Bo Liu1, Jingping Liu1, Kun Liu2, Hao Huang2, Yexin Li3, Xiqi Hu4, Ke Wang5, Hui Cao6, Quan Cheng7. 1. Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China. 2. Department of Neurosurgery, The Second People's Hospital of Hunan Province, The Hospital of Hunan University of Chinese Medicine, Changsha, China. 3. Department of Neurosurgery, The Central Hospital of Shaoyang, Shaoyang, China. 4. Department of Neurosurgery, Yiyang Central Hospital, Yiyang, China. 5. Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China. 6. Department of Psychiatry, The Second People's Hospital of Hunan Province, The Hospital of Hunan University of Chinese Medicine, Changsha, China. 7. Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China; Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, China. Electronic address: chengquan@csu.edu.cn.
Abstract
BACKGROUND: A pseudogene is a gene copy that has lost its original coding ability. Pseudogenes participate in numerous biological processes including oncogenesis. OBJECTIVES: We screened for prognostic pseudogenes for lower-grade glioma (LGG) and explored the potential molecular mechanisms. METHODS: LGG data downloaded from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) databases were used as training and validation dataset, respectively. Univariate Cox proportional hazard regression was performed to identify pseudogenes with significant prognostic value. Robust likelihood-based survival model and LASSO regression were performed to screen for the most survival-relevant pseudogenes. A risk score model was constructed based on the prognostic pseudogenes to predict the prognosis of LGG patients. RESULTS: Five pseudogenes (PKMP3, AC027612.4, HILS1, RP5-1132H15.3 and HSPB1P1) were identified as prognostic gene-signatures. Using the risk score model established based on the five pseudogenes, LGG patients were stratified into distinct prognosis groups in both TCGA and CGGA datasets (P < 0.0001). Univariate and multivariate Cox regression analyses confirmed that the risk score generated from the model was an independent prognostic factor in LGG patients (p < 0.05). Furthermore, functional analysis revealed the potential biological mechanisms mediated by the five prognostic pseudogenes. CONCLUSIONS: Five novel pseudogenes capable of predicting survival in LGG patients were identified. Our findings provide novel insights into the biological role of pseudogenes in LGG.
BACKGROUND: A pseudogene is a gene copy that has lost its original coding ability. Pseudogenes participate in numerous biological processes including oncogenesis. OBJECTIVES: We screened for prognostic pseudogenes for lower-grade glioma (LGG) and explored the potential molecular mechanisms. METHODS: LGG data downloaded from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) databases were used as training and validation dataset, respectively. Univariate Cox proportional hazard regression was performed to identify pseudogenes with significant prognostic value. Robust likelihood-based survival model and LASSO regression were performed to screen for the most survival-relevant pseudogenes. A risk score model was constructed based on the prognostic pseudogenes to predict the prognosis of LGG patients. RESULTS: Five pseudogenes (PKMP3, AC027612.4, HILS1, RP5-1132H15.3 and HSPB1P1) were identified as prognostic gene-signatures. Using the risk score model established based on the five pseudogenes, LGG patients were stratified into distinct prognosis groups in both TCGA and CGGA datasets (P < 0.0001). Univariate and multivariate Cox regression analyses confirmed that the risk score generated from the model was an independent prognostic factor in LGG patients (p < 0.05). Furthermore, functional analysis revealed the potential biological mechanisms mediated by the five prognostic pseudogenes. CONCLUSIONS: Five novel pseudogenes capable of predicting survival in LGG patients were identified. Our findings provide novel insights into the biological role of pseudogenes in LGG.
Authors: Maciej Stasiak; Tomasz Kolenda; Joanna Kozłowska-Masłoń; Joanna Sobocińska; Paulina Poter; Kacper Guglas; Anna Paszkowska; Renata Bliźniak; Anna Teresiak; Urszula Kazimierczak; Katarzyna Lamperska Journal: Life (Basel) Date: 2021-12-07