Exploring the prognostic value and biological pathways of transcriptomics and radiomics patterns in glioblastoma multiforme
Jixin Luan,
Di Zhang,
Bing Liu,
Aocai Yang,
Kuan Lv,
Pianpian Hu,
Hongwei Yu,
Amir Shmuel,
Chuanchen Zhang,
Guolin Ma
Affiliations
Jixin Luan
Department of Radiology, China-Japan Friendship Hospital, Beijing, China; China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
Di Zhang
Department of Radiology, Liaocheng People's Hospital, Shandong First Medical University & Shandong Academy of Medical Sciences, Shandong, China
Bing Liu
Department of Radiology, China-Japan Friendship Hospital, Beijing, China; China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
Aocai Yang
Department of Radiology, China-Japan Friendship Hospital, Beijing, China; China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
Kuan Lv
Department of Radiology, China-Japan Friendship Hospital, Beijing, China; Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China
Pianpian Hu
Department of Radiology, China-Japan Friendship Hospital, Beijing, China; Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China
Hongwei Yu
Department of Radiology, China-Japan Friendship Hospital, Beijing, China; China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
Amir Shmuel
McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada; Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
Chuanchen Zhang
Department of Radiology, Liaocheng People's Hospital, Shandong First Medical University & Shandong Academy of Medical Sciences, Shandong, China; Corresponding author. Department of Radiology, Liaocheng People's Hospital, Shandong First Medical University & Shandong Academy of Medical Sciences, No. 67, Dongchang West Road, Liaocheng District, Shandong Province, 252000, China.
Guolin Ma
Department of Radiology, China-Japan Friendship Hospital, Beijing, China; China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China; Corresponding author.Department of Radiology, China-Japan Friendship Hospital, No. 2 East Yinghua Road, Chaoyang District, Beijing 100029, China.
Objectives: To develop a multi-omics prognostic model integrating transcriptomics and radiomics for predicting overall survival in patients with glioblastoma multiforme (GBM), and investigate the biological pathways of radiomics patterns. Materials and methods: Transcription profiles of GBM patients and normal controls were used to obtain differentially expressed mRNAs and long non-coding RNAs (lncRNAs). Radiomics features were extracted from magnetic resonance imaging (MRI). Least absolute shrinkage and selection operator (LASSO) Cox regression was employed to select survival-associated features for the construction of transcriptomics and radiomics signatures. Genes associated with GBM prognosis were identified through the analysis of lncRNA-mRNA co-expression networks and Weighted Gene Co-expression Network Analysis (WGCNA), and their biological pathways were investigated using Genomes enrichment analysis. Transcriptomics, radiomics, and clinical data were integrated to evaluate the multi-omics prognostic model's performance. Results: LASSO Cox regression yielded 21 survival-related features, including 19 transcriptomics features and 2 radiomics features. Based on transcriptomics and radiomics signature, GBM patients were classified as high-risk or low-risk. The genes obtained from the co-expression network screen were associated with microtubule binding, while those from the WGCNA screen were associated with growth factor receptor binding. In the training set, the AUC values for the multi-omics model and clinical model were 0.964 and 0.830, respectively, while in the validation set, they were 0.907 and 0.787. The multi-omics prognostic model outperformed the clinical prognostic model. Conclusions: The co-expression network and WGCNA methods revealed genes associated with multiple biological pathways in GBM. The multi-omics prognostic model demonstrated excellent performance and indicated significant potential for clinical application.
