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Reliable molecular signatures are needed to improve the early and accurate diagnosis of autism spectrum disorder (ASD), and indicate physicians to provide timely intervention. This study aimed to identify a robust blood small nuclear RNA (snRNA) signature in diagnosing ASD. 186 blood samples in the microarray dataset were randomly divided into the training set (n = 112) and validation set (n = 72). Then, the microarray probe expression profiles were re-annotated into the expression profiles of 1253 snRNAs though probe sequence mapping. In the training set, least absolute shrinkage and selection operator (LASSO) penalized generalized linear model was adopted to identify the 9-snRNA signature (RNU1-16P, RNU6-1031P, RNU6-258P, RNU6-335P, RNU6-485P, RNU6-549P, RNU6-98P, RNU6ATAC26P, and RNVU1-15), and a diagnostic score was calculated for each sample according to the snRNA expression levels and the model coefficients. The score demonstrated a good diagnostic ability for ASD in the training set (area under receiver operating characteristic curve (AUC) = 0.90), validation set (AUC = 0.87), and the overall (AUC = 0.88). Moreover, the blood samples of 23 ASD patients and 23 age- and gender-matched controls were collected as the external validation set, in which the signature also showed a good diagnostic ability for ASD (AUC = 0.88). In subgroup analysis, the signature was robust when considering the confounders of gender, age, and disease subtypes, and displayed a significantly better performance among the female and younger cases (P = .039; P = .002). In comparison with a 55-gene signature deriving from the same dataset, the snRNA signature showed a better diagnostic ability (
0.88 vs 0.80, P = .049). In conclusion, this study identified a novel and robust blood snRNA signature in diagnosing ASD, which might help improve the diagnostic accuracy for ASD in clinical practice. Nevertheless, a large-scale prospective study was needed to validate our results.
This article was published in the following journal.
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Short chains of RNA (100-300 nucleotides long) that are abundant in the nucleus and usually complexed with proteins in snRNPs (RIBONUCLEOPROTEINS, SMALL NUCLEAR). Many function in the processing of messenger RNA precursors. Others, the snoRNAs (RNA, SMALL NUCLEOLAR), are involved with the processing of ribosomal RNA precursors.
A nuclear RNA-protein complex that plays a role in RNA processing. In the nucleoplasm, the U2 snRNP along with other small nuclear ribonucleoproteins (U1, U4-U6, and U5) assemble into SPLICEOSOMES that remove introns from pre-mRNA by splicing. The U2 snRNA forms base pairs with conserved sequence motifs at the branch point, which associates with a heat- and RNAase-sensitive factor in an early step of splicing.
A nuclear RNA-protein complex that plays a role in RNA processing. In the nucleoplasm, the U1 snRNP along with other small nuclear ribonucleoproteins (U2, U4-U6, and U5) assemble into SPLICEOSOMES that remove introns from pre-mRNA by splicing. The U1 snRNA forms base pairs with conserved sequence motifs at the 5'-splice site and recognizes both the 5'- and 3'-splice sites and may have a fundamental role in aligning the two sites for the splicing reaction.
Small, punctate nuclear structures found in close proximity to COILED BODIES. They are enriched with SMN COMPLEX PROTEINS and may play a role in the processing of SMALL NUCLEAR RIBONUCLEOPROTEINS.
Highly conserved nuclear RNA-protein complexes that function in RNA processing in the nucleus, including pre-mRNA splicing and pre-mRNA 3'-end processing in the nucleoplasm, and pre-rRNA processing in the nucleolus (see RIBONUCLEOPROTEINS, SMALL NUCLEOLAR).
A DNA microarray or biochip is a collection of microscopic DNA spots attached to a solid surface used to measure the expression levels of large numbers of genes simultaneously or to genotype multiple regions of a genome.
Bioinformatics is the application of computer software and hardware to the management of biological data to create useful information. Computers are used to gather, store, analyze and integrate biological and genetic information which can then be applied...