Yang, R., Ge, H., Zhu, Y. & Liu, S. PTX3 promotes breast cancer cell proliferation and metastasis by regulating PKCζbreast cancer, pentraxin 3, protein kinase Cζ, proliferation, metastasis. Experimental Therapeutic Med. 27 10.3892/etm.2024.12412 (2024). Yen, M. C. et al. S100B expression in breast cancer as a predictive marker for cancer metastasis. Int. J. Oncol. 10.3892/ijo.2017.4226 (2017). Rybinska, I. et al. SAA1-dependent reprogramming of adipocytes by tumor cells is associated with triple negative breast cancer aggressiveness. Int. J. Cancer . 154 , 1842–1856. 10.1002/ijc.34859 (2024). Malle, E. & De Beer, F. C. Human serum amyloid A (SAA) protein: a prominent acute-phase reactant for clinical practice. Eur. J. Clin. Invest. 26 , 427–435. 10.1046/j.1365-2362.1996.159291.x (2003). Wu, L. et al. CXCL9 influences the tumor immune microenvironment by stimulating JAK/STAT pathway in triple-negative breast cancer. Cancer Immunol. Immunother. 72 , 1479–1492. 10.1007/s00262-022-03343-w (2022). Razis, E. et al. The Role of CXCL13 and CXCL9 in Early Breast Cancer. Clin. Breast. Cancer. 20 , e36–e53. 10.1016/j.clbc.2019.08.008 (2020). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 07 May, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 02 Mar, 2025 Reviews received at journal 27 Feb, 2025 Reviewers agreed at journal 17 Feb, 2025 Reviewers agreed at journal 17 Feb, 2025 Reviews received at journal 19 Nov, 2024 Reviewers agreed at journal 19 Nov, 2024 Reviewers invited by journal 19 Nov, 2024 Editor assigned by journal 19 Nov, 2024 Editor invited by journal 12 Nov, 2024 Submission checks completed at journal 12 Nov, 2024 First submitted to journal 23 Oct, 2024 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-5318112","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":381172173,"identity":"4f30a923-1d3f-471c-a7f8-5ef9a6ebeae3","order_by":0,"name":"Tingyao Dou","email":"","orcid":"","institution":"Department of First Clinical Medicine, Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Tingyao","middleName":"","lastName":"Dou","suffix":""},{"id":381172174,"identity":"0af8f1d0-2dee-4b35-9b8a-fc7bb616ac75","order_by":1,"name":"Yaodong Chen","email":"","orcid":"","institution":"Department of Ultrasonic Imaging, First Hospital of Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yaodong","middleName":"","lastName":"Chen","suffix":""},{"id":381172176,"identity":"ca009c70-0ea7-4b6e-8bd7-96aed622759b","order_by":2,"name":"Lunhang Liu","email":"","orcid":"","institution":"Department of First Clinical Medicine, Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lunhang","middleName":"","lastName":"Liu","suffix":""},{"id":381172177,"identity":"9feedfe2-d8d8-420b-a52f-91cb21c8a698","order_by":3,"name":"Yaochen Zhang","email":"","orcid":"","institution":"Department of First Clinical Medicine, Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yaochen","middleName":"","lastName":"Zhang","suffix":""},{"id":381172178,"identity":"e019985c-29ed-48bc-a9a8-66186f7d0f7a","order_by":4,"name":"Wanru Pei","email":"","orcid":"","institution":"Department of First Clinical Medicine, Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wanru","middleName":"","lastName":"Pei","suffix":""},{"id":381172179,"identity":"ba46f622-514c-4ab5-9357-87705d157866","order_by":5,"name":"Jing Li","email":"","orcid":"","institution":"Department of Breast Surgery, First Hospital of Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Li","suffix":""},{"id":381172180,"identity":"3839b7dc-8243-4bc9-83ad-db66cb784c2d","order_by":6,"name":"Yan Lei","email":"","orcid":"","institution":"Department of Breast Surgery, First Hospital of Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Lei","suffix":""},{"id":381172181,"identity":"54b59d09-372c-4e47-9234-f21bcfe3ebed","order_by":7,"name":"Yanhong Wang","email":"","orcid":"","institution":"Department of Microbiology and Immunology, School of Basic Medical Sciences, ShanxiMedical University","correspondingAuthor":false,"prefix":"","firstName":"Yanhong","middleName":"","lastName":"Wang","suffix":""},{"id":381172182,"identity":"dc06f540-26e2-45e9-8604-53d5e9a60109","order_by":8,"name":"Hongyan Jia","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYHACxgMPQOQMBsYHCRU1xOk5kADRwmzw4MwxErQwSDCwST5sYSasXH5G8oMDCRWHGZhn95hVJDawMfC3dyfg1WJwI83gQMKZwwyMc86Y3UjcIcMgcebsBvxapBMMDiS23Qb6JQeo5Qwbg4FELn4t8rPTP8C1FCS2MRPWwnA7B2ELA1FaDO6/KQD65T9QS1qxRMKZYzwE/SLfc3zjgw8VaQyGM5I3fvxRUSPH395LwGFQUL+xgcMAxOAhSjnEOgb2B8SrHgWjYBSMghEFAG3xUElinV8MAAAAAElFTkSuQmCC","orcid":"","institution":"Department of Breast Surgery, First Hospital of Shanxi Medical University","correspondingAuthor":true,"prefix":"","firstName":"Hongyan","middleName":"","lastName":"Jia","suffix":""}],"badges":[],"createdAt":"2024-10-23 10:23:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5318112/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5318112/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-00891-w","type":"published","date":"2025-05-07T15:57:37+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":71565133,"identity":"fb585795-ede8-481a-bd61-17266a086dd2","added_by":"auto","created_at":"2024-12-16 17:31:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":454260,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic of research strategy\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5318112/v1/d3e5be40d319775a3fbbfa84.png"},{"id":71565134,"identity":"acc73555-6e03-4c22-8496-b9d00d6d74b7","added_by":"auto","created_at":"2024-12-16 17:31:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1084597,"visible":true,"origin":"","legend":"\u003cp\u003eUsing GEO and IMMPORT databases to screen out DEIRGs in breast cancer. a. Heatmap of differentially expressed genes. b. Volcanic map of high and low expression of DEGs, in which the blue dot is a gene with low expression, and the red dot is a gene with high expression in breast cancer. c. Wayne map of the intersection of differentially expressed genes and immune-related genes .\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5318112/v1/f87d20a082123f849e3b211e.png"},{"id":71565135,"identity":"b8c796b3-66b7-443f-80b7-de3f9219efe3","added_by":"auto","created_at":"2024-12-16 17:31:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":7492871,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival curve and expression of DEIRGs related to overall survival rate in breast cancer. a. Survival curve of gene overall survival rate in GEPIA database. b. Survival curve of gene overall survival rate in Kaplan-Meier Plotter platforms. c. Scatter plot of gene expression in breast cancer and adjacent tissues. d. Violin plot of gene expression in different stages of breast cancer.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5318112/v1/15932c026b2227e47dd77593.png"},{"id":71565136,"identity":"981ee613-b40d-4420-adf6-ef348f4e105d","added_by":"auto","created_at":"2024-12-16 17:31:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":7896021,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis between immune genes and expression products. \u0026nbsp;a. Correlation heat map. b. Correlation network diagram. The thicker the line between genes, the greater the correlation coefficient. c. Immunohistochemical staining of CXCL9 in cancer and adjacent tissues. Left: strong positive in cancer, right: weak positive in adjacent tissues. d. Scatter plot and fitting line equation of CXCL9 sequencing amount and number of positive cells in immunohistochemical staining.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5318112/v1/e72b1baf9c8e743efd197e82.png"},{"id":82537967,"identity":"6c1eeb65-a6e9-4da7-87e8-7b6e261db2b3","added_by":"auto","created_at":"2025-05-12 16:10:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":19636103,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5318112/v1/53608e9d-3fbe-4cc9-85c7-8742500e2d44.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Radiogenomic analysis of the correlation between clinical, ultrasound characteristics and immune-related genes in breast cancer","fulltext":[{"header":"Background","content":"\u003cp\u003eAccording to the American Cancer Society, breast cancer is still the highest incidence of cancer among women in the world in 2024, accounting for 32% of the total incidence of female cancer. Still, the mortality rate of breast cancer only accounts for 15% of all cancers, ranking second \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. This is due to the variety of treatment methods for breast cancer. Breast cancer treatment includes not only traditional surgical resection, radiotherapy, and chemotherapy, but also endocrine therapy, targeted therapy, and immunotherapy, which bring hope to breast cancer patients \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. It is well known that the survival of patients with early breast cancer is much better than that of late and advanced breast cancer. Therefore, breast cancer screening for all women can increase the early detection rate and diagnosis rate of breast cancer to reduce the mortality rate of breast cancer.\u003c/p\u003e \u003cp\u003eIn China, the most routine examination of the breast is breast ultrasound. By using advanced and clear two-dimensional ultrasound equipment, experienced ultrasound doctors can observe the presence or absence of breast hyperplasia, mammary duct dilatation, and breast nodules by performing breast tomography \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Ultrasound can accurately detect small nodules that doctors cannot accurately examine. The possibility of malignant breast nodules is judged by whether the shape of the nodules is regular, whether the aspect ratio is greater than 1, whether the posterior echo is attenuated, whether there is a rich blood flow signal, echo pattern, and so on. Now, more advanced breast ultrasound instruments are being developed, such as Mindray Nuewa R9 Pro ultrasound diagnostic instrument, which can accurately detect the micro blood flow of breast nodules (including blood flow index, perforator vessels, etc.) and hardness, which is more helpful for ultrasound doctors to judge the nature of breast nodules. At present, studies have been made to use various ultrasonic characteristics of breast nodules to predict molecular type, stage, gene expression, and even tissue hypoxia in breast cancer \u003csup\u003e[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Therefore, some ultrasound characteristics have good predictive value for breast cancer, but more in-depth research still needs to be widely used in clinics in the future.\u003c/p\u003e \u003cp\u003eSo far, the treatment of breast cancer has come to the era of precision therapy. The emergence and clinical application of immunotherapy such as PD-1/PD-L1 and CTLA-4 inhibitors \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e have benefited many cancer patients and brought hope to breast cancer immunotherapy. The mechanism of immunotherapy is to enhance the anti-tumor activity of CD8\u003csup\u003e+\u003c/sup\u003eT cells by presenting tumor-associated antigens to T lymphocytes through dendritic cells in patients, thereby inhibiting the development of breast cancer \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. TIM-3, TIGIT, and VISTA are relatively clear immune-related genes in breast cancer patients \u003csup\u003e[\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e, but their therapeutic effects are unclear.