Gong Weiwei, Kuang Mingqin, Chen Hongxi, Luo Yiheng, You Keli, Zhang Bin, Liu Yueyang
Department of Hematology and Oncology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
Gynecology and Oncology Department of Ganzhou Cancer Hospital, Ganzhou, Jiangxi, China.
Front Mol Biosci. 2024 Aug 5;11:1426274. doi: 10.3389/fmolb.2024.1426274. eCollection 2024.
Ovarian cancer (OC) is a common gynecological malignancy with poor prognosis and substantial tumor heterogeneity. Due to the complex tumor immune microenvironment (TIME) among ovarian cancer, only a few patients have an immune response to immunotherapy. To investigate the differences in immune function and identify potential biomarkers in OC, we established a prognostic risk scoring model (PRSM) with differential expression of immune-related genes (IRGs) to identify critical prognostic IRG signatures.
Single-sample gene set enrichment analysis (ssGSEA) was used to investigate the infiltration of various immune cells in 372 OC patients. Then, COX regression analysis and Lasso regression analysis were used to screen IRGs and construct PRSM. Next, the immunotherapy sensitivity of different risk groups regarding the immune checkpoint expression and tumor mutation burden was evaluated. Finally, a nomogram was created to guide the clinical evaluation of the patient prognosis.
In this study, 320 immune-related genes (IRGs) were identified, 13 of which were selectively incorporated into a Prognostic Risk Scoring Model (PRSM). This model revealed that the patients in the high-risk group were characterized as having poorer prognosis, lower expression of immune checkpoints, and decreased tumor mutation load levels compared with those in the low-risk group. The nomogram based on the risk score can distinguish the risk subtypes and individual prognosis of patients with OC. Additionally, M1 macrophages may be the critical target for immunotherapy in OC patients.
With the in-depth analysis of the immune microenvironment of OC, the PRSM was constructed to predict the OC patient prognosis and identify the subgroup of the patients benefiting from immunotherapy.
卵巢癌（OC）是一种常见的妇科恶性肿瘤，预后较差且肿瘤异质性显著。由于卵巢癌中复杂的肿瘤免疫微环境（TIME），只有少数患者对免疫疗法有免疫反应。为了研究免疫功能的差异并确定OC中的潜在生物标志物，我们建立了一个具有免疫相关基因（IRG）差异表达的预后风险评分模型（PRSM），以识别关键的预后IRG特征。
采用单样本基因集富集分析（ssGSEA）研究372例OC患者中各种免疫细胞的浸润情况。然后，使用COX回归分析和Lasso回归分析筛选IRG并构建PRSM。接下来，评估不同风险组在免疫检查点表达和肿瘤突变负担方面的免疫治疗敏感性。最后，创建了一个列线图以指导对患者预后的临床评估。
在本研究中，共鉴定出320个免疫相关基因（IRG），其中13个被选择性纳入预后风险评分模型（PRSM）。该模型显示，与低风险组相比，高风险组患者的预后较差，免疫检查点表达较低，肿瘤突变负荷水平降低。基于风险评分的列线图可以区分OC患者的风险亚型和个体预后。此外，M1巨噬细胞可能是OC患者免疫治疗的关键靶点。
通过对OC免疫微环境的深入分析，构建了PRSM以预测OC患者的预后，并识别受益于免疫治疗的患者亚组。
