ReviewWorld journal of gastrointestinal oncology2023
Machine learning algorithm to construct cuproptosis- and immune-related prognosis prediction model for colon cancer.
Review in World journal of gastrointestinal oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 12 citations in OpenAlex.
- An m6A-programmed cell death signature predicts prognosis and identifies STK25 as a therapeutic target in colon adenocarcinoma.Oncology letters · 2026Article
- Mechanism and role of regulated cell death in tumor immunity and immunotherapy.Cancer communications (London, England) · 2025Review
- Involvement of copper in cell death and cancer.Apoptosis : an international journal on programmed cell death · 2025Review
- Prognostic models of immune-related cell death and stress unveil mechanisms driving macrophage phenotypic evolution in colorectal cancer.Journal of translational medicine · 2025Article
- The gene signature linked to lactate metabolism predicts the prognosis and correlates with the immune status of head and neck squamous cell carcinoma.Frontiers in genetics · 2025Article
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- Using Bioinformatics and Machine Learning to Predict the Genetic Characteristics of Ferroptosis-Cuproptosis-Related Genes Associated with Sleep Deprivation.Nature and science of sleep · 2024Article
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Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOver the past few years, research into the pathogenesis of colon cancer has progressed rapidly, and cuproptosis is an emerging mode of cellular apoptosis. Exploring the relationship between colon cancer and cuproptosis benefits in identifying novel biomarkers and even improving the outcome of the disease.
aimTo look at the prognostic relationship between colon cancer and the genes associated with cuproptosis and the immune system in patients. The main purpose was to assess whether reasonable induction of these biomarkers reduces mortality among patients with colon cancers.
methodData obtained from The Cancer Genome Atlas and Gene Expression Omnibus and the Genotype-Tissue Expression were used in differential analysis to explore differential expression genes associated with cuproptosis and immune activation. The least absolute shrinkage and selection operator and Cox regression algorithm was applied to build a cuproptosis- and immune-related combination model, and the model was utilized for principal component analysis and survival analysis to observe the survival and prognosis of the patients. A series of statistically meaningful transcriptional analysis results demonstrated an intrinsic relationship between cuproptosis and the micro-environment of colon cancer.
resultsOnce prognostic characteristics were obtained, the CDKN2A and DLAT genes related to cuproptosis were strongly linked to colon cancer: The first was a risk factor, whereas the second was a protective factor. The finding of the validation analysis showed that the comprehensive model associated with cuproptosis and immunity was statistically significant. Within the component expressions, the expressions of HSPA1A, CDKN2A, and UCN3 differed markedly. Transcription analysis primarily reflects the differential activation of related immune cells and pathways. Furthermore, genes linked to immune checkpoint inhibitors were expressed differently between the subgroups, which may reveal the mechanism of worse prognosis and the different sensitivities of chemotherapy.
conclusionThe prognosis of the high-risk group evaluated in the combined model was poorer, and cuproptosis was highly correlated with the prognosis of colon cancer. It is possible that we may be able to improve patients' prognosis by regulating the gene expression to intervene the risk score.
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