Chen Weidong, Liao Yan, Yao Hao, Zou Yutong, Fang Ji, Zhang Jiongfeng, Guo Zehao, Tu Jian, Chen Junkai, Huo Zijun, Wen Lili, Xie Xianbiao
Department of Musculoskeletal Oncology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China.
Guangdong Provincial Key Laboratory of Orthopedics and Traumatology, Guangzhou, China.
J Immunother Cancer. 2025 Aug 22;13(8):e011091. doi: 10.1136/jitc-2024-011091.
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.
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.
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.
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.
骨肉瘤是一种侵袭性很强的癌症，现有治疗方法的疗效已趋于平稳。多组学整合分析可以为各种癌症识别新的治疗靶点，因此在骨肉瘤治疗方面显示出潜力。本研究旨在利用多组学整合开发一种新的风险模型，表征骨肉瘤的免疫特征以发现新的治疗靶点。
进行代谢组学分析以识别骨肉瘤中的关键代谢物。分析转录组测序数据集以识别与关键代谢途径相关的预后基因并开发预后风险模型。然后根据风险模型将患者分为具有不同临床结局的高风险组和低风险组。使用单样本基因集富集分析、利用表达数据估计恶性肿瘤组织中的基质和免疫细胞（ESTIMATE）算法以及xCell算法来评估免疫细胞浸润和活性。使用单细胞RNA测序来探索肿瘤微环境内的细胞间相互作用。进行体外共培养功能试验以验证巨噬细胞迁移抑制因子（MIF）在巨噬细胞极化和趋化作用中的作用。在体内研究中评估MIF抑制与免疫检查点阻断联合在小鼠模型中的有效性。
骨肉瘤患者中乳酸水平升高与较差的总生存期相关。我们将溶质载体家族7成员7（SLC7A7）和细胞色素P450 27A1（CYP27A1）鉴定为预后乳酸代谢基因，并开发了一种风险模型将患者分为具有不同结局的高风险组和低风险组。生物信息学分析突出了两组之间免疫浸润模式和活性的差异。值得注意的是，两组之间巨噬细胞的浸润和表型差异显著，并且MIF被确定为这一过程中的关键介质。在骨肉瘤细胞中，乳酸通过组蛋白H3K9乳酸化调节MIF表达。将MIF抑制剂4-异戊基吡啶（4-IPP）与程序性细胞死亡蛋白1（PD-1）单克隆抗体治疗相结合显示出显著的抗肿瘤作用。
MIF通过调节巨噬细胞极化和趋化作用作为一种新的治疗靶点。乳酸通过组蛋白乳酸化调节MIF表达。靶向MIF有望提高抗PD-1治疗的疗效。
