Body Mass Index and Breast Cancer Risk: A Retrospective Multi-Institutional Analysis in Saudi Arabia

Background: The purpose of this study was to evaluate the association between the body mass index (BMI) of breast cancer patients and non-cancer females of the Eastern Province of Saudi Arabia. Methods: The weight, height and age was obtained from the patient records of 706 newly diagnosed breast cancer patients and of 20,872 non-cancer female patients who consulted the two largest hospitals in the Eastern Province of Saudi Arabia between 2006 and 2012. Factorial analysis of variance (ANOVA) was used to assess the association between the BMI, age and breast cancer status. Results: The mean BMI of the non-cancer females was 29.4 and the percentage of obese patients of the different age groups ranged from 23.9% to 66.5%. The BMI increased significantly with age. The ANOVA revealed that breast cancer patients older than 50 years had a significantly lower BMI compared to their non-cancer counterparts (p = 0.01). Conclusion: Our data confirm the high BMI of the Saudi Arabian female population. The reason for our finding of a lower BMI of postmenopausal breast cancer patients compared to their non-cancer counterparts is unclear. Future studies are warranted to assess the impact of possible confounding factors on the association between obesity and breast cancer risk. An interesting factor to investigate in future studies would particularly be the use of the anti-diabetic and cancer-protective drug metformin considering that diabetes mellitus is endemic in Saudi Arabia with a prevalence of 30%.

Segmentation Method of Breast Masses on Ultrasonographic Images Using Level Set Method Based on Statistical Model

It is important to segment mass region accurately in a computer-aided diagnosis (CADx) scheme for evaluating the likelihood of malignancy of the mass on ultrasonographic breast image. The purpose of this study was to develop a novel level set method for segmentation of breast mass on ultrasonographic image. Our database consisted of 151 ultrasonographic images with 70 malignant and 81 benign breast masses. In a novel level set method, an energy function was defined with region-based, edge-based, and regularizing terms. The region-based term analyzed global information, whereas the edge-based term analyzed local information. The regularizing term also controlled the length of the boundary curve. The region of breast mass was segmented so that the energy based on those terms was minimized. With our proposed method, true positive (TP) ratio, false positive (FP) ratio, jaccard similarity (JS), and Dice similarity coefficient (DSC) were 92.2%, 9.1%, 84.2%, and 91.3%, respectively. These results tended to be substantially higher than those with two conventional segmentation methods. Our proposed method based on the novel level set method was shown to segment mass region accurately on ultrasonographic breast image.

Evaluation of Breast Masses Using Mammography and Sonography as First Line Investigations

Purpose: To study the specificity of mammography and ultrasonography separately and in combination for detection of breast masses (ultrasonography-mammography correlation);To study the investigations to evaluate various breast masses;To describe suitable indications, advantages and limitations of each technique compared with other available modalities;To study the mimics of breast masses;To have histopathology follow-up and retrospective evaluation with imaging findings to improve diagnostic skills in series of 166 patients complaining of breast mass. Material: The prospective clinical study was carried out in the department of Radiodiagnosis for a period of 2 year extending from December 2010 to December 2012 infemale patients complaining of breast mass. Well informed written consent was obtained from them. Histopathology follow up was obtained from either biopsy or post operative tissue. USG machine: Philips HD 11 XE USG of the breasts and axillary region done in supine position in presence of female attendant;Mammography machine: Allengers machine with Agfa special mammography cassettes. Cranio caudal and Medio-Lateral Oblique views are taken in the presence of female attendant. MRI: PHILIPS 1.5 T machine;CT: SIEMENS duel slice CT machine. Results: Ultrasonography and mammography was done in most of the cases were sufficient to diagnose the lesion in most of the cases especially in benign breast masses. MRI and CT scan was used in special cases to know the extent of the lesions, in mimics of breast masses, bony extensions, primary muscular and bony lesions. Total 166 patients complaining of breast mass in one or both breasts were examined and evaluated with USG and mammography. The lesions were confirmed on histopathology (FNAC/biopsy). Out of 30 diagnosed malignancies two lesions were missed on mammography and four lesions were missed on ultrasonography. One of them was missed on both. For malignancies specificity of mammography is 93.3% and that of ultrasonography is 86.67%. Combining both the modalities specificity is near 97%. Out of total 92 abnormal breasts 12 were missed on USG and 20 were missed on mammography. Combining both the modalities only 2 lesions were missed and were diagnosed on histopathology alone. Overall specificity for USG in breast masses is 86.9% and for mammography it is 78.6%. Combining both the modalities the specificity is 97.6%. The “p” value is obtained which is highly significant for combination of ultrasonography and mammography in comparison with any individual modality (p = 0.0059 & p = 0.0001 respectively). Conclusion: Our study confirms the higher combined sensitivity rate for ultrasonography and mammography for detection of breast masses including malignancies. USG is useful in cystic lesions, ectasias, infections, pregnancy-lactation, and dense breast evaluation and for image guidance, whereas mammography is useful in detecting microcalcifications, spiculated masses for early detection of malignancies and for stereotactic biopsies. To suggest single modality, ultrasonography is better in younger population and BIRAD 1, 2 & 3 lesions. Whereas, mammography is better in older population and BIRAD 4 & 5 lesions. However, sono-mammographic correlation is best in both.

