Establishment of a chemokine-based prognostic model and identification of CXCL10+ M1 macrophages as predictors of neoadjuvant therapy efficacy in colorectal cancer
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View abstract on PubMed
Summary
This summary is machine-generated.A new prognostic model using 15 chemokines helps predict colorectal cancer patient outcomes and chemotherapy resistance. Identifying CXCL10+ M1 macrophages may indicate who benefits most from neoadjuvant therapy.
Area Of Science
- Oncology
- Immunology
- Genomics
Background
- Neoadjuvant therapy offers benefits but not all colorectal cancer patients respond effectively.
- Chemokines significantly influence the tumor microenvironment and patient prognosis.
- A chemokine-based prognostic model is needed for risk stratification and personalized treatment in colorectal cancer.
Purpose Of The Study
- To develop and validate a chemokine-based prognostic model for colorectal cancer.
- To explore the relationship between the chemokine signature and the tumor immune landscape, genetic alterations, and drug sensitivity.
- To identify potential biomarkers for predicting response to neoadjuvant therapy.
Main Methods
- Utilized LASSO-Cox regression with data from TCGA and GEO databases to build a chemokine signature.
- Integrated single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing data.
- Analyzed immune cell infiltration, somatic mutations, copy number variations, intercellular communication, and stemness.
- Validated findings using multiplex immunofluorescence.
Main Results
- A 15-chemokine prognostic model was successfully constructed and validated.
- High-risk scores correlated with poorer prognosis, advanced TNM/clinical stages, and increased chemotherapy resistance.
- scRNA-seq identified CXCL10+ M1 macrophages as a potential indicator for neoadjuvant therapy response.
Conclusions
- A novel chemokine-based prognostic model integrating multi-omics data was developed for colorectal cancer.
- Epithelial cell heterogeneity impacts neoadjuvant therapy outcomes.
- CXCL10+ M1 macrophages show promise as predictors of neoadjuvant therapy response, aiding personalized treatment strategies.
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