\u003c/p\u003e \u003cp\u003eRadiogenomics is a new research field, which studies the relationship between tumor imaging characteristics and genomic characteristics. Radiogenomics of tumors can non-invasively reveal potential biological functional characteristics closely related to imaging characteristics and reveal potential carcinogenic mechanisms. It improves the accuracy of patient treatment by improving non-invasive prediction and survival prediction at the molecular level of the tumor \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. At present, the use of radiogenomics to explore the diagnosis and treatment of breast cancer has been widely studied \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. However, radiogenomics mainly uses the parameters of breast nuclear magnetic resonance \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e, and only a few studies use the characteristics of breast ultrasound.\u003c/p\u003e \u003cp\u003eIn this study, we explored the correlation between clinical and ultrasonographic characteristics of breast cancer and immune-related genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Firstly, the Gene Expression Omnibus (GEO) database was used to screen the differentially expressed genes (DEGs) in breast cancer and adjacent normal tissues. Then the immune-related genes (IRGs) in the IMMPORT database were used to intersect the two to obtain the differentially expressed immune-related genes (DEIRGs). The The Gene Expression Profiling Interactive Analysis (GEPIA) and Kaplan-Meier (KM) plotter platforms were used to screen the DEIRGs related to the overall survival of breast cancer. At the same time, the ultrasonic characteristics and pathological specimens of breast cancer patients were collected, and the obtained pathological specimens were subjected to transcriptome sequencing and immunohistochemical staining. The expression of the screened genes was extracted from the tumor specimens of the patients in our hospital, and the clinical characteristics and ultrasonic characteristics of the patients were statistically analyzed. The conclusion of this study was to screen for DEIRGs related to the overall survival rate of breast cancer, which can be further studied in breast cancer immunotherapy. Some ultrasonic and clinical characteristics of breast cancer were significantly correlated with immune-related genes. Further analysis provides new ideas for the diagnosis and treatment of breast cancer.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003eFrom January 2023 to December 2023, breast patients with BI-RADS\u0026thinsp;\u0026ge;\u0026thinsp;4 breast nodules were enrolled in the First Hospital of Shanxi Medical University. The ultrasound characteristics of the breast tumor were collected by experienced ultrasound doctors before the breast tumor puncture. Patients who had received breast cancer-related treatment (including breast biopsy, surgery, chemotherapy, radiotherapy, and endocrine therapy) were excluded to avoid the impact of treatment on the ultrasound characteristics of the tumor. Patients with puncture results of breast malignant tumors were included in this study. Preoperative puncture or intraoperative frozen pathological samples were obtained after obtaining the patient's informed consent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData analysis screened differentially expressed immune-related genes\u003c/h2\u003e \u003cp\u003eGEO database contains a large number of published gene expression data sets in various countries around the world, providing free open download and application for the public \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. The data of DEGs screened in this study were derived from the GEO database, including 8 tumor tissues and 8 adjacent normal tissues \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. IMMPORT Shared Data is an authoritative database of immunology, providing immunological data and analysis tools that can be used to query immune genes \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. The immune-related genes involved in this study were derived from the IMMPORT database \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. The DEGs between breast cancer and adjacent normal tissues were determined by R package \u0026ldquo;limma\u0026rdquo;, and the criteria was |logFC|\u0026gt; 2, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01. The R package \u0026ldquo;dplyr\u0026rdquo; was used to find differentially expressed genes with immune-related genes. Using the gene data of breast cancer in the public database GEO and the immune gene data in the IMMPORT database for analysis and screening, some DEIRGs in breast cancer were initially obtained.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eGEPIA and Kaplan-Meier plotter platforms survival analysis\u003c/h2\u003e \u003cp\u003eGEPIA is a web server for cancer and normal gene expression analysis and interaction analysis. GEPIA collates the expression values of each searchable gene in different tumor samples. It can calculate the expression level of a gene in a certain tumor, analyze the relationship between genes and tumor prognosis, and co-expression between genes \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. The KM survival curve contains gene expression data and survival information of breast cancer patients. The survival information on KM includes OS, RFS, DMFS, and PPS of people with cancer. It is a powerful tool for analyzing patient survival outcomes \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. We obtained the relationship between the DEIRGs and the overall survival rate of breast cancer patients through GEPIA and KM, and further screened the DEIRGs that affect the survival and prognosis of breast cancer patients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eUltrasonic characteristics\u003c/h2\u003e \u003cp\u003eIn this study, Mindray Nuewa R9 Pro ultrasonic diagnostic instrument (Mindray Medical, China) was used to collect and store ultrasonic data, including B-mode ultrasound, microvascular imaging, strain shear wave elastography (SWE), and contrast-enhanced ultrasound. B-mode ultrasound characteristics are qualitative analysis, including aspect ratio (\u0026gt;\u0026thinsp;1 and \u0026le;\u0026thinsp;1), with or without edge burrs, and calcification. Microvascular imaging characteristics, SWE characteristics, and contrast-enhanced ultrasound characteristics (CEUS) belong to quantitative analysis. Microvascular imaging characteristics include color Doppler flow imaging (CDFI) and ultramicro angiography (UMA). Microvascular imaging can display blood flow direction, blood flow velocity, and blood flow dispersion. The faster the blood flow velocity, the higher the sensitivity of the blood flow pixel ratio. The characteristics of CDFI and UMA include: using the built-in analysis software of the ultrasonic diagnostic instrument, manually outlining the boundary of the lesion as the region of interest (ROI), and obtaining the color pixel percentage (CPP) of the lesion, hereinafter referred to as CDFI-CPP and UMA-CPP. The characteristics of SWE include Young's modulus E\u003csub\u003emean\u003c/sub\u003e, E\u003csub\u003emax\u003c/sub\u003e, E\u003csub\u003emin\u003c/sub\u003e, and E\u003csub\u003esd\u003c/sub\u003e \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. The E\u003csub\u003emean\u003c/sub\u003e value reflects the average Young's modulus of the whole area of the breast mass; the E\u003csub\u003emax\u003c/sub\u003e value reflects the Young's modulus value of the hardest part of the breast mass; the E\u003csub\u003emin\u003c/sub\u003e value is the minimum value of Young's modulus of SWE. E\u003csub\u003esd\u003c/sub\u003e value reflects the degree of variation of Young's modulus in different regions of breast mass. CEUS include peak intensity (PKI), area under the curve (AUC), contrast agent development time (AT), and time to peak (TTP), which are used to indicate the blood supply of local lesions \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eWhole transcriptome sequencing\u003c/h2\u003e \u003cp\u003eThe expression of DEIRGs related to breast cancer survival was analyzed by whole transcriptome sequencing of frozen pathological samples obtained by puncture or surgery in 38 breast cancer patients included in the study. Following the manufacturer\u0026rsquo;s instructions, TRlzol\u0026reg; Reagent was used to extract total RNA. High-quality RNA was used to construct the sequencing library. RNA Purification Kit was used to purify nucleic acid. The kit used in this experiment used Illumina\u0026reg; Stranded Total RNA Prep, Ligation with Ribo-Zero Plus, and Microbiome for library preparation. They were sequenced by using the NovaSeq Reagent Kit.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eImmunohistochemical staining\u003c/h2\u003e \u003cp\u003eImmunohistochemical staining was performed on the paraffin sections of the selected genes with high expression in breast cancer to analyze the expression of immune-related genes related to the overall survival rate of breast cancer. The pathological sections used for immunohistochemical analysis were all from the First Hospital of Shanxi Medical University. Breast cancer patients included in this study were screened again, and patients who underwent neoadjuvant therapy and did not undergo surgical treatment were excluded. Immunohistochemical staining was performed on 22 cases of breast cancer and adjacent tissues. Firstly, paraffin-embedded slides were dewaxed and rehydrated with xylene and graded series of ethanol (100%, 95%, 80%, 75%). Then the citrate repair solution was boiled at the high temperature (180℃) in a pressure cooker for 2 min and cooled under flowing water to repair the antigen on the slice. Then the peroxidase blocker was added dropwise at room temperature in the dark for 15 minutes. Then incubated with anti-CXCL9 (Zhongshan Jinqiao, China) primary antibody (1:100) in a wet chamber at 37\u0026deg;C for 1.5 hours. The slides were incubated with the secondary antibody ( Zhongshan Jinqiao, China) for 30 minutes at room temperature. Washed with PBS 3 times between each step, 5 mins each time. For the color reaction, the slide was incubated with the DAB solution. Subsequently, the slides were counter-stained with hematoxylin, dehydrated with graded alcohol series, and covered with neutral balsam.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eSPSS 27.0 and R language (version 4.2.1) were used for statistical analysis and mapping. The patient's age and tumor size were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (x\u0026thinsp;\u0026plusmn;\u0026thinsp;s), and menopause, stage, and classification were expressed as number and percentage (%).The expression of gene was expressed as median and quartiles (M(Q1, Q2)). For the hypothesis test of classification characteristics (such as staging, with or without edge burrs, calcification, etc.), non-parametric T-test was used for data characteristics. Two independent samples were tested by the Mann-Whitney U test, Kolmogorov-Smirnov, or Moses extreme reaction test according to different data characteristics. The Kruskal-Wallis test was used for multiple independent samples. Spearman correlation analysis was used to test the hypothesis of continuous variables (such as gene expression and characteristic parameters of ultrasound). Sanger Box biomedical big data analysis platform was used to analyze the correlation between differentially expressed genes and draw heat maps \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eClinical characteristics of patients\u003c/h2\u003e \u003cp\u003eThis study included 38 patients with breast cancer. The clinical characteristics of the patients (including age, menopausal status, mean maximum diameter of the tumor, and molecular typing ) are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The average size of breast cancer is 23.42 mm (range 10.4-43.3mm). These patients are female, and paraffin pathology results are invasive ductal carcinoma.