{"title":"多组学整合分析确定肿瘤细胞来源的MIF作为治疗靶点,并增强骨肉瘤的抗pd -1治疗。","authors":"Weidong Chen, Yan Liao, Hao Yao, Yutong Zou, Ji Fang, Jiongfeng Zhang, Zehao Guo, Jian Tu, Junkai Chen, Zijun Huo, Lili Wen, Xianbiao Xie","doi":"10.1136/jitc-2024-011091","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Osteosarcoma is a highly aggressive cancer, and the efficacy of existing therapies has plateaued. Multiomics integration analysis can identify novel therapeutic targets for various cancers and therefore shows potential toward osteosarcoma treatment. This study aimed to leverage multiomics integration to develop a new risk model, characterizing the immune features of osteosarcoma to uncover novel therapeutic targets.</p><p><strong>Methods: </strong>Metabolomics profiling was conducted to identify key metabolites in osteosarcoma. Transcriptomic sequencing datasets were analyzed to identify prognostic genes related to key metabolic pathways and develop a prognostic risk model. Patients were then divided into high-risk and low-risk groups with distinct clinical outcomes based on the risk model. The single-sample gene set enrichment analysis, Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithm, and xCell algorithms were used to evaluate the immune cell infiltration and activity. Single-cell RNA sequencing was used to explore cell-to-cell interactions within the tumor microenvironment. In vitro coculture functional assays were performed to validate the role of macrophage migration inhibitory factor (MIF) in macrophage polarization and chemotaxis. In vivo studies were used to evaluate the effectiveness of MIF inhibition in combination with immune checkpoint blockade in murine models.</p><p><strong>Results: </strong>Elevated lactate levels in osteosarcoma patients correlated with poorer overall survival. We identified SLC7A7 and CYP27A1 as prognostic lactate metabolism genes and developed a risk model to stratify patients into high-risk and low-risk groups with distinct outcomes. Bioinformatics analyses highlighted the differences in immune infiltration patterns and activity between the groups. Notably, the infiltration and phenotype of macrophages varied significantly between the groups, and MIF was identified as a critical mediator in this process. In osteosarcoma cells, lactate regulated MIF expression through histone H3K9 lactylation. Combining the MIF inhibitor 4-IPP with a programmed cell death 1 (PD-1) monoclonal antibody treatment demonstrated a significant antitumor effect.</p><p><strong>Conclusion: </strong>MIF acts as a novel therapeutic target by regulating macrophage polarization and chemotaxis. Lactate regulated MIF expression through histone lactylation. Targeting MIF holds promise for enhancing the efficacy of anti-PD-1 treatment.</p>","PeriodicalId":14820,"journal":{"name":"Journal for Immunotherapy of Cancer","volume":"13 8","pages":""},"PeriodicalIF":10.6000,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12374663/pdf/","citationCount":"0","resultStr":"{\"title\":\"Multiomics integration analysis identifies tumor cell-derived MIF as a therapeutic target and potentiates anti-PD-1 therapy in osteosarcoma.\",\"authors\":\"Weidong Chen, Yan Liao, Hao Yao, Yutong Zou, Ji Fang, Jiongfeng Zhang, Zehao Guo, Jian Tu, Junkai Chen, Zijun Huo, Lili Wen, Xianbiao Xie\",\"doi\":\"10.1136/jitc-2024-011091\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Osteosarcoma is a highly aggressive cancer, and the efficacy of existing therapies has plateaued. Multiomics integration analysis can identify novel therapeutic targets for various cancers and therefore shows potential toward osteosarcoma treatment. This study aimed to leverage multiomics integration to develop a new risk model, characterizing the immune features of osteosarcoma to uncover novel therapeutic targets.</p><p><strong>Methods: </strong>Metabolomics profiling was conducted to identify key metabolites in osteosarcoma. Transcriptomic sequencing datasets were analyzed to identify prognostic genes related to key metabolic pathways and develop a prognostic risk model. Patients were then divided into high-risk and low-risk groups with distinct clinical outcomes based on the risk model. The single-sample gene set enrichment analysis, Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithm, and xCell algorithms were used to evaluate the immune cell infiltration and activity. Single-cell RNA sequencing was used to explore cell-to-cell interactions within the tumor microenvironment. In vitro coculture functional assays were performed to validate the role of macrophage migration inhibitory factor (MIF) in macrophage polarization and chemotaxis. In vivo studies were used to evaluate the effectiveness of MIF inhibition in combination with immune checkpoint blockade in murine models.