Prognostic value and their clinical implication of 89-gene signature in glioma
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Muhammad Shahid1,*, Kyoung Min Cho2,*, Minh Nam Nguyen3, Tae Gyu Choi3, Yong Hwa Jo3, Saurav Nath Aryal1, Ji Youn Yoo1, Hyeong Rok Yun1, Jae Woong Lee1, Young Gyu Eun4, Ju-Seog Lee5, Insug Kang1,3, Joohun Ha1,3, Hwi-Joong Yoon2, Si-Young Kim2, Sung Soo Kim1,3
1Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, Republic of Korea
2Department of Internal Medicine, Graduate School, Kyung Hee University, Seoul, Republic of Korea
3Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, Republic of Korea
4Department of Otolaryngology-Head and Neck Surgery, Kyung Hee University Medical Center, Seoul, Republic of Korea
5Department of Systems Biology, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
*These authors contributed equally to this work
Sung Soo Kim, email: [email protected]
Keywords: glioma, gene expression profile, prognosis, chemosensitivity
Received: March 03, 2016 Accepted: May 20, 2016 Published: June 13, 2016
Gliomas are the most common and aggressive primary tumors in adults. The current approaches, such as histological classification and molecular genetics, have limitation in prediction of individual therapeutic outcomes due to heterogeneity within the tumor groups. Recent studies have proposed several gene signatures to predict glioma’s prognosis. However, most of the gene expression profiling studies have been performed on relatively small number of patients and combined probes from diverse microarray chips. Here, we identified prognostic 89 common genes from diverse microarray chips. The 89-gene signature classified patients into good and bad prognostic groups which differed in the overall survival significantly, reflecting the biological characteristics and heterogeneity. The robustness and accuracy of the gene signature as an independent prognostic factor was validated in three microarray and one RNA-seq data sets independently. By incorporating into histological classification and molecular marker, the 89-gene signature could further stratify patients with 1p/19q co-deletion and IDH1 mutation. Additionally, subset analyses suggested that the 89-gene signature could predict patients who would benefit from adjuvant chemotherapy. Conclusively, we propose that the 89-gene signature would have an independent and accurate prognostic value for clinical use. This study also offers opportunities for novel targeted treatment of individual patients.
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