Zheng Xingju, Xu Shilin, Wu JiaYing
Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, China.
Department of Oncology, Xichang People's Hospital, Liangshan High-Tech Tumor Hospital, Xichang, China.
Front Neurol. 2022 Jul 12;13:905761. doi: 10.3389/fneur.2022.905761. eCollection 2022.
Bioinformatics tools are used to create a clinical prediction model for cervical cancer metastasis and to investigate the neurovascular-related genes that are involved in brain metastasis of cervical cancer. One hundred eighteen patients with cervical cancer were divided into two groups based on the presence or absence of metastases, and the clinical data and imaging findings of the two groups were compared retrospectively. The nomogram-based model was successfully constructed by taking into account four clinical characteristics (age, stage, N, and T) as well as one imaging characteristic (original_glszm_GrayLevelVariance Rad-score). In patients with cervical cancer, headaches and vomiting were more often reported in the brain metastasis group than in the other metastasis groups. According to the TCGA data, mRNA differential gene expression analysis of patients with cervical cancer revealed an increase in the expression of neurovascular-related gene Adrenoceptor Beta 1 () in the brain metastasis group. An analysis of the correlation between imaging features and expression revealed that expression was significantly higher in the low Rad-score group compared with the high Rad-score group ( = 0.025). Therefore, expression in cervical cancer was correlated with imaging features and was associated as a risk factor for cerebral neurovascular metastases. This study developed a nomogram prediction model for cervical cancer metastasis using age, stage, N, T and original_glszm_GrayLevelVariance. As a risk factor associated with the development of cerebral neurovascular metastases of cervical cancer, expression was significantly higher in brain metastases from cervical cancer.
生物信息学工具用于创建宫颈癌转移的临床预测模型，并研究参与宫颈癌脑转移的神经血管相关基因。118例宫颈癌患者根据有无转移分为两组，回顾性比较两组的临床资料和影像学表现。通过考虑四个临床特征（年龄、分期、N和T）以及一个影像学特征（original_glszm_GrayLevelVariance Rad-score）成功构建了基于列线图的模型。在宫颈癌患者中，脑转移组比其他转移组更常出现头痛和呕吐。根据TCGA数据，宫颈癌患者的mRNA差异基因表达分析显示，脑转移组中神经血管相关基因肾上腺素能受体β1（）的表达增加。影像学特征与表达之间的相关性分析显示，低Rad-score组的表达明显高于高Rad-score组（=0.025）。因此，宫颈癌中的表达与影像学特征相关，并作为脑神经血管转移的危险因素。本研究利用年龄、分期、N、T和original_glszm_GrayLevelVariance建立了宫颈癌转移的列线图预测模型。作为与宫颈癌脑神经血管转移发生相关的危险因素，其在宫颈癌脑转移中的表达明显更高。
