The Zeb1-Cxcl1 axis impairs the antitumor immune response by inducing M2 macrophage polarization in breast cancer.

Zeb1, a key epithelial-mesenchymal transition (EMT) regulator, has recently been found to be involved in M2 macrophage polarization in the tumor immune microenvironment, thereby promoting tumor development. However, the underlying mechanism of Zeb1-induced M2 macrophage polarization remains largely unexplored. To identify the potential role of Zeb1 in remodeling the tumor immune microenvironment in breast cancer, we crossed the floxed Zeb1 allele homozygously into PyMT mice to generate PyMT;Zeb1cKO (MMTV-Cre;PyMT;Zeb1fl/fl ) mice. We found that the recruitment of M2-type tumor-associated macrophages (TAMs) was significantly reduced in tumors from PyMT;Zeb1cKO mice, and their tumor suppressive effects were weakened. Mechanistically, Zeb1 played a crucial role in transcriptionally promoting the production of Cxcl1 in tumor cells. In turn, Cxcl1 activated the Cxcr2-Jak-Stat3 pathway to induce M2 polarization of TAMs in a paracrine manner, which eventually led to T-cell inactivation and impaired the antitumor immune response in breast cancer. Our results collectively revealed an important role of Zeb1 in remodeling the tumor microenvironment, suggesting a novel therapeutic intervention for the treatment of advanced breast cancer.

[Primary Extranodal Diffuse Large B-Cell Lymphoma in the Rituximab Era: a Single-Center Retrospective Analysis].

OBJECTIVE: To investigate the clinical features and prognostic factors of patients with primary extranodal diffuse large B-cell lymphoma (DLBCL) in the rituximab era. METHODS: The continuous data of newly diagnosed DLBCL patients with complete case data and first-line treated with rituximab, cyclophosphamide, epirubicin, vincristine, prednisone (R-CHOP) or R-CHOP treatment admitted to the Affiliated Hospital of Inner Mongolia Medical University from January 2013 to November 2023 were retrospectively analyzed. The clinical and molecular immunological features and prognosis of extranodal DLBCL were analyzed, Logistics regression model was used to analyzed the influencing factors of patients prognosis. RESULTS: A total of 237 patients were enrolled, of which 54.4% (129 cases) were primary extranodal sources of DLBCL, and the most common extranodal sites were as follows: stomach (19.4%), colon (14.7%), tonsils (12.4%), skin/muscle (9.3%), central (7.7%), nasal/nasopharynx (6.2%), bone marrow (5.4%), testes (4.7%). The 3-year PFS and OS of DLBCL patients with extranodal involvement of bone marrow, central, liver, gastrointestinal or pulmonary origin were significantly lower than those of other patients with extranodal DLBCL of non-special site origin, and the difference was statistically significant (PFS: 65.2% vs 76.7%, P =0.008; OS: 82.6% vs 88.3%, P =0.04). Multivariate analysis showed that the prognostic factors affecting OS included NCCN-IPI score >3 (OR : 0.142, 95%CI : 0.041-0.495, P =0.002), non-germinal center source (OR : 2.675，95%CI :1.069-6.694，P =0.036), and DEL patients (OR : 0.327, 95%CI : 0.129-0.830, P =0.019). An NCCN-IPI score >3 was the only independent adverse prognostic factor for PFS (OR : 0.235, 95%CI : 0.116-0.474, P < 0.001). CONCLUSION: Patients with primary extranodal source DLBCL are more common in gastrointestinal involvement, and the overall prognosis is worse than that of patients with lymph node origin. NCCN-IPI score is an important independent adverse prognostic factor for predicting overall survival and progression-free survival in patients with primary extranodal diffuse large B-cell lymphoma.

A Fuzzy Expert System to Predict the Risk of Postpartum Hemorrhage.

INTRODUCTION: The American College of Obstetricians and Gynecologists (ACOG) defines postpartum hemorrhage (PPH) as a blood loss of >500mL following vaginal delivery or >1000mL following cesarean section. PPH is widely recognized as a common cause of maternal death. However, there is currently no effective method to predict its risk of occurrence. AIM: To develop a fuzzy expert system to predict the risk of developing PPH and to evaluate its performance in the clinical setting. METHODS: This system was developed using MATLAB software. Mamdani inference was used to simulate reasoning of experts in the field. To evaluate the performance of the system, a dataset of 1705 patients admitted at the Labor and Delivery ward of The Second Affiliated Hospital of Nanjing Medical University from 2017-10 to 2018-04, was considered. RESULTS: The Negative Predictive value (NPV), Positive Predictive value PPV), Specificity and Sensitivity were calculated and were 99.72%, 18.50%, 87.48% and 92.16% respectively. CONCLUSIONS: Our findings suggest that the fuzzy expert system can be used to predict PPH in clinical settings and thus decrease maternal mortality rate due to hemorrhage.