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical characteristics of patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.82\u0026thinsp;\u0026plusmn;\u0026thinsp;9.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60.53%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39.47%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMenopause\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor size(mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.42\u0026thinsp;\u0026plusmn;\u0026thinsp;8.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;20mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;20mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive lymph nodes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNM Stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅠA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅡA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅡB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmunohistochemical results\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLuminalA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLuminalB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHer-2 +(HR+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHer-2 +(HR-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriple-Negative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eER positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHER-2 positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKi-67\u0026thinsp;\u0026ge;\u0026thinsp;20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKi-67\u0026lt;20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eData are displayed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (x\u0026thinsp;\u0026plusmn;\u0026thinsp;s)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eER estrogen receptor, PR progesterone receptor, HER-2 human epidermal growth factor receptor 2\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDEGs and IRGs in breast cancer\u003c/h2\u003e \u003cp\u003eGene expression data of 8 cases of breast cancer and 8 cases of adjacent normal tissues were downloaded from the GEO database. A total of 174 differentially expressed genes were screened out (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Compared with normal adjacent tissues, 111 differentially expressed genes were lowly expressed in breast cancer, and 63 differentially expressed genes were highly expressed in breast cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Using the data in the IMMPORT database, 31 DEIRGs were screened (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). The specific name, modified P value, logFC, and up or down-regulation of 31 genes are listed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Among them, 20 genes were lowly expressed in breast cancer, and 11 genes were highly expressed.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDEIRGs in breast cancer and their correlation with overall survival\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eadj.P.Val\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003elogFC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003echange\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOS(logrank)GEPIA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOS(logrank)KM\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMP8A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.008947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.0094\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCL28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.002098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCR8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.007918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.002701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCXCL10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.003527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.0061\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCXCL11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.008336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.042\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCXCL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.002391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0039\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.042\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCXCL9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.005165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0049\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.0011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDEFB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.002404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.00064\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDMBT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.0086\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.008056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFAM3D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.008056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.0049\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFGF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.008452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.009983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.000021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL21R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.001299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.0005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL22RA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.006937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.077\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLIFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.006145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.00034\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMIA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.006937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.0003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMSR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.027\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNPPA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.006557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNR3C2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.006422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.027\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.036\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOLR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.005477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePENK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.002763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-4.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.0019\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePF4V1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.005165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNot found\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.001125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTX3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.002854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.037\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.026\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS100B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.005223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.031\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.0012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.004015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0068\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.048\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.0042\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNFRSF9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.000035\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSLP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.001353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eThe expression of DEIRGs and their relationship with overall survival\u003c/h2\u003e \u003cp\u003eThe expression of these 31 genes and their correlation with overall survival were obtained in GEPIA and Kaplan-Meier plotter platforms. A total of 8 genes were associated with overall survival (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in both database platforms (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), of which 7 genes were lowly expressed in breast cancer and 1 gene was lowly expressed, which was consistent with previous results. The curves of these 8 genes in the two databases with the overall survival of patients are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea and b. The results showed that the overall survival of patients with high expression of these 8 genes was higher. The specific expression of these 8 immune-related genes in breast cancer was obtained in GEPIA (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec) and the relationship between different breast cancer stages (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). The expression of CXCL2, S100B, and SAA1 in different stages of breast cancer was statistically different. We extracted the expression of these 8 genes in the transcriptome sequencing database for subsequent analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation between DEIRGs and clinical characteristics of breast cancer\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the relationship between 8 immune-related genes and different clinical characteristics. The age-related genes were S100B and CXCL9. Low expression of S100B ( median, 0.62vs0.72, P\u0026thinsp;=\u0026thinsp;0.035 ) and high expression of CXCL9 ( median, 26.31vs4.59, P\u0026thinsp;=\u0026thinsp;0.004 ) were found in patients aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years. The NR3C2 gene was associated with tumor size. Patients with tumor size\u0026thinsp;\u0026ge;\u0026thinsp;20 mm usually had low expression of NR3C2 ( median, 0.95 vs 2.63, P\u0026thinsp;=\u0026thinsp;0.001 ). The genes related to breast cancer stages were NR3C2, S100B, and CXCL9. As the clinical stage of the patients became later, the expression of NR3C2 gradually decreased ( median, 2.46vs1.97vs0.93, P\u0026thinsp;=\u0026thinsp;0.034 ), the expression of S100B increased first and then decreased ( median, 0.76vs1.24vs0.52, P\u0026thinsp;=\u0026thinsp;0.048 ), and the expression of CXCL9 also increased first and then decreased ( median, 5.55vs40.79vs22.31, P\u0026thinsp;=\u0026thinsp;0.042 ). The expression of the MIA gene was related to the expression of PR in immunohistochemistry, and the expression of the MIA gene was high in PR-positive patients ( median, 2.72vs0.65, P\u0026thinsp;=\u0026thinsp;0.024 ). Patients with low expression of NR3C2 tended to have positive Her-2 expression ( median, 1.94 vs 1.16, P\u0026thinsp;=\u0026thinsp;0.016 ) and Ki-67 expression\u0026thinsp;\u0026ge;\u0026thinsp;20% ( median, 2.73 vs 1.16, P\u0026thinsp;=\u0026thinsp;0.009 ). These 8 immune genes were not significantly different from whether the patient was menopausal, whether there was lymph node metastasis, and whether the ER expression was positive ( P\u0026thinsp;\u0026gt;\u0026thinsp;0.05 ).