Isocitrate dehydrogenase (IDH) mutant glioblastoma (GBM), accounts for ~10% GBMs, arises from lower grade diffuse glioma and preferentially appears in younger patients. Here, we aim to establish a robust gene expression-based molecular classification of IDH-mutant GBM. A total of 33 samples from the Chinese Glioma Genome Atlas RNA-sequencing data were selected as training set, and 21 cases from Chinese Glioma Genome Atlas microarray data were used as validation set. Consensus clustering identified three groups with distinguished prognostic and molecular features. G1 group, with a poorer clinical outcome, mainly contained TERT promoter wild-type and male cases. G2 and G3 groups had better prognosis differed in gender. Gene ontology analysis showed that genes enriched in G1 group were involved in DNA replication, cell division and cycle. On the basis of the differential genes between G1 and G2/G3 groups, a six-gene signature was developed with a Cox proportional hazards model. Kaplan-Meier analysis found that the acquired signature could differentiate the outcome of low- and high-risk cases. Moreover, the signature could also serve as an independent prognostic factor for IDH-mutant GBM in the multivariate Cox regression analysis. Gene ontology and gene set enrichment analyses revealed that gene sets correlated with high-risk group were involved in cell cycle, cell proliferation, DNA replication and repair. These finding highlights heterogeneity within IDH-mutant GBMs and will advance our molecular understanding of this lethal cancer.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6642368PMC
http://dx.doi.org/10.1093/carcin/bgz032DOI Listing

Publication Analysis

Top Keywords

molecular classification
8
classification idh-mutant
8
idh-mutant gbm
8
chinese glioma
8
glioma genome
8
genome atlas
8
gene ontology
8
group involved
8
dna replication
8
gene
6

Similar Publications

Computational Methods for Lineage Reconstruction.

Methods Mol Biol

January 2025

Centro Nacional de Análisis Genómico, Barcelona, Spain.

The recent development of genetic lineage recorders, designed to register the genealogical history of cells using induced somatic mutations, has opened the possibility of reconstructing complete animal cell lineages. To reconstruct a cell lineage tree from a molecular recorder, it is crucial to use an appropriate reconstruction algorithm. Current approaches include algorithms specifically designed for cell lineage reconstruction and the repurposing of phylogenetic algorithms.

View Article and Find Full Text PDF

Measurements of cell phylogeny based on natural or induced mutations, known as lineage barcodes, in conjunction with molecular phenotype have become increasingly feasible for a large number of single cells. In this chapter, we delve into Quantitative Fate Mapping (QFM) and its computational pipeline, which enables the interrogation of the dynamics of progenitor cells and their fate restriction during development. The methods described here include inferring cell phylogeny with the Phylotime model, and reconstructing progenitor state hierarchy, commitment time, population size, and commitment bias with the ICE-FASE algorithm.

View Article and Find Full Text PDF

Purpose: 10-15% of prostate cancers (PCa) harbor recurrent FOXA1 aberrations whereby the alteration type and the effect on the forkhead( FKH) domain impacts protein-function. We developed a FOXA1 classification system to inform clinical management.

Experimental Design: 5,014 PCa were examined using whole exome and transcriptome sequencing from the Caris database.

View Article and Find Full Text PDF

Objectives: We aimed to classify genetic variants in RYR1 and CACNA1S associated with malignant hyperthermia using biobank genotyping data in patients exposed to triggering anesthetics without malignant hyperthermia phenotype.

Methods: We identified individuals who underwent surgery and were exposed to triggering anesthetics without malignant hyperthermia phenotype and who had RYR1 or CACNA1S genotyping data available in our biobank. We classified all variants in the cohort using a Bayesian framework of the American College of Medical Genetics and Genomics and the Association of Molecular Pathologists guidelines for variant classification and updated the posterior probabilities from this model with the new information from our biobank cohort.

View Article and Find Full Text PDF

TarIKGC: A Target Identification Tool Using Semantics-Enhanced Knowledge Graph Completion with Application to CDK2 Inhibitor Discovery.

J Med Chem

January 2025

State Key Laboratory of Anti-Infective Drug Discovery and Development, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China.

Target identification is a critical stage in the drug discovery pipeline. Various computational methodologies have been dedicated to enhancing the classification performance of compound-target interactions, yet significant room remains for improving the recommendation performance. To address this challenge, we developed TarIKGC, a tool for target prioritization that leverages semantics enhanced knowledge graph (KG) completion.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!