Integrated machine learning reveals the role of tryptophan metabolism in clear cell renal cell carcinoma and its association with patient prognosis

Background: 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.

Methods: 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.

Results: 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.

Conclusion: 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.