Hongshi Cao

Tumor-associated macrophages (TAMs) are central constituents of the tumor microenvironment (TME), recruited from circulating monocytes through chemotactic signals, and they execute complex, multifaceted functions throughout tumor progression. Functionally heterogeneous, TAMs are broadly classified into distinct subtypes that display a dynamic duality, capable of shifting between tumor-suppressive and tumor-promoting states, though the pro-tumorigenic functions tend to dominate across multiple cancer types. The polarization of TAMs is modulated by diverse cytokines and signaling networks within the TME. Key pro-tumor mechanisms include activating proliferative signaling pathways, enhancing invasive and metastatic potential, establishing an immunosuppressive TME through immune cell interactions, and conferring therapy resistance. The spatial heterogeneity of TAMs further underscores the predictive relevance. Translational research increasingly focuses on TAM-targeting strategies such as inhibiting recruitment, depleting subsets, or reprogramming function. Emerging approaches, including nanomedicine-based targeting, macrophage-mediated therapies, and novel drug formulations, highlight the importance of combining conventional treatments with immune checkpoint inhibitors (ICIs). Such combinations help overcome therapeutic resistance and improve clinical outcomes. This review systematically summarizes recent advances in TAM biology and plasticity, biomarkers from single-cell and spatial analyses for distinguishing TAM subsets, and their prognostic relevance in immunotherapy. It also discusses TAM-targeting strategies and their synergistic potential with existing therapies. Together, these insights lay the foundation for next-generation cancer treatments that precisely target TAMs to overcome therapy resistance and improve patient survival.

Purpose Maintenance hemodialysis (MHD), which can cause various complications, is a common alternative therapy for patients with ESRD. This research built a prediction model of hypoproteinemia among ESRD patients based on machine learning algorithms. Method A total of 468 patients were selected as subjects. The “hypoproteinemia risk factor data extraction table” was drawn up after a literature review. Univariate analysis was used to screen independent risk factors as prediction variables. After hyper parameter adjustment by k-fold (k = 5) cross-validation and grid search, random forest (RF), support vector machine (SVM), back propagation (BP) neural network and logistic regression (LR) prediction models were developed. The model was evaluated by 6 dimensions, including AUROC, accuracy, precision, sensitivity, specificity and F1 score, and an importance matrix diagram was used to describe the importance. Result The incidence of hypoproteinemia in total was 30.8%. According to univariate analysis, the difference between the hypoproteinemia and nonhypoproteinemia groups was significant in 18 aspects, including age, weight, dialysis duration, and dialysis frequency. In the training set, the AUROC values of the RF, SVM, and LR models were all greater than 0.8 unlike the BP neural network (0.798). The RF model had the highest AUC value (0.924). The specificities of the LR and RF models were similar (0.846 and 0.839, respectively), while the RF model had the best accuracy (0.924) and balanced F1 score (0.751). The models had higher performance indexes in the test set than in the training set, with the RF and BP models performing better in AUROC (0.981, 0.948) and the RF model being better in accuracy, specificity balanced F1 score and precision. The top 5 prediction variables were hypersensitivity C reactive protein, age, weight, usage of high-throughput dialyzers, and dialysis age. ConclusionTheRF model performed best. The model could help recognize characteristics related to hypoproteinemia during clinical practice, thereby enhancing nurses’ risk perception and improving accurate screening, primary prevention and early intervention.