Breast non-mass-like lesions on contrast-enhanced ultrasonography: Feature analysis, breast image reporting and data system classification assessment

BACKGROUND Breast non-mass-like lesions(NMLs)account for 9.2%of all breast lesions.The specificity of the ultrasound diagnosis of NMLs is low,and it cannot be objectively classified according to the 5th Edition of the Breast Imaging Reporting and Data System(BI-RADS).Contrast-enhanced ultrasound(CEUS)can help to differentiate and classify breast lesions but there are few studies on NMLs alone.AIM To analyze the features of benign and malignant breast NMLs in grayscale ultrasonography(US),color Doppler flow imaging(CDFI)and CEUS,and to explore the efficacy of the combined diagnosis of NMLs and the effect of CEUS on the BI-RADS classification of NMLs.METHODS A total of 51 breast NMLs verified by pathology were analyzed in our hospital from January 2017 to April 2019.All lesions were examined by US,CDFI and CEUS,and their features from those examinations were analyzed.With pathology as the gold standard,binary logic regression was used to analyze the independent risk factors for malignant breast NMLs,and a regression equation was established to calculate the efficiency of combined diagnosis.Based on the regression equation,the combined diagnostic efficiency of US combined with CEUS(US+CEUS)was determined.The initial BI-RADS-US classification of NMLs was adjusted according to the independent risk factors identified by CEUS,and the diagnostic efficiency of CEUS combined with BI-RADS(CEUS+BI-RADS)was calculated based on the results.ROC curves were drawn to compare the diagnostic values of the three methods,including US,US+CEUS,and CEUS+BI-RADS,for benign and malignant NMLs.RESULTS Microcalcification,enhancement time,enhancement intensity,lesion scope,and peripheral blood vessels were significantly different between benign and malignant NMLs.Among these features,microcalcification,higher enhancement,and lesion scope were identified as independent risk factors for malignant breast NMLs.When US,US+CEUS,and CEUS+BI-RADS were used to identify the benign and malignant breast NMLs,their sensitivity rates were 82.6%,91.3%,and 87.0%,respectively;their specificity rates were 71.4%,89.2%,and 92.9%,respectively;their positive predictive values were 70.4%,87.5%,and 90.9%,respectively;their negative predictive values were 83.3%,92.6%,and 89.7%,respectively;their accuracy rates were 76.5%,90.2%,and 90.2%,respectively;and their corresponding areas under ROC curves were 0.752,0.877 and 0.903,respectively.Z tests showed that the area under the ROC curve of US was statistically smaller than that of US+CEUS and CEUS+BI-RADS,and there was no statistical difference between US+CEUS and CEUS+BI-RADS.CONCLUSION US combined with CEUS can improve diagnostic efficiency for NMLs.The adjustment of the BI-RADS classification according to the features of contrastenhanced US of NMLs enables the diagnostic results to be simple and intuitive,facilitates the management of NMLs,and effectively reduces the incidence of unnecessary biopsy.