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation between DEIRGs and clinical characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinicopathological characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCXCL2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMIA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNR3C2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePTX3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eS100B\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSAA1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSAA2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCXCL9\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.45(0.26,0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.32(0.38,6.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.19(0.86,3.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.16(0.10,0.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.62(0.17,1.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11.31(5.58,47.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.89(0.24,2.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e4.59(1.49,12.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.79(0.53,1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.25(0.49,4.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.32(0.86,2.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.18(0.10, 0.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.72(0.40,1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e14.79(8.36,31.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.13(0.49,2.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e26.31(8.88,61.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.035\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMenopause\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.70(0.36,1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.02(0.38,5.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.73(0.70,3.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.19(0.09,0.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.66(0.23,1.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e12.96(6.69,31.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.00(0.38,1.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e12.51(2.83,44.51)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.72(0.50,1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.59(0.56,6.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.16(0.88,2.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.15(0.10,0.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.72(0.42,0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e15.41(8.40,34.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.60(0.45,2.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e26.31(7.56,52.32)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.317\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTumor size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;20mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.62(0.44,0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.88(0.33,3.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.95(0.62,1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.15(0.08,0.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.64(0.34,0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e13.14(7.87,23.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.98(0.50,1.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e33.92(9.06,58.64)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;20mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.15(0.36,2.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.56(0.61,8.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.63(1.52,4.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.33(0.11,0.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.92(0.39,2.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e24.01(7.30,44.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.60(0.38,3.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e6.92(3.92,16.73)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePositive lymph nodes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.79(0.47,1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.10(0.43,3.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.49(0.86,2.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.28(0.11,0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.64(0.34,1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e14.11(6.69,24.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.89(0.49,2.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e14.14(4.17,61.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.66(0.33,1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.50(0.55,6.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.48(0.88,3.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.15(0.06,0.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.74(0.41,1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e16.69(8.29,40.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.66(0.34,2.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e15.60(4.66,41.81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.828\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eTNM Stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eⅠA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.60(0.31,1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.61(0.66,7.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.46(1.33,4.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.17(0.09,0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.76(0.49,1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e24.01(6.91,40.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.70(0.40,2.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e5.55(2.26,14.59)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eⅡA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.89(0.49,2.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.89(0.35,9.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.97(0.92,2.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.20(0.10,0.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.24(0.39,2.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11.72(7.56,60.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.09(0.27,4.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e40.79(6.76,111.49)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eⅡB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.59(0.45,0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.86(0.36,3.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.93(0.57,1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.15(0.09,0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.52(0.21,0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e14.04(6.98,23.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.91(0.55,1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e22.31(3.80,57.46)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.034\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.048\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.042\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.72(0.42,1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.47(0.43,4.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.25(0.86,3.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.17(0.10,0.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.73(0.42,1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e14.11(8.20,31.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.07(0.42,1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e14.14(5.05,44.51)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.70(0.34,1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00(0.45,9.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.75(1.14,2.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.21(0.07,0.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.40(0.18,2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e13.02(6.70,54.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.11(0.37,4.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e13.94(3.84,52.32)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.75(0.41,1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.72(0.85,5.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.41(0.86,3.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.18(0.11,0.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.74(0.52,1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e19.74(8.43,33.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.14(0.47,1.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e13.58(4.90,40.79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.62(0.35,1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.65(0.36,6.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.51(0.86,2.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.15(0.08,0.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.44(0.20,0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9.24(5.90,24.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.87(0.37,2.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e22.31(3.92,57.46)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.658\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eHer-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.61(0.34,0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00(0.45,3.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.16(0.86,1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.15(0.11,0.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.72(0.42,0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e13.14(6.98,36.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.70(0.50,2.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e17.95(5.45,59.83)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.79(0.43,1.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.53(0.34,5.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.94(0.88,3.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.23(0.09,0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.68(0.23,1.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e14.86(8.23,31.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.07(0.50,2.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e14.14(2.83,42.76)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.427\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eKi-67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.66(0.44,0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.83(0.33,3.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.16(0.71,2.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.16(0.10,0.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.69(0.33,1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e12.27(6.70,24.