Histological Grade Of Breast Cancer Research Articles

Ki-67 and Histological Grade in Breast Cancer

AIMS: The aim of the study was to investigate the prognostic value of Ki-67 and histological grade in patients with estrogen receptor (ER) positive and human epidermal growth factor receptor 2 (HER2) negative early breast cancer. Although the proliferation activity of different tumors assessed with antigen Ki-67 was extensively studied in the last decade and showed that Ki67 expression was a useful prognostic factor in breast cancer, there is still a controversy about the utility value of Ki-67. METHODS: The retrospective study covered the period from 2016 to 2022. Year included 106 patients from Brcko District, with early estrogen-positive and HER2 receptors-negative breast cancer. The average patients’ age was 62.37 ± 11.65 years. Patients were divided into groups with high/low Ki-67 and high/low histological grade. Multiple linear regression analysis was performed for disease relapse and mortality. RESULTS: Patients with high Ki-67 were more frequent postmenopausal, had higher histological grade, cancer ˂2 cm, more frequent lymph nodes’ metastases, more frequently underwent to axillary surgery, and had a higher mortality rate. Patients with high histological grade were older, more frequent postmenopausal, had more frequent metastases to the lymph nodes, more frequent occurrence of high Ki-67, more frequent relapse of disease, and more frequent of death. CONCLUSION: Combination of high Ki-67 with high histological grade in ER positive and HER2-negative early breast cancer is an important prognostic factor.

Development of a Nomogram-Integrated Model Incorporating Intra-tumoral and Peri-tumoral Ultrasound Radiomics Alongside Clinical Parameters for the Prediction of Histological Grading in Invasive Breast Cancer

To develop a comprehensive nomogram to predict the histological grading of breast cancer and further examine its clinical significance by integrating both intra-tumoral and peri-tumoral ultrasound radiomics features. In a retrospective study 468 female breast cancer patients were analyzed from 2017 to 2020 at the Second Affiliated Hospital of Harbin Medical University. Patients were grouped into high-grade (n = 215) and low-grade (n = 253) categories based on pathological evaluation. Tumor regions of interest were defined and expanded automatically to peri-tumor regions of interest. Ultrasound radiomics features were extracted independently. To ensure rigor, cases were randomly divided into 80% training and 20% test sets. Optimal features were selected using statistical and machine learning methods. Intra-tumor, peri-tumor, and combined radiomics models were constructed. To determine the best predictors of breast cancer histological grading, we screened the features using single- and multi-factor logistic regression analyses. Finally, a nomogram was developed and evaluated for its predictive value in this context. By applying logistic regression, we integrated ultrasound, clinicopathologic, and radiomics features to generate a nomogram. The combined model outperformed others, achieving areas under the curve of 0.934 and 0.812 in training and test sets. Calibration curves also showed high accuracy and reliability. A nomogram constructed through the integration of combined intra-tumor-peri-tumor ultrasound radiomics features along with clinicopathologic characteristics exhibited remarkable performance in distinguishing the histologic grades of invasive breast cancer.

Exploration of the relationship between tumor-infiltrating lymphocyte score and histological grade in breast cancer

BackgroundThe histological grade is an important factor in the prognosis of invasive breast cancer and is vital to accurately identify the histological grade and reclassify of Grade2 status in breast cancer patients.MethodsIn this study, data were collected from 556 invasive breast cancer patients, and then randomly divided into training cohort (n = 335) and validation cohort (n = 221). All patients were divided into actual low risk group (Grade1) and high risk group (Grade2/3) based on traditional histological grade, and tumor-infiltrating lymphocyte score (TILs-score) obtained from multiphoton images, and the TILs assessment method proposed by International Immuno-Oncology Biomarker Working Group (TILs-WG) were also used to differentiate between high risk group and low risk group of histological grade in patients with invasive breast cancer. Furthermore, TILs-score was used to reclassify Grade2 (G2) into G2 /Low risk and G2/High risk. The coefficients for each TILs in the training cohort were retrieved using ridge regression and TILs-score was created based on the coefficients of the three kinds of TILs.ResultsStatistical analysis shows that TILs-score is significantly correlated with histological grade, and is an independent predictor of histological grade (odds ratio [OR], 2.548; 95%CI, 1.648–3.941; P < 0.0001), but TILs-WG is not an independent predictive factor for grade (P > 0.05 in the univariate analysis). Moreover, the risk of G2/High risk group is higher than that of G2/Low risk group, and the survival rate of patients with G2/Low risk is similar to that of Grade1, while the survival rate of patients with G2/High risk is even worse than that of patients with G3.ConclusionOur results suggest that TILs-score can be used to predict the histological grade of breast cancer and potentially to guide the therapeutic management of breast cancer patients.

