Development of a TLR-Based Model That Can Predict Prognosis, Tumor Microenvironment, and Drug Response for Esophageal Squamous Cell Carcinoma.

The toll-like receptor (TLR) family is an important class of proteins involved in the immune response. However, little is known about the association between TLRs and Esophageal squamous cell cancer (ESCC). We explored differentially expressed genes (DEGs) between ESCC and esophagus tissues in TCGA and GTEx database. By taking the intersection with TLR gene set and using univariate Cox analysis and multivariate Cox regression analysis to discriminate the hub genes, we created a TLR-prognostic model. Our model separated patients with ESCC into high- and low-risk score (RS) groups. Prognostic analysis was performed with Kaplan-Meier curves. The two groups were also compared regarding tumor immune microenvironment and drug sensitivity. Six hub genes (including CD36, LGR4, MAP2K3, NINJ1, PIK3R1, and TRAF3) were screened to construct a TLR-prognostic model. High-RS group had a worse survival (p < 0.01), lower immune checkpoint expression (p < 0.05), immune cell abundance (p < 0.05) and decreased sensitivity to Epirubicin (p < 0.001), 5-fluorouracil (p < 0.0001), Sorafenib (p < 0.01) and Oxaliplatin (p < 0.05). We constructed a TLR-based model, which could be used to assess the prognosis of patients with ESCC, provide new insights into drug treatment for ESCC patients and investigate the TME and drug response.