The Effect of Body Mass Index on Survival in Breast Cancer

Background: Obesity is a well-known risk factor for breast cancer recurrence and poor prognosis. The objective of this study was to evaluate the effect of body mass index (BMI) on survival in breast cancer patients. Methods: We performed a retrospective analysis of 50 breast cancer patients treated in our hospital from January 2012 to December 2013. Patients were divided according to body mass index when diagnosed into: normal weight BMI 25 Kg/m2, over weight BMI ≥ 25 Kg/m2 to 2, obesity BMI ≥ 30 Kg/m2. In this study the effect of body mass index on progression free survival (PFS) and overall survival (OS) was evaluated. Results: The disease free survival (DFS) and overall survival (OS) decreased in overweight and obese patients. Both overweight and obesity were predictors for increased risks of breast cancer relapse and mortality with a median disease free survival for overweight 29 mons and obese patients 11 mons and a median overall survival for overweight patients 49 mons and obese patients 39 mons. Conclusion: Obesity and overweight are associated with poorer disease free survival and overall survival in patients with breast cancer.

Optimization of Gas Chromatography-electron Ionization-tandem Mass Spectrometry for Determining Toxic Non-ortho Polychlorinated Biphenyls in Breast Milk

One of the most commonly used non-invasive methods for assessing human exposure to pollution is the analysis of human milk.Human milk analyses help estimate the exposure of infants[1].This is why breast milk is receives scientific interest,and various methods for determining different pollutants from the environment are being developed[2,3].

The Role of RNA Epitranscriptomics and the RNA Fat Mass and Obesity-Associated Demethylase in Triple Negative Breast Cancer

Breast cancer is one of the most commonly diagnosed cancers and one of the most significant sources of cancer mortality. Triple negative breast cancer (TNBC) is a particularly aggressive subtype that has proven difficult to treat with standard chemotherapies. Obesity has also been shown to exacerbate breast cancer, and diagnoses of these two diseases frequently overlap. Both conditions are regulated in part by the fat mass and obesity-associated (FTO) demethylase, an RNA demethylase which may drive breast cancers through epigenetic alterations to gene expression. Methods of inhibiting FTO have been researched in vitro and in vivo as an alternative or adjunct to chemotherapies in multiple cancers, including breast cancer. Translating knowledge of the role of FTO in breast cancer and the development of novel agents may allow for improvements in the treatment of this refractory cancer. This review therefore aims to provide an overview of existing and developing chemical inhibitors of FTO that could be innovatively studied for the treatment of TNBC and associated comorbidity.

Complex Cystic Breast Masses: An Ultrasound Imaging Review

Cystic lesions are very commonly encountered entities in the breast. Among these, Complex Cystic Breast Masses (CCBMs), which contain both anechoic and echogenic components, can result in a variety of imaging appearances. These include cystic lesions with thick walls and/or internal septa, intracystic nodular lesions, and solid-cystic masses of varying compositions. Ultrasound is the mainstay for evaluating cystic lesions, and thus recognizing the imaging features appropriately and suggesting suitable interventional procedures are included in their management. In this pictorial essay, we describe the wide range of ultrasound appearances of CCBMs with a number of clinically encountered examples from our institution. This article would enhance the understanding of readers in possible differentials to be included in their clinical practice and to suggest appropriate further intervention, when deemed necessary.