Development and prognostic validation of a three-level NHG-like deep learning-based model for histological grading of breast cancer

BackgroundHistological grade is a well-known prognostic factor that is routinely assessed in breast tumours. However, manual assessment of Nottingham Histological Grade (NHG) has high inter-assessor and inter-laboratory variability, causing uncertainty in grade assignments. To address this challenge, we developed and validated a three-level NHG-like deep learning-based histological grade model (predGrade). The primary performance evaluation focuses on prognostic performance.MethodsThis observational study is based on two patient cohorts (SöS-BC-4, N = 2421 (training and internal test); SCAN-B-Lund, N = 1262 (test)) that include routine histological whole-slide images (WSIs) together with patient outcomes. A deep convolutional neural network (CNN) model with an attention mechanism was optimised for the classification of the three-level histological grading (NHG) from haematoxylin and eosin-stained WSIs. The prognostic performance was evaluated by time-to-event analysis of recurrence-free survival and compared to clinical NHG grade assignments in the internal test set as well as in the fully independent external test cohort.ResultsWe observed effect sizes (hazard ratio) for grade 3 versus 1, for the conventional NHG method (HR = 2.60 (1.18–5.70 95%CI, p-value = 0.017)) and the deep learning model (HR = 2.27, 95%CI 1.07–4.82, p-value = 0.033) on the internal test set after adjusting for established clinicopathological risk factors. In the external test set, the unadjusted HR for clinical NHG 2 versus 1 was estimated to be 2.59 (p-value = 0.004) and clinical NHG 3 versus 1 was estimated to be 3.58 (p-value < 0.001). For predGrade, the unadjusted HR for predGrade 2 versus 1 HR = 2.52 (p-value = 0.030), and 4.07 (p-value = 0.001) for preGrade 3 versus 1 was observed in the independent external test set. In multivariable analysis, HR estimates for neither clinical NHG nor predGrade were found to be significant (p-value > 0.05). We tested for differences in HR estimates between NHG and predGrade in the independent test set and found no significant difference between the two classification models (p-value > 0.05), confirming similar prognostic performance between conventional NHG and predGrade.ConclusionRoutine histopathology assessment of NHG has a high degree of inter-assessor variability, motivating the development of model-based decision support to improve reproducibility in histological grading. We found that the proposed model (predGrade) provides a similar prognostic performance as clinical NHG. The results indicate that deep CNN-based models can be applied for breast cancer histological grading.

A Comprehensive Model Based on Dynamic Contrast-Enhanced Magnetic Resonance Imaging Can Better Predict the Preoperative Histological Grade of Breast Cancer Than a Radiomics Model.

