Construction of a Prognostic Model based on CSC-related Genes in Patients with Colorectal Cancer

Colorectal cancer (CRC) is one of the most common and deadly malignancies. Lack of efficient biomarkers for prognosis has limited the improvement of survival outcome in patients with CRC. Numerous studies have demonstrated the important roles of cancer stem cells (CSCs) in both treatment resistance and disease recurrence of CRC. Thus, the current study aims to construct a prognostic model based on expression level of CSC-related genes for precise molecular subtyping of CRC patients with different prognoses, TME infiltration patterns and therapeutic responses. The RNA sequencing data and clinical information were obtained from UCSC Xena database, followed by identification of differential expressed genes, univariate Cox regression, and LASSO regression to identify prognostic CSC-related genes and construct a novel prognostic risk scoring model consisting of 21 CSC-related genes. The patients in high-risk group suffered poor survival outcome (<i>P</i><0.0001). Moreover, the performance of CSC-related prognostic model was validated in individual GEO datasets including GSE41258 and GSE39582 (<i>P</i><0.05). Furthermore, patients with high-risk score exhibited lower response rate to immune checkpoint inhibitors as compared to those in low-risk group (17.4% vs. 28.2%), indicating the potential of CSC-related prognostic model to predict the immunotherapy response. Collectively, our findings provide an effective model to predict the immunotherapy response and survival outcome in patients with CRC.

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Publication Details

Journal
Journal of Cancer
Published
2025-04-13
DOI
https://doi.org/10.7150/jca.108188
Primary Topic
Cancer Cells and Metastasis
Type
article
Field-Weighted Citation Impact
0.00

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article

Construction of a Prognostic Model based on CSC-related Genes in Patients with Colorectal Cancer

Jierong Chen, Caifeng Yue, Ziyue Li, Mingfeng Li et al.
Journal of Cancer
Cancer Cells and Metastasis
article

Construction of a Prognostic Model based on CSC-related Genes in Patients with Colorectal Cancer

Jierong Chen, Caifeng Yue, Ziyue Li, Mingfeng Li, Yun‐Miao Guo, Qizhou Lian, Yingying He, Guangtao Zheng
article en

Abstract

Colorectal cancer (CRC) is one of the most common and deadly malignancies. Lack of efficient biomarkers for prognosis has limited the improvement of survival outcome in patients with CRC. Numerous studies have demonstrated the important roles of cancer stem cells (CSCs) in both treatment resistance and disease recurrence of CRC. Thus, the current study aims to construct a prognostic model based on expression level of CSC-related genes for precise molecular subtyping of CRC patients with different prognoses, TME infiltration patterns and therapeutic responses. The RNA sequencing data and clinical information were obtained from UCSC Xena database, followed by identification of differential expressed genes, univariate Cox regression, and LASSO regression to identify prognostic CSC-related genes and construct a novel prognostic risk scoring model consisting of 21 CSC-related genes. The patients in high-risk group suffered poor survival outcome (<i>P</i><0.0001). Moreover, the performance of CSC-related prognostic model was validated in individual GEO datasets including GSE41258 and GSE39582 (<i>P</i><0.05). Furthermore, patients with high-risk score exhibited lower response rate to immune checkpoint inhibitors as compared to those in low-risk group (17.4% vs. 28.2%), indicating the potential of CSC-related prognostic model to predict the immunotherapy response. Collectively, our findings provide an effective model to predict the immunotherapy response and survival outcome in patients with CRC.

Journal of CancerVol. 16(7)
Guangdong Medical College (CN), Central People's Hospital of Zhanjiang (CN), Guangdong Academy of Medical Sciences (CN), Guangzhou Medical University (CN)
Basic and Applied Basic Research Foundation of Guangdong Province
Good health and well-being
Openalex Percentile: Top 87%
Cancer Cells and Metastasis
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