Dissecting Oxidative Stress-Related Heterogeneity and Constructing a Prognostic Signature for Colorectal Cancer

Abstract

Background: While recent studies have highlighted oxidative stress (OS) as a critical factor influencing tumor dynamics, its specific interactions within the tumor microenvironment (TME) of colorectal cancer (CRC) remains elusive. This study seeks to unveil the impact of OS on the CRC TME and to develop an advanced OS-related risk signature (OSRRS) model.Methods: We analyzed OS-related pathway activities using both single-cell and bulk RNA-seq data. We then utilized an unsupervised clustering algorithm to identified subtypes associated with OS. Based on genes associated with the OS pathways, we constructed an OSRRS model employing the LASSO Cox analysis. For validation, we employed quantitative real-time polymerase chain reaction (qRT-PCR) coupled with immunohistochemical (IHC) analyses on tissue microarrays (TMA) to validate the expression of the identified genes.Results: Examination of the single-cell RNA-seq GSE132465 dataset revealed an universal elevation in OS-associated pathway activities within malignant cells. By integrating this with the bulk RNA-seq TCGA-CRC dataset, we demarcated two unique OS-specific clusters. This led to the establishment of a 12-gene OSRRS using the LASSO Cox method. The robustness of our model was further verified using the GSE39582 and GSE17538 cohorts. Notably, an increased expression of the UCN gene was observed in CRC specimens, as validated by qRT-PCR and IHC assays on TMA.Conclusion: In this research, we delineated two distinct subtypes of CRC associated with OS. The developed OSRRS holds promise as a prospective therapeutic target for CRC management. Collectively, these results shed light on the intricate role of OS in CRC pathology.

Note:
Funding declaration: This work was supported by The Youth Project of Shandong Provincial Natural Science Foundation (No. ZR2023QH414).

Conflict of Interests: None declared.

Keywords: oxidative stress, colorectal cancer, scRNA-seq, TME, risk signature