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.96(0.35,1.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e23.42(4.93,52.32)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.93(0.35,1.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.60(1.31,9.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.73(1.17,4.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.26(0.07,0.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.89(0.55,1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e25.84(9.88,39.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.60(0.58,3.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e9.84(3.10,32.79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eData refers to the numbers of subject included medians and quartiles (M (Q1, Q2)) for continuous variables\u003c/p\u003e \u003cp\u003eP values indicate comparisons between two groups using the Mann-Whitney U test, Kolmogorov-Smirnov, or Moses extreme reaction test and multiple independent samples using the Kruskal\u0026ndash;Wallis test\u003c/p\u003e \u003cp\u003eER estrogen receptor, PR progesterone receptor, HER-2 human epidermal growth factor receptor 2\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation between DEIRGs and ultrasound characteristics of breast cancer\u003c/h2\u003e \u003cp\u003eThe correlation between 8 immune-related genes and ultrasound characteristics in clinically collected breast cancer patients is shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. In Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, we divided the patients into two groups according to the characteristics of B-mode ultrasound: whether the aspect ratio of the tumor is greater than or equal to 1, whether there are burrs at the edge of the tumor, and whether there is calcification inside the tumor, and analyzed whether the immune-related genes were differentially expressed in the two groups. In Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the correlation of two continuous variables between the expression of immune genes and the characteristics of microvascular ultrasound, SWE, and contrast-enhanced ultrasound was analyzed.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation between immune-related genes and B-mode ultrasound characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUltrasound characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCXCL2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMIA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNR3C2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePTX3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eS100B\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSAA1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSAA2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCXCL9\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eBurrs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.69(0.41,1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.16(0.53,4.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.41(0.86,3.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.15(0.08,0.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.66(0.32,1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e14.04(7.56,30.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.05(0.38,1.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e14.06(5.08,42.11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.89(0.42,1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.77(0.31,10.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.99(1.11,2.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.35(0.16,0.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.80(0.67,2.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e25.81(8.11,89.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.03(1.22,10.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e50.61(2.73,319.57)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAspect ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.63(0.41,0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.78(0.70,6.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.09(0.79,2.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.15(0.10,0.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.74(0.41,1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e14.04(7.57,35.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.09(0.48,1.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e24.53(4.41,54.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.10(0.46,1.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.66(0.33,3.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.97(1.19,3.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.20(0.09,0.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.62(0.27,1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e16.79(7.70,30.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.13(0.37,2.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e10.77(4.54,30.75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.345\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCalcification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.63(0.32,1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.34(0.23,5.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.98(0.79,2.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.20(0.11,0.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.72(0.32,1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9.45(5.50,20.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.87(0.32,2.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e11.44(3.69,47.74)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.75(0.47,1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.16(0.55,5.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.53(1.03,3.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.16(0.07,0.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.73(0.43,1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e22.21(8.38,35.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.54(0.52,2.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e17.62(5.54,52.80)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eData refers to the numbers of subject included medians and quartiles (M (Q1, Q2)) for continuous variables P values indicate comparisons between two groups using the Mann-Whitney U test, Kolmogorov-Smirnov, or Moses extreme reaction test\u003c/p\u003e \u003cp\u003eThe SAA2 gene was associated with the presence of burrs at the edge of the tumor, and the SAA2 expression was low in tumors with burrs at the edge (median, 1.05 vs 2.03, P\u0026thinsp;=\u0026thinsp;0.04). Whether the aspect ratio of the tumor was greater than 1 and whether there was calcification inside the tumor were not correlated with these 8 immune-related genes (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). In the linear correlation analysis between SWE and immune genes, PTX3 had a linear correlation with E\u003csub\u003emax\u003c/sub\u003e (r=-0.346, P\u0026thinsp;=\u0026thinsp;0.033), and the two were negatively correlated. In CEUS, PKI values were negatively correlated with the expression levels of NR3C2 (r=-0.450, P\u0026thinsp;=\u0026thinsp;0.005) and PTX3 (r=-0.347, P\u0026thinsp;=\u0026thinsp;0.033). NR3C2 expression was also negatively correlated with AUC (r=-0.390, P\u0026thinsp;=\u0026thinsp;0.015), and positively correlated with AT (r\u0026thinsp;=\u0026thinsp;0.407, P\u0026thinsp;=\u0026thinsp;0.011) and TTP (r\u0026thinsp;=\u0026thinsp;0.384, P\u0026thinsp;=\u0026thinsp;0.017). There was no linear correlation between the expression of these 8 immune-related genes and the remaining ultrasound parameters(r\u0026lt;0.3, P\u0026gt;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation between DEIRGs and microvascular ultrasound, SWE, and contrast-enhanced ultrasound characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUltrasound characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCXCL2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMIA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNR3C2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePTX3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eS100B\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSAA1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSAA2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCXCL9\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCDFI-CPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.143\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.391\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eUMA-CPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.320\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eE\u003csub\u003emean\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eE\u003csub\u003emax\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-0.346\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.217\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.033\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eE\u003csub\u003emin\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.522\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eE\u003csub\u003esd\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.220\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePkl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-0.450\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-0.347\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.033\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-0.390\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.407\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.147\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.377\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTTP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.384\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.190\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.017\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e451.