ArticleThe journal of pathology. Clinical research2022
Identification of a prognostic signature in colorectal cancer using combinatorial algorithm-driven analysis.
Article in The journal of pathology. Clinical research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- High KPNA2 expression predicts poor prognosis in pathological T4 colorectal cancer by importing c-Myc into the nucleus to suppress p53-dependent p21 expression.British journal of cancer · 2026Article
- UCP1-Dependent Thermogenic Adipose Tissue in Human Disease: Adipose-Centered Mechanisms, Biomarker Limitations, and Translational Perspectives.International journal of molecular sciences · 2026Review
- Integrative single-cell RNA-seq and transcriptomic analysis uncovers cellular heterogeneity and immunotherapy resistance mechanisms in colorectal cancer.Discover oncology · 2026Article
- The evolving view of thermogenic fat and its implications in cancer and metabolic diseases.Signal transduction and targeted therapy · 2022Review
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Colorectal carcinoma is one of the most common types of malignancy and a leading cause of cancer-related death. Although clinicopathological parameters provide invaluable prognostic information, the accuracy of prognosis can be improved by using molecular biomarker signatures. Using a large dataset of immunohistochemistry-based biomarkers (n = 66), this study has developed an effective methodology for identifying optimal biomarker combinations as a prognostic tool. Biomarkers were screened and assigned to related subsets before being analysed using an iterative algorithm customised for evaluating combinatorial interactions between biomarkers based on their combined statistical power. A signature consisting of six biomarkers was identified as the best combination in terms of prognostic power. The combination of biomarkers (STAT1, UCP1, p-cofilin, LIMK2, FOXP3, and ICOS) was significantly associated with overall survival when computed as a linear variable (χ
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