Histological grade is an important prognostic factor for patients with breast cancer and can affect clinical decision-making. From a clinical perspective, developing an efficient and non-invasive method for evaluating histological grading is desirable, facilitating improved clinical decision-making by physicians. This study aimed to develop an integrated model based on radiomics and clinical imaging features for preoperative prediction of histological grade invasive breast cancer. In this retrospective study, we recruited 211 patients with invasive breast cancer and randomly assigned them to either a training group (n=147) or a validation group (n=64) with a 7:3 ratio. Patients were classified as having low-grade tumors, which included grade I and II tumors, or high-grade tumors, which included grade III tumors. Three models were constructed based on basic clinical features, radiomics features, and the sum of the two. To assess diagnostic performance of the radiomics models, we employed measures such as receiver operating characteristic (ROC) curve, decision curve analysis (DCA), accuracy, sensitivity, and specificity, and the predictive performance of the three models was compared using the DeLong test and net reclassification improvement (NRI). The area under the curve (AUC) of the clinical model, radiomics model, and comprehensive model was 0.682, 0.833, and 0.882 in the training set and 0.741, 0.751, and 0.836 in the validation set, respectively. NRI analysis confirmed that the combined model was better than the other two models in predicting the histological grade of breast cancer (NRI=21.4% in the testing cohort). Compared with the other models, the comprehensive model based on the combination of basic clinical features and radiomics features exhibits more significant potential for predicting histological grade and can better assist clinicians in optimal decision-making.

A classifier-combined method for grading breast cancer based on Dempster-Shafer evidence theory.

Preoperative non-invasive histologic grading of breast cancer is essential. This study aimed to explore the effectiveness of a machine learning classification method based on Dempster-Shafer (D-S) evidence theory for the histologic grading of breast cancer. A total of 489 contrast-enhanced magnetic resonance imaging (MRI) slices with breast cancer lesions (including 171 grade Ⅰ, 140 grade Ⅱ, and 178 grade Ⅲ lesions) were used for analysis. All the lesions were segmented by two radiologists in consensus. For each slice, the quantitative pharmacokinetic parameters based on a modified Tofts model and the textural features of the segmented lesion on the image were extracted. Principal component analysis was then used to reduce feature dimensionality and obtain new features from the pharmacokinetic parameters and texture features. The basic confidence assignments of different classifiers were combined using D-S evidence theory based on the accuracy of three classifiers: support vector machine (SVM), Random Forest, and k-nearest neighbor (KNN). The performance of the machine learning techniques was evaluated in terms of accuracy, sensitivity, specificity, and the area under the curve. The three classifiers showed varying accuracy across different categories. The accuracy of using D-S evidence theory in combination with multiple classifiers reached 92.86%, which was higher than that of using SVM (82.76%), Random Forest (78.85%), or KNN (87.82%) individually. The average area under the curve of using the D-S evidence theory combined with multiple classifiers reached 0.896, which was larger than that of using SVM (0.829), Random Forest (0.727), or KNN (0.835) individually. Multiple classifiers can be effectively combined based on D-S evidence theory to improve the prediction of histologic grade in breast cancer.

Influence of the histological grade of breast cancer on the tacticsof reconstructive surgery after radical mastectomy

Influence of the histological grade of breast cancer on the tacticsof reconstructive surgery after radical mastectomy

Pink1 promotes cell proliferation and affects glycolysis in breast cancer.

Phosphatase and tensin homolog (PTEN)-induced kinase 1 (Pink1) is regarded as a tumor suppressor and plays an important role in cancer cell biology, while relatively few studies have examined Pink1 in breast cancer, especially in vivo. The aims of this study were to investigate Pink1 expression in different subtypes of breast cancer tissues and cell lines and explore the effect of Pink1 protein on breast cancer. In these experiments, Pink1 expression was investigated using the tissue microarray immunohistochemistry (TMA-IHC) method in 150 samples of breast cancer tissues with different subtypes, and strong staining of Pink1 was significantly correlated with the histological grade of breast cancer (p = 0.015). In addition, Pink1 messenger RNA (mRNA) displayed much higher expression levels in breast cancer cell lines than in MCF-10A breast epithelial cells. Moreover, proteomic data obtained by isobaric tags for relative and absolute quantification (iTRAQ) showed that Pink1 deletion induced a distinct proteomic profile in MDA-MB-231 cells, and enrichment analysis showed that the differential proteins were concentrated mainly in energy metabolism-related pathways. Moreover, Seahorse XF analysis showed that Pink1 knockout reduced the glycolytic ability of MDA-MB-231 cells. Our findings indicated that Pink1 may be an indicator of malignancy in breast cancer and that it presents oncogenic properties in breast cancer, which raises another perspective for understanding the regulatory role of Pink1 in breast cancer.