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eP values indicate comparisons between two groups using Spearman correlation analysis\u003c/p\u003e \u003cp\u003er indicate correlation coefficient between the two groups\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eThe correlation between DEIRGs and expression products\u003c/h2\u003e \u003cp\u003eSince some ultrasound and clinical characteristics are related to a variety of immune-related genes, we performed correlation analysis on the expression levels of these 8 immune-related genes and plotted heat maps (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). The results showed that the expression levels of CXCL2, MIA, S100 B, SAA1, and SAA2 were correlated with each other. The expression of CXCL2 was also related to NR3C2 and PTX3. The expression of NR3C2 was related to PTX3 and CXCL2. The expression of CXCL9 was related to S100 B and SAA2. We analyzed the correlation between genes by network diagram and found that there was a correlation between S100B and CXCL9 related to breast cancer stage, and there was a correlation between NR3C2 and PTX3 related to PKI (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Based on previous studies, we found that CXCL9 was significantly correlated with age and stage in clinical characteristics of breast cancer, and it was highly expressed in breast cancer. Therefore, we performed anti-CXCL9 immunohistochemical staining on paraffin sections of patients to analyze their gene expression products. The results of immunohistochemistry showed that the expression of CXCL9 in breast cancer tissues was stronger than that in adjacent breast tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). We randomly selected three fields of vision under a 400X microscope to count brown-positive cells on the slides of cancer tissues. The results showed that the gene sequencing amount (GSA) of CXCL9 was significantly correlated with the number of positive cells (NPCs) obtained by immunohistochemical staining (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A correlation scatter plot and linear equation is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study found that the expression of immune-related genes was related to the clinical and ultrasonic characteristics of breast cancer patients. In the correlation analysis with clinical characteristics, NR3C2 expression was associated with tumor size, stage, Her-2, and Ki-67 expression, MIA was associated with PR expression, and S100B and CXCL9 were associated with age and stage of patients. This suggests that the expression of these genes can affect the stage and molecular typing of patients, thus affecting the survival and prognosis of patients. In the correlation analysis with ultrasound characteristics, SAA2 was related to the presence or absence of burrs at the edge of the tumor. NR3C2 was correlated with CEU parameters, including PKI, AUC, AT, and TTP. PTX3 was also associated with E\u003csub\u003emax\u003c/sub\u003e and PKI (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These parameters have predictive value for the expression of immune genes in patients and can predict tumor prognosis and develop more accurate treatment plans. Therefore, when the peak intensity and the area under the curve are larger, the contrast agent development time and time to peak are shorter, and the expression of the NR3C2 gene is lower. The lower the expression of NR3C2, the larger the tumor size, the later the stage, Her-2 tends to be positive, and the higher the expression of Ki-67. It prompts us to predict the expression of immune gene NR3C2 by CEUS, and then predict the stage and type of tumor. We can also detect the expression of NR3C2 in advanced or Her-2 positive or high Ki-67 expression breast cancer, to guide the follow-up treatment of breast cancer patients. In future studies, we can explore a new therapeutic approach to increase NR3C2 gene expression.\u003c/p\u003e \u003cp\u003ePreviously, many studies have used radiogenomics to screen differentially expressed genes in breast cancer by using breast ultrasound or breast nuclear magnetic resonance \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. The innovation of our study is to analyze the correlation between immune-related genes and clinical and ultrasound characteristics of tumors and to further screen immune-related genes. This is helpful to explore the guiding value of ultrasound characteristics in the diagnosis and treatment of breast cancer. Our study shows that PKI, AUC, AT, and TTP can predict the expression of NR3C2 and can be used to guide subsequent treatment. The expression products of immune-related genes, such as chemokines, can promote tumor angiogenesis \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. These new tumor vessels showed higher peaks and areas under the curve on breast ultrasound, faster contrast agent development time and peak time, so we believe that contrast-enhanced ultrasound parameters can be used to select immunotherapy. Breast cancer is a highly heterogeneous malignant tumor, but the treatment of breast cancer now mainly depends on the results of immunohistochemistry. Breast ultrasound can focus on the overall situation of the tumor. Our study predicted the expression of immune genes through macroscopic ultrasound characteristics to guide the follow-up treatment of breast cancer. For example, the positive expression of Her-2 is associated with low expression of NR3C2. Therefore, in the treatment of Her-2 positive breast cancer, in addition to conventional chemotherapy and targeted therapy, we can use a new treatment method to promote the expression of NR3C2 to improve the survival rate of patients. Our study not only screened IRGs that can predict the prognosis of patients, but also explored the ultrasonic characteristics associated with the expression of these genes. This is conducive to the diagnosis of breast cancer, the choice of treatment methods, and the prediction of patient survival in the future, to formulate accurate individualized monitoring and treatment plans for patients.\u003c/p\u003e \u003cp\u003eEndothelial cells are the basic components of blood vessels. Endothelial cell proliferation is conducive to the formation of new blood vessels, so it is a good target for predicting angiogenesis. Dysfunction of neovascularization is associated with cancer, inflammation, and immune disorders \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. It has been reported that the interaction between breast cancer cells and tumor endothelial cells can induce tumor angiogenesis and regulate the immune response in the tumor microenvironment, such as promoting the secretion of immune regulatory factors \u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. A large number of studies have shown that infiltrating immune cell subsets in the tumor microenvironment can regulate tumor angiogenesis and remodeling \u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. Our study found that the blood flow characteristics observed by CEU may predict the expression of immune genes in tumors. Abnormal expression of immune genes can also affect tumor angiogenesis, which is manifested in changes in CEUS, which is consistent with previous research results. Therefore, we found that there was a correlation between CEUS and IRGs in breast cancer. CEUS can be used as a promising imaging biomarker to predict the prognosis and treatment strategies of tumor patients.\u003c/p\u003e \u003cp\u003eIn our study, the high expression of MIA (MIA SH3 domain containing) is associated with PR positive in breast cancer immunohistochemistry. The role of MIA in breast cancer has not been studied experimentally, but in esophageal cancer, MIA can be used as a biomarker for the prognosis of poorly differentiated esophageal cancer \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. A large number of studies have shown that NR3C2 (Nuclear receptor subfamily 3 group C member 2) is lowly expressed in breast cancer compared with normal tissues. It is a potential prognostic biomarker and is related to the choice of subsequent treatment options for patients \u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Studies have shown that miR-301b-3p can target NR3C2, thereby promoting the proliferation, migration, and invasion of breast cancer cells \u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. In our study, the low expression of NR3C2 was associated with tumor size\u0026thinsp;\u0026ge;\u0026thinsp;20mm, later stage, Her-2 positive, and Ki-67\u0026thinsp;\u0026ge;\u0026thinsp;20% in clinical characteristics. It was negatively correlated with PKI and AUC in CEUS, and positively correlated with AT and TTP, indicating that the low expression of NR3C2 was associated with more abundant tumor blood vessels, which was consistent with previous research conclusions. Therefore, we believe that the role of NR3C2 in breast cancer can be further studied and can be predicted by ultrasound characteristics. PTX3 (Pentraxin 3) is an immune-related gene. Its expression product PTX3 protein has innate immune function and angiogenesis, which can inhibit or promote angiogenesis \u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. Studies have shown that the expression level of PTX3 is associated with breast cancer staging. Overexpression of PTX3 promotes tumor invasion, proliferation, and stemness \u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. In our study, the expression of the PTX3 gene was negatively correlated with the E\u003csub\u003emax\u003c/sub\u003e, indicating that the higher the expression of the PTX3, the lower the maximum hardness of the tumor. The expression of PTX3 gene was also negatively correlated with the value of PKI in CEUs, indicating that high expression of PTX3 could inhibit vascular function. Studies have shown that high expression of S100B ( S100 calcium binding protein B ) can inhibit the metastasis of ER-negative breast cancer and is a biomarker for predicting breast cancer metastasis \u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. In our study, patients with high expression of S100B had earlier onset (\u0026lt;\u0026thinsp;60 years) and earlier tumor stage. In triple-negative breast cancer, SAA1 (serum amyloid A1) expression is associated with invasiveness, cancer-associated adipocyte infiltration, inflammation, lipolysis, tumor stemness, and tumor microenvironment \u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. There is no research on SAA2 (serum amyloid A2) and breast cancer, but the SAA gene expression product is synthesized by cytokines released by activated monocytes and macrophages \u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. Our study found that SAA2 was associated with the presence or absence of burrs at the edge of the tumor by ultrasound. Studies have shown that CXCL9 (C-X-C motif chemokine ligand 9) can stimulate the JAK/STAT signaling pathway and is a biomarker for the prognosis and efficacy of immunotherapy in patients with triple negative breast cancer \u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. In this study, the expression of the CXCL9 gene was also statistically different from the age of onset and tumor stage. Therefore, the expression of immune genes is related to the clinical and ultrasonic characteristics of patients. We can predict the expression of immune genes based on ultrasonic characteristics, to guide the choice of treatment strategies and predict the survival rate of patients.\u003c/p\u003e \u003cp\u003eOur study had several limitations. First, the number of breast cancer patients included was small. Among the 38 patients included, only 3 were triple-negative breast cancer. The number was relatively small and non-parametric tests for multiple independent samples could not be performed, so some statistical results were missing. And the 38 cases of breast cancer pathology results were invasive ductal carcinoma, pathological type was single. Second, there was a selective bias. In the inclusion and exclusion criteria of our study, patients with locally advanced breast cancer with a tumor size of more than 50mm and patients with distant metastasis of breast cancer were excluded. Therefore, our results may not apply to all breast cancers, and further studies are needed to expand the number of cases. Third, there are few classifications of ultrasonic characteristics and direct correlation analysis of numerical variables. We believe that further processing and classification of these values can lead to more clear conclusions. Fourth, there was no correlation analysis between the protein of gene expression product and the clinical and ultrasonic characteristics of patients, and further research can be carried out after expanding the database.