Targeting IL-33 reprograms the tumor microenvironment and potentiates antitumor response to anti-PD-L1 immunotherapy

Correlation of Wound Prognosis with serum IL-6, ICAM-1 and sST2 in Patients with Diabetic Foot and Construction of a Nomogram Model

Diabetes foot (DF) is one of the most serious chronic complications of diabetes. This study explored the relationship between serum interleukin-6 (IL-6), intercellular adhesion molecule-1 (ICAM-1) and soluble suppression of tumorigenicity 2 (sST2) and wound prognosis in 210 DF patients between January 2019 and January 2024. 210 DF patients were divided into the good prognosis (n = 147) and poor prognosis (n = 63) group according to the prognosis. Comparative analysis revealed that levels of serum IL-6, ICAM-1 and sST2 in the poor prognosis group were all higher than those in the good prognosis group significantly (P < .05). Multivariate logistic regression identified these 3 biomarkers as independent risk factors for poor wound healing (P < .05). Positive correlations between serum IL-6 (r = 0.269), ICAM-1 (r = 0.302), sST2 (r = 0.289) levels and poor prognosis were confirmed through Pearson's correlation analysis. A prediction model was established to analyse their predictive value. The training and validation sets ROC curves had AUCs of 0.79 (0.71-0.87) and 0.75 (0.59-0.91) respectively. Calibration curves were plotted to evaluate the consistency of the model, and the results showed that the predictive value of the nomogram model was similar to that of the actual one. Decision curves were plotted, which showed that the nomogram had higher positive net benefit in the range of 20% to 60%. This study suggest that serum IL-6, ICAM-1, and sST2 levels may serve as valuable prognostic indicators for wound healing progression in DF patients, with combined biomarker assessment showing potential clinical utility for outcome prediction. The total sample size (n = 210), with validation set (n = 63) of this study are relatively limited and the representativeness is restricted, which may affect the universality of the research conclusions.

Tumor-associated macrophages remodel the suppressive tumor immune microenvironment and targeted therapy for immunotherapy

Despite the significant advances in the development of immune checkpoint inhibitors (ICI), primary and acquired ICI resistance remains the primary impediment to effective cancer immunotherapy. Residing in the tumor microenvironment (TME), tumor-associated macrophages (TAMs) play a pivotal role in tumor progression by regulating diverse signaling pathways. Notably, accumulating evidence has confirmed that TAMs interplay with various cellular components within the TME directly or indirectly to maintain the dynamic balance of the M1/M2 ratio and shape an immunosuppressive TME, consequently conferring immune evasion and immunotherapy tolerance. Detailed investigation of the communication network around TAMs could provide potential molecular targets and optimize ICI therapies. In this review, we systematically summarize the latest advances in understanding the origin and functional plasticity of TAMs, with a focus on the key signaling pathways driving macrophage polarization and the diverse stimuli that regulate this dynamic process. Moreover, we elaborate on the intricate interplay between TAMs and other cellular constituents within the TME, that is driving tumor initiation, progression and immune evasion, exploring novel targets for cancer immunotherapy. We further discuss current challenges and future research directions, emphasizing the need to decode TAM-TME interactions and translate preclinical findings into clinical breakthroughs. In conclusion, while TAM-targeted therapies hold significant promise for enhancing immunotherapy outcomes, addressing key challenges-such as TAM heterogeneity, context-dependent plasticity, and therapeutic resistance-remains critical to achieving optimal clinical efficacy.

The Role of Eosinophils, Eosinophil-Related Cytokines and AI in Predicting Immunotherapy Efficacy in NSCLC Cancer

Immunotherapy and chemoimmunotherapy are standard treatments for non-oncogene-addicted advanced non-small cell lung cancer (NSCLC). Currently, a limited number of biomarkers, including programmed death-ligand 1 (PD-L1) expression, microsatellite instability (MSI), and tumor mutational burden (TMB), are used in clinical practice to predict benefits from immune checkpoint inhibitors (ICIs). It is therefore necessary to search for novel biomarkers that could be helpful to identify patients who respond to immunotherapy. In this context, research efforts are focusing on different cells and mechanisms involved in anti-tumor immune response. Herein, we provide un updated literature review on the role of eosinophils in cancer development and immune response, and the functions of some cytokines, including IL-31 and IL-33, in eosinophil activation. We discuss available data demonstrating a correlation between eosinophils and clinical outcomes of ICIs in lung cancer. In this context, we underscore the role of absolute eosinophil count (AEC) and tumor-associated tissue eosinophilia (TATE) as promising biomarkers able to predict the efficacy and toxicities from immunotherapy. The role of eosinophils and cytokines in NSCLC, treated with ICIs, is not yet fully understood, and further research may be crucial to determine their role as biomarkers of response. Artificial intelligence, through the analysis of big data, could be exploited in the future to elucidate the role of eosinophils and cytokines in lung cancer.

Identification of a novel immunogenic cell death-related classifier to predict prognosis and optimize precision treatment in hepatocellular carcinoma

Accumulating studies have highlighted the biological significance of immunogenic cell death (ICD) in cancer immunity. However, the influence of ICD on tumor microenvironment (TME) formation and immune response in Hepatocellular carcinoma (HCC) remains largely unexplored. In this study, we systematically analyzed the mRNA profiles of ICD-related genes in 1847 HCC patients and identified three molecular subtypes with significantly different immune features and prognostic stratification. A reliable risk model named ICD score was constructed via machine learning algorithms to assess the immunological status, therapeutic responses, and clinical outcomes of individual HCC patients. High ICD score indicated an immune-excluded TME phenotype, with lower anticancer immunity and shorter survival time. In contrast, low ICD score corresponded to abundant immune cell infiltration, high sensitivity to immunotherapy and a positive prognosis, indicating an "immune-hot" phenotype. Pan-cancer analysis further validated a negative association between ICD score and the immune cell infiltration levels. In conclusion, our findings revealed that the ICD score could serve as a robust prognostic biomarker to predict the benefits of immunotherapy and optimize the clinical decision-making of HCC patients.