In recent years， central venous access has been widely employed for chemotherapy and nutritional therapy in breast cancer patients. Standardized management of central venous access is a critical prerequisite for ensuring safe administration of chemotherapy. To address this issue， the Nursing Collaboration Group of the Breast Cancer Specialty Committee of the Hebei Anti-Cancer Association has established a management consensus focusing on five key aspects： access selection， implantation details， usage and maintenance， identification and management of common complications， and patient education. This consensus aims to offer scientific and practical guidance for nursing staff at all levels of healthcare institutions， thereby improving the management of central venous access for breast cancer patients and enhancing their quality of life and clinical outcomes.
To investigate the role of LINC01232 in triple negative breast cancer （TNBC）and explore the regulatory relationship with miR-516a-5p/BCL9 axis.
Methods
The Gene Expression Omnibus （GEO） database was screened for differentially expressed genes associated with TNBC malignant progression. Real-time quantitative RT-PCR was performed to detect the expression of LINC01232 in TNBC，and cell function experiments were performed to explore the potential effect of LINC01232 on TNBC progression.Bioinformatics analysis was performed to analyze the association between LINC01232 and miR-516a-5p and B-cell lymphokine 9 （BCL9）， and their binding activities were verified using dual-luciferase reporter gene and RNA immunoprecipitation assays.
Results
Compared with adjacent normal tissue， the expression of LINC01232 was abnormally elevated in TNBC tissue （0.797±0.449 vs 2.524±1.099， t=11.273， P＜0.001）.When LINC01232 was targeted for knockdown， breast cancer cell proliferation among different groups showed a statistically significant difference （F=884.428， P＜0.001）， with an interaction between subgroups and time points （F=54.276， P＜0.001）. Compared with the control group， cell proliferation ability was reduced in the LINC01232 knockdown group at 72， 96 h（t=8.933， P＜0.001； t=8.378， P＜0.001）. Clone formation assay showed that the number of clones in the LINC01232 knockdown group was reduced compared with the control group （t=40.455，48.039， both P＜0.001）. Transwell assay showed that the number of migrated and invaded cells in the LINC01232 knockdown group was significantly reduced compared with the control group （539.3±0.1 vs 344.3±0.6， t=35.308， P＜0.001）. LINC01232 was able to competitively bind to miR-516a-5p and inhibit the expression of miR-516a-5p， thus upregulating the expression of BCL9. The results of MTS experiments showed that there was a statistical difference between sh-NC， sh-LINC01232 and sh-LINC01232 and miR-516a-5p inhibitor co-transfection groups （F = 4412.680， P ＜0.001） and an interaction between subgroups and time points （F= 64.100， P ＜0.001）. Knockdown of LINC01232 at 72， 96 h significantly inhibited the proliferation of breast cancer cells （t = 4.011， P = 0.007； t = 13.993， P ＜0.001）， and cotransfection somewhat restored the proliferation ability of breast cancer cells （t=-1.734， P=0.134； t=-1.091， P=0.317）. In the clone formation experiment， Statistical differences existed between groups （F=891.520， P＜0.001）， and pairwise comparison within groups showed the knockdown of LINC01232 inhibited the proliferation of breast cancer cells （t=44.111， P＜0.001）， and co-transfection restored the proliferative ability of breast cancer cells to a certain extent （t=-0.220， P=0.810）.The results of Transwell assay showed that statistical differences were observed in the migrated cells and invasive cells among three groups （F=2114.691，P＜0.001； F=810.413， P＜0.001）， and pairwise comparisons within the groups showed that knockdown of LINC01232 reduced the migration and invasion of breast cancer cells （t=55.103， P＜0.001， t=51.879， P＜0.001）， and the migration and invasion ability of breast cancer cells was reduced after cotransfection （t=-0.223， P=0.822， t=-9.470， P＜0.001）.
Conclusions
In triple negative breast cancer cells， LINC01232 promotes the migration and invasion capacity of TNBC cells in vitro via the miR-516a-5p/BCL9 axis.
