Emerging glyco-risk prediction model to forecast response to immune checkpoint inhibitors in colorectal cancer
Qiu P., Chen X., Xiao C., Zhang M., Wang H., Wang C.
Cohort Study, published in J Cancer Res Clin Oncol (2023) — summary generated from the PubMed abstract.
Early human evidence such as case series or small samples is exploring possible benefits.
- Level A · Stronger Clinical Evidence
- Level B · Emerging clinical evidence with positive signals
- Level C · Early human research exploring benefits
- Level D · Scientific groundwork from lab and animal studies
- Emerging · Emerging topic under active research
This page is generated from the PubMed record. The Thai description is an automated summary of bibliographic fields and the abstract, not a full translation, and is not medical advice.
- Study type
- Cohort Study
- Journal
- J Cancer Res Clin Oncol (2023)
- Reported sample size
- —
- Source database
- Europe PMC
- PMID
- 36757621
- PMCID
- PMC11796738
- DOI
- 10.1007/s00432-023-04626-0
- Citations
- 7
Abstract (original English)
Background Aberrant glycosylation is one of the most common post-translational modifications leading to heterogeneity in colorectal cancer (CRC). This study aims to construct a risk prediction model based on glycosyltransferase to forecast the response to immune checkpoint inhibitors in CRC patients. Methods Based on the TCGA dataset and glycosyltransferase genes, the NMF algorithm and WGCNA were used to identify molecular subtypes and co-expressed genes, respectively. Lasso and multivariate COX regression were used to identify prognostic glycosyltransferase genes and construct a glyco-risk prediction model in CRC patients. Univariate and multivariate Cox regression, Kaplan-Meier, and ROC curves were applied to further verify the prognostic performance of the model in CRC patients in the training and validation sets. We compared the responsiveness of immunotherapy and chemotherapy between the two groups. In vitro experiments and clinical specimens verified the specific function of the key glycosyltransferase genes in CRC. Results The CRC cohort was divided into two subtypes with prominent differences in survival based on the well-robust seven-gene glyco-risk prediction model (composed of ALG1L2, HAS1, PYGL, COLGALT2, B3GNT4, POFUT2, and GALNT7). The nomograms based on the risk model could predict the prognosis of CRC patients independently of other clinicopathologic characteris
What this study does not prove
- • This study does not prove SVF is an approved treatment or a replacement for standard care.
Evidence level
Early human evidence such as case series or small samples is exploring possible benefits.
How we grade evidenceBrowse all related research
Filter the research library by this study's title keywords, author, or publication year.