Derivation and Clinical Validation of a Redox-Driven Prognostic Signature for Colorectal Cancer

Abstract

Colorectal cancer (CRC), a seriously threat that endangers public health, has a striking tendency to relapse and metastasize. Redox-related signaling pathways have recently been extensively studied in cancers. However, the study and potential role of redox in CRC remain unelucidated. We developed and validated a risk model for prognosis and recurrence prediction in CRC patients via identifying gene signatures driven by redox-related signaling pathways. The redox-driven prognostic signature (RDPS) was demonstrated to be an independent risk factor for patient survival (including OS and RFS) in four public cohorts and one clinical in-house cohort. Additionally, there was an intimate association between the risk score and tumor immune infiltration, with higher risk score accompanied with less immune cell infiltration. In this study, we used redox-related factors as an entry point, which may provide a broader perspective for prognosis prediction in CRC and have the potential to provide more promising evidence for immunotherapy.

Keywords: colorectal cancer; gene signature; immune infiltration; prognosis; redox.

Figure 1

The flowchart of this study.

Figure 2

Identification of redox-associated pathways and genes between the normal group and tumor group in the TCGA-CRC cohort. (A) Redox-related gene-set enrichment analysis in the normal and tumor groups. (B) Differential analysis of redox-related genes in TCGA-CRC. (C) Univariate Cox regression revealed 17 redox-related genes with significant prognostic significance.

Figure 3

Construction and evaluation of RDPS. (A) Ten-time cross-validations to tune the parameter selection in the LASSO model. The two dotted vertical lines are drawn at the optimal values by minimum criteria (left) and 1−SE criteria (right). (B) LASSO coefficient profiles of the candidate genes for risk score construction. (C–F) Kaplan–Meier curves for OS according to the risk score in four cohorts.

Figure 4

Power of RDPS in multivariate Cox regression analysis in CRC patients. The risk score was an independent risk factor for prognosis in TCGA-CRC (A), GSE17536 (B), GSE29621 (C), and GSE39582 (D) cohorts.

Figure 5

Evaluation of RDPS in predicting OS in four cohorts. (A–D) Time-dependent ROC analysis for predicting OS at 1, 3, and 5 years. (E–H) Calibration plots for comparing the actual probabilities and the predicted probabilities of OS at 1, 3, and 5 years.

Figure 6

Evaluation of the ability of risk scores to predict CRC recurrence in four public cohorts. (A–D) Kaplan–Meier curves of RFS according to the RDPS model in four cohorts. (E–H) Multivariate Cox regression analysis of the risk score in four cohorts.

Figure 7

Validation of our discovery in a clinical in-house cohort. (A, B) Kaplan–Meier curves of OS (A) and RFS (B) according to the RDPS. (C, D) Multivariate Cox regression analysis of the risk score for OS (C) and RFS (D). (E) Time-dependent ROC analysis for predicting RFS at 1, 3, and 5 years. (F) Calibration plots for comparing the actual probabilities and the predicted probabilities of OS at 1, 3, and 5 years.

Figure 8

Landscapes of frequently mutated genes (FMGs) in high and low risk-score groups. (A) Oncoplot depicts the differences in FMGs of CRC among the fourcohorts. The right panel shows the mutation rate, and genes are ordered by their mutation frequencies. (B) The mutation frequency of the driver genes in high- andlow-risk groups (*p < 0.05). (C) Amplified and homozygously deleted genes in the high- and low-risk groups.

Figure 9

GSEA functional pathway analysis. (A, B) Significantly enriched Gene Ontology terms between high (A) and low (B) risk groups. (C, D) Significantly enriched Kyoto Encyclopedia of Genes and Genomes terms between high (C) and low (D) risk groups.

Figure 10

Immune infiltration analysis. (A) Assessment of infiltration abundance of nine immune cells and 27 immune checkpoints in patients with high or low RDPS scores. (B) Abundance of immune cell infiltrates in the high- and low-risk groups. (C) Differential expression analysis of immune checkpoints. The difference of TMB (D) and IPS (E) in different risk groups. (F) Correlation analysis of immune cells or checkpoints with risk scores (ns, p > 0.05; *p < 0.05; **p < 0.01; ***p < 0.001). NS, none significance.