Novel Approach for Quantitative Measurement of Matrix Metalloprotease-1 (MMP1) in Human Breast Cancer Cells Using Mass Spectrometry

Identification and quantification of low abundance growth factors and regulators in complex biological samples still present a challenging task in analytical biochemistry. Immunoassays are often used for such purpose but immunoassays face limitation of both availability and qualities of antibody reagents that are necessary for development of immune assays. With genomics data base available, mass spectrometry (MS) can analyze protein tryptic peptides directly for quantitative determination of proteins. In this study, we report a method for detection of matrix metalloproteinase 1 (MMP1), an important extracellular matrix modulator, in human breast cancer cells by quadrupole time-of-flight (Q-TOF) MS. Absolute quantification of MMP1 was conducted using the selected reaction monitoring (SRM) on a triple quadrupole (Triple-Quad) MS via transitions selected from MMP1 tryptic peptides using non isotope labeled MMP1 protein as a titration standard. In comparison with immune based assay, this MS method showed picogram level sensitivity for quantitative determination of MMP1 intotal cell lysates. Our results demonstrated the feasibility of absolute quantification of low abundance proteins using label-free protein standard by mass spectrometry. Therefore, this method provides not only advantages of high sensitivity but also cost saving in comparison with the commonly used mass spectrometry that currently employs isotype labeled proteins for quantitative analysis.

Optimizing Breast Mass Segmentation Algorithms with Generative Adversarial Nets

Breast cancer is the most ordinary malignant tumor in women worldwide. Early breast cancer screening is the key to reduce mortality. Clinical trials have shown that Computer Aided Design improves the accuracy of breast cancer detection. Segmentation of mammography is a critical step in Computer Aided Design. In recent years, FCN has been applied in the field of image segmentation. Generative Adversarial Networks is also popularized for its ability on generate images which is difficult to distinguish from real images, and have been applied in the image semantic segmentation domain. We apply the Dilated Convolutions to the partial convolutional layer of the Multi-FCN and use the ideas of Generative Adversarial Networks to train and correct our segmentation network. Experiments show that the Dice index of the model DMulti- FCN-CRF-Adversarial Training on the datasets InBreast and DDSMBCRP can be increased to 91.15% and 91.8%.

Analysis of Machine Learning Techniques Applied to the Classification of Masses and Microcalcification Clusters in Breast Cancer Computer-Aided Detection

Breast cancer is one of the most common and deadliest types of cancer among women and early detection is of major importance to decrease mortality rates. Microcalcification clusters and masses are two major indicators of malignancy in the early stages of this disease, when mammography is typically used as the screening technology. Computer-Aided Diagnosis (CAD) systems can support the radiologists’ work, by performing a double-reading process, which provides a second opinion that the physician can take into account in the detection process. This paper presents a CAD model based on computer vision procedures for locating suspicious regions that are later analyzed by artificial neural networks, support vector machines and linear discriminant analysis, to classify them into benign or malignant, based on a set of features that are extracted from lesions to characterize their visual content. A genetic algorithm is used to find the subset of features that provide the greatest discriminant power. Our results show that the SVM presented the highest overall accuracy and specificity for classifying microcalcification clusters, while the NN outperformed the rest for mass-classification in the same parameters. Overall accuracy, sensitivity and specificity were measured.

Noncausal relationship between body weight and breast cancer based on bidirectional Mendelian randomization evidence

Background:Some observational associations between body weight and breast cancer have attracted attention.However,the causal relationship between these 2 factors remains unclear,and more clinical outcomes are needed for its validation.Methods:Based on statistical data from a Genome Wide Association Study,we performed a bidirectional Mendelian randomization analysis to assess the bidirectional causal relationship between body weight and breast cancer using 4 methods,with inverse variance weighting as the primarymethod.To verify the robustness and reliability of the causal relationship,we performed a sensitivity analysis using horizontal pleiotropy,outlier,and one-by-one elimination tests.Results:The inverse variance weighting results revealed no significant positive causal relationship between body weight and breast cancer.Similarly,the reverse analysis revealed no causal effect of breast cancer on body weight.Conclusions:The relationship between body weight and breast cancer may be attributed to confounding factors.

