Prognosis-determinant candidate genes identified by whole genome scanning in eyes with pterygia.
OBJECTIVE
To identify the genes that can differentiate primary from recurrent pterygia.


METHODS
The transcriptional differences of primary and recurrent pterygia were first determined by microarray analyses. Computational analyses were used to extract the biological significance of the genes accurately, and a significant functional classification of the genes was made by unsupervised methodologies. After confirming the functional classification for primary and recurrent pterygia by a clustering algorithm, a support vector machine (SVM) algorithm was applied. Based on a machine learning technique, the minimum number of genes that can accurately classify primary and recurrent pterygia was determined.


RESULTS
Clustering analyses classified primary and recurrent pterygia transcriptomes and identified 10 clusters associated with distinct biological processes. When the SVM algorithm was applied to the microarray-analyzed products from three primary and three recurrent cases, periostin, TIMP-2, and L-3-phosphoserine phosphatase homolog (PSPHL) were identified as the minimum set of predictors with 100% accuracy. A differential expression of these genes in primary and recurrent pterygia was confirmed by immunohistochemistry. When the 24 patients with primary disease and the 8 patients with recurrent disease were analyzed with this gene set, an accuracy of classification of 84.38% was achieved.


CONCLUSIONS
Periostin, TIMP-2, and PSPHL can be used as predictor genes for the recurrence of pterygia. Their biological activities may explain the events leading to recurrences of pterygia and thus may be genes to target for pharmaceutical interventions.
Kuo
Chuan-Hui
CH
Division of Ophthalmology and Visual Science, Tottori University Medical School, Yonago, Japan.
Miyazaki
Dai
D
Nawata
Nobuhiko
N
Tominaga
Takeshi
T
Yamasaki
Atsushi
A
Sasaki
Yuji
Y
Inoue
Yoshitsugu
Y
D000368:Aged; D000465:Algorithms; D015815:Cell Adhesion Molecules / Q000235:genetics*; D016000:Cluster Analysis; D005260:Female; D005819:Genetic Markers; D015894:Genome, Human; D023281:Genomics; D006801:Humans; D008297:Male; D008875:Middle Aged; D020411:Oligonucleotide Array Sequence Analysis; D010744:Phosphoric Monoester Hydrolases / Q000235:genetics*; D011237:Predictive Value of Tests; D011379:Prognosis; D011625:Pterygium / Q000175:diagnosis / Q000235:genetics*; D012008:Recurrence; D019716:Tissue Inhibitor of Metalloproteinase-2 / Q000235:genetics*
D015815:Cell Adhesion Molecules; D005819:Genetic Markers; C512835:POSTN protein, human; D019716:Tissue Inhibitor of Metalloproteinase-2; D010744:Phosphoric Monoester Hydrolases; C022394:phosphoserine phosphatase