To establish a prediction model for HER-2 expression status in breast cancer using habitat analysis based on enhanced MRI radiomic features.
Methods
A retrospective analysis was conducted on the second-phase DCE-T1 WI data of 168 breast cancer patients who underwent enhanced MRI examinations in the Affiliated Suzhou Hospital of Nanjing Medical University from January 2018 to May 2023.Among them， 100 cases were HER-2-negative， and 68 cases were HER-2-positive. After preprocessing the images， the whole tumor volume of interest （VOI） was manually segmented. Twenty-four regional radiomic features were extracted and clustered into habitat subregions using a Gaussian Mixture Model （GMM） combined with the Bayesian Information Criterion （BIC）. Radiomic features were separately extracted from the subregions and the entire tumor region. The data were randomly divided into a training set （117 cases） and a validation set（51 cases） at the ratio of 7 ∶3. Logistic regression （LR）， support vector machine （SVM）， and k-nearest neighbor （KNN） algorithms were used to construct habitat prediction models and whole-tumor prediction models. The optimal model was selected based on the area under the curve （AUC） value in the validation set，the receiver operating characteristic （ROC） curve was drawn. DeLong’s test was used to compare the AUC of the two prediction models， and decision curve analysis （DCA） was performed to evaluate the clinical utility of the models.
Results
Each tumor VOI was segmented into three habitat subregions. SVM was identified as the best modeling method. The habitat prediction model-SVM achieved an AUC of 0.949 （95%CI：0.915-0.984） in the training set and 0.844 （95%CI： 0.725-0.963） in the validation set. The best whole-tumor prediction model-SVM achieved an AUC of 0.870 （95%CI： 0.809-0.931） in the training set and 0.735 （95%CI： 0.588-0.882） in the validation set. The habitat prediction model-SVM demonstrated superior accuracy， sensitivity and specificity compared with the whole-tumor prediction model-SVM in both the training and validation sets.DeLong’s test indicated that the AUC differences between the models in the training set were statistically significant （Z=2.134， P=0.033）. DCA results showed that the habitat prediction model-SVM provided the highest net benefit.
Conclusions
This study established a prediction model for HER-2 expression status in breast cancer based on habitat analysis using enhanced MRI radiomic features， which may provide a reference for precision treatment of breast cancer patients.
To investigate the clinical efficacy， prognosis and safety of apatinib combined with doxorubicin （A） + cyclophosphamide （C） sequential albumin-bound paclitaxel （T） as a neoadjuvant chemotherapy regimen in patients with triple negative breast cancer （TNBC）.
Methods
A retrospective analysis was conducted in 70 TNBC patients treated in Cangzhou Central Hospital from July 2016 to January 2020. Patients were divided into two groups based on their treatment regimens：34 patients in the control group received preoperative AC-T chemotherapy alone， while 36 patients in the observation group received AC-T chemotherapy combined with apatinib （antiangiogenic agent）. Adverse events during chemotherapy were recorded. Blood samples were collected before and after chemotherapy to measure vascular endothelial growth factor （VEGF）， thymidine kinase 1 （TK1）， and carcinoembryonic antigen （CEA） levels. Objective response rate （ORR）， pathological complete response （pCR） rate， and surgical approaches were recorded. Intergroup comparisons of pCR， ORR， and breast-conserving surgery rate were analyzed using chi-square test. Logistic regression （univariate and multivariate） was employed to identify independent predictors of pCR. Differences in serum biomarker levels between groups were compared using t test，while adverse events were analyzed by ranksum test. Cox proportional hazards regression （univariate and multivariate） was performed to identify independent prognostic factors of DFS and OS.
