Gao Tingting, Zhao Peng, Han Suxia
Department of Radiation Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China.
Department of Obstetrics and Gynecology, The Second Affiliated Hospital of Air Force Medical University, Xi'an, 710038, China.
Sci Rep. 2025 May 31;15(1):19123. doi: 10.1038/s41598-025-99930-9.
Platinum-based therapy is an integral part of the standard treatment for ovarian cancer. However, despite extensive research spanning several decades, the identification of dependable predictive biomarkers for platinum response in clinical practice has proven to be a formidable challenge. Recently, the development of single-cell technology has enabled more precise investigations into the heterogeneity of cancer. In this study, we isolated cancer cells from the single-cell transcriptomic data of platinum-sensitive and platinum-resistant patients with ovarian cancer. Differential gene analysis of platinum-sensitive and platinum-resistant cancer cells revealed that several of the differentially expressed genes had previously been reported in other studies to be associated with platinum resistant. Gene set enrichment analysis revealed the up-regulation of pathways involved in processes such as autophagy, cell cycle regulation, and DNA damage repair, which are known to promote platinum resistance in ovarian cancer. Based on these findings, we hypothesized that these differentially expressed genes could be used to predict the response of ovarian cancer patients to platinum-based chemotherapy. To validate this hypothesis, we explored 7 different machine learning models for predicting platinum chemotherapy response at varying feature gene counts. Ultimately, the random forest model performed the best, with 5 genes (PAX2, TFPI2, APOA1, ADIRF and CRISP3) and achieve an AUC of 0.993 in test cohort and 0.989 in GSE63885 independent validation cohorts. We named this model GPPS (Genes to Predict Platinum response Signature). Furthermore, we discovered that the GPPS model can also predict patient prognosis.
铂类疗法是卵巢癌标准治疗的重要组成部分。然而，尽管经过了数十年的广泛研究，但在临床实践中确定可靠的铂类反应预测生物标志物已被证明是一项艰巨的挑战。最近，单细胞技术的发展使人们能够更精确地研究癌症的异质性。在本研究中，我们从铂敏感和铂耐药的卵巢癌患者的单细胞转录组数据中分离出癌细胞。对铂敏感和铂耐药癌细胞的差异基因分析表明，一些差异表达基因先前在其他研究中已被报道与铂耐药相关。基因集富集分析显示，自噬、细胞周期调控和DNA损伤修复等过程中涉及的通路上调，这些通路已知会促进卵巢癌的铂耐药。基于这些发现，我们假设这些差异表达基因可用于预测卵巢癌患者对铂类化疗的反应。为了验证这一假设，我们探索了7种不同的机器学习模型，用于在不同特征基因数量下预测铂类化疗反应。最终，随机森林模型表现最佳，5个基因（PAX2、TFPI2、APOA1、ADIRF和CRISP3）在测试队列中的AUC为0.993，在GSE63885独立验证队列中的AUC为0.989。我们将此模型命名为GPPS（预测铂反应特征基因）。此外，我们发现GPPS模型还可以预测患者的预后。