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe expression of immune-related genes was related to the clinical characteristics of breast cancer patients, such as age of onset and tumor stage. The detection of immune-related genes in patients who meet these characteristics is conducive to the development of personalized precision treatment strategies for patients. Immune-related genes can be predicted by ultrasound characteristics, such as whether there are burrs at the edge of the tumor, E\u003csub\u003emax\u003c/sub\u003e, PKI, AUC, AT, and TTP. These ultrasonic characteristics can be used to predict the expression of certain immune-related genes in patients, to predict the survival and recurrence of breast cancer patients, and to monitor the expression of genes in tumors. Therefore, the use of ultrasound characteristics to predict the expression of immune genes helps to identify potential immune-related biomarkers and develop effective treatment plans for patients.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n\u003ch2\u003eEthics declarations\u003c/h2\u003e\n\u003cp\u003eThis research was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee at the First Hospital of Shanxi Medical University (Approval ID: NO.KYLL-2024-034).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n\u003ch2\u003eConsent to publish\u003c/h2\u003e\n\u003cp\u003eAll authors gave consent for publication.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThe research fund was provided by Research Project Supported by Shanxi Scholarship Council of China (2021\u0026thinsp;\u0026minus;\u0026thinsp;157), Open Fund from Key Laboratory of Cellular Physiology (CPOF202310), Wu Jieping Medical Fund (320.6750.2023-18-121), Shanxi natural Science Project youth project (202303021222339), The central government leads the local science and technology development fund project (YDZJSX2024D068), China Postdoctoral Science Foundation (2021M691993), Doctoral Research Project of Shanxi Medical University (XD1901), Postgraduate Practice Innovation Project in Shanxi Province (2024SJ166).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eHJ contributed to the notion of this research. TD designed the research strategy and completed the first manuscript. YC revised the manuscript. LL and YZ performed IHC and data analysis. WP, JL, YL, and YW helped perform the analysis with constructive discussions. All authors contributed to the article and approved the submitted manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe results of this study are partially based on data available at the GEO (https://www.ncbi.nlm.nih.gov/geo/) and IMMPORT Shared Data (https://www.immport.org/home). The whole transcriptome sequencing datasets have not yet reached the stage of being made public, but are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel, R. L., Giaquinto, A. N., Jemal, A. \u0026amp; Cancer statistics \u003cem\u003eCA: A Cancer Journal for Clinicians\u003c/em\u003e 74, 12\u0026ndash;49, doi: (2024). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3322/caac.21820\u003c/span\u003e\u003cspan address=\"10.3322/caac.21820\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaughan, K. L. \u0026amp; Ham, P. Lutterbie Ma Fau - Ham, P. S. S. Treatment of breast cancer.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo, R., Lu, G., Qin, B. \u0026amp; Fei, B. Ultrasound Imaging Technologies for Breast Cancer Detection and Management: A Review. \u003cem\u003eUltrasound. Med. Biol.\u003c/em\u003e \u003cb\u003e44\u003c/b\u003e, 37\u0026ndash;70. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ultrasmedbio.2017.09.012\u003c/span\u003e\u003cspan address=\"10.1016/j.ultrasmedbio.2017.09.012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCui, H. et al. Radiogenomic analysis of prediction HER2 status in breast cancer by linking ultrasound radiomic feature module with biological functions. \u003cem\u003eJ. Translational Med.\u003c/em\u003e \u003cb\u003e21\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12967-022-03840-7\u003c/span\u003e\u003cspan address=\"10.1186/s12967-022-03840-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBene, I. et al. Radiomic Signatures Derived from Hybrid Contrast-Enhanced Ultrasound Images (CEUS) for the Assessment of Histological Characteristics of Breast Cancer: A Pilot Study. \u003cem\u003eCancers\u003c/em\u003e. \u003cb\u003e14\u003c/b\u003e, 3905. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/cancers14163905\u003c/span\u003e\u003cspan address=\"10.3390/cancers14163905\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoo, J. et al. Tumor stiffness measured by shear wave elastography correlates with tumor hypoxia as well as histologic biomarkers in breast cancer. \u003cem\u003eCancer Imaging\u003c/em\u003e. \u003cb\u003e20\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s40644-020-00362-7\u003c/span\u003e\u003cspan address=\"10.1186/s40644-020-00362-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWillsmore, Z. N. et al. Combined anti-PD‐1 and anti‐CTLA‐4 checkpoint blockade: Treatment of melanoma and immune mechanisms of action. \u003cem\u003eEur. J. Immunol.\u003c/em\u003e \u003cb\u003e51\u003c/b\u003e, 544\u0026ndash;556. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/eji.202048747\u003c/span\u003e\u003cspan address=\"10.1002/eji.202048747\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNencioni, A. et al. The use of dendritic cells in cancer immunotherapy. \u003cem\u003eCrit. Rev. Oncol. Hematol.\u003c/em\u003e \u003cb\u003e65\u003c/b\u003e, 191\u0026ndash;199. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.critrevonc.2007.10.002\u003c/span\u003e\u003cspan address=\"10.1016/j.critrevonc.2007.10.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZong, L. et al. Expression of the immune checkpoint VISTA in breast cancer. \u003cem\u003eCancer Immunol. Immunother.\u003c/em\u003e \u003cb\u003e69\u003c/b\u003e, 1437\u0026ndash;1446. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00262-020-02554-3\u003c/span\u003e\u003cspan address=\"10.1007/s00262-020-02554-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang, L. et al. Expression and Clinical Significance of TIGIT in Primary Breast Cancer. \u003cem\u003eInt. J. Gen. Med. Volume\u003c/em\u003e. \u003cb\u003e16\u003c/b\u003e, 2405\u0026ndash;2417. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2147/ijgm.s407725\u003c/span\u003e\u003cspan address=\"10.2147/ijgm.s407725\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Pulido, M. TIM-3 Regulates CD103\u0026thinsp;+\u0026thinsp;Dendritic Cell Function and Response to Chemotherapy in Breast Cancer. \u003cem\u003eCancer Cell.\u003c/em\u003e \u003cb\u003e33\u003c/b\u003e, 60\u0026ndash;74e66. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ccell.2017.11.019\u003c/span\u003e\u003cspan address=\"10.1016/j.ccell.2017.11.019\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStory, M. D., Durante, M. \u0026amp; Radiogenomics \u003cem\u003eMed. Phys.\u003c/em\u003e \u003cb\u003e45\u003c/b\u003e, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/mp.13064\u003c/span\u003e\u003cspan address=\"10.1002/mp.13064\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrimm, L. J. \u0026amp; Mazurowski, M. A. Breast Cancer Radiogenomics: Current Status and Future Directions. \u003cem\u003eAcad. Radiol.\u003c/em\u003e \u003cb\u003e27\u003c/b\u003e, 39\u0026ndash;46. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.acra.2019.09.012\u003c/span\u003e\u003cspan address=\"10.1016/j.acra.2019.09.012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBismeijer, T. et al. Radiogenomic Analysis of Breast Cancer by Linking MRI Phenotypes with Tumor Gene Expression. \u003cem\u003eRadiology\u003c/em\u003e. \u003cb\u003e296\u003c/b\u003e, 277\u0026ndash;287. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1148/radiol.2020191453\u003c/span\u003e\u003cspan address=\"10.1148/radiol.2020191453\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYeh, A. C. et al. Radiogenomics of breast cancer using dynamic contrast enhanced MRI and gene expression profiling. \u003cem\u003eCancer Imaging\u003c/em\u003e. \u003cb\u003e19\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s40644-019-0233-5\u003c/span\u003e\u003cspan address=\"10.1186/s40644-019-0233-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEdgar, R. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cb\u003e30\u003c/b\u003e, 207\u0026ndash;210. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/nar/30.1.207\u003c/span\u003e\u003cspan address=\"10.1093/nar/30.1.207\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2002).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarrett, T. et al. NCBI GEO: archive for functional genomics data sets\u0026mdash;update. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cb\u003e41\u003c/b\u003e, D991\u0026ndash;D995. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/nar/gks1193\u003c/span\u003e\u003cspan address=\"10.1093/nar/gks1193\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRitchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cb\u003e43\u003c/b\u003e, e47\u0026ndash;e47. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/nar/gkv007\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkv007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhattacharya, S. et al. ImmPort, toward repurposing of open access immunological assay data for translational and clinical research. \u003cem\u003eSci. Data\u003c/em\u003e. \u003cb\u003e5\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/sdata.2018.15\u003c/span\u003e\u003cspan address=\"10.1038/sdata.2018.15\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang, Z. et al. GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cb\u003e45\u003c/b\u003e, W98\u0026ndash;W102. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/nar/gkx247\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkx247\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKishore, J., Goel, M. \u0026amp; Khanna, P. Understanding survival analysis: Kaplan-Meier estimate. \u003cem\u003eInt. J. Ayurveda Res.\u003c/em\u003e \u003cb\u003e1\u003c/b\u003e, 274. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4103/0974-7788.76794\u003c/span\u003e\u003cspan address=\"10.4103/0974-7788.76794\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi, X. Q., Li, J. L., Wan, W. B. \u0026amp; Huang, Y. A. Set of Shear Wave Elastography Quantitative Parameters Combined with Ultrasound BI-RADS to Assess Benign and Malignant Breast Lesions. \u003cem\u003eUltrasound. Med. Biol.\u003c/em\u003e \u003cb\u003e41\u003c/b\u003e, 960\u0026ndash;966. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ultrasmedbio.2014.11.014\u003c/span\u003e\u003cspan address=\"10.1016/j.ultrasmedbio.2014.11.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKristiansen, M. U., Martiniussen, M. A. \u0026amp; Larsen, A. S. F. Contrast-enhanced ultrasound of breast tumors: an initial experience. \u003cem\u003eActa Radiol. Open.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 205846012210974. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/20584601221097458\u003c/span\u003e\u003cspan address=\"10.1177/20584601221097458\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShen, W. et al. Sangerbox: A comprehensive, interaction-friendly clinical bioinformatics analysis platform. \u003cem\u003eiMeta\u003c/em\u003e 1, e36, doi: (2022). