Logists.2.5 Data AnalysisNormalized data from each array were analyzed using two-class differentiation, which is applicable to analyses of small samples. We applied the random variance 16985061 were then further processed using H E staining. A Leica AS LMD microdissection system (Leica) was used to capture samples from the chordomas and notochord tissues. Target tissue samples identified by H E staining were outlined by free-hand tracing, cut from the slide by the laser and collected into the cap of a 0.2 ml PCR tube immediately. All the samples were stored at 280uC until they were processed for RNA extraction in 1 batch.2.7 Bioinformatic AnalysisWe applied the Gene Ontology (GO) classification of genes to determine the functions of the intersecting genes and uncover the miRNA-gene regulatory network on the basis of biological process and molecular function. In detail, the two-sided Fisher’s exact test and x2 test were used to classify the GO category, and the FDR was calculated to correct the P value. We chose only GOs that had P,0.01. To identify the pathways of intersecting genes, Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www.genome.ad. jp/kegg/) enrichment analysis was performed. This analysis provides a better understanding of gene expression information as a complete network. The Fisher’s exact test, x2 test, and the threshold of significance were defined by the P value and FDR. The screening criterion was P,0.05.2.4 RNA Extraction and Microarray ExperimentsTotal RNA was isolated from PFPE samples by using RecoverAllTM Total Nucleic Acid Isolation Kit (Life Technologies, Carlsbad, CA, USA) for mRNA and miRNA microarray analysis, according to the manufacturer’s instructions. Total RNA quality and integrity were confirmed by denaturing gel electrophoresis. Total RNA was purified using RNeasy Mini Kit (Qiagen, Hilden, Germany) and amplified using a sensation kit (Genisphere, Hatfield, PA, USA). We ensured that the purification method retained low-molecular-weight (LMW) RNA. MiRNA expression profiling was performed using Affymetrix Gene Chip miRNA 2.0 arrays (Santa Clara, CA, USA) containing 1,105 human mature miRNAs in miRBase 15 (http://microrna.sanger.ac.uk). Messenger RNA expression profiling was performed using Affymetrix GeneChip Human Gene 1.0 ST Array (Santa Clara), which contains 764,885.Logists.2.5 Data AnalysisNormalized data from each array were analyzed using two-class differentiation, which is applicable to analyses of small samples. We applied the random variance 10781694 model (RVM) t-test to filter differentially expressed miRNAs and mRNAs for the 2 groups [15]. Fold change and the false discovery rate (FDR)-adjusted P values (P,0.05) were used to screen miRNAs and mRNAs with significantly different expression. Hierarchical clustering of miRNAs and mRNAs with significantly different expression was performed using the Cluster 3.0 software and visualized with Treeview v1.60.2.6 Integrated Analysis of miRNA TargetsThe differentially expressed miRNAs were then selected for target prediction by using TargetScan database version 6.0 (http://www.targetscan.org/). To improve the accuracy of target prediction, we further combined the analysis of differentially expressed mRNA with target prediction of the differentially expressed miRNAs. The intersecting gene set was subject to bioinformatic analysis.2.3 Laser Capture MicrodissectionAll tissues were separated using laser capture microdissection (LCM). Briefly, a series of 10-mm-thick sections was cut from paraffin-embedded tissues and the sections were affixed to crosslinked polyethylene foils that were attached to glass slides (Leica, Wetzlar, Germany). The slides 16985061 were then further processed using H E staining. A Leica AS LMD microdissection system (Leica) was used to capture samples from the chordomas and notochord tissues. Target tissue samples identified by H E staining were outlined by free-hand tracing, cut from the slide by the laser and collected into the cap of a 0.2 ml PCR tube immediately. All the samples were stored at 280uC until they were processed for RNA extraction in 1 batch.2.7 Bioinformatic AnalysisWe applied the Gene Ontology (GO) classification of genes to determine the functions of the intersecting genes and uncover the miRNA-gene regulatory network on the basis of biological process and molecular function. In detail, the two-sided Fisher’s exact test and x2 test were used to classify the GO category, and the FDR was calculated to correct the P value. We chose only GOs that had P,0.01. To identify the pathways of intersecting genes, Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www.genome.ad. jp/kegg/) enrichment analysis was performed. This analysis provides a better understanding of gene expression information as a complete network. The Fisher’s exact test, x2 test, and the threshold of significance were defined by the P value and FDR. The screening criterion was P,0.05.2.4 RNA Extraction and Microarray ExperimentsTotal RNA was isolated from PFPE samples by using RecoverAllTM Total Nucleic Acid Isolation Kit (Life Technologies, Carlsbad, CA, USA) for mRNA and miRNA microarray analysis, according to the manufacturer’s instructions. Total RNA quality and integrity were confirmed by denaturing gel electrophoresis. Total RNA was purified using RNeasy Mini Kit (Qiagen, Hilden, Germany) and amplified using a sensation kit (Genisphere, Hatfield, PA, USA). We ensured that the purification method retained low-molecular-weight (LMW) RNA. MiRNA expression profiling was performed using Affymetrix Gene Chip miRNA 2.0 arrays (Santa Clara, CA, USA) containing 1,105 human mature miRNAs in miRBase 15 (http://microrna.sanger.ac.uk). Messenger RNA expression profiling was performed using Affymetrix GeneChip Human Gene 1.0 ST Array (Santa Clara), which contains 764,885.
