代谢重编程是肿瘤生长、转移、进展和预后不佳的标志。然而，在透明细胞肾细胞癌(ccRCC)中，代谢相关分子模式和机制仍不清楚。本研究的目的是鉴定代谢相关分子模式，并调查代谢相关聚类的特征和预后价值。我们在TCGA数据库上综合分析了ccRCC的差异表达基因(DEGs)和代谢相关基因(MAGs)。采用一致性聚类方法构建了代谢相关的分子模式。然后，分析了生物功能、分子特征、评估/免疫/基质评分、免疫细胞浸润、免疫治疗和化疗的反应。我们还鉴定了亚聚类之间的DEGs，并基于LASSO回归cox分析、单变量和多变量cox回归分析构建了一个预测不良风险的签名和风险模型。然后，通过校准曲线构建和验证了一个预测的诊断图表。我们鉴定了1942个ccRCC肿瘤与正常样本之间的DEGs(1004个上调和838个下调)，并从这些DEGs中筛选出了254个MAGs。然后，将526个ccRCC患者分为两个亚聚类。在第2聚类中富集了7个与代谢相关的通路。第2聚类具有较高的评估/免疫/基质评分和较差的生存率。而第1聚类具有更高的免疫细胞浸润、免疫检查点、IFN、HLA、免疫激活相关基因的表达、对抗CTLA4治疗和化疗的反应。此外，我们鉴定了两个代谢相关亚聚类之间的295个DEGs，并构建了一个15个基因的签名和9个风险因素。然后，计算了一个风险评分，并将患者分为TCGA-KIRC和E-MTAB-1980数据集中的高风险组和低风险组。通过ROC曲线验证了风险评分的预测能力。最后，临床病理特征(年龄和分期)、风险评分和分子聚类被鉴定为独立的预后变量，并用于构建1年、3年、5年的整体生存预测诊断图。校准曲线用于验证诊断图的预测能力。我们的发现鉴定了ccRCC的两个代谢相关分子亚聚类，有助于估计免疫治疗和化疗后的反应以及预后。© 2023年 BioMed Central Ltd., part of Springer Nature.

Metabolism reprogramming is a hallmark that associates tumor growth, metastasis, progressive, and poor prognosis. However, the metabolism-related molecular patterns and mechanism in clear cell renal cell carcinoma (ccRCC) remain unclear. Herein, the purpose of this study was to identify metabolism-related molecular pattern and to investigate the characteristics and prognostic values of the metabolism-related clustering.We comprehensively analyzed the differentially expressed genes (DEGs), and metabolism-related genes (MAGs) in ccRCC based on the TCGA database. Consensus clustering was used to construct a metabolism-related molecular pattern. Then, the biological function, molecular characteristics, Estimate/immune/stomal scores, immune cell infiltration, response to immunotherapy, and chemotherapy were analyzed. We also identified the DEGs between subclusters and constructed a poor signature and risk model based on LASSO regression cox analysis and univariable and multivariable cox regression analyses. Then, a predictive nomogram was constructed and validated by calibration curves.A total of 1942 DEGs (1004 upregulated and 838 downregulated) between ccRCC tumor and normal samples were identified, and 254 MRGs were screened out from those DEGs. Then, 526 ccRCC patients were divided into two subclusters. The 7 metabolism-related pathways enriched in cluster 2. And cluster 2 with high Estimate/immune/stomal scores and poor survival. While, cluster 1 with higher immune cell infiltrating, expression of the immune checkpoint, IFN, HLA, immune activation-related genes, response to anti-CTLA4 treatment, and chemotherapy. Moreover, we identified 295 DEGs between two metabolism-related subclusters and constructed a 15-gene signature and 9 risk factors. Then, a risk score was calculated and the patients into high- and low-risk groups in TCGA-KIRC and E-MTAB-1980 datasets. And the prediction viability of the risk score was validated by ROC curves. Finally, the clinicopathological characteristics (age and stage), risk score, and molecular clustering, were identified as independent prognostic variables, and were used to construct a nomogram for 1-, 3-, 5-year overall survival predicting. The calibration curves were used to verify the performance of the predicted ability of the nomogram.Our finding identified two metabolism-related molecular subclusters for ccRCC, which facilitates the estimation of response to immunotherapy and chemotherapy, and prognosis after treatment.© 2023. BioMed Central Ltd., part of Springer Nature.