Apparent diffusion coefficient as a potential non-invasive predictor of histological grade of breast cancer

Apparent diffusion coefficient as a potential non-invasive predictor of histological grade of breast cancer

Association Between Cytological and Histological Grading of Breast Cancer and its Usefulness in Patient Treatment

Introduction: Breast cancer is the most common cancer in women throughout the globe, and the modern therapeutic available options have been widely researched currently. For a developing nation with limited resources, such as Pakistan, a quick, affordable, and least invasive technique like FNAC may give useful information. Aim: The aim of this study was; 1 Perform and evaluate cytomorphological classification of all breast cancer patients using the Robinson's three-tier grading system. 2 Histopathological evaluation and classification of all samples of MRM by using a modified BR classification system. 3 Correlation of histo-morphological and cytomorphological classification systems. Place and Duration: In the Pathology department of Khyber Teaching Hospital Peshawar for six months duration from February 2018 to July 2018. Material and methods: A prospective study was conducted in which two independent observers classified all breast cancer patients on the basis of cytology and histology of the tissue and cyto-histological correlation. Results: This study provided good individual correlation and inter-observer agreement in the cytopathological classification of breast cancer aspirates. Conclusion: Cytopathological classification may be useful for these patients and should therefore be included in routine reports. Keywords: Breast carcinoma; Cytopathology; Robinson classification system

Correlation between the characteristics of ultrasonic contrast and the regional distribution of tumor vascular heterogeneity in breast cancer

Abstract To clarify the correlation between qualitative and quantitative parameters of contrast-enhanced ultrasound and regional distribution differences of microvessel density (MVD) and vasculogenic mimic (VM) in breast cancer. 119 female patients with suspected breast cancer were involved. The clinicopathological parameters of patients were collected. Contrast-enhanced ultrasound was performed on patients. After the operation, according to the unified standard, the marginal zone, the central region and the paracancerous area of the breast cancer were sampled respectively. The MVD were labeled by immunohistochemistry with CD34, and tumor VM was labeled by CD34/PAS double staining. The distributions of MVD and VM in the marginal zone, the central area and the paracancerous area were decreased (P < 0.05, respectively), but the correlations between MVD, VM and contrast-enhanced ultrasound were not consistent. The quantitative parameters of contrast-enhanced ultrasound in marginal zone and central area were correlated with MVD. The positive rate of VM in marginal zone was also higher when breast cancer mass was larger and clinical stage was higher. When histological grade of breast cancer was high, the positive rates of VM in three regions were also high. The correlation between contrast-enhanced ultrasound and MVD was better than that of VM, but VM was associated with more prognostic factors than MVD.

Relationship Between Vitamin D Receptor TaqI Polymorphism and the Clinicopathological Features of Breast Cancer Patients at Sanglah Hospital, Denpasar, Bali

