Bulk and single-cell RNA sequencing analyses coupled with multiple machine learning to develop a glycosyltransferase associated signature in colorectal cancer
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Summary
This summary is machine-generated.This study identifies key glycosyltransferases (GTs) in colorectal cancer (CRC) and develops a Glycosyltransferase-Associated Risk Signature (GARS) for improved prognosis and immunotherapy response prediction.
Area Of Science
- Oncology
- Genomics
- Bioinformatics
Background
- Colorectal cancer (CRC) remains a significant health challenge.
- Identifying novel prognostic markers is crucial for effective CRC management.
Purpose Of The Study
- To identify key glycosyltransferases (GTs) in CRC.
- To establish a robust prognostic signature derived from GTs.
- To predict immunotherapy response in CRC patients.
Main Methods
- Single-cell RNA sequencing analysis using AUCell, UCell, singscore, ssgsea, and AddModuleScore algorithms.
- Univariate Cox and lasso regression for GT selection.
- Machine learning for prognostic model development and validation across external cohorts.
- Analysis of tumor microenvironment, mutational profiles, and pathway variations.
Main Results
- CRC patients were classified into distinct subgroups with varying prognoses and immune profiles.
- A Glycosyltransferase-Associated Risk Signature (GARS) was developed, outperforming traditional prognostic factors.
- GARS correlates with malignancy and predicts immunotherapy efficacy.
Conclusions
- A novel prognostic model based on GTs (GARS) was developed for CRC.
- This model aids in forecasting immunotherapy response.
- It offers a new strategy for CRC patient management.
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