Zhang ZhenWei, Chen MianMian, Peng XiaoLian
Jinjiang Municipal Hospital(Shanghai Sixth People's Hospital Fujian Campus), No. 16, Luoshan Section, Jinguang Road, Luoshan Street, Jinjiang City, Quanzhou, Fujian, China.
Heliyon. 2024 Jun 20;10(13):e33367. doi: 10.1016/j.heliyon.2024.e33367. eCollection 2024 Jul 15.
Ovarian cancer represents a severe gynecological malignancy with a dire prognosis, underscoring the imperative need for dependable biomarkers that can accurately predict drug response and guide therapeutic choices. In this study, we harnessed online single-cell RNA sequencing (scRNAseq) and bulk RNA sequencing (RNAseq) datasets, applying the Scissor algorithm to identify cells responsive to paclitaxel. From these cells, we derived a gene signature, subsequently used to construct a prognostic model that demonstrated high sensitivity and specificity in predicting patient outcomes. Moreover, we conducted pathway and functional enrichment analyses to uncover potential molecular mechanisms driving the prognostic gene signature. This study illustrates the critical role of scRNAseq and bulk RNAseq in developing precise prognostic models for ovarian cancer, potentially transforming clinical decision-making.
卵巢癌是一种严重的妇科恶性肿瘤，预后极差，这凸显了对可靠生物标志物的迫切需求，这些生物标志物能够准确预测药物反应并指导治疗选择。在本研究中，我们利用在线单细胞RNA测序（scRNAseq）和批量RNA测序（RNAseq）数据集，应用Scissor算法来识别对紫杉醇有反应的细胞。从这些细胞中，我们得出了一个基因特征，随后用于构建一个预后模型，该模型在预测患者预后方面表现出高敏感性和特异性。此外，我们进行了通路和功能富集分析，以揭示驱动预后基因特征的潜在分子机制。本研究说明了scRNAseq和批量RNAseq在开发卵巢癌精确预后模型中的关键作用，可能会改变临床决策。