Introduction: Breast cancer is a malignant tumor of the mammary gland. Breast cancer is associated with vitamin D receptor (VDR) TaqI polymorphism. This study aims to prove the relationship between TaqI polymorphism and the clinicopathology of breast cancer in Bali. Methods: This study is an observational analytic study with a cross-sectional design conducted from February to August 2021. A total of 39 sample DNA isolates from breast cancer patients at Sanglah Hospital were enrolled in the study. The independent variable was the VDR TaqI gene polymorphism, while the dependent variable was the clinicopathological breast cancer (clinical stage and histological grading). The examination was carried out by PCR and sequencing. Clinical staging and histological grading of breast cancer were taken from the patient's medical record. The results were analyzed using descriptive and bivariate analysis. Results: TaqI polymorphism of breast cancer at Sanglah Hospital Denpasar was dominated by the wild type TT genotype as much as 59%. This study did not find the CC genotype. The results of the statistical analysis did not find a significant relationship (p&gt;0.05) between TaqI polymorphisms with TT and TC genotypes and clinicopathology of breast cancer but found a significant relationship (p=0.047) with histological grading. The TT and TC genotypes were found to increase the risk of breast cancer severity by 3,810 compared to individuals without the TT and TC genotypes. Analysis of confounding variables with clinicopathological breast cancer found no significant relationship (p&gt;0.05) between the age of diagnosis, menstrual status, and hormonal contraceptive use with the characteristics of the sample. Conclusion: It was concluded that breast cancer patients at Sanglah Hospital Bali had TaqI polymorphisms with TT and TC genotypes. TT and TC genotypes are associated with histological grading and can increase the severity of histological grading in breast cancer patients at Sanglah Hospital Denpasar Bali.

Nomogram model combining macro and micro tumor-associated collagen signatures obtained from multiphoton images to predict the histologic grade in breast cancer.

The purpose of this study is to develop and validate a new nomogram model combining macro and micro tumor-associated collagen signatures obtained from multiphoton images to differentiate tumor grade in patients with invasive breast cancer. A total of 543 patients were included in this study. We used computer-generated random numbers to assign 328 of these patients to the training cohort and 215 patients to the validation cohort. Macroscopic tumor-associated collagen signatures (TACS1-8) were obtained by multiphoton microscopy at the invasion front and inside of the breast primary tumor. TACS corresponding microscopic features (TCMF) including morphology and texture features were extracted from the segmented regions of interest using Matlab 2016b. Using ridge regression analysis, we obtained a TACS-score for each patient based on the combined TACS1-8, and the least absolute shrinkage and selection operator (LASSO) regression was applied to select the most robust TCMF features to build a TCMF-score. Univariate logistic regression analysis demonstrates that the TACS-score and TCMF-score are significantly associated with histologic grade (odds ratio, 2.994; 95% CI, 2.013-4.452; P < 0.001; 4.245, 2.876-6.264, P < 0.001 in the training cohort). The nomogram (collagen) model combining the TACS-score and TCMF-score could stratify patients into Grade1 and Grade2/3 groups with the AUC of 0.859 and 0.863 in the training and validation cohorts. The predictive performance can be further improved by combining the clinical factors, achieving the AUC of 0.874 in both data cohorts. The nomogram model combining the TACS-score and TCMF-score can be useful in differentiating breast tumor patients with Grade1 and Grade2/3.

Intravoxel incoherent motion magnetic resonance imaging for breast cancer: A comparison with molecular subtypes and histological grades

PurposeThe purpose of this paper is to investigate whether the IVIM parameters (D, D *, f) helps to determine the molecular subtypes and histological grades of breast cancer. MethodsFifty-one patients with breast cancer were included in the study. All subjects were examined by 3 T Magnetic Resonance Imaging (MRI). Diffusion-weighted imaging (DWI) was undertaken with 16 b-values. IVIM parameters [D (true diffusion coefficient), D* (pseudo-diffusion coefficient), f (perfusion fraction)] were calculated. Histopathological reports were reviewed to histological grade, histological type, and immunohistochemistry. IVIM parameters of tumors with different histological grades and molecular subtypes were compared. ResultsD* and f were significantly different between molecular subtypes (p = 0.019, p = 0.03 respectively). D* and f were higher in the HER-2 group and lower in Triple negative (−) group (D*:36.8 × 10−3 ± 5.3 × 10−3 mm2/s, f:29.5%, D*:29.8 × 10−3 ± 5.6 × 10−3 mm2/s, f:21.5% respectively). There was a significant difference in D* and f between HER-2 and Triple (−) subgroups (p = 0,028, p = 0.024, respectively). D* was also significantly different between the HER-2 group and the Luminal group (p = 0,041). While histological grades increase, D and f values tend to decrease, and D* tends to increase. While the Ki-67 index increases, D* and f values tend to increase, and D tend to decrease. ConclusionD* and f values measured with IVIM imaging were useful for assessing breast cancer molecular subtyping. IVIM imaging may be an alternative to breast biopsy for sub-typing of breast cancer with further research.

