Li Fan, Hu Haiyi, Li Liyang, Ding Lifeng, Lu Zeyi, Mao Xudong, Wang Ruyue, Luo Wenqin, Lin Yudong, Li Yang, Chen Xianjiong, Zhu Ziwei, Lu Yi, Zhou Chenghao, Wang Mingchao, Xia Liqun, Li Gonghui, Gao Lei
Department of Urology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, 3 Qingchun Road, Hangzhou, 310016, China.
School of Medicine, University of New South Wales, Sydney, Australia.
Biol Direct. 2024 Dec 21;19(1):132. doi: 10.1186/s13062-024-00576-w.
Precision oncology's implementation in clinical practice faces significant constraints due to the inadequacies in tools for detailed patient stratification and personalized treatment methodologies. Dysregulated tryptophan metabolism has emerged as a crucial factor in tumor progression, encompassing immune suppression, proliferation, metastasis, and metabolic reprogramming. However, its precise role in clear cell renal cell carcinoma (ccRCC) remains unclear, and predictive models or signatures based on tryptophan metabolism are conspicuously lacking.
The influence of tryptophan metabolism on tumor cells was explored using single-cell RNA sequencing data. Genes involved in tryptophan metabolism were identified across both single-cell and bulk-cell dimensions through weighted gene co-expression network analysis (WGCNA) and its single-cell data variant (hdWGCNA). Subsequently, a tryptophan metabolism-related signature was developed using an integrated machine-learning approach. This signature was then examined in multi-omics data to assess its associations with patient clinical features, prognosis, cancer malignancy-related pathways, immune microenvironment, genomic characteristics, and responses to immunotherapy and targeted therapy. Finally, the genes within the signature were validated through experiments including qRT-PCR, Western blot, CCK8 assay, and transwell assay.
Dysregulated tryptophan metabolism was identified as a potential driver of the malignant transformation of normal epithelial cells. The tryptophan metabolism-related signature (TMRS) demonstrated robust predictive capability for overall survival (OS) and progression-free survival (PFS) across multiple datasets. Moreover, a high TMRS risk score correlated with increased tumor malignancy, significant metabolic reprogramming, an inflamed yet dysfunctional immune microenvironment, heightened genomic instability, resistance to immunotherapy, and increased sensitivity to certain targeted therapeutics. Experimental validation revealed differential expression of genes within the signature between RCC and adjacent normal tissues, with reduced expression of DDAH1 linked to enhanced proliferation and metastasis of tumor cells.
This study investigated the potential impact of dysregulated tryptophan metabolism on clear cell renal cell carcinoma, leading to the development of a tryptophan metabolism-related signature that may provide insights into patient prognosis, tumor biological status, and personalized treatment strategies. This signature serves as a valuable reference for further exploring the role of tryptophan metabolism in renal cell carcinoma and for the development of clinical applications based on this metabolic pathway.
由于用于详细患者分层和个性化治疗方法的工具存在不足，精准肿瘤学在临床实践中的实施面临重大限制。色氨酸代谢失调已成为肿瘤进展的关键因素，包括免疫抑制、增殖、转移和代谢重编程。然而，其在透明细胞肾细胞癌（ccRCC）中的精确作用仍不清楚，且明显缺乏基于色氨酸代谢的预测模型或特征。
利用单细胞RNA测序数据探索色氨酸代谢对肿瘤细胞的影响。通过加权基因共表达网络分析（WGCNA）及其单细胞数据变体（hdWGCNA），在单细胞和批量细胞层面鉴定参与色氨酸代谢的基因。随后，采用综合机器学习方法开发了一个与色氨酸代谢相关的特征。然后在多组学数据中检查该特征，以评估其与患者临床特征、预后、癌症恶性相关途径、免疫微环境、基因组特征以及对免疫治疗和靶向治疗反应的关联。最后，通过qRT-PCR、蛋白质免疫印迹、CCK8测定和transwell测定等实验对该特征中的基因进行验证。
色氨酸代谢失调被确定为正常上皮细胞恶性转化的潜在驱动因素。与色氨酸代谢相关的特征（TMRS）在多个数据集中对总生存期（OS）和无进展生存期（PFS）显示出强大的预测能力。此外，高TMRS风险评分与肿瘤恶性程度增加、显著的代谢重编程、炎症但功能失调的免疫微环境、基因组不稳定性增强、对免疫治疗的抗性以及对某些靶向治疗的敏感性增加相关。实验验证显示，RCC与相邻正常组织之间该特征中的基因存在差异表达，DDAH1表达降低与肿瘤细胞增殖和转移增强有关。
本研究调查了色氨酸代谢失调对透明细胞肾细胞癌的潜在影响，从而开发出一个与色氨酸代谢相关的特征，该特征可能为患者预后、肿瘤生物学状态和个性化治疗策略提供见解。该特征为进一步探索色氨酸代谢在肾细胞癌中的作用以及基于此代谢途径开发临床应用提供了有价值的参考。
