ArticleHuman mutation2026
Multiomics Identification of Radioresistance-Associated Biomarkers and Prognostic Model Construction in Rectal Cancer.
Article in Human mutation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Review
- Multiomics Identification of Radioresistance-Associated Biomarkers and Prognostic Model Construction in Rectal Cancer.Human mutation · 2026Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Radiotherapy remains a cornerstone in the local management of rectal cancer (RC); however, resistance to radiation significantly compromises therapeutic efficacy and adversely affects patient prognosis. Identification of biomarkers associated with radioresistance and the development of a prognostic model based on radiotherapy-related genes in RC remain critical for enhancing treatment outcomes. Methods: Data related to RC were obtained from public repositories, including bulk RNA-seq data from 993 patients across four Gene Expression Omnibus (GEO) and one The Cancer Genome Atlas (TCGA) cohort, as well as single-cell RNA-seq data from six samples. A prognostic model was developed using differential expression analysis, functional enrichment analysis, and least absolute shrinkage and selection operator regression analysis. Associations between risk score and prognosis were assessed through gene set variation analysis, gene set enrichment analysis, and construction of a nomogram to identify potential therapeutic targets for RC. Results: Prognosis-related genes were determined through analysis of clinical data from patients with RC in the GEO and TCGA datasets, leading to the development of a risk score model. The risk score demonstrated significant associations with immune cell infiltration, chemotherapy drug sensitivity, and multiple signaling pathways. Protein expression levels of the key genes in patients with RC were verified using the Human Protein Atlas database. Furthermore, immunohistochemical evaluation in animal models provided additional validation. Conclusion: Molecular characteristics and mechanisms underlying radiotherapy response in RC were clarified through multiomics analysis. Five key genes were identified as potentially related to radiotherapy sensitivity in RC. These prognostic genes may serve as novel biomarkers and potential targets for diagnosis, prognostic evaluation, and clinical management of RC.
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