Research Paper Volume 15, Issue 15 pp 7593—7615

Development of tryptophan metabolism patterns to predict prognosis and immunotherapeutic responses in hepatocellular carcinoma

Guo Long1,2, , Dong Wang3, , Jianing Tang1,2, , Weifeng Tang4, ,

  • 1 Department of Liver Surgery, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China
  • 2 National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China
  • 3 Liver Disease Center, The Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong, China
  • 4 Department of Gastroenterology, The Second Hospital of Zhuzhou, Zhuzhou 412005, Hunan, China

Received: February 14, 2023       Accepted: July 14, 2023       Published: August 3, 2023
How to Cite

Copyright: © 2023 Long et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Tryptophan metabolism is associated with tumorigenesis and tumor immune response in various cancers. Liver is the main place where tryptophan catabolism is performed. However, the role of tryptophan metabolism in hepatocellular carcinoma (HCC) has not been well clarified. In the present study, we described the mutations of 42 tryptophan metabolism-related genes (TRPGs) in HCC cohorts. Then, HCC patients were well distributed into two subtypes based on the expression profiles of the 42 TRPGs. The clinicopathological characteristics and tumor microenvironmental landscape of the two subtypes were profiled. We also established a TRPGs scoring system and identified four hallmark TRPGs, including ACSL3, ADH1B, ALDH2, and HADHA. Univariate and multivariate Cox regression analysis revealed that the TRPG signature was an independent prognostic indicator for HCC patients. Besides, the predictive accuracy of the TRPG signature was assessed by the receiver operating characteristic curve (ROC) analysis. These results showed that the TRPG risk model had an excellent capability in predicting survival in both TCGA and GEO HCC cohorts. Moreover, we discovered that the TRPG signature was significantly related to the different immune infiltration and therapeutic drug sensitivity. The functional experiments and immunohistochemistry staining analysis also validated the results above. Our comprehensive analysis enhanced our understanding of TRPGs in HCC. A novel predictive model based on TRPGs was built, which may be considered as a beneficial tool for predicting the clinical outcomes of HCC patients.


HCC: Hepatocellular carcinoma; TCGA: The Cancer Genome Atlas; TRPGs: Tryptophan metabolism-related genes; Trp: Tryptophan; TME: Tumor microenvironment; GEO: Gene Expression Omnibus; CDF: Cumulative distribution function; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; GSEA: Gene Set Enrichment Analysis; ESTIMATE: Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression Data; GSVA: Gene Set variation analysis; SNV: Single Nucleotide Variation; CNV: Copy number variation; MF: Molecular function; OS: Overall survival; DSS: Disease-specific survival; DFI: Disease-free interval; PFI: Progression-free interval; ROC: Receiver operating characteristic; GSVA: Gene Set Variation Analysis; PCA: Principal component analysis; ROC: Receiver operating characteristic curve.