A comparative study of improvements Pre-filter methods bring on feature selection using microarray data

Identification of key pathways regulated by a set of competitive long non-coding RNAs in oral squamous cell carcinoma

Objective

The aim of this study was to identify important pathways regulated by a set of long non-coding RNAs (lncRNAs) in oral squamous cell carcinoma (OSCC).

Methods

A lncRNA-mediated competitive endogenous RNA network (LMCN) was constructed using information on microRNA (miRNA)–mRNA interactions and lncRNA–miRNA intersections from the E-GEOD-37991 transcription profiling data in the ArrayExpress database. A random walk with restart ranking algorithm was then applied to evaluate the influences of protein-coding genes regulated by competitive lncRNAs. Pathway enrichment scores were calculated based on the propagation scores of protein-coding genes. Finally, permutation tests were used to estimate the significance of the pathways.

Results

We obtained lncRNA–mRNA interactions based on miRNAs common to both miRNA–mRNA interactions and lncRNA–miRNA intersections, and used interactions with a z-score > 0.7 to construct a LMCN. Ten lncRNAs were identified as source nodes in the LMCN, and nine pathways with enrichment scores >0.8, including ‘Cell cycle’, ‘Endocytosis’, and ‘Pathways in cancer’, were significantly enriched by these source nodes.

Conclusions

Nine significant pathways regulated by a set of competitive lncRNAs were identified in OSCC, which may play important roles in the development of OSCC via the cell cycle and endocytosis.

Personalized analysis of pathway aberrance induced by sevoflurane and propofol

Anesthetic agents are used in surgical operations to reversibly reduce consciousness and pain. Sevoflurane is an inhalational anesthetic. Propofol is a short-acting intravenous general anesthetic. The mechanism of anesthetic agents at pathway level on individual patients has not been reported to date. In the present study, pathway aberrance in the human atrial tissue in response to anesthetics was examined. Microarray data of anesthesia-treated samples were downloaded from the Array Express database. Pathway information was obtained from the Reactome Pathway Database. The individual pathway aberrance score (iPAS) was introduced to identify dysregulated pathways in individual patients. The present data demonstrated 157 dysregulated pathways in the sevoflurane group, and 44 pathways were identified with the least P-values. A subset of 49 differentially expressed genes (DEGs) that were shared between the expression profiling results and the dysregulated pathways results were constructed into a co-expression network. The top 5 ranked DEGs, nuclear receptor subfamily 4 group A member 3 (NR4A3), JUNB proto-oncogene, MYC proto-oncogene, tachykinin precursor 1 and nicotinamide phosphoribosyltransferase, were identified as important in the topology analysis. In the propofol group, 87 dysregulated pathways were identified and 44 pathways had the least P-values. In total 28 DEGs were constructed into a co-expression network, of which 5 DEGs were important in the topology analysis, NR4A3, suppressor of cytokine signaling 3, cyclin dependent kinase inhibitor 1A, C-C motif chemokine ligand 2 and C-X-C motif chemokine ligand 1. A total of 72 dysregulated pathways were identified in common in the two groups. In conclusion, the two types of anesthetics induced partially similar mechanisms. The pathways enriched by DEGs, particularly those that were unique to sevoflurane and propofol, may affect surgical outcomes and aid the prevention of complications from anesthetics.