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

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.

Screening and identifying a novel M-MDSCs-related gene signature for predicting prognostic risk and immunotherapeutic responses in patients with lung adenocarcinoma

Background: Lung adenocarcinoma (LUAD) shows intratumoral heterogeneity, a highly complex phenomenon that known to be a challenge during cancer therapy. Considering the key role of monocytic myeloid-derived suppressor cells (M-MDSCs) in the tumor microenvironment (TME), we aimed to build a prognostic risk model using M-MDSCs-related genes. Methods: M-MDSCs-related genes were extracted from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Utilized univariate survival analysis and random forest algorithm to screen candidate genes. A least absolute shrinkage and selection operator (LASSO) Cox regression analysis was selected to build the risk model. Patients were scored and classified into high- and low-risk groups based on the median risk scores. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis along with R packages "estimate" and "ssGSEA" were performed to reveal the mechanism of risk difference. Prognostic biomarkers and tumor mutation burden (TMB) were combined to predict the prognosis. Nomogram was carried out to predict the survival probability of patients in 1, 3, and 5 years. Results: 8 genes (VPREB3, TPBG, LRFN4, CD83, GIMAP6, PRMT8, WASF1, and F12) were identified as prognostic biomarkers. The GEO validation dataset demonstrated the risk model had good generalization effect. Significantly enrichment level of cell cycle-related pathway and lower content of CD8⁺ T cells infiltration in the high-risk group when compared to low-risk group. Morever, the patients were from the intersection of high-TMB and low-risk groups showed the best prognosis. The nomogram demonstrated good consistency with practical outcomes in predicting the survival rate over 1, 3, and 5 years. Conclusion: The risk model demonstrate good prognostic predictive ability. The patients from the intersection of low-risk and high-TMB groups are not only more sensitive response to but also more likely to benefit from immune-checkpoint-inhibitors (ICIs) treatment.