Journal
MOLECULAR THERAPY-NUCLEIC ACIDS
Volume 31, Issue -, Pages 224-240Publisher
CELL PRESS
DOI: 10.1016/j.omtn.2022.12.014
Keywords
-
Categories
Ask authors/readers for more resources
A refined molecular classification scheme for gastric cancer (GC) was proposed, which utilized integrated optimal algorithms and multi-omics data to guide personalized therapies. Three subtypes linked to distinct clinical outcomes were identified based on selected important features from mRNA, microRNA, and DNA methylation data. These subtypes were validated in multiple independent cohorts and showed better clinical prediction performance compared to The Cancer Genome Atlas classification.
Gastric cancer (GC) is a heterogeneous disease and a leading cause of cancer-related deaths. Discovering robust, clinically relevant molecular classifications is critical for guiding personalized therapies for GC. Here, we propose a refined molecular classification scheme for GC using integrated optimal algorithms and multi-omics data. Based on the important features of mRNA, microRNA, and DNA methylation data selected by the multivariate Cox regression model, three subtypes linked to distinct clinical outcomes were identified by combining similarity network fusion and consensus clustering methods. Three subtypes were validated by an extreme gradient boosting machine learning prediction model with 125 differentially expressed genes in multiple independent cohorts. The molecular characteristics of mutation signatures, characteristic gene sets, driver genes, and chemotherapy sensitivity for each subtype were also identified: subtype 1 was associated with favorable prognosis and characterized by high ARID1A and PIK3CA mutations, subtype 2 was associated with a poor prognosis and harbored high recurrent TP53 mutations, and subtype 3 was associated with high CHD1, APOA1 mutations, and a poor prognosis. The proposed three-subtype scheme achieved a better clinical prediction performance (area under the curve value = 0.71) than The Cancer Genome Atlas classification, which may provide a practical subtyping framework to improve the treatment of GC.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available