Abstract
Background
The application of precision oncology in clinical settings is currently constrained by limitations in tools for granular patient stratification and personalized treatment approaches. Dysregulated tryptophan metabolism has been identified as a key player in tumor development, including immune suppression, proliferation, metastasis, and metabolic reprogramming. Nonetheless, its exact function in clear cell renal cell carcinoma (ccRCC) is yet to be fully understood, and there is a notable absence of predictive models or signatures derived from it.
Methods
The role of tryptophan metabolism on tumor cells was investigated using single-cell RNA sequencing data. Genes associated with tryptophan metabolism were identified across both single-cell and bulk cell dimensions through the application of weighted gene co-expression network analysis (WGCNA) and its single cell data variant (hdWGCNA). A signature related to tryptophan metabolism was subsequently developed utilizing an integrated machine learning approach. This signature was examined in multi-omics data for its associations with patient clinical features, prognosis, cancer malignancy-related pathways, immune microenvironment, genomic characteristics, and responses to immunotherapy and targeted therapy. Finally, genes within the signature were validated through experiments including qRT-PCR, Western blot, CCK8 assay, and transwell assay.
Results
The dysregulated tryptophan metabolism was identified as a potential contributor to the malignant transformation of normal epithelial cells. The tryptophan metabolism-related signature (TMRS) exhibited strong predictive ability for overall survival (OS) and progression-free survival (PFS) in multiple datasets. Furthermore, elevated TMRS risk score was associated with increased tumor malignancy, significant metabolic reprogramming, an inflamed yet dysfunctional immune microenvironment, greater genomic instability, resistance to immunotherapy, and heightened sensitivity to specific targeted therapeutics. Experimental validation demonstrated differential expression of genes within the signature between RCC and adjacent normal tissues, with decreased expression of the gene DDAH1 linked to increased proliferation and metastasis of tumor cells.
Conclusion
This study explored the influence of abnormal tryptophan metabolism on clear cell renal cell carcinoma, and constructed a signature related to tryptophan metabolism that can accurately predict patient prognosis, evaluate tumor biological status, and guide patient personalized treatment, which is conducive to enabling more patients to benefit from precision oncology.