Data Sheet 1_Development of CSOARG: a single-cell and multi-omics-based machine learning model for ovarian cancer prognosis and drug response prediction.pdf

ObjectiveOvarian cancer is the most deadly gynaecological malignancy. This study aims to generate a predictive model for prognosis and therapeutic responses in ovarian cancer using defined specific genes.
MethodsThe cellular senescence-associated gene sets and the ovarian aging-associated gene sets from the TCGA and GEO databases were analyzed using Cox regression with LASSO approach and employed to construct a prognostic model of Cellular Senescence and Ovarian Aging-Related Genes (CSOARG). Immunology analysis, functional enrichment, single-cell analysis, and therapeutic responses of ovarian cancer were conducted using the data from public databases. A machine learning model based on the expression levels of prognostic genes combined with clinical features was developed to predict the five-year overall survival. Patients with high- and low-risk scores were separated by the median risk score. Defined genes were verified by qRT-PCR and Western blot. The cellular behavior was evaluated by CCK-8, migration, and wound-healing assays.
ResultsAfter a series of calculations, an 8-gene CSOARG model was generated. CSOARG was correlated with genomic instability that harbored homologous recombination deficiency. The area under the curve (AUC) for 5-year overall survival was 0.68. Patients in the high-risk score group had a higher IC50 of chemotherapeutic and targeted therapeutical agents, worse responses to chemotherapy and immunotherapy, and exhibited a poor prognosis. A hub gene WNK1 was validated and acted as an oncogene affecting ovarian cancer cell viability and migration.
ConclusionsThese findings demonstrate that a novel CSOARG model can effectively predict the prognosis and therapeutical responses of patients with ovarian cancer, which may assist clinicians in implementing better practices.

研究目的：卵巢癌是致死率最高的妇科恶性肿瘤。本研究旨在通过已明确的特定基因，构建卵巢癌预后与治疗反应预测模型。
研究方法：本研究采用结合最小绝对收缩和选择算子（Least Absolute Shrinkage and Selection Operator, LASSO）的Cox回归分析方法，对来自癌症基因组图谱（The Cancer Genome Atlas, TCGA）与基因表达综合数据库（Gene Expression Omnibus, GEO）的细胞衰老相关基因集及卵巢衰老相关基因集进行分析，以此构建细胞衰老与卵巢衰老相关基因（Cellular Senescence and Ovarian Aging-Related Genes, CSOARG）预后模型。利用公共数据库数据开展卵巢癌免疫分析、功能富集分析、单细胞分析及治疗反应分析。基于预后基因表达水平结合临床特征构建机器学习模型，以预测患者5年总生存期。以中位风险评分将患者划分为高风险评分组与低风险评分组。通过实时定量聚合酶链反应（quantitative real-time polymerase chain reaction, qRT-PCR）与蛋白质印迹（Western Blot）对已明确的基因进行验证。采用细胞计数试剂盒-8（Cell Counting Kit-8, CCK-8）实验、迁移实验及划痕愈合实验评估细胞行为。
研究结果：经一系列计算后，成功构建包含8个基因的CSOARG模型。CSOARG与存在同源重组缺陷的基因组不稳定性显著相关。该模型预测5年总生存期的曲线下面积（Area Under the Curve, AUC）为0.68。高风险评分组患者对化疗及靶向治疗药物的半抑制浓度（Half Maximal Inhibitory Concentration, IC50）更高，对化疗与免疫治疗的反应更差，且预后不良。核心基因WNK1被验证可作为癌基因，影响卵巢癌细胞的存活能力与迁移能力。
研究结论：本研究结果表明，新型CSOARG模型可有效预测卵巢癌患者的预后与治疗反应，可为临床医师制定优化诊疗方案提供辅助依据。