Jiang Haonan, Awuti Guzhanuer, Guo Xiaoqing
Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai 200092, China.
Department of Gynecological Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai 200092, China.
ACS Omega. 2023 Aug 30;8(36):33017-33031. doi: 10.1021/acsomega.3c04856. eCollection 2023 Sep 12.
Ovarian cancer (OC) is the deadliest gynecological malignancy in the world, and immunotherapy is emerging as a promising treatment. Immunophenoscore (IPS) is a robust biomarker distinguishing sensitive responders from immunotherapy. In this study, we aimed to construct a prognostic model for predicting overall survival (OS) and identifying patients who would benefit from immunotherapy. First, we combined The Cancer Genome Atlas (TCGA) and The Cancer Immune Atlas (TCIA) data sets and incorporated 229 OC samples into a training cohort. The validation cohort included 240 OC samples from the Gene Expression Omnibus (GEO) cohort. The training cohort was divided into high- and low-IPS subgroups to obtain differentially expressed genes (DEGs). DEGs with OS were identified by Univariate Cox regression analysis. The least absolute shrinkage and selection operator (LASSO) Cox regression was used to construct the prognostic model. Then, immune and mutation analyses were performed to explore the relationship between the model and the tumor microenvironment (TME) and tumor mutation burden (TMB). Eighty-three DEGs were obtained between the high-and low-IPS subgroups, where 17 DEGs were associated with OS. The five essential genes were selected to establish the prognostic model, which showed high accuracy for predicting OS and could be an independent survival indicator. OC samples that were divided by risk scores showed distinguished immune status, TME, TMB, immunotherapy response, and chemotherapy sensitivity. Similar results were validated in the GEO cohort. We developed an immunophenoscore-related signature associated with the TME to predict OS and response to immunotherapy in OC.
卵巢癌（OC）是世界上最致命的妇科恶性肿瘤，免疫疗法正成为一种有前景的治疗方法。免疫表型评分（IPS）是一种强大的生物标志物，可区分免疫疗法的敏感反应者。在本研究中，我们旨在构建一个预测总生存期（OS）的预后模型，并识别将从免疫疗法中获益的患者。首先，我们合并了癌症基因组图谱（TCGA）和癌症免疫图谱（TCIA）数据集，并将229个OC样本纳入训练队列。验证队列包括来自基因表达综合数据库（GEO）队列的240个OC样本。将训练队列分为高IPS和低IPS亚组以获得差异表达基因（DEG）。通过单变量Cox回归分析确定与OS相关的DEG。使用最小绝对收缩和选择算子（LASSO）Cox回归构建预后模型。然后，进行免疫和突变分析以探索该模型与肿瘤微环境（TME）和肿瘤突变负担（TMB）之间的关系。在高IPS和低IPS亚组之间获得了83个DEG，其中17个DEG与OS相关。选择五个关键基因建立预后模型，该模型在预测OS方面显示出高准确性，并且可以作为独立的生存指标。根据风险评分划分的OC样本显示出不同的免疫状态、TME、TMB、免疫疗法反应和化疗敏感性。在GEO队列中验证了类似的结果。我们开发了一种与TME相关的免疫表型评分特征，以预测OC中的OS和对免疫疗法的反应。