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/imt2.36\u003c/span\u003e\u003cspan address=\"10.1002/imt2.36\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePark, A. Y. et al. Radiogenomic Analysis of Breast Cancer by Using B-Mode and Vascular US and RNA Sequencing. \u003cem\u003eRadiology\u003c/em\u003e. \u003cb\u003e295\u003c/b\u003e, 24\u0026ndash;34. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1148/radiol.2020191368\u003c/span\u003e\u003cspan address=\"10.1148/radiol.2020191368\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStrieter, R. M. et al. Cancer CXC chemokine networks and tumour angiogenesis. \u003cem\u003eEur. J. Cancer\u003c/em\u003e. \u003cb\u003e42\u003c/b\u003e, 768\u0026ndash;778. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ejca.2006.01.006\u003c/span\u003e\u003cspan address=\"10.1016/j.ejca.2006.01.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2006).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNaito, H., Iba, T. \u0026amp; Takakura, N. Mechanisms of new blood-vessel formation and proliferative heterogeneity of endothelial cells. \u003cem\u003eInt. Immunol.\u003c/em\u003e \u003cb\u003e32\u003c/b\u003e, 295\u0026ndash;305. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/intimm/dxaa008\u003c/span\u003e\u003cspan address=\"10.1093/intimm/dxaa008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTerceiro, L. E. L. et al. The Breast Tumor Microenvironment: A Key Player in Metastatic Spread. \u003cem\u003eCancers\u003c/em\u003e. \u003cb\u003e13\u003c/b\u003e, 4798. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/cancers13194798\u003c/span\u003e\u003cspan address=\"10.3390/cancers13194798\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStockmann, C., Schadendorf, D., Klose, R. \u0026amp; Helfrich, I. The Impact of the Immune System on Tumor: Angiogenesis and Vascular Remodeling. \u003cem\u003eFront. Oncol.\u003c/em\u003e \u003cb\u003e4\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fonc.2014.00069\u003c/span\u003e\u003cspan address=\"10.3389/fonc.2014.00069\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYou, J. et al. TNFSF15 and MIA Variant Associated with Immunotherapy and Prognostic Evaluation in Esophageal Cancer. \u003cem\u003eJournal of Oncology\u003c/em\u003e 1\u0026ndash;12, doi: (2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1155/2023/1248024\u003c/span\u003e\u003cspan address=\"10.1155/2023/1248024\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu, J., Hu, F., Zhou, Y. \u0026amp; Guo, X. NR3C2-Related Transcriptome Profile and Clinical Outcome in Invasive Breast Carcinoma. \u003cem\u003eBioMed Research International\u003c/em\u003e 1\u0026ndash;13, doi: (2021). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1155/2021/9025481\u003c/span\u003e\u003cspan address=\"10.1155/2021/9025481\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFan, Y. et al. miR-301b-3p Regulates Breast Cancer Cell Proliferation, Migration, and Invasion by Targeting NR3C2. \u003cem\u003eJ. Oncol.\u003c/em\u003e \u003cb\u003e2021\u003c/b\u003e, 1\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1155/2021/8810517\u003c/span\u003e\u003cspan address=\"10.1155/2021/8810517\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGiacomini, A., Ghedini, G. C., Presta, M. \u0026amp; Ronca, R. Long pentraxin 3: A novel multifaceted player in cancer. \u003cem\u003eBiochim. et Biophys. Acta (BBA) - Reviews Cancer\u003c/em\u003e. \u003cb\u003e1869\u003c/b\u003e, 53\u0026ndash;63. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.bbcan.2017.11.004\u003c/span\u003e\u003cspan address=\"10.1016/j.bbcan.2017.11.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGiacomini, A. et al. The PTX3/TLR4 autocrine loop as a novel therapeutic target in triple negative breast cancer. \u003cem\u003eExperimental Hematol. Oncol.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s40164-023-00441-y\u003c/span\u003e\u003cspan address=\"10.1186/s40164-023-00441-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu, J., Yang, R., Ge, H., Zhu, Y. \u0026amp; Liu, S. PTX3 promotes breast cancer cell proliferation and metastasis by regulating PKCζbreast cancer, pentraxin 3, protein kinase Cζ, proliferation, metastasis. \u003cem\u003eExperimental Therapeutic Med.\u003c/em\u003e \u003cb\u003e27\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3892/etm.2024.12412\u003c/span\u003e\u003cspan address=\"10.3892/etm.2024.12412\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYen, M. C. et al. S100B expression in breast cancer as a predictive marker for cancer metastasis. \u003cem\u003eInt. J. Oncol.\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3892/ijo.2017.4226\u003c/span\u003e\u003cspan address=\"10.3892/ijo.2017.4226\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRybinska, I. et al. SAA1-dependent reprogramming of adipocytes by tumor cells is associated with triple negative breast cancer aggressiveness. \u003cem\u003eInt. J. Cancer\u003c/em\u003e. \u003cb\u003e154\u003c/b\u003e, 1842\u0026ndash;1856. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/ijc.34859\u003c/span\u003e\u003cspan address=\"10.1002/ijc.34859\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMalle, E. \u0026amp; De Beer, F. C. Human serum amyloid A (SAA) protein: a prominent acute-phase reactant for clinical practice. \u003cem\u003eEur. J. Clin. Invest.\u003c/em\u003e \u003cb\u003e26\u003c/b\u003e, 427\u0026ndash;435. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1046/j.1365-2362.1996.159291.x\u003c/span\u003e\u003cspan address=\"10.1046/j.1365-2362.1996.159291.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2003).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu, L. et al. CXCL9 influences the tumor immune microenvironment by stimulating JAK/STAT pathway in triple-negative breast cancer. \u003cem\u003eCancer Immunol. Immunother.\u003c/em\u003e \u003cb\u003e72\u003c/b\u003e, 1479\u0026ndash;1492. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00262-022-03343-w\u003c/span\u003e\u003cspan address=\"10.1007/s00262-022-03343-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRazis, E. et al. The Role of CXCL13 and CXCL9 in Early Breast Cancer. \u003cem\u003eClin. Breast. Cancer.\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e, e36\u0026ndash;e53. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.clbc.2019.08.008\u003c/span\u003e\u003cspan address=\"10.1016/j.clbc.2019.08.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Breast cancer, Breast ultrasound, Immune gene, Immunotherapy, Radiogenomics","lastPublishedDoi":"10.21203/rs.3.rs-5318112/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5318112/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Breast cancer is the most commonly diagnosed cancer worldwide. Breast cancer screening, early diagnosis, and standardized treatment can effectively reduce the mortality of patients. Breast color Doppler ultrasound plays a significant role in the non-invasive screening and diagnosis of breast cancer. The application of immunotherapy for breast cancer can significantly prolong the overall survival rates of patients with advanced breast cancer, which is an important research area of breast cancer treatment. This study analyzed the correlation between the clinical and ultrasound characteristics of breast cancer and immune-related genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e First, differential expression of immune-related genes were obtained using the GEO and IMMPORT database. Then, differentially expressed immune-related genes related to the overall survival of breast cancer were obtained using the GEPIA and Kaplan-Meier plotter platforms. Additionally, clinical, ultrasound characteristics and pathological specimens of breast cancer patients’ tumors were collected. Transcriptome sequencing and immunohistochemical staining were performed on the tumor specimens to obtain gene expression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e CXCL2, MIA, NR3C2, PTX3, S100B, SAA1, SAA1, and CXCL9 genes were correlated with each other and with clinical and ultrasonic characteristics. The high expression of MIA was related to the positive expression of PR in breast cancer. The low expression of NR3C2 was correlated with the clinical characteristics of tumor size≥20mm, later stage, Her-2 positive, Ki-67≥20%. NR3C2 was negatively correlated with the value of PKI and AUC in contrast-enhanced ultrasound parameters, and positively correlated with the value of AT and TTP. The expression of the PTX3 gene was also negatively correlated with the value of PKI and E\u003csub\u003emax\u003c/sub\u003e of shear wave elastography. SAA2 was related to the presence or absence of burrs on the edge of the tumor characterized by ultrasound. The expression of the CXCL9 gene was associated with the age of onset and tumor stage.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e In this study, 8 differentially expressed immune-related genes related to the overall survival rate of breast cancer were screened, which can be further studied in the subsequent immunotherapy of breast cancer. Some clinical and ultrasonic characteristics of breast cancer were significantly correlated with immune-related genes, such as NR3C2, SAA2, and CXCL9. Further analysis of these genes provides new ideas for the diagnosis and treatment of breast cancer.\u003c/p\u003e","manuscriptTitle":"Radiogenomic analysis of the correlation between clinical, ultrasound characteristics and immune-related genes in breast cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-16 17:31:26","doi":"10.21203/rs.3.rs-5318112/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-03-03T04:33:03+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-27T15:02:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"15604527924211730397073579224616465472","date":"2025-02-17T19:26:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"292729961370180846348697211139635075844","date":"2025-02-17T07:06:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-19T18:14:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"154069887869640573326166051981607826029","date":"2024-11-19T16:41:13+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-19T10:17:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-19T05:31:43+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-11-12T11:05:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-12T07:08:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-10-23T10:20:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"13550393-fe94-4579-85e5-d6ca734bee44","owner":[],"postedDate":"December 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":40598469,"name":"Biological sciences/Cancer"},{"id":40598470,"name":"Biological sciences/Genetics"},{"id":40598471,"name":"Biological sciences/Immunology"},{"id":40598472,"name":"Health sciences/Medical research"},{"id":40598473,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2025-05-12T16:08:05+00:00","versionOfRecord":{"articleIdentity":"rs-5318112","link":"https://doi.org/10.1038/s41598-025-00891-w","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-05-07 15:57:37","publishedOnDateReadable":"May 7th, 2025"},"versionCreatedAt":"2024-12-16 17:31:26","video":"","vorDoi":"10.1038/s41598-025-00891-w","vorDoiUrl":"https://doi.org/10.1038/s41598-025-00891-w","workflowStages":[]},"version":"v1","identity":"rs-5318112","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5318112","identity":"rs-5318112","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}