AUTHOR=Luo Peng , Chen Guojun , Shi Zhaoqi , Yang Jin , Wang Xianfa , Pan Junhai , Zhu Linghua 

TITLE=Comprehensive multi-omics analysis of tryptophan metabolism-related gene expression signature to predict prognosis in gastric cancer

JOURNAL=Frontiers in Pharmacology

VOLUME=Volume 14 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2023.1267186

DOI=10.3389/fphar.2023.1267186

ISSN=1663-9812

ABSTRACT=Introduction
The 5-year survival of gastric cancer (GC) patients with advanced stage remains poor. Some evidence has indicated that tryptophan metabolism may induce cancer progression through immunosuppressive responses and promoting the malignancy of cancer cells. The role of tryptophan and its metabolism should be explored for in-depth understanding of molecular mechanism during GC development.
Material and methods
We utilized the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) dataset to screen tryptophan metabolism-associated genes via single sample gene set enrichment analysis (ssGSEA) and correlation analysis. Consensus clustering analysis was employed to construct different molecular subtypes.  Most common differentially expressed genes (DEGs) were determined from the molecular subtypes. Univariate cox analysis as well as lasso were performed to establish a tryptophan metabolism-associated gene signature. Gene Set Enrichment Analysis (GSEA) was utilized to evaluate signaling pathways. ESTIMATE, ssGSEA, and TIDE were used for the evaluation of gastric tumor microenvironment.
Results
Two tryptophan metabolism-associated gene molecular subtypes were constructed. Compared to C2 subtype, C1 subtype showed better prognosis with increased CD4 positive memory T cells as well as activated dendritic cells (DCs) infiltration and suppressed M2-phenotype macrophages inside the tumor microenvironment. Immune checkpoint was downregulated in the C1 subtype. 8 key genes including EFNA3, GPX3, RGS2, CXCR4, SGCE, ADH4, CST2, and GPC3 were screened for the establishment of prognostic risk model. 
Conclusions
This study concluded that the tryptophan metabolism-associated genes can be applied in GC prognostic prediction. The risk model established in the current study was highly accurate in GC survival prediction.