ArticleTranslational cancer research2025
Molecular dynamics simulation and single-cell and spatial transcriptomics validate immune and prognostic biomarkers in colorectal cancer and construct a clinical prognostic model.
Article in Translational cancer research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Colorectal cancer (CRC) represents a huge global health challenge characterized by significant morbidity and mortality. The urgent need to identify biomarkers through integrative validation research to enhance diagnostic accuracy and prognostic stratification has prompted the exploration of immune and prognostic genes. This study aimed to systematically identify differentially expressed genes (DEGs) associated with both immunity and prognosis in CRC, validate their clinical significance, and construct a reliable prognostic model. Methods: This research sought to identify DEGs associated with immunity and prognosis in CRC. We examined clinical and RNA sequencing data from 698 CRC patients obtained from The Cancer Genome Atlas (TCGA). Utilizing the Xiantao Academic Platform, we conducted differential expression analysis and identified hub genes associated with immunity and prognosis through Least Absolute Shrinkage and Selection Operator (LASSO) and Cox regression analyses, alongside five machine learning algorithms to construct a prognostic model. The hub genes were validated using the Gene Expression Omnibus (GEO) database, molecular docking, molecular dynamics simulation, single-cell and spatial transcription analyses. Results: LASSO and Cox regression analyses, along with five machine learning algorithms, were employed to identify significant genes linked to immunity and prognosis, yielding three hub genes: Conclusions: This comprehensive study highlights the potential of
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