Khalili-Tanha Ghazaleh, Khalili-Tanha Nima, Farahani Masoumeh, Rezaei-Tavirani Mostafa, Nazari Elham
Department of Medical Genetics and Molecular Medicine, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
Department of Small Animal Clinical Sciences, Western College of Veterinary Medicine, University of Saskatchewan, 52Campus Drive, Saskatoon, SK S7N 5B4, Canada.
Asian Pac J Cancer Prev. 2024 Dec 1;25(12):4201-4210. doi: 10.31557/APJCP.2024.25.12.4201.
Glioblastoma (GBM) is the most aggressive cancer in the central nervous system in glial cells. Finding novel biomarkers in GBM offers numerous advantages that can contribute to early detection, personalized treatment, improved patient outcomes, and advancements in cancer research and drug development. Integrating machine learning with RNAseq data in medicine holds significant potential for identifying novel biomarkers in various diseases, including cancer.
Gene expression raw data was used to detect differentially expressed genes (DEGs) within a cohort of 532 GBM patients. The molecular pathway analysis, disease ontology, and protein-protein interactions of DEGs were assessed. Machine learning methods were performed to identify candidate genes. Survival curves were estimated using the Kaplan-Meier method and Cox proportional hazard to find prognostic biomarkers.
The molecular pathway analysis revealed that key dysregulated genes are in GPCRs, class A rhodopsin-like, MAPK signaling pathway, and calcium regulation in cardiac cells. Additionally, survival analysis showed that ten downregulated genes, including CPLX3, GPR162, LCNL1, SLC5A5, GPR61, GPR68, IL1RL2, HCRTR1, AIPL1, and SYTL1, and also ten upregulated genes, including C1orf92, CATSPER1, CCDC19, EPS8L1, FAIM3, FAM70B, FCN3, GPR157, IGFBP1, and MYBPH decreased the overall survival in GBM patients. Furthermore, the machine learning detected twenty genes, among which LRRTM2 and OPRL1 were candidates with high correlation coefficients.
Our data suggest that genes belonging to G Protein-Coupled Receptors play a critical role in various aspects of glioblastoma progression and pathogenesis. Four members of GPCRs, including GPR162, GPR61, GPR68, and GPR157, can be considered prognostic biomarkers. Additionally, the combination of A2BP1 and GPR157 was reported as a diagnostic marker.
胶质母细胞瘤（GBM）是中枢神经系统中最具侵袭性的神经胶质细胞癌。在GBM中寻找新的生物标志物具有诸多优势，有助于早期检测、个性化治疗、改善患者预后以及推动癌症研究和药物开发。将机器学习与医学中的RNAseq数据相结合，在识别包括癌症在内的各种疾病的新生物标志物方面具有巨大潜力。
使用基因表达原始数据检测532例GBM患者队列中的差异表达基因（DEG）。评估DEG的分子通路分析、疾病本体和蛋白质-蛋白质相互作用。采用机器学习方法识别候选基因。使用Kaplan-Meier方法和Cox比例风险估计生存曲线，以寻找预后生物标志物。
分子通路分析显示，关键的失调基因存在于G蛋白偶联受体（GPCR）、A类视紫红质样、丝裂原活化蛋白激酶（MAPK）信号通路和心肌细胞钙调节中。此外，生存分析表明，包括CPLX3、GPR162、LCNL1、SLC5A5、GPR61、GPR68、IL1RL2、HCRTR1、AIPL1和SYTL1在内的10个下调基因，以及包括C1orf92、CATSPER1、CCDC19、EPS8L1、FAIM3、FAM70B、FCN3、GPR157、IGFBP1和MYBPH在内的10个上调基因，均降低了GBM患者的总生存率。此外，机器学习检测到20个基因，其中LRRTM2和OPRL1是具有高相关系数的候选基因。
我们的数据表明，属于G蛋白偶联受体的基因在胶质母细胞瘤进展和发病机制的各个方面起着关键作用。GPCR的四个成员，包括GPR162、GPR61、GPR68和GPR157，可被视为预后生物标志物。此外，A2BP1和GPR157的组合被报道为一种诊断标志物。
