Integrated analysis of single-cell and bulk RNA-sequencing to predict prognosis and therapeutic response for colorectal cancer
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Le résumé fourni par la source
Colorectal cancer (CRC) is a prevalent malignant tumor characterized by high global incidence and mortality rates. Furthermore, it is imperative to comprehend the molecular mechanisms underlying its development and to identify effective prognostic markers. These efforts are crucial for pinpointing potential therapeutic targets and enhancing patient survival rates. Therefore, we develop a novel prognostic model aimed at providing new theoretical support for clinical prognosis evaluation and treatment. We downloaded data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Subsequently, we performed single-cell analysis and developed a prognostic model associated with colorectal cancer. We divided the scRNA-seq dataset (GSE221575) into 19 cell clusters and classified these clusters into 11 distinct cell types using marker genes. Using univariate Cox regression and LASSO (Least Absolute Shrinkage and Selection Operator) analyses, we developed a prognostic model consisting of 9 genes. Based on our 9-gene model, we divided patients into high-risk and low-risk groups using the median risk score. The high-risk group demonstrated significant positive correlations with M0 macrophages, CD8+ T cells, and M2 macrophages. The enrichment analyses indicate significant enrichment of immune-related pathways in the high-risk group, including HEDGEHOG_SIGNALING, Wnt signaling pathway, and cell adhesion molecules. Drug sensitivity analysis revealed that the low-risk group was sensitive to 5 chemotherapeutic drugs, while the high-risk group was sensitive to only 1. Additionally, we developed a highly reliable nomogram for clinical application. This suggests that the risk score derived from our modeling analysis is highly effective for stratifying colorectal cancer samples. This study comprehensively applied bioinformatics methods to construct a risk score model. The model showed good predictive performance, offering potential guidance for individualized treatment of colorectal cancer patients. Furthermore, it may provide valuable insights into the disease's pathogenesis and identify potential therapeutic targets for further research.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Integrated analysis of single-cell and bulk RNA-sequencing to predict prognosis and therapeutic response for colorectal cancer
- Date Crossref
- 07/03/2025
- Éditeur
- Springer Science and Business Media LLC
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Guangdong Academy of Medical Sciences pays non établi dans la noticeÉtablissement de santé
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Guangdong Provincial People's Hospital pays non établi dans la noticeÉtablissement de santé
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Southern Medical University Department of Gastrointestinal Surgery pays non établi dans la noticeUniversité ou école supérieure
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Guangdong Medical College pays non établi dans la noticeUniversité ou école supérieure
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Guangdong University of Technology pays non établi dans la noticeUniversité ou école supérieure
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South China University of Technology pays non établi dans la noticeUniversité ou école supérieure
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The First Clinical Medical College pays non établi dans la noticeUniversité ou école supérieure
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School of Medicine pays non établi dans la noticeUniversité ou école supérieure
Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital et Department of Gastrointestinal Surgery — Southern Medical University, avec 5 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.