ArticleFrontiers in oncology2022
Prognostic risk analysis related to radioresistance genes in colorectal cancer.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.
- FBXW7 Gene Mutation and Expression in Colorectal Cancer (CRC): A Systematic Review from Molecular Mechanisms to Clinical Translation.International journal of molecular sciences · 2025Pooled it
- Article
- A 13-gene prognostic model developed using machine learning to predict the response to neoadjuvant chemoradiotherapy in rectal carcinoma.Cancer cell international · 2026Article
- C5aR1 and cGAS/STING and their possible involvement in radiosensitivity of colorectal cancer.iScience · 2026Review
- Integration of single-cell and bulk RNA-seq via machine learning to reveal ferroptosis- and lipid metabolism-driven immune landscape heterogeneity and predict immunotherapy response in colon cancer.Frontiers in immunology · 2025Article
- Article
- [Molecular pathology of colorectal cancer].Pathologie (Heidelberg, Germany) · 2023Review
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Radiotherapy (RT) is one of the most important treatments for patients with colorectal cancer (CRC). Radioresistance is the crucial cause of poor therapeutic outcomes in colorectal cancer. However, the underlying mechanism of radioresistance in colorectal cancer is still poorly defined. Herein we established a radioresistant colorectal cancer cell line and performed transcriptomics analyses to search for the underlying genes that contribute to radioresistance and investigate its association with the prognosis of CRC patients. Methods: The radioresistant cell line was developed from the parental HCT116 cell by a stepwise increased dose of irradiation. Differential gene analysis was performed using cellular transcriptome data to identify genes associated with radioresistance, from which extracellular matrix (ECM) and cell adhesion-related genes were screened. Survival data from a CRC cohort in the TCGA database were used for further model gene screening and validation. The correlation between the risk score model and tumor microenvironment, clinical phenotype, drug treatment sensitivity, and tumor mutation status were also investigated. Results: A total of 493 different expression genes were identified from the radioresistant and wild-type cell line, of which 94 genes were associated with ECM and cell adhesion-related genes. The five model genes Conclusions: The risk score model built with five radioresistance genes in this study, including
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