Simultaneous Paragonimus infection involving the breast and lung:A case report

BACKGROUND Paragonimiasis is a food-borne parasitic infection caused by lung flukes of the genus Paragonimus. Although the most common site of infection is the pleuropulmonary area, the parasite can also reach other parts of the body on its journey from the intestines to the lungs, ending up in locations such as the brain,abdomen, skin, and subcutaneous tissues. Ectopic paragonimiasis is difficult to diagnose due to the rarity of this disease.CASE SUMMARY Here, we report a rare case of simultaneous breast and pulmonary paragonimiasis in a woman presenting painless breast mass and lung nodule with a history of eating raw trout. To confirm the diagnosis, serologic testing and tissue confirmation of the breast mass were performed. The patient was treated with surgical resection of the mass and praziquantel medication.CONCLUSION Ectopic paragonimiasis is difficult to diagnose due to the rarity of this disease.Thus, thorough history-taking and clinical suspicion of parasitic infection are important.

Breast cancer: Epidemiology, risk factors and screening

Breast cancer is a global health concern with a significant impact on the well-being of women. Worldwide, the past several decades have witnessed changes in the incidence and mortality of breast cancer. Additionally,epidemiological data reveal distinct geographic and demographic disparities globally. A range of modifiable and non-modifiable risk factors are established as being associated with an increased risk of developing breast cancer.This review discusses genetic, hormonal, behavioral, environmental, and breast-related risk factors. Screening plays a critical role in the effective management of breast cancer. Various screening modalities, including mammography,ultrasound, magnetic resonance imaging(MRI), and physical examination, have different applications, and a combination of these modalities is applied in practice. Current screening recommendations are based on factors including age and risk, with a significant emphasis on minimizing potential harms to achieve an optimal benefits-to-harms ratio. This review provides a comprehensive insight into the epidemiology, risk factors, and screening of breast cancer. Understanding these elements is crucial for improving breast cancer management and reducing its burden on affected individuals and healthcare systems.

Increased Cellular Invasion and Proliferation via Estrogen Receptor after 17-<i>β</i>-Estradiol Treatment in Breast Cancer Cells Using Stable Isotopic Labeling with Amino Acids in Cell Culture (SILAC)

17-β-estradiol (estrogen) is a steroid hormone important to human development;however, high levels of this molecule are associated with increased risk of breast cancer primarily due to estrogen’s ability to bind and activate the estrogen receptor (ER) and initiate gene transcription. Currently, estrogen mechanisms of action are classified as genomic and non-genomic and occur in an ER-dependent and ER-independent manner. In this study, we examine estrogen signaling pathways, by measuring changes in protein expression as a function of time of exposure to estrogen in both ER-positive (MCF-7) and ER-negative (MDA-MB-231) cell lines. Using a robust experimental design utilizing isotopic labeling, two-dimensional LC-MS, and bioinformatics analysis, we report genomic and non-genomic ER regulated estrogen responsive proteins. We find a little over 200 proteins differentially expressed after estrogen treatment. Cell proliferation, transcription, actin filament capping and cell to cell signaling are significantly enriched in the MCF-7 cell line alone. Translational elongation and proteolysis are enriched in both cell lines. Subsets of the proteins presented in this study are for the first time directly associated with estrogen signaling in mammary carcinoma cells. We find that estrogen affected the expression of proteins involved in numerous processes that are related to tumorigenesis such as increased cellular division and invasion in an ER-dependent manner. Moreover, we identified negative regulation of apoptosis as a non-genomic process of estrogen. This study complements gene expression studies and highlights the need for both genomic and proteomic analyses in unraveling the complex mechanisms by which estrogen affects progression of breast cancer.

