Tumor-associated macrophages (TAMs) facilitate cancer progression by promoting tumor invasion, angiogenesis, metastasis, inflammatory responses, and immunosuppression. Folate receptor β (FRβ) is overexpressed in TAMs. However, the clinical significance of FRβ-positive macrophages in lung cancer remains poorly understood. In this study, we verified that FRβ overexpression in lung cancer TAMs was associated with poor prognosis. We utilized a folate-modified lipoplex comprising a folate-modified liposome (F-PLP) delivering a BIM-S plasmid to target both lung cancer cells and FRβ-positive macrophages in the tumor microenvironment. Transfection of LL/2 cells and MH-S cells with F-PLP/pBIM induced cell apoptosis. Injection of F-PLP/pBIM into LL/2 and A549 lung cancer models significantly depleted FRβ-positive macrophages and reduced tumor growth. Treatment of tumor-bearing mice with F-PLP/pBIM significantly inhibited tumor growth in vivo by inducing tumor cell and macrophage apoptosis, reducing tumor proliferation, and inhibiting tumor angiogenesis. In addition, a preliminary safety evaluation demonstrated a good safety profile of F-PLP/pBIM as a gene therapy administered intravenously. This work describes a novel application of lipoplexes in lung cancer targeted therapy that influences the tumor microenvironment by targeting TAMs.

Pancreatic cancer remains a persistently high mortality rate, with limited efficacy through traditional therapies, necessitating exploration of its pathogenesis from a new biological perspective. The tumor microenvironment plays a decisive role in the malignant progression of pancreatic cancer, and the nervous system, as a key component of the microenvironment, has an active and bidirectional interaction with tumor cells, known as the “neuro-tumor interaction.” Pancreatic ductal adenocarcinoma (PDAC), the most common type of pancreatic cancer, is highly rich in neural components. The neuro-tumor interaction not only drives the unique neural infiltration of PDAC but also profoundly affects tumor proliferation, invasion, metastasis, immune escape, and pain perception. Recent studies have revealed that tumor cells, Schwann cells, cancer-associated fibroblasts, and immune cells form a “perineural niche” through neurotrophic factors, chemotactic axes, cell adhesion/extracellular matrix remodeling, and neurotransmitters, driving tripartite neural-immune-cancer interaction and providing targets for new therapeutic interventions. This review systematically summarized the key molecular and cellular mechanisms of neural-immune-cancer interactions in pancreatic cancer and specifically discussed several translational strategies, including neurotrophic factor blockade targeting NGF/TrkA and GDNF/RET, myeloid cell reprogramming targeting CXCR2/CXCL to improve T cell infiltration, and potential combination strategies that combine neuromodulatory drugs (e.g., β-blockers or CRGP antagonists) with immune checkpoint inhibitors. These strategies have shown feasibility in preclinical studies or PDAC models and warrant further validation in stratified clinical trials.

Background: PSA screening has limited specificity in the 10–20 ng/mL range, making biopsy decisions difficult. Metabolic disorders and inflammation may improve risk prediction. We aimed to develop a model integrating multidimensional indicators to predict prostate cancer and high−grade disease in men with tPSA >10 ng/mL. Methods: This retrospective study included 461 men who underwent prostate biopsy (tPSA >10 ng/mL). Patients were randomly split into training (70%) and validation (30%) sets. Two logistic regression models were built: Model 1 for benign vs. malignant; Model 2 (in malignant cases) for high−grade (ISUP ≥3) vs. low−grade. Variables with VIF>5 were excluded. Backward stepwise (AIC) and univariate P < 0.05 guided variable selection. Performance was assessed by AUC, calibration, DCA, and compared with fPSA% alone using DeLong test. Result: Among 461 patients, 252 (54.7%) had prostate cancer, and 139 (55.2% of malignant cases) had high−grade disease. Malignant patients were older, had higher BMI, TyG index, NLR, and tPSA, and lower fPSA% (all P < 0.05). High−grade patients showed similar metabolic differences but no tPSA difference versus low−grade. Model 1 retained age, TyG index, NLR, fPSA%, smoking, hypertension, and lesion location. Model 2 retained age, TyG index, LDH, and smoking history. Validation AUC was 0.871 for Model 1 and 0.779 for Model 2. At optimal thresholds, sensitivity/specificity were 80.3%/74.6% (Model 1) and 71.7%/62.1% (Model 2). Both models showed good calibration (Hosmer−Lemeshow P>0.05). Decision curve analysis and DeLong test confirmed that each full model provided higher net benefit than fPSA% alone ( P < 0.05). Conclusion: Two nomograms using routine clinical and laboratory variables may assist in risk stratification for prostate cancer and high−grade disease in men with tPSA >10 ng/mL, outperforming fPSA% alone. These tools can help reduce unnecessary biopsies.

Background: Portal vein tumor thrombus (PVTT) marks advanced hepatocellular carcinoma (HCC) and carries a dismal prognosis. Survival varies widely even within this stage, yet simple tools for individualized risk stratification remain scarce. Methods: We retrospectively enrolled 134 HCC patients with PVTT and randomly divided them into a training set (n = 94) and a validation set (n = 40). Clinical predictors were selected by variance inflation factor screening and backward elimination Cox regression. A radiomics score (Rad-score) was constructed from portal-venous phase computed tomography (CT) images using Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression with 10-fold cross-validation. Three Cox models were built: a clinical model, an imaging model based solely on the Rad-score, and a combined model integrating both. Discrimination was assessed by C-index and time-dependent area under the curve (AUC). Calibration was examined with bootstrap-based calibration curves. Decision curve analysis evaluated net benefit. A nomogram was developed from the combined model. Results: Four clinical variables (alpha-fetoprotein (AFP), body mass index (BMI), high-density lipoprotein cholesterol (HDL-C), and alkaline phosphatase (ALP)) and two CT texture features (GLRLM_SRHGE and GLZLM_SZHGE) were retained as independent predictors. The combined model gave the highest C-index in both the training set (0.843) and the internal validation set (0.815). Its 1-year AUC reached 0.953 and 0.947 in the two sets. Calibration slopes ranged from 1.044 to 1.291 across time points, indicating a tendency toward mild overdispersion; nevertheless, decision curve analysis confirmed net benefit across clinically relevant thresholds. The combined model offered greater net benefit than either single-domain model across a 0–50% threshold range. A nomogram incorporating all five predictors was generated for individualized 12- and 24-month survival prediction. Conclusions: A combined model integrating routine laboratory variables and a CT-based radiomics score improved survival prediction over clinical or imaging models alone. The corresponding nomogram uses inputs from a basic blood panel and a single portal-venous phase CT, suggesting its potential as a low-cost prognostic stratification tool for HCC patients with PVTT, although external validation in prospective multicenter cohorts is required before clinical implementation.