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<p>While recent studies have highlighted oxidative stress (OS) as a pivotal factor influencing tumor dynamics, its specific interactions within the tumor microenvironment (TME) of colorectal cancer (CRC) remain 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. We analyzed OS-related pathway activities using both single-cell and bulk RNA-seq data. An unsupervised clustering algorithm was utilized to identify OS-related subtypes. 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 confirm the expression of the identified gene. Examination of the single-cell RNA-seq GSE132465 dataset revealed a universal elevation in OS-associated pathway activities within malignant cells. By integrating this with the bulk RNA-seq TCGA-CRC dataset, we identified 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, increased expression of the UCN gene was observed in CRC specimens, as confirmed by qRT-PCR and IHC assays on TMA. In this research, we delineated two distinct subtypes of CRC associated with OS. The developed OSRRS holds promise as a candidate prognostic and stratification tool for CRC management. Collectively, these results shed light on the intricate role of OS in CRC pathology.</p>

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Deciphering oxidative stress-related heterogeneity and developing a prognostic signature for colorectal cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月4日

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  • Linyun Ma,
  • Wenqiang Luo,
  • Enrui Liu,
  • Shuang Zhang,
  • Lei Zheng,
  • Zhen Liu,
  • Qiyou Guo,
  • Zhenlu Li,
  • Han Gao

摘要

While recent studies have highlighted oxidative stress (OS) as a pivotal factor influencing tumor dynamics, its specific interactions within the tumor microenvironment (TME) of colorectal cancer (CRC) remain 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. We analyzed OS-related pathway activities using both single-cell and bulk RNA-seq data. An unsupervised clustering algorithm was utilized to identify OS-related subtypes. 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 confirm the expression of the identified gene. Examination of the single-cell RNA-seq GSE132465 dataset revealed a universal elevation in OS-associated pathway activities within malignant cells. By integrating this with the bulk RNA-seq TCGA-CRC dataset, we identified 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, increased expression of the UCN gene was observed in CRC specimens, as confirmed by qRT-PCR and IHC assays on TMA. In this research, we delineated two distinct subtypes of CRC associated with OS. The developed OSRRS holds promise as a candidate prognostic and stratification tool for CRC management. Collectively, these results shed light on the intricate role of OS in CRC pathology.