Preliminary results of acoustic radiation force impulses (ARFI) ultrasound imaging of solid suspicious breast lesions

Objective： The aim of our study was to make the qualitative and quantitative analysis to breast lesions using acoustic radiation force impulses （ARFI）, and assess the diagnostic value of ARFI for differentiation between benign and malignant solid breast masses, meanwhile evaluate the influences of ARFI with breast imaging reporting and data system （BI-RADS） of suspicious masses. Methods： Seventy-five women with 86 breast lesions underwent conventional breast ultra- sound examination. Then B-mode BI-RADS features and assessments were recorded and standard breast US supplemented by ARFI elastographic examination were repeated. The data were recorded and analyzed as following： area ratio of breast lesion, the shear-wave velocity, the ratio of the shear-wave velocity between lesions and surrounding normal tissues, and according to the elastographic data reconsidered the BI-RADS category, all the results have been correlated with pathological results and make statistical evaluations of ARFI for differentiation between benign and malignant solid breast masses. Meantime our study has correlated the adjusted BI-RADS category of suspicious breast lesions with the pathological results and made assessment. Results： Thirty-eight patients were malignant breast carcinoma （31 invasive ductal carcinoma, 5 intraductal carcinoma in situ, 2 medullary carcinoma, 2 invasive Iobular carcinoma）, 48 patients were benign breast lesions （23 fibroadenoma, 12 benign nodular hyperplasia, 5 phyllodes tumor, 6 adenosis, 2 intraductal papilloma）. Underwent conventional breast ultrasound exam, 42 cases were BI-RADS category 3, 23 cases were BI-RADS category 4. When adding elastographic data, 46 cases were BI-RADS category 3 and 20 cases were BI-RADS category 4. Compared with pathological results showed for both the specificity of BIRADS features and the area under ROC curve has risen. Virtual touch tissue imaging （VTI） and virtual touch tissue quantification （VTQ） data showed the area ratio （AR） between elastographic lesions area and B-mode lesions area, SWV （maximal shear-wave velocity of lesions）, R-SWV （shear-wave velocity ratio between lesions and surrounding normal tissues） in benign breast lesions were lower than those in malignant lesions which has statistical significance and the cut-off point were 1.1,4.65 m/s, 5.18 respectively. Conclusion： The ARFI elastography can provide the reliable qualitative and quantitative analysis about hardness of breast lesions, supply the new BI-RADS category features to suspicious breast masses and serve as an effective diagnostic tool for differentiation between benign and malignant solid masses.

THE CLINICAL SIGNIFICANCE OF ^(99m)Tc-MIBI BREAST IMAGING IN THE DIAGNOSIS OF EARLY BREAST CANCER

Objective: To find an effective, sensitive, specific and noninvasive diagnostic method of breast cancer. Methods: 109 masses of 102 patients with breast lesions smaller than 2 cm in diameter were divided into three groups to undergo 99mTc-MIBI imaging and compared with the results of pathology examination. 20 cases without breast lesions were selected as control. Abnormal condensation of 99mTc-MIBI in the breast reaching 10% higher than that in the counterpart of the healthy breast was regarded as positive. Results: Of 32 breast cancers, positive imaging appeared in 25. Negative imaging were found in 31 of 38 benign breast lesions. Of 39 occult breast lesions, positive imaging appeared in 6 and 3 of them were breast cancer, 2 of 3 patients with slightly increased 99mTc-MIBI imaging threshold were breast cancer also. No positive imaging was found in the control group. The diagnostic accuracy, sensitivity, specificity, positive predictive value, negative predictive value of 99mTc-MIBI was 88.4%, 89.2%, 88.0%, 75.0% and 95.3%, respectively. Conclusion: 99mTc-MIBI imaging had higher sensitivity and accuracy in the diagnosis of breast cancer and differentiation between benign and malignant breast lesions. It could provide useful information for the diagnosis of clinically suspected breast cancer.