Application of Artificial Intelligence Technology in Pathological Image Analysis of Breast Tissue

Breast histopathological examination usually relies on experienced pathologists. In recent years, artificial intelligence technology has been applied to digital pathological images to help doctors diagnose breast tumor. This paper mainly introduces the research progress of artificial intelligence technology in pathological classification and histological grade of breast cancer. In view of the simplification of the pathological image analysis object of breast tissue, a multimodal breast cancer aided pathological diagnosis model is proposed. The proposed model combines the patient’s immunohistochemical diagnosis data and pathological image data with artificial intelligence to analyze and diagnose. To achieve a deeper evaluation of the patient’s condition on the basis of simple tumor screening, optimize the clinical treatment plan to make it more targeted. Discuss the main problems faced by artificial intelligence technology in the field of breast pathological image analysis, and prospect its development prospects.

Correlation of Lymphocyte-Monocyte Ratio with Grading of Breast Cancer Malignancy

Introduction: Breast cancer cases are increase and fast becoming the leading cause of oncologic mortality among women worldwide. Delay in management will worsen the prognosis of breast cancer patients. The poor prognosis is correlate with high grade of breast cancer malignancy. Histology can be used as an indicator to assess the grade of breast cancer malignancy. In addition, a high predictive value of lymphocyte-monocyte ratio (LMR) has been suggested to indicate favorable prognosis of various cancer, and used as parameters for assessing breast cancer prognosis.&#x0D; Method: This study is a quantitative research, using an observational approach. Laboratory examination results are collected from 43 breast cancer patients who will conduct treatment at Urip Sumoharjo Hospital from June to September 2019. Data are processed by the Spearman rank test using SPSS 22.0.&#x0D; Results: Correlation test results between lymphocyte-monocyte ratio with histological grading of breast cancer obtained a P value of 0.376.&#x0D; Conclusions: Lymphocytes to monocytes ratio (LMR) value cannot be utilize as a parameter for evaluating prognosis of breast cancer.

Corelation Between Cytological and Histological Grading of Breast Cancer and its Utility in Patient’s Management

Corelation Between Cytological and Histological Grading of Breast Cancer and its Utility in Patient’s Management

Anti-angiogenic efficacy in invasive breast carcinoma patients depends on clinicopathological determinants

PurposeThe biggest problem with the occurrence of breast cancer is late diagnosis, which is associated with high mortality rates. The aim of the study was to appraise the number of circulating endothelial precursors and the concentration of vascular endothelial growth factor A (VEGF-A) and the soluble forms of its receptors, sVEGFR1 and sVEGFR2, in breast cancer patients with respect to clinicopathological features. Material and methodsThe study involved 85 women of Caucasian ethnicity aged 45–66 with primary breast cancer without distant metastases (M0). Inclusion criteria were as follows: histopathological examination confirming the diagnosis of primary breast cancer, without previous radiotherapy and chemotherapy. Immunohistochemistry evaluation of oestrogen and progesterone receptors, human epidermal growth factor receptor 2, Ki67 expression was made in all cases. In the EDTA-plasma, the concentrations of VEGF-A and its soluble receptors, sVEGFR1 and sVEGFR2, were measured applying immunoassay techniques. Circulating endothelial progenitor cells (EPCs) were identified with the immune-phenotype CD45−, CD34+, CD133+, CD31+ using flow cytometry. ResultsOlder women with breast cancer had significantly higher concentrations of VEGF-A as well as sVEGFR2 compared with their younger counterparts. A significantly higher concentration of the soluble form of VEGF receptor type 1 in patients with T1 breast cancer in relation to T2 cases was noted. Also, negative correlations between circulating EPCs and histological grading as well as a soluble form of VEGFR2 with histological grading of breast cancer according to the Elston-Ellis classification were observed. ConclusionsAnti-angiogenic potential is divergent in relation to the clinicopathological determinants.

