The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Changsha, Changsha, 410008, Hunan, People's Republic of China.
Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, People's Republic of China.
Sci Rep. 2021 Mar 30;11(1):7207. doi: 10.1038/s41598-021-86294-z.
Growing evidence suggest that transcription factors (TFs) play vital roles in serous ovarian cancer (SOC). In the present study, TFs mRNA expression profiles of 564 SOC subjects in the TCGA database, and 70 SOC subjects in the GEO database were screened. A 17-TFs related prognostic signature was constructed using lasso cox regression and validated in the TCGA and GEO cohorts. Consensus clustering analysis was applied to establish a cluster model. The 17-TFs related prognostic signature, risk score and cluster models were effective at accurately distinguishing the overall survival of SOC. Analysis of genomic alterations were used to elaborate on the association between the 17-TFs related prognostic signature and genomic aberrations. The GSEA assay results suggested that there was a significant difference in the inflammatory and immune response pathways between the high-risk and low-risk score groups. The potential immune infiltration, immunotherapy, and chemotherapy responses were analyzed due to the significant difference in the regulation of lymphocyte migration and T cell-mediated cytotoxicity between the two groups. The results indicated that patients with low-risk score were more likely to respond anti-PD-1, etoposide, paclitaxel, and veliparib but not to gemcitabine, doxorubicin, docetaxel, and cisplatin. Also, the prognostic nomogram model revealed that the risk score was a good prognostic indicator for SOC patients. In conclusion, we explored the prognostic values of TFs in SOC and developed a 17-TFs related prognostic signature to predict the survival of SOC patients.
越来越多的证据表明，转录因子（TFs）在浆液性卵巢癌（SOC）中发挥着重要作用。本研究筛选了 TCGA 数据库中 564 例 SOC 患者和 GEO 数据库中 70 例 SOC 患者的 TFs mRNA 表达谱。使用lasso cox 回归构建了一个与 17 个 TFs 相关的预后签名，并在 TCGA 和 GEO 队列中进行了验证。应用共识聚类分析建立了一个聚类模型。该 17 个 TFs 相关的预后签名、风险评分和聚类模型能够准确区分 SOC 的总生存期。分析基因组改变用于阐述 17 个 TFs 相关预后签名与基因组异常之间的关系。GSEA 检测结果表明，高危和低危评分组之间在炎症和免疫反应途径上存在显著差异。由于两组间淋巴细胞迁移和 T 细胞介导的细胞毒性的调节存在显著差异，分析了潜在的免疫浸润、免疫治疗和化疗反应。结果表明，低风险评分的患者更有可能对抗 PD-1、依托泊苷、紫杉醇和 veliparib 产生反应，但对吉西他滨、阿霉素、多西他赛和顺铂没有反应。此外，预后列线图模型表明，风险评分是 SOC 患者的良好预后指标。总之，我们探讨了 TFs 在 SOC 中的预后价值，并开发了一个与 17 个 TFs 相关的预后签名来预测 SOC 患者的生存情况。
