Gene expression–based classification and regulatory networks of pediatric acute lymphoblastic leukemia

Abstract

Pediatric acute lymphoblastic leukemia (ALL) contains cytogenetically distinct subtypes that respond differently to cytotoxic drugs. Subtype classification can be also achieved through gene expression profiling. However, how to apply such classifiers to a single patient and correctly diagnose the disease subtype in an independent patient group has not been addressed. Furthermore, the underlying regulatory mechanisms responsible for the subtype-specific gene expression patterns are still largely unknown. Here, by combining 3 published microarray datasets on 535 mostly white children's samples and generating a new dataset on 100 Chinese children's ALL samples, we were able to (1) identify a 62-gene classifier with 97.6% accuracy from the white children's samples and validated it on the completely independent set of 100 Chinese samples, and (2) uncover potential regulatory networks of ALL subtypes. The classifier we identified was, thus far, the only one that could be applied directly to a single sample and that sustained validation in a large independent patient group. Our results also suggest that the etiology of ALL is largely the same among different ethnic groups, and that the transcription factor hubs in the predicted regulatory network might play important roles in regulating gene expression and development of ALL. (Blood. 2009;114: 4486-4493)

Noncoding RNA–related polymorphisms in pediatric acute lymphoblastic leukemia susceptibility

“…Articles transcription factor in ALL) (28). Therefore, alterations in the sequence of mir-499-5p and mir-499-3p, which affect expression of these miRNAs and/or the binding of mir-499-3p to target mRNAs, may have functional consequences for ALL.…”

ArabidopsisFloral Initiator SKB1 Confers High Salt Tolerance by Regulating Transcription and Pre-mRNA Splicing through Altering Histone H4R3 and Small Nuclear Ribonucleoprotein LSM4 Methylation

“…Array scanning and data analysis were performed as previously described (Zheng and Wang, 2008;Li et al, 2009). Two independent biological replicates were performed, and only genes whose expression alteration was consistent in two microarray assays were selected as differentially expressed genes.…”

Genome-wide analysis of microRNA and mRNA expression signatures in cancer

“…Genomewide studies have identified a large number of cancer-specific alternative splicing events or fusion proteins, which are ideal diagnostic markers and therapeutic targets [129][130][131] . In 2000, Perou and colleagues reported a new method to classify breast cancer by gene expression profiling [132] and started to use gene expression microarrays for the diagnosis of breast and other cancers [133,134] . Colorectal cancer (CRC) subtyping has also been addressed using genome-wide gene expression profiling in large-scale patient samples [135] (Table 3).…”

“…ALL is a heterogeneous disease with more than 12 subtypes. A 62-gene classifier was created in white children's samples for subtype classification and validated on a completely independent set of 100 Chinese samples [134] . Breast cancer can also be classified into different subtypes using miRNA and mRNA expression data at The Cancer Genome Atlas (TCGA) database [136] .…”