van Lieshout Laura, van de Stolpe Anja, van der Ploeg Phyllis, Bowtell David, de Hullu Joanne, Piek Jurgen
Department of Obstetrics and Gynecology, Catharina Cancer Institute, Catharina Hospital, 5602ZA Eindhoven, The Netherlands.
Radboud Institute for Health Sciences, Department of Obstetrics and Gynecology, Radboud University Medical Center, 6500HB Nijmegen, The Netherlands.
Cancers (Basel). 2020 Sep 18;12(9):2660. doi: 10.3390/cancers12092660.
We investigated signal transduction pathway (STP) activity in high-grade serous ovarian carcinoma (HGSC) in relation to progression-free survival (PFS) and overall survival (OS). We made use of signal transduction pathway activity analysis (STA analysis), a novel method to quantify functional STP activity. Activity of the following pathways was measured: androgen receptor (AR), estrogen receptor (ER), phosphoinositide 3-kinase (PI3K), Hedgehog (Hh), Notch, nuclear factor-kappa B (NF-κB), transforming growth factor beta (TGF-β), and Wnt. We selected HGSC samples from publicly available datasets of ovarian cancer tissue, and used repeated -means clustering to identify pathway activity clusters. PFS and OS of the clusters were analyzed. We used a subset of publicly available dataset GSE9891 ( = 140), where repeated -means clustering based on PI3K and NF-κB pathway activity in HGSC samples resulted in two stable clusters. The cluster with low PI3K and high NF-κB pathway activity ( = 72) had a more favorable prognosis for both PFS ( = 0.004) and OS ( = 0.001) compared to the high-PI3K and low-NF-κB pathway activity cluster ( = 68). The low PI3K and high NF-κB pathway activity of the favorable prognosis cluster may indicate a more active immune response, while the high PI3K and low NF-κB pathway activity of the unfavorable prognosis cluster may indicate high cell division.
我们研究了高级别浆液性卵巢癌（HGSC）中信号转导通路（STP）活性与无进展生存期（PFS）和总生存期（OS）的关系。我们采用了信号转导通路活性分析（STA分析），这是一种量化功能性STP活性的新方法。测量了以下通路的活性：雄激素受体（AR）、雌激素受体（ER）、磷酸肌醇3激酶（PI3K）、刺猬信号通路（Hh）、Notch、核因子κB（NF-κB）、转化生长因子β（TGF-β）和Wnt。我们从公开可用的卵巢癌组织数据集中选择HGSC样本，并使用重复均值聚类来识别通路活性簇。分析了这些簇的PFS和OS。我们使用了公开可用数据集GSE9891的一个子集（n = 140），其中基于HGSC样本中PI3K和NF-κB通路活性的重复均值聚类产生了两个稳定的簇。与高PI3K和低NF-κB通路活性簇（n = 68）相比，PI3K低且NF-κB通路活性高的簇（n = 72）在PFS（P = 0.004）和OS（P = 0.001）方面预后更优。预后良好簇的低PI3K和高NF-κB通路活性可能表明免疫反应更活跃，而预后不良簇的高PI3K和低NF-κB通路活性可能表明细胞分裂活跃。
