由于卵巢癌的异质性和复杂的免疫微环境，个体化医疗仍然是一个未满足的需求，在免疫治疗时代越来越受到关注。一个主要障碍是缺乏可靠的生物标志物来识别将从该治疗中显着受益的患者。虽然传统的临床病理因素作为卵巢癌预后指标的功效有限，但多组学分析为理解肿瘤和免疫成分之间的相互作用提供了一条有希望的途径。在这里，我们的目标是利用卵巢癌患者的个体蛋白质组和转录组图谱来开发一种有效的基于蛋白质的特征，能够预测和区分对免疫治疗的反应。该工作流程基于反相蛋白阵列 (RPPA) 和 RNA 测序进行了演示癌症基因组图谱 (TCGA) 中卵巢癌患者的概况。该算法首先使用免疫相关基因集对患者进行聚类，这使我们能够识别感兴趣的免疫相关蛋白质。接下来，采用涉及 LASSO 和 Cox 回归的多阶段过程来提取包含五种免疫相关蛋白的预后特征。根据该特征，我们随后计算了每位患者的风险评分，并通过将该模型与传统的临床病理特征进行比较来评估其预后表现。我们在 377 名卵巢癌患者的队列中开发并验证了基于蛋白质的预后特征。在预后方面，风险特征优于传统的临床病理因素，如年龄、分级、分期、微卫星不稳定性（MSI）和同源重组缺陷（HRD）状态。高风险组患者的总生存率显着不利（p＜＜0.001）。此外，我们的特征有效地将患者分为具有不同免疫状况的亚组。高风险组表现出更高水平的 CD8 T 细胞浸润，并且免疫治疗反应者的比例可能更高。 TGF-β途径和癌症相关成纤维细胞的共同激活可能会损害细胞毒性T细胞消除癌细胞的能力，导致高危人群的预后不良。基于蛋白质的特征不仅有助于评估预后，而且还为卵巢癌的肿瘤免疫微环境提供了有价值的见解。我们的研究结果共同强调了彻底了解卵巢癌中免疫抑制肿瘤微环境的重要性，以指导开发更有效的免疫疗法。© 2024。作者。

Personalized medicine remains an unmet need in ovarian cancer due to its heterogeneous nature and complex immune microenvironments, which has gained increasing attention in the era of immunotherapy. A key obstacle is the lack of reliable biomarkers to identify patients who would benefit significantly from the therapy. While conventional clinicopathological factors have exhibited limited efficacy as prognostic indicators in ovarian cancer, multi-omics profiling presents a promising avenue for comprehending the interplay between the tumor and immune components. Here we aimed to leverage the individual proteomic and transcriptomic profiles of ovarian cancer patients to develop an effective protein-based signature capable of prognostication and distinguishing responses to immunotherapy.The workflow was demonstrated based on the Reverse Phase Protein Array (RPPA) and RNA-sequencing profiles of ovarian cancer patients from The Cancer Genome Atlas (TCGA). The algorithm began by clustering patients using immune-related gene sets, which allowed us to identify immune-related proteins of interest. Next, a multi-stage process involving LASSO and Cox regression was employed to distill a prognostic signature encompassing five immune-related proteins. Based on the signature, we subsequently calculated the risk score for each patient and evaluated its prognostic performance by comparing this model with conventional clinicopathological characteristics.We developed and validated a protein-based prognostic signature in a cohort of 377 ovarian cancer patients. The risk signature outperformed conventional clinicopathological factors, such as age, grade, stage, microsatellite instability (MSI), and homologous recombination deficiency (HRD) status, in terms of prognoses. Patients in the high-risk group had significantly unfavorable overall survival (p < 0.001). Moreover, our signature effectively stratified patients into subgroups with distinct immune landscapes. The high-risk group exhibited higher levels of CD8 T-cell infiltration and a potentially greater proportion of immunotherapy responders. The co-activation of the TGF-β pathway and cancer-associated fibroblasts could impair the ability of cytotoxic T cells to eliminate cancer cells, leading to poor outcomes in the high-risk group.The protein-based signature not only aids in evaluating the prognosis but also provides valuable insights into the tumor immune microenvironments in ovarian cancer. Together our findings highlight the importance of a thorough understanding of the immunosuppressive tumor microenvironment in ovarian cancer to guide the development of more effective immunotherapies.© 2024. The Author(s).