Purpose: To develop a gene expression-based classifier for neuroblastoma patients that reliably predicts courses of the disease.
Patients And Methods: Two hundred fifty-one neuroblastoma specimens were analyzed using a customized oligonucleotide microarray comprising 10,163 probes for transcripts with differential expression in clinical subgroups of the disease. Subsequently, the prediction analysis for microarrays (PAM) was applied to a first set of patients with maximally divergent clinical courses (n = 77). The classification accuracy was estimated by a complete 10-times-repeated 10-fold cross validation, and a 144-gene predictor was constructed from this set. This classifier's predictive power was evaluated in an independent second set (n = 174) by comparing results of the gene expression-based classification with those of risk stratification systems of current trials from Germany, Japan, and the United States.
Results: The first set of patients was accurately predicted by PAM (cross-validated accuracy, 99%). Within the second set, the PAM classifier significantly separated cohorts with distinct courses (3-year event-free survival [EFS] 0.86 +/- 0.03 [favorable; n = 115] v 0.52 +/- 0.07 [unfavorable; n = 59] and 3-year overall survival 0.99 +/- 0.01 v 0.84 +/- 0.05; both P < .0001) and separated risk groups of current neuroblastoma trials into subgroups with divergent outcome (NB2004: low-risk 3-year EFS 0.86 +/- 0.04 v 0.25 +/- 0.15, P < .0001; intermediate-risk 1.00 v 0.57 +/- 0.19, P = .018; high-risk 0.81 +/- 0.10 v 0.56 +/- 0.08, P = .06). In a multivariate Cox regression model, the PAM predictor classified patients of the second set more accurately than risk stratification of current trials from Germany, Japan, and the United States (P < .001; hazard ratio, 4.756 [95% CI, 2.544 to 8.893]).
Conclusion: Integration of gene expression-based class prediction of neuroblastoma patients may improve risk estimation of current neuroblastoma trials.
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http://dx.doi.org/10.1200/JCO.2006.06.1879 | DOI Listing |
Biochem Biophys Rep
March 2025
Department of Genetics and Molecular Biology, Faculty of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
Introduction: Gastric cancer (GC) is among the deadliest malignancies globally, characterized by hypoxia-driven pathways that promote cancer progression, including stemness mechanisms facilitating invasion and metastasis. This study aimed to develop a prognostic decision tree using genes implicated in hypoxia and stemness pathways to predict outcomes in GC patients.
Materials And Methods: GC RNA-seq data from The Cancer Genome Atlas (TCGA) were analyzed to compute hypoxia and stemness scores using Gene Set Variation Analysis (GSVA) and the mRNA expression-based stemness index (mRNAsi).
Nat Commun
January 2025
Department of Chemistry, Boston College, Chestnut Hill, MA, USA.
Recent advances in gene editing and precise regulation of gene expression based on CRISPR technologies have provided powerful tools for the understanding and manipulation of gene functions. Fusing RNA aptamers to the sgRNA of CRISPR can recruit cognate RNA-binding protein (RBP) effectors to target genomic sites, and the expression of sgRNA containing different RNA aptamers permit simultaneous multiplexed and multifunctional gene regulations. Here, we report an intracellular directed evolution platform for RNA aptamers against intracellularly expressed RBPs.
View Article and Find Full Text PDFInt J Mol Sci
December 2024
Bioinformatics and Molecular Design Research Center (BMDRC), Incheon 21983, Republic of Korea.
Understanding drug-target interactions is crucial for identifying novel lead compounds, enhancing efficacy, and reducing toxicity. Phenotype-based approaches, like analyzing drug-induced gene expression changes, have shown effectiveness in drug discovery and precision medicine. However, experimentally determining gene expression for all relevant chemicals is impractical, limiting large-scale gene expression-based screening.
View Article and Find Full Text PDFExp Hematol
January 2025
Department of Neuroscience, Karolinska Institutet, 171 77 Stockholm, Sweden. Electronic address:
T-cell acute lymphoblastic leukemia (T-ALL), which constitutes of 10-15% of all pediatric ALL cases, is known for its complex pathology due to pervasive genetic and chromosomal abnormalities. Although most children are successfully cured, chromosomal rearrangements involving the KMT2A gene is considered a poor prognostic factor. In a cohort of 171 pediatric T-ALL samples we have studied differences in gene and splice variant patterns in KMT2A rearranged (KMT2A-r) T-ALL compared to KMT2A negative (KMT2A-wt) T-ALL samples.
View Article and Find Full Text PDFAdv Sci (Weinh)
January 2025
State Key Laboratory of Animal Nutrition and Feeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China.
High soluble protein expression in heterologous hosts is crucial for various research and applications. Despite considerable research on the impact of codon usage on expression levels, the relationship between protein sequence and expression is often overlooked. In this study, a novel connection between protein expression and sequence is uncovered, leading to the development of SRAB (Strength of Relative Amino Acid Bias) based on AEI (Amino Acid Expression Index).
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