Affiliations: Department of Head and Neck Surgery, Taizhou People's Hospital, Tai Zhou, Jiangsu 225300, China
Correspondence:
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Corresponding author: Jiusheng Chu, Department of Head and Neck Surgery, Taizhou People's Hospital, No. 210 Yinchun Road, Tai Zhou, Jiangsu 225300, China. Tel.: +86 0523 86361511; E-mail:chujsh_2000@sina.com
Abstract: OBJECTIVE: The present study was performed to identify a
gene set for predicting the relapse in laryngeal carcinoma using large data
analysis methods.
METHODS: Two gene expression profile data of laryngeal
carcinoma (GSE27020 and GSE25727) were downloaded from public database.
Genes associated with tumor relapse, namely informative genes, were
identified by Cox regression analysis. Then the protein-protein interaction
(PPI) network consisting of informative genes was constructed. Afterwards,
the optimized support vector machine (SVM) classifier was constructed to
classify the relapsed laryngeal carcinoma samples based on genes in specific
PPI network. Furthermore, the efficiency of the SVM classifier was verified
by other two independent datasets.
RESULTS: A total of 331 informative genes were obtained
from GSE27020 and GSE25757 datasets. A PPI network specific to laryngeal
carcinoma relapse was constructed which contained informative genes and
critical non-informative genes. The top 10 genes in specific PPI network
were APP, NTRK1, TP53, PTEN, FN1, ELAVL1, HSP90AA1, XPO1, LDHA and CDK2 ranked by BC (betweenness centrality) value. The
optimized SVM classifier including top 80 genes showed accuracy of 100%
to classify the relapsed cases from laryngeal carcinoma samples. Next, the
efficiency of the SVM classifier to predict relapse samples was verified in
another independent datasets, which showed accuracy of 97.47%. The
informative genes in the optimized SVM classifier were enriched in several
pathways associated with tumor progression.
CONCLUSION: A 80-gene set was identified as biomarker to
predict the relapse of laryngeal carcinoma, which would be potentially
applied in decision of different treatments for patients with different
relapse risks.
Keywords: Laryngeal carcinoma, prognosis factor, relapse, support vector machine
