Oxidative stress genes in patients with esophageal squamous cell carcinoma: construction of a novel prognostic signature and characterization of tumor microenvironment infiltration

Abstract

Background: Oxidative stress plays an important role in the progression of various types of tumors. However, its role in esophageal squamous cell carcinoma (ESCC) has seldom been explored. This study aimed to discover prognostic markers associated with oxidative stress in ESCC to improve the prediction of prognosis and help in the selection of effective immunotherapy for patients.

Results: A consensus cluster was constructed using 14 prognostic differentially expressed oxidative stress-related genes (DEOSGs) that were remarkably related to the prognosis of patients with ESCC. The infiltration levels of neutrophils, plasma cells, and activated mast cells, along with immune score, stromal score, and estimated score, were higher in cluster 1 than in cluster 2. A prognostic signature based on 10 prognostic DEOSGs was devised that could evaluate the prognosis of patients with ESCC. Calculated risk score proved to be an independent clinical prognostic factor in the training, testing, and entire sets. P53 signaling pathway was highly enriched in the high-risk group. The calculated risk score was positively related to the infiltration levels of resting mast cells, memory B cells, and activated natural killer (NK) cells and negatively associated with the infiltration levels of M1 and M2 macrophages. The relationship between clinical characteristics and risk score has not been certified. The half-maximal inhibitory concentration (IC50) values for sorafenib and gefitinib were lower for patients in the low-risk group.

Conclusion: Our prognostic signature based on 10 prognostic DEOSGs could predict the disease outcomes of patients with ESCC and had strong clinical value. Our study improves the understanding of oxidative stress in tumor immune microenvironment (TIME) and provides insights for developing improved and efficient immunotherapy strategies.

Keywords: Esophageal squamous cell carcinoma; Immune infiltrates; Marker; Oxidative stress; Prognosis.

Fig. 1

A Volcano map of DEOSGs between normal and ESCC samples. B Network between DEOSGs. C, D Bubble plots of GO analyses (C) and KEGG analyses (D)

Fig. 2

A The prognostic DEOSGs extracted by Univariate Cox regression analysis. B The independent prognostic DEOSGs extracted by Multivariate Cox regression analysis. C, D Unsupervised clustering of 14 prognostic DEOSGs in the GEO-ESCC cohort: C Relative change in area under CDF curve for k = 2 to 9; D The ESCC cohort from GEO was divided into two distinct clusters when k = 2. E Kaplan–Meier survival curve of patients between cluster 1 and 2. F Comparison of the relationship between the clinical characteristics of two clusters and heatmap of 14 prognostic DEOSGs. Blue represents down-regulation and red represents up-regulation of genes

Fig. 3

A–G The infiltrating levels of 22 immune cell types in cluster1 vs cluster 2: B resting mast cells, C memory B cells, D NK cells, E plasma cells, F activated mast cells, G neutrophils. The comparison of immune-related scores between cluster 1 and cluster 2 (H, J): (H) estimated score, I immune score, J stromal score

Fig. 4

A, B The prognostic signature constructed by the minimum criterion of LASSO Cox regression algorithm

Fig. 5

A–C Risk score distribution of patients between high- and low-risk groups in the training (A), testing (B), and entire sets (C), respectively. D–I Survival status of patients between high- and low-risk groups in the training (D, G), testing (E, H), and entire sets (F, I), respectively. J–L Kaplan–Meier survival curve of patients between high- and low-risk groups in the training (J), testing (K), and entire set (L), respectively

Fig. 6

A–H Kaplan–Meier survival curves stratified by gender (A, E), stage T (B, F), N (C, G), or TNM (D, H) between low- and high-risk groups in the training set. I–P Kaplan–Meier survival curves stratified by gender (I, M), stage T (J, N), N (K, O), or TNM (L, P) between low- and high-risk groups in the testing set. Q–X Kaplan–Meier survival curves stratified by gender (Q, U), stage T (R, V), N (S, W), or TNM (T, X) between low- and high-risk groups in the entire set

Fig. 7

A–C Time-dependent ROC curve analyses of risk score in the training (A), testing (B), and entire sets (C), respectively. D–F Univariate Cox analyses of clinical factors and risk score with OS in the training (D), testing (E), and entire sets (F), respectively. (G–I) Multivariate Cox analyses of clinical factors and risk score with OS in the training (G), testing (H), and entire sets (I), respectively. (J–L) One-year ROC curve analyses of gender, clinical stage, T stage, N stage and risk score in the training (J), testing (K), and entire sets (L), respectively

Fig. 8

A GSVA enrichment analysis between high- and low-risk groups in the training. The heatmap was used to visualize these biological processes, and red represented activated pathways and blue represented inhibited pathways. B–F The correlation between risk score and the infiltration levels of immune cells: (B) memory B cells, C resting mast cells, D activated NK cells, E macrophages M1, F macrophages M2. G–J The sensitivity to drugs in high- and low-risk score patients: (G) Sorafenib, H Gefitinib, I Cytarabine, J Elesclomol