Classification of colon cancer patients into consensus molecular subtypes using support vector machines.
Turk J Biol
; 47(6): 406-412, 2023.
Article
in En
| MEDLINE
| ID: mdl-38681775
ABSTRACT
Background/aim:
The molecular heterogeneity of colon cancer has made classification of tumors a requirement for effective treatment. One of the approaches for molecular subtyping of colon cancer patients is the consensus molecular subtypes (CMS), developed by the Colorectal Cancer Subtyping Consortium. CMS-specific RNA-Seq-dependent classification approaches are recent, with relatively low sensitivity and specificity. In this study, we aimed to classify patients into CMS groups using their RNA-seq profiles. Materials andmethods:
We first identified subtype-specific and survival-associated genes using the Fuzzy C-Means algorithm and log-rank test. We then classified patients using support vector machines with backward elimination methodology.Results:
We optimized RNA-seq-based classification using 25 genes with a minimum classification error rate. In this study, we reported the classification performance using precision, sensitivity, specificity, false discovery rate, and balanced accuracy metrics.Conclusion:
We present a gene list for colon cancer classification with minimum classification error rates and observed the lowest sensitivity but the highest specificity with CMS3-associated genes, which significantly differed due to the low number of patients in the clinic for this group.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Language:
En
Journal:
Turk J Biol
Year:
2023
Document type:
Article