Mo XinKai, Wang Na, He Zanjing, Kang Wenjun, Wang Lu, Han Xia, Yang Liu
Department of Clinical Laboratory, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250117, Shandong, PR China.
Department of Medical Laboratory Science, Xinjiang Bayingoleng Mongolian Autonomous Prefecture People's Hospital, Xinjiang, China.
Heliyon. 2023 Jun 7;9(6):e16873. doi: 10.1016/j.heliyon.2023.e16873. eCollection 2023 Jun.
The efficacy of therapy in cervical cancer (CESC) is blocked by high molecular heterogeneity. Thus, the sub-molecular characterization remains primarily explored for personalizing the treatment of CESC patients.
Datasets with 741 CESC patients were obtained from TCGA and GEO databases. The NMF algorithm, random forest algorithm, and multivariate Cox analysis were utilized to construct a classifier for defining the sub-molecular characterization. Then, the biological characteristics, genomic variations, prognosis, and immune landscape in molecular subtypes were explored. The significance of classifier genes was validated by quantitative Real-Time PCR, cell transfection, cell colony formation assay, wound healing assay, cell proliferation assay, and Western blot.
The CESC patients were classified into two subtypes, and the high classifier-score patients with significant differences in ECM-receptor interaction, PI3K-Akt signaling pathway, and MAPK signaling pathway showed a poorer prognosis in OS (p < 0.001), DFI (p = 0.016), PFI (p < 0.001) and DSS (p < 0.001), and with high the M0 Macrophage and resting Mast cells infiltration and low HLA family gene expression. Moreover, the constructed classifier owns a high identified accuracy in the tumor/normal groups (AUC: 0.993), the tumor/CIN1-CIN3 groups (AUC: 0.963), and normal/CIN1-CIN3 groups (AUC: 0.962), and the total prediction performance is better than currently published signatures in CESC (C-index: 0,763). The combined prediction performance further indicated that Nomogram (AUC = 0.837) is superior to the classifier (AUC = 0.835) and Stage (AUC = 0.568), and the C-index of calibration curves is 0.784. The potential biological function of classifier genes indicated that silencing GALNT2 inhibited the cancer cell's proliferation, migration, and colony formation; Conversely, the cancer cell's proliferation, migration, and colony formation were increased after the upregulation of GALNT2. The Epithelial-Mesenchymal Transition Experiment showed that GALNT2 knockdown might reduce the levels of Snail and Vimentin proteins and increase E-cadherin; Conversely, the levels of Snail and Vimentin proteins were increased, E-cadherin was reduced by GALNT2 upregulation.
The classifier we constructed may help improve our understanding of subtype characteristics and provide a new strategy for developing CESC therapeutics. Remarkably, GALNT2 may be an option to directly target drivers in CESC cancer therapy.
宫颈癌（CESC）治疗的疗效因高度的分子异质性而受阻。因此，仍主要在探索分子亚特征以实现CESC患者治疗的个性化。
从TCGA和GEO数据库中获取了包含741例CESC患者的数据集。利用非负矩阵分解（NMF）算法、随机森林算法和多变量Cox分析构建用于定义分子亚特征的分类器。然后，探索了分子亚型中的生物学特征、基因组变异、预后和免疫格局。通过定量实时聚合酶链反应（qRT-PCR）、细胞转染、细胞集落形成试验、伤口愈合试验、细胞增殖试验和蛋白质免疫印迹法验证了分类器基因的重要性。
CESC患者被分为两个亚型，分类器评分高的患者在细胞外基质受体相互作用、PI3K-Akt信号通路和MAPK信号通路方面存在显著差异，其总生存期（OS，p < 0.001）、无病生存期（DFI，p = 0.016）、无进展生存期（PFI，p < 0.001）和疾病特异性生存期（DSS，p < 0.001）预后较差，且M0巨噬细胞和静止肥大细胞浸润高，HLA家族基因表达低。此外，构建的分类器在肿瘤/正常组（曲线下面积[AUC]：0.993）、肿瘤/CIN1-CIN3组（AUC：0.963）和正常/CIN1-CIN3组（AUC：0.962）中具有较高的识别准确率，总体预测性能优于目前发表的CESC特征（C指数：0.763）。联合预测性能进一步表明，列线图（AUC = 0.837）优于分类器（AUC = 0.835）和分期（AUC = 0.568），校准曲线的C指数为0.784。分类器基因的潜在生物学功能表明，沉默GALNT2可抑制癌细胞的增殖、迁移和集落形成；相反，上调GALNT2后癌细胞的增殖、迁移和集落形成增加。上皮-间质转化实验表明，敲低GALNT2可能会降低Snail和波形蛋白的蛋白水平并增加E-钙黏蛋白；相反，上调GALNT2会增加Snail和波形蛋白的蛋白水平，降低E-钙黏蛋白。
我们构建的分类器可能有助于提高我们对亚型特征的理解，并为开发CESC治疗方法提供新策略。值得注意的是，GALNT2可能是CESC癌症治疗中直接靶向驱动因素的一个选择。