Second Primary Lung Cancer After Breast Cancer: A Population-Based Study of 6,269 Women.

Purpose: Breast cancer (BC) and lung cancer are the most two common cancers with highest morbidity and mortality for women. With prolonged survival, there comes the possibility that BC patients will develop second primary cancers. We evaluate the characteristics, incidence and survival of second primary non-small cell lung cancer (BC-NSCLC) and small cell lung cancer (BC-SCLC) after breast cancer.Patients and methods: Second primary lung cancer risks using standardized incidence ratios (SIRs) [95% confidence intervals (95% CIs)] were calculated among breast cancer patients in SEER-18 (2000–2014). Survival outcomes were also analyzed for both BC-NSCLC and BC-SCLC.Results: A total of 6,269 second lung cancer patients after a localized or regional BC were identified. The incidence rate was modestly higher compared to the general population (SIR = 1.03; 95%CI: 1.00–1.06). For ER-, PR- and HER2- groups, SIRs were 1.26, 1.16, 1.13, respectively (all p < 0.05). Triple negative breast cancer (TNBC) patients have an even higher incidence rate of lung cancer (SIR = 1.59, 95%CI: 1.29–1.94). Elevated SIRs were also observed among the following groups: within 1 year after BC diagnosed, a young age at BC diagnosed, black people, poorly or undifferentiated histological grade of breast cancer. Median survival (MST) after localized, regional and distant BC-NSCLC was 68.0, 26.0, and 6.0m. Five-year survival rates for BC-NSCLC were 53.9, 29.8 and 5.7% in each stage, which were significantly higher compared to first primary NSCLC (all p < 0.001). ER-/PR- or TNBC were unfavorable prognostic factors for BC-NSCLC. The survival rates of BC-SCLC were no significant different compared to first primary SCLC.Conclusion: BC patients, especially for TNBC, are at a high risk of developing second primary lung cancers. BC history may be a favorable prognostic factor for NSCLC (but not SCLC) patients. Clinicians should closely follow up BC patients with high-risk factors.

Extent of breast cancer type 1 promoter methylation correlates with clinicopathological features in breast cancers.

The current meta-analysis investigated the correlation between breast cancer type 1 (BRCA1) promoter methylation and the clinicopathological features of breast cancer (BC). An electronic literature search was performed to identify and select cohort studies, by employing stringent inclusion and exclusion criteria, for data relevant to promoter methylation of BRCA1 and BC. Statistical analysis of the extracted data was performed using comprehensive meta-analysis 2.0 software (CMA 2.0) (Biostat Inc., Englewood, New Jersey, USA). A total of 125 published studies were retrieved from the literature search, and finally, 18 cohort studies meeting our inclusion criteria were incorporated into our meta-analysis. The 18 studies contained a total of 3213 BC patients. Meta-analysis results revealed that BRCA1 promoter methylation in BC patients with high and moderately differentiated tumors (I-II) was significantly lower than patients with poorly-differentiation tumors (III) (odds ratio [OR] =0.450, 95% confidence interval [95% CI] =0.241-0.838, P = 0.012). BRCA1 promoter methylation in BC patients with lymph node (LN) metastasis was significantly higher than patients without LN metastasis (OR = 2.244, 95% CI = 1.278-3.940, P = 0.005). The results of ethnicity-based subgroup analysis showed a significant difference in histological grade of BC on Asians, LN metastasis of BC in Asians and Caucasians, subtypes of BC in Caucasians, and age at diagnosis of BC patients in Caucasians (all P < 0.05). Our meta-analysis revealed that BRCA1 promoter methylation status is linked to tumor grade and LN metastasis of BC.