Results
The observation group had significantly higher pCR and breast-conserving surgery rates compared with the control group （50.0% vs 23.5%， χ2=5.717，P=0.017；44.4% vs 20.6%，χ2=4.511，P=0.034）. Tumor size， neoadjuvant chemotherapy regimen， and pre-treatment Ki-67 levels were identified as independent predictors of pCR （OR=0.85，95%CI：0.77-0.95，P=0.003；OR=11.00，95%CI：2.37-51.12， P=0.002；OR=0.04，95%CI：0-0.44，P=0.008）. The VEGF and CEA levels after neoadjuvant chemotherapy were significantly higher in the observation group than those in the control group（279.08±29.03 vs 143.15±15.73，t=24.160，P＜0.001； 2.20±0.22 vs 1.78±0.23， t=7.840，P＜0.001）. No serious adverse reactions of grade Ⅲor above were observed in either group. The incidences of recorded adverse events， including nausea/vomiting， diarrhea， leukopenia， and proteinuria， showed no statistically significant differences between the two groups （Z=-0.463， -0.202， -0.547， -0.814； P=0.643， 0.840， 0.584， 0.416）.Patients in the observation group had a significantly higher breast-conserving surgery rate compared with the control group （44.4% vs 20.6%， χ2=4.511，P=0.034）. Apatinib combined with AC-T， postmenopausal status and early stage breast cancer were protective factors of DFS （HR=0.10， 95%CI：0.02-0.51， P=0.006； HR=0.12，95%CI：0.03-0.47， P=0.002； HR=8.87，95%CI：1.87-43.77， P=0.007）.
Conclusion
The regimen of apatinib combined with AC-T for neoadjuvant chemotherapy demonstrated favorable clinical efficacy，promising prognosis and controllable safety in TNBC patients.
To investigate the diagnostic value of ultrasound radiomics features for the detection of non-mass breast cancer in dense breasts.
Methods
We retrospectively analyzed 2D ultrasound images of 619 patients with non-mass breast lesions （NML） in dense breasts in Dongguan People’s Hospital and Affiliated Tumor Hospital of Xinjiang Medical University between January 1st， 2017 and January 30th，2023. They were randomized into two groups using a 7 ∶3 ratio（434 cases in the training group and 185 cases in the validation group）. Totally 848 imaging features were extracted. The least absolute shrinkage and selection operator （LASSO） regression model was used to screen the features， and the radiomics model was built by the LASSO-Logistic regression. The joint model was constructed by integrating it with clinical and ultrasound features. The diagnostic efficacy of the model was evaluated by the receiver operating characteristic （ROC）curve. The consistency of the model was assessed with the calibration curve and the clinical value of the model was assessed by decision curve analysis （DCA）. The DeLong test was used to compare those models.
Results
The postoperative pathological results showed that among 619 cases of breast NML， 304 cases were malignant and 315 cases were benign. The results of univariate and multivariate Logistic regression analyses indicated that age， lesion length， microcalcification， distortion of surrounding structures， and blood flow features were independent predictors of malignancy （OR=1.053，8.197，0.701，3.479，1.195；95%CI：1.027-1.080， 4.895-14.154， 0.573-0.857， 2.044-6.044， 1.536-2.408； all P＜0.050）. A total of 12 radiomics features with non-zero coefficients were screened out. The selected clinical factors and radiomics features were integrated to create a joint prediction model. The area under the ROC curve of the joint model was 0.89（95%CI：0.86-0.92） in the training group， and 0.83 （95%CI： 0.78-0.89） in the validation group. The area under the DCA curve of the joint model was the largest（0.12 in the training group and 0.22 in the validation group）. The calibration curve showed that the joint model had a better consistency between the predicted results and the actual pathological results compared with other models. The DeLong test demonstrated that there were statistically significant differences comparing the joint model with other models （the clinical model， the ultrasound model and the radiomics model） （Z=-3.974，-3.338，-3.468；all P＜0.050）.
Conclusions
The joint model integrating clinical factors and radiomics features of ultrasound has good efficacy in identifying the benign and malignant nature of NML in dense breasts， which can provide guidance for clinical decision-making in breast cancer.
