Table_4_Crosstalk Between Metabolism and Immune Activity Reveals Four Subtypes With Therapeutic Implications in Clear Cell Renal Cell Carcinoma.xlsx

Clear cell renal cell carcinoma (ccRCC) is characterized by metabolic dysregulation and distinct immunological signatures. The interplay between metabolic and immune processes in the tumor microenvironment (TME) causes the complexity and heterogeneity of immunotherapy responses observed during ccRCC treatment. Herein, we initially identified two distinct metabolic subtypes (C1 and C2 subtypes) and immune subtypes (I1 and I2 subtypes) based on the occurrence of differentially expressed metabolism-related prognostic genes and immune-related components. Notably, we observed that immune regulators with upregulated expression actively participated in multiple metabolic pathways. Therefore, we further delineated four immunometabolism-based ccRCC subtypes (M1, M2, M3, and M4 subtypes) according to the results of the above classification. Generally, we found that high metabolic activity could suppress immune infiltration. Immunometabolism subtype classification was associated with immunotherapy response, with patients possessing the immune-inflamed, metabolic-desert subtype (M3 subtype) that benefits the most from immunotherapy. Moreover, differences in the shifts in the immunometabolism subtype after immunotherapy were observed in the responder and non-responder groups, with patients from the responder group transferring to subtypes with immune-inflamed characteristics and less active metabolic activity (M3 or M4 subtype). Immunometabolism subtypes could also serve as biomarkers for predicting immunotherapy response. To decipher the genomic and epigenomic features of the four subtypes, we analyzed multiomics data, including miRNA expression, DNA methylation status, copy number variations occurrence, and somatic mutation profiles. Patients with the M2 subtype possessed the highest VHL gene mutation rates and were more likely to be sensitive to sunitinib therapy. Moreover, we developed non-invasive radiomic models to reveal the status of immune activity and metabolism. In addition, we constructed a radiomic prognostic score (PRS) for predicting ccRCC survival based on the seven radiomic features. PRS was further demonstrated to be closely linked to immunometabolism subtype classification, immune score, and tumor mutation burden. The prognostic value of the PRS and the association of the PRS with immune activity and metabolism were validated in our cohort. Overall, our study established four immunometabolism subtypes, thereby revealing the crosstalk between immune and metabolic activities and providing new insights into personal therapy selection.

透明细胞肾细胞癌（clear cell renal cell carcinoma, ccRCC）以代谢失调与独特的免疫特征为主要特征。其肿瘤微环境（tumor microenvironment, TME）内代谢与免疫过程的相互作用，导致了ccRCC免疫治疗应答过程中观察到的复杂性与异质性。本研究首先基于差异表达的代谢相关预后基因与免疫相关成分，鉴定出两种明确的代谢亚型（C1、C2亚型）与免疫亚型（I1、I2亚型）。值得注意的是，表达上调的免疫调控因子可积极参与多条代谢通路。据此，我们基于上述分类结果，进一步划定了四种免疫代谢相关ccRCC亚型（M1、M2、M3及M4亚型）。整体而言，我们发现高代谢活性可抑制免疫浸润。免疫代谢亚型分类与免疫治疗应答密切相关，其中携带免疫炎症型、代谢荒漠型特征的M3亚型患者，可从免疫治疗中获得最大获益。此外，免疫治疗后免疫代谢亚型的转变在应答组与非应答组中存在显著差异：应答组患者的亚型向具有免疫炎症特征且代谢活性较低的M3或M4亚型转变。免疫代谢亚型还可作为预测免疫治疗应答的生物标志物。为解析这四种亚型的基因组与表观基因组特征，我们分析了多组学数据，包括miRNA（microRNA）表达、DNA甲基化状态、拷贝数变异发生情况以及体细胞突变谱。M2亚型患者的VHL基因突变率最高，且更易对舒尼替尼治疗产生应答。此外，我们构建了无创放射组学模型，用于揭示免疫活性与代谢状态。同时，我们基于7种放射组学特征，构建了用于预测ccRCC患者生存的放射组学预后评分（radiomic prognostic score, PRS）。研究进一步证实，PRS与免疫代谢亚型分类、免疫评分及肿瘤突变负荷密切相关。我们的队列数据验证了PRS的预后价值，以及其与免疫活性、代谢状态的关联。综上，本研究确立了四种免疫代谢亚型，揭示了免疫与代谢活动之间的串扰，并为个性化治疗方案的选择提供了全新的见解。