Background: Immunotherapy, including adoptive cell therapy (ACT) and immune checkpoint inhibitors (ICIs), has a limited effect in most patients with colorectal cancer (CRC), and the efficacy is further limited in patients with liver metastasis. Lack of antitumor lymphocyte infiltration could be a major cause, and there remains an urgent need for more potent and safer therapies for CRC. Methods: In this study, the antitumoral synergism of low molecular weight heparin (LMWH) combined with immunotherapy in the microsatellite stable (MSS) highly aggressive murine model of CRC was fully evaluated. Results: Dual LMWH and ACT objectively mediated the stagnation of tumor growth and inhibition of liver metastasis, neither LMWH nor ACT alone had any antitumoral activity on them. The combination of LMWH and ACT obviously increased the infiltration of intratumor CD8⁺ T cells, as revealed by multiplex immunohistochemistry, purified CD8⁺ T-cell transfer assay, and IVIM in vivo imaging. Mechanistically, evaluation of changes in the tumor microenvironment revealed that LMWH improved tumor vascular normalization and facilitated the trafficking of activated CD8⁺ T cells into tumors. Similarly, LMWH combined with anti-programmed cell death protein 1 (PD-1) therapy provided superior antitumor activity as compared with the single PD-1 blockade in murine CT26 tumor models. Conclusions: LMWH could enhance ACT and ICIs-based immunotherapy by increasing lymphocyte infiltration into tumors, especially cytotoxic CD8⁺ T cells. These results indicate that combining LMWH with an immunotherapy strategy presents a promising and safe approach for CRC treatment, especially in MSS tumors.

Pancreatic cancer is characterized by a complex tumor microenvironment that hinders effective immunotherapy. Identifying key factors that regulate the immunosuppressive landscape is crucial for improving treatment strategies. We constructed a prognostic and risk assessment model for pancreatic cancer using 101 machine learning algorithms, identifying OSBPL3 as a key gene associated with disease progression and prognosis. We integrated multi-dataset analyses, single-cell transcriptomic data, and functional experiments to explore the role of OSBPL3 in pancreatic cancer. Our risk prediction model, developed using machine learning algorithms, demonstrated high predictive accuracy across multiple datasets. Notably, the “rf” algorithm model showed an AUC of 1 in the training set and AUCs of 0.887 and 0.977 in two validation datasets. Ridge regression analysis identified OSBPL3 as a core prognostic feature gene. High OSBPL3 expression in pancreatic cancer samples was associated with immunosuppressive characteristics, including reduced CD8 + T cell infiltration and increased immunosuppressive cell populations such as Treg cells and M2 macrophages. Transcriptomic sequencing following OSBPL3 knockdown revealed enrichment of immune-related pathways, suggesting OSBPL3’s influence on the immune microenvironment. Single-cell analyses further confirmed OSBPL3’s role in shaping the immunosuppressive landscape by modulating Treg cells and M2 macrophages. Additionally, OSBPL3 expression was linked to resistance to immunotherapy, with high OSBPL3 expression associated with lower Immunophenoscore (IPS) scores, indicating poor responsiveness to immunotherapy. Our study reveals OSBPL3 as a critical regulator of the immunosuppressive microenvironment in pancreatic cancer and a potential therapeutic target. Targeting OSBPL3 may enhance the efficacy of immunotherapy and improve patient outcomes. Further research is warranted to explore OSBPL3 as a biomarker for predicting immunotherapy response and as a potential therapeutic target in combination with anti-PD1 therapy.

Background: Pancreatic cancer, a highly malignant tumor with poor prognosis, lacks effective early diagnosis and treatment strategies. Sphingolipids have emerged as key players in tumorigenesis, with certain sphingolipid-related genes linked to patient survival. This study aims to identify prognostic glycosphingolipid (GSL)-related genes and construct a predictive model to improve survival prediction and guide personalized treatment. By providing potential biomarkers, our findings may enhance clinical decision-making and offer new insights into pancreatic cancer diagnosis and therapy. Methods: This study utilized 150 pancreatic cancer samples from The Cancer Genome Atlas-Pancreatic Adenocarcinoma (TCGA-PAAD) and 69 from GSE62452 [Gene Expression Omnibus (GEO)] for training and validation. Cox univariate regression identified sphingolipid-related genes with prognostic value. Over 100 machine learning algorithms, including Cox models, support vector machines (SVM), and random forests (RF), were applied to construct an optimal survival prediction model for pancreatic ductal adenocarcinoma (PDAC). Model accuracy was evaluated using the concordance index (C-index). Enrichment, immune infiltration, mutation spectrum, and cell communication analyses were performed to explore sphingolipid mechanisms in pancreatic cancer. Results: Using 10 machine learning algorithms, we developed over 100 models to predict sphingolipid-related survival in pancreatic cancer. A robust prognostic model was constructed, incorporating three GSL-related genes (MET, GBA2, DEFB1), represented by the equation: weighted score = 0.469 * MET + (−0.357) * GBA2 + 0.103 * DEFB1. The model demonstrated strong predictive performance, with a C-index of 0.854 for overall survival in 150 pancreatic cancer patients from the TCGA database and 0.652 in 69 patients from the GEO validation set. Pathway enrichment analysis revealed that high-risk patients were significantly enriched in oncogenic and immune-related pathways. Mutation spectrum analysis indicated a higher mutation load in high-risk patients, with mutations concentrated in common oncogenic pathways. Immune infiltration analysis showed that the risk score positively correlated with immune-suppressive genes but negatively correlated with immune-killing cell infiltration. Cell communication analysis highlighted elevated activity in the macrophage migration inhibitory factor (MIF) pathway within high-risk groups, associated with tumor proliferation and immune escape. In conclusion, this study establishes a sphingolipid-based prognostic model with significant potential for predicting pancreatic cancer outcomes. Conclusions: The sphingolipid-based model accurately predicts pancreatic cancer survival and suggests sphingolipids promote tumor progression by mediating immune-suppressive microenvironments, aiding prognostic prediction and personalized treatment.