Comprehensive analysis of tryptophan metabolism-related gene signature to predict prognosis in Esophageal squamous cell carcinoma based on Multi-omics

Zhengjie Wu¹Zhiping Liu²Yukun Wang³Geling Teng⁴Xiaodong Li⁵Tong Lu⁵Fangning Hu⁵Shuo Wu⁵Guanqiang Ma⁵Hua Zhang^6*

• ¹Tracheal and bronchial surgery, Shandong Public Health Clinical Center, Jinan, Shandong Province, China
• ²Department of Oncology, Shandong Provincial Third Hospital, Cheeloo College of Medicine, Shandong University., Jinan, China
• ³Department of Geriatrics, Qingdao Binhai College Affiliated Hospital, Qingdao, China
• ⁴Department of Respiratory and Critical Care Medicine, Shandong Provincial Public Health Clinical Center., Jinan, China
• ⁵Department of Thoracic Surgery, Shandong Provincial Public Health Clinical Center, Jinan, China
• ⁶Department of Thoracic Surgery, Shandong Public Health Clinical Center, Jinan, China

Background: Tryptophan (Trp) metabolism plays a vital role in tumor development and outcomes. However, Trp in esophageal squamous cell carcinoma (ESCC) remains poorly understood. We aimed to explore the role and mechanism of Trp metabolism in ESCC.We integrated single-cell RNA (scRNA) sequencing, bulk transcriptome, proteomics, and microbiome data from public databases. Tryptophan-related cell population and their interactions were explored using "seurat" R package in single-cell level. LASSO and univariate Cox regression were used to select prognostic TrpGs and construct a risk model. The overall survival (OS), immune infiltration, checkpoint expression, drug sensitivity, and microbiota composition between high-and low-risk groups were evaluated. Independent prognostic factors were identified via multivariate Cox analysis and validated by qPCR analysis, and a nomogram was constructed for survival prediction.: We identified 28 differentially expressed Tryptophan-related genes (DE-TrpGs), and fibroblasts emerged as the cell type with the highest TrpGs score, although reduced in ESCC. Eighteen DE-TrpGs showed downregulation in tumor fibroblasts at the single-cell level. Fibroblast-epithelial communication involved LAMININ, HSPG, and AGRN pathways. Five prognostic TrpGs (MAOA, AKR1A1, ALDH9A1, HAAO, ALDH2) were selected to construct the risk model. The expression of MAOA, AKR1A1, ALDH9A1, HAAO, and ALDH2 was significantly downregulated in ESCC tumor tissues compared to non-tumor tissues. High-risk patients showed poorer OS, distinct immune cell infiltration, elevated expression of KIR2DL1, LGALS9, TNFRSF18, and TNFRSF4, increased sensitivity to imatinib and Axitinib, resistance to multiple chemotherapeutics, and reduced Fusobacteria and Tenericutes abundance. HAAO, ALDH2, and lymph node stage were independent prognostic factors used to develop a predictive nomogram. Conclusions: We identified a Trp metabolism-associated fibroblast population in the ESCC TME and developed a five-gene TrpGs signature for prognostic prediction in ESCC patients.