title: Integrated Cox's model for predicting survival time of glioblastoma multiforme.
abstract: Glioblastoma multiforme is the most common primary brain tumor and is highly lethal. This study aims to figure out signatures for predicting the survival time of patients with glioblastoma multiforme. Clinical information, messenger RNA expression, microRNA expression, and single-nucleotide polymorphism array data of patients with glioblastoma multiforme were retrieved from The Cancer Genome Atlas. Patients were separated into two groups by using 1 year as a cutoff, and a logistic regression model was used to figure out any variables that can predict whether the patient was able to live longer than 1 year. Furthermore, Cox's model was used to find out features that were correlated with the survival time. Finally, a Cox model integrated the significant clinical variables, messenger RNA expression, microRNA expression, and single-nucleotide polymorphism was built. Although the classification method failed, signatures of clinical features, messenger RNA expression levels, and microRNA expression levels were figured out by using Cox's model. However, no single-nucleotide polymorphisms related to prognosis were found. The selected clinical features were age at initial diagnosis, Karnofsky score, and race, all of which had been suggested to correlate with survival time. Both of the two significant microRNAs, microRNA-221 and microRNA-222, were targeted to p27Kip1protein, which implied the important role of p27Kip1on the prognosis of glioblastoma multiforme patients. Our results suggested that survival modeling was more suitable than classification to figure out prognostic biomarkers for patients with glioblastoma multiforme. An integrated model containing clinical features, messenger RNA levels, and microRNA expression levels was built, which has the potential to be used in clinics and thus to improve the survival status of glioblastoma multiforme patients.
authors.affiliation: 1 Department of Neurology, Taihe Hospital, Hubei University of Medicine, Shiyan, P.R. China.
authors.forename: Zhibing
authors.initials: Z
authors.lastname: Ai
authors.affiliation: 2 Department of Development and Planning, Taihe Hospital, Hubei University of Medicine, Shiyan, P.R. China.
authors.forename: Longti
authors.initials: L
authors.lastname: Li
authors.affiliation: 3 Department of Neurosurgery, Taihe Hospital, Hubei University of Medicine, Shiyan, P.R. China.
authors.forename: Rui
authors.initials: R
authors.lastname: Fu
authors.affiliation: 4 Department of Neurology, The Affiliated Huai'an Hospital of Xuzhou Medical University and The Second People's Hospital of Huai'an, Huai'an, P.R. China.
authors.forename: Jing-Min
authors.initials: JM
authors.lastname: Lu
authors.affiliation: 5 Department of Clinical Oncology, Huai'an First People's Hospital, Nanjing Medical University, Huai'an, P.R. China.
authors.forename: Jing-Dong
authors.initials: JD
authors.lastname: He
authors.affiliation: 6 Department of Spinal Surgery, Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, China.
authors.forename: Sen
authors.initials: S
authors.lastname: Li
mesh_terms: D001932:Brain Neoplasms / Q000235:genetics / Q000401:mortality*; D050760:Cyclin-Dependent Kinase Inhibitor p27 / Q000502:physiology; D005260:Female; D005909:Glioblastoma / Q000235:genetics / Q000401:mortality*; D006801:Humans; D016015:Logistic Models; D008297:Male; D035683:MicroRNAs / Q000032:analysis; D020641:Polymorphism, Single Nucleotide; D011379:Prognosis; D016016:Proportional Hazards Models
chemical_list: D050760:Cyclin-Dependent Kinase Inhibitor p27; D035683:MicroRNAs; C436291:CDKN1B protein, human; C525709:MIR221, human; C525710:MIR222, human
keywords: Cox’s model; Glioblastoma multiforme; logistic regression; messenger RNA expression; microRNA expression; single-nucleotide polymorphism; survival analysis