Department of gynaecology, Shengjing Hospital of China Medical University, Shenyang, China.
BMC Cancer. 2024 Feb 14;24(1):207. doi: 10.1186/s12885-024-11908-4.
Ovarian cancer (OC) is a complex disease with significant tumor heterogeneity with the worst prognosis and highest mortality among all gynecological cancers. Glycosylation is a specific post-translational modification that plays an important role in tumor progression, immune escape and metastatic spread. The aim of this work was to identify the major glycosylation-related genes (GRGs) in OC and construct an effective GRGs signature to predict prognosis and immunotherapy.
AUCell algorithm was used to identify glycosylation-related genes (GRGs) based on the scRNA-seq and bulk RNA-seq data. An effective GRGs signature was conducted using COX and LASSO regression algorithm. The texting dataset and clinical sample data were used to assessed the accuracy of GRGs signature. We evaluated the differences in immune cell infiltration, enrichment of immune checkpoints, immunotherapy response, and gene mutation status among different risk groups. Finally, RT-qPCR, Wound-healing assay, Transwell assay were performed to verify the effect of the CYBRD1 on OC.
A total of 1187 GRGs were obtained and a GRGs signature including 16 genes was established. The OC patients were divided into high- and low- risk group based on the median riskscore and the patients in high-risk group have poor outcome. We also found that the patients in low-risk group have higher immune cell infiltration, enrichment of immune checkpoints and immunotherapy response. The results of laboratory test showed that CYBRD1 can promote the invasion, and migration of OC and is closely related to the poor prognosis of OC patients.
Our study established a GRGs signature consisting of 16 genes based on the scRNA-seq and bulk RNA-seq data, which provides a new perspective on the prognosis prediction and treatment strategy for OC.
卵巢癌（OC）是一种复杂的疾病，具有显著的肿瘤异质性，其预后最差，死亡率在所有妇科癌症中最高。糖基化是一种特定的翻译后修饰，在肿瘤进展、免疫逃逸和转移扩散中发挥重要作用。本研究旨在鉴定 OC 中主要的糖基化相关基因（GRGs），并构建有效的 GRGs 特征以预测预后和免疫治疗。
基于 scRNA-seq 和批量 RNA-seq 数据，使用 AUCell 算法鉴定糖基化相关基因（GRGs）。使用 COX 和 LASSO 回归算法构建有效的 GRGs 特征。使用文本数据集和临床样本数据评估 GRGs 特征的准确性。我们评估了不同风险组之间免疫细胞浸润、免疫检查点富集、免疫治疗反应和基因突变状态的差异。最后，通过 RT-qPCR、划痕愈合实验和 Transwell 实验验证了 CYBRD1 对 OC 的影响。
共获得 1187 个 GRGs，建立了一个包含 16 个基因的 GRGs 特征。根据中位风险评分将 OC 患者分为高风险和低风险组，高风险组患者的预后较差。我们还发现，低风险组患者的免疫细胞浸润、免疫检查点富集和免疫治疗反应更高。实验室测试结果表明，CYBRD1 可促进 OC 的侵袭和迁移，与 OC 患者的不良预后密切相关。
本研究基于 scRNA-seq 和批量 RNA-seq 数据建立了一个由 16 个基因组成的 GRGs 特征，为 OC 的预后预测和治疗策略提供了新的视角。