</p><p><strong>Results: </strong>Elevated lactate levels in osteosarcoma patients correlated with poorer overall survival. We identified SLC7A7 and CYP27A1 as prognostic lactate metabolism genes and developed a risk model to stratify patients into high-risk and low-risk groups with distinct outcomes. Bioinformatics analyses highlighted the differences in immune infiltration patterns and activity between the groups. Notably, the infiltration and phenotype of macrophages varied significantly between the groups, and MIF was identified as a critical mediator in this process. In osteosarcoma cells, lactate regulated MIF expression through histone H3K9 lactylation. Combining the MIF inhibitor 4-IPP with a programmed cell death 1 (PD-1) monoclonal antibody treatment demonstrated a significant antitumor effect.</p><p><strong>Conclusion: </strong>MIF acts as a novel therapeutic target by regulating macrophage polarization and chemotaxis. Lactate regulated MIF expression through histone lactylation. Targeting MIF holds promise for enhancing the efficacy of anti-PD-1 treatment.</p>\",\"PeriodicalId\":14820,\"journal\":{\"name\":\"Journal for Immunotherapy of Cancer\",\"volume\":\"13 8\",\"pages\":\"\"},\"PeriodicalIF\":10.6000,\"publicationDate\":\"2025-08-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12374663/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal for Immunotherapy of Cancer\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1136/jitc-2024-011091\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"IMMUNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal for Immunotherapy of Cancer","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1136/jitc-2024-011091","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"IMMUNOLOGY","Score":null,"Total":0}
Multiomics integration analysis identifies tumor cell-derived MIF as a therapeutic target and potentiates anti-PD-1 therapy in osteosarcoma.
Background: Osteosarcoma is a highly aggressive cancer, and the efficacy of existing therapies has plateaued. Multiomics integration analysis can identify novel therapeutic targets for various cancers and therefore shows potential toward osteosarcoma treatment. This study aimed to leverage multiomics integration to develop a new risk model, characterizing the immune features of osteosarcoma to uncover novel therapeutic targets.
Methods: Metabolomics profiling was conducted to identify key metabolites in osteosarcoma. Transcriptomic sequencing datasets were analyzed to identify prognostic genes related to key metabolic pathways and develop a prognostic risk model. Patients were then divided into high-risk and low-risk groups with distinct clinical outcomes based on the risk model. The single-sample gene set enrichment analysis, Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithm, and xCell algorithms were used to evaluate the immune cell infiltration and activity. Single-cell RNA sequencing was used to explore cell-to-cell interactions within the tumor microenvironment. In vitro coculture functional assays were performed to validate the role of macrophage migration inhibitory factor (MIF) in macrophage polarization and chemotaxis. In vivo studies were used to evaluate the effectiveness of MIF inhibition in combination with immune checkpoint blockade in murine models.
Results: Elevated lactate levels in osteosarcoma patients correlated with poorer overall survival. We identified SLC7A7 and CYP27A1 as prognostic lactate metabolism genes and developed a risk model to stratify patients into high-risk and low-risk groups with distinct outcomes. Bioinformatics analyses highlighted the differences in immune infiltration patterns and activity between the groups. Notably, the infiltration and phenotype of macrophages varied significantly between the groups, and MIF was identified as a critical mediator in this process. In osteosarcoma cells, lactate regulated MIF expression through histone H3K9 lactylation. Combining the MIF inhibitor 4-IPP with a programmed cell death 1 (PD-1) monoclonal antibody treatment demonstrated a significant antitumor effect.
Conclusion: MIF acts as a novel therapeutic target by regulating macrophage polarization and chemotaxis. Lactate regulated MIF expression through histone lactylation. Targeting MIF holds promise for enhancing the efficacy of anti-PD-1 treatment.
期刊介绍:
The Journal for ImmunoTherapy of Cancer (JITC) is a peer-reviewed publication that promotes scientific exchange and deepens knowledge in the constantly evolving fields of tumor immunology and cancer immunotherapy. With an open access format, JITC encourages widespread access to its findings. The journal covers a wide range of topics, spanning from basic science to translational and clinical research. Key areas of interest include tumor-host interactions, the intricate tumor microenvironment, animal models, the identification of predictive and prognostic immune biomarkers, groundbreaking pharmaceutical and cellular therapies, innovative vaccines, combination immune-based treatments, and the study of immune-related toxicity.