Simply Fine-Tuned Deep Learning-Based Classification for Breast Cancer with Mammograms

A lump growing in the breast may be referred to as a breast mass related to the tumor.However,not all tumors are cancerous or malignant.Breast masses can cause discomfort and pain,depending on the size and texture of the breast.With an appropriate diagnosis,non-cancerous breast masses can be diagnosed earlier to prevent their cultivation from being malignant.With the development of the artificial neural network,the deep discriminative model,such as a convolutional neural network,may evaluate the breast lesion to distinguish benign and malignant cancers frommammogram breast masses images.This work accomplished breastmasses classification relative to benign and malignant cancers using a digital database for screening mammography image datasets.A residual neural network 50(ResNet50)model along with an adaptive gradient algorithm,adaptive moment estimation,and stochastic gradient descent optimizers,as well as data augmentations and fine-tuning methods,were implemented.In addition,a learning rate scheduler and 5-fold cross-validation were applied with 60 training procedures to determine the best models.The results of training accuracy,p-value,test accuracy,area under the curve,sensitivity,precision,F1-score,specificity,and kappa for adaptive gradient algorithm 25%,75%,100%,and stochastic gradient descent 100%fine-tunings indicate that the classifier is feasible for categorizing breast cancer into benign and malignant from the mammographic breast masses images.

直接稀释-电感耦合等离子体质谱法测定母乳中17种元素的含量

提出了直接稀释-电感耦合等离子体质谱法(ICP-MS)同时测定母乳中钒、铬、锰、钴、镍、铜、锌、砷、硒、锶、钡、钼、镉、锡、锑、汞、铅等17种元素含量的方法。取母乳0.5 mL,用含2%(体积分数)硝酸和0.01%(体积分数)四甲基氢氧化铵的0.01%(体积分数)曲拉通X-100溶液定容至10 mL,涡旋混匀,以转速4000 r·min^(-1)离心5 min,将上清液用0.45μm水相滤膜过滤,在动能歧视(KED)模式下采用ICP-MS测定滤液中上述17种元素的含量,在线加入含6μg·L^(-1)钪、锗、铑、铋的混合内标使用液。结果表明:各元素的质量浓度在一定范围内与所对应的信号强度与内标元素信号强度比呈线性关系,检出限(3s)为0.019~0.768μg·L^(-1);方法用于分析美国国家标准参考材料,除锌元素外,其他元素的测定值均在认定值的不确定度范围内,并且日内和日间的变异系数均小于13%;按照标准加入法进行回收试验,回收率为81.3%~119%。

GZMB在乳腺癌和乳腺良性肿物患者中的表达及与T淋巴细胞水平的关系研究

目的:探讨颗粒酶B(granzyme B,GZMB)在乳腺癌和乳腺良性肿物患者中的表达及与T淋巴细胞水平的关系。方法:选取2022年1月至2023年8月南阳市第一人民医院收治的乳腺癌患者63例为观察组,根据肿瘤分期分为T_(1)N_(0)M_(0)组、T_(2)N_(0)M_(0)组、T_(2)N_(1)M_(0)组、T_(3)N_(0)M_(0)组及T_(3)N_(1)M_(0)组;选择同期治疗的乳腺良性肿物患者9例为对照组;采用流式细胞仪测定各组GZMB及T淋巴细胞水平;对乳腺癌患者GZMB与T淋巴细胞参数完成相关性分析。结果:观察组乳腺癌患者GZMB水平高于对照组(P<0.05);CD_(3)^(+)、CD_(4)^(+)及CD_(4)^(+)/CD_(8)^(+)水平低于对照组(P<0.05);乳腺癌不同分期下CD_(8)^(+)水平无统计差异(P>0.05);乳腺癌患者中T_(3)N_(1)M_(0)组GZMB水平高于其余4组(P<0.05),CD_(3)^(+)、CD_(4)^(+)及CD_(4)^(+)/CD_(8)^(+)水平低于其余4组(P<0.05);乳腺癌患者分期越高,GZMB水平越高,CD_(3)^(+)、CD_(4)^(+)及CD_(4)^(+)/CD_(8)^(+)水平越低;Pearson相关性结果表明,乳腺癌患者GZMB水平与CD_(3)^(+)、CD_(4)^(+)及CD_(4)^(+)/CD_(8)^(+)水平呈负相关(P<0.05)。结论:GZMB在乳腺癌患者中呈高表达,其表达水平在不同分期中存在差异,且与T淋巴细胞水平存在相关性,可指导临床诊疗。