ArticleTechnology in cancer research & treatment
Article in Technology in cancer research & treatment. 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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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.
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Authors and funding
4 authors.
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Abstract
IntroductionColorectal cancer (CRC) remains a leading cause of cancer-related mortality globally, with drug resistance and poor prognosis significantly limiting treatment efficacy. To address this unmet clinical need, this study aimed to screen potential biomarkers for CRC drug resistance and prognosis through integrated bioinformatics analysis and clinical sample validation.MethodsWe analyzed Gene Expression Omnibus (GEO) database GSE153412 to screen differentially expressed genes (DEGs) between 5-fluorouracil (5-FU)-resistant and sensitive CRC cells (|log2FC| > 1.0, adj P < 0.05). Gene set enrichment analysis (GSEA) was used for pathway enrichment, Weighted gene co-expression network analysis (WGCNA) to identify resistance-related modules (correlation > 0.7, P < 0.01), and Protein-protein interaction (PPI) networks to screen hub genes. Their prognostic value was evaluated in TCGA-COAD, along with IC50 correlation. Finally, qPCR verified biomarker expression in clinical CRC samples.ResultsThere were altogether 1033 DEGs screened. Through GSEA, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Gene Ontology (GO) terms enriched by the DEGs were obtained. By PPI network construction, hub genes were screened. In TCGA-COAD datasets,
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