RNA-seq and Microarray in Transcriptome Profiling of Anterior Cruciate Ligament Tears: Implications for Prognostic Biomarkers Discovery
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ABSTRACT: Injury to anterior cruciate ligament (ACL) is common in young individuals and a frequent cause of functional instability and early onset of osteoarthritis. The healing potential of an injured ACL is known to decay over time. The molecular origin of this healing deficiency largely remains elusive but plausibly involves gene transcripts associated with tissue healing. To explore this possibility, we set out to identify transcript expression differences in injured ACL remnants recovered at the time of surgical reconstruction, via microarray (n=24) and RNA-seq (n=8) technologies in transcriptome profiling. We found that time-from-injury was an important determinant of changes in gene expression signatures predominately resulting in repression of several biological processes as identified by gene ontology. The most interesting observation was a time-dependent decline in the gene transcripts as well as the biological processes common to both microarray and RNA-seq analyses. Compared to acute tears, in chronic several important biological processes were namely extracellular matrix organization, angiogenesis, cell adhesion, wound healing, mesenchyme transition, and response to hypoxia. Furthermore, the cross-platform concordance in terms of differentially expressed transcripts or enriched pathways was linearly correlated (r=0.64). Microfluidic digital PCR confirmed the expression of selected differentially expressed transcripts. These intriguing findings suggest an initial attempt of the injured ACL to repair, which drops with time. These findings have implications for efforts to repair the ACL and may be relevant for its reconstruction. These findings also emphasize the utility of differentially expressed transcripts as prognostic biomarkers in patients with ACL injury.
ORGANISM(S): Homo sapiens
PROVIDER: GSE65469 | GEO | 2015/01/30
SECONDARY ACCESSION(S): PRJNA274125
REPOSITORIES: GEO
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