Frontiers in Pharmacology (Oct 2023)

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

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

DOI
https://doi.org/10.3389/fphar.2023.1267186
Journal volume & issue
Vol. 14

Abstract

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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 promote the malignancy of cancer cells. The role of tryptophan and its metabolism should be explored for an in-depth understanding of molecular mechanisms 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 the gastric tumor microenvironment.Results: Two tryptophan metabolism-associated gene molecular subtypes were constructed. Compared to the C2 subtype, the 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. The immune checkpoint was downregulated in the C1 subtype. A total of eight key genes, EFNA3, GPX3, RGS2, CXCR4, SGCE, ADH4, CST2, and GPC3, were screened for the establishment of a prognostic risk model.Conclusion: 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.

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