Li Li, Zhang Weiwei, Sun Yanjun, Zhang Weiling, Lu Mengmeng, Wang Jiaqian, Jin Yunfeng, Xi Qinghua
Department of Obstetrics and Gynecology, Affiliated Hospital of Nantong University, Nantong, Jiangsu, 226001, China.
Department of Obstetrics and Gynecology, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, Jiangsu, 226001, China.
Heliyon. 2024 Mar 21;10(7):e28442. doi: 10.1016/j.heliyon.2024.e28442. eCollection 2024 Apr 15.
According to statistics, ovarian cancer (OV) is the most prevalent type of gynecologic malignancy and has the highest mortality rate of all gynecologic tumors. Although several studies have shown that oxidative stress (OS) contributes significantly to the onset and progression of cancer, the role of OS in OV needs to be investigated further. Thus, it is critical to comprehend the function of OS-related genes in OV.
In this study, all data related to the transcriptome and clinical status of the patients were retrieved from "The Cancer Genome Atlas" (TCGA) and "Gene Expression Omnibus" (GEO) databases. Using the unsupervised cluster analysis technique, all patients with OV were classified into two different subtypes (categories) based on the OS gene. All hub genes were screened using the weighted gene co-expression network analysis (WGCNA). Since the hub genes and the differentially expressed genes (DEGs) in both categories were found to intersect, the univariate Cox regression analysis was implemented. A multivariate Cox analysis was also performed to construct a novel clinical prognosis model, which was validated using data from the GEO cohort. In addition, the relationship between risk score and immune cell infiltration level was evaluated using CIBERSORT. Finally, qRT-PCR was used to confirm the expression of the genes used to construct the model.
Two subtypes of OS were obtained. The findings indicated that OS-C1 had a better survival outcome than OS-C2. The results of WGCNA yielded 112 hub genes. For univariate COX regression analyses, 49 OS-related trait genes were obtained. Finally, a clinical prognostic model containing two genes was constructed. This model could differentiate between patients with OV having varying years of survival in the TCGA and GEO cohorts. The model risk score was verified as an independent prognostic indicator. According to the results of CIBERSORT, many tumor-infiltrating immune cells were found to be significantly related to the risk score. Furthermore, the results revealed that patients with low-risk OV in the CTLA4 treatment group had a high likelihood of benefiting from immunotherapy. qRT-PCR results also showed that the expression of and was high in the OV samples.
Analysis of the results suggested that the newly developed model, which contained two characteristic OS-related genes, could successfully predict the survival outcomes of all patients with OV. The findings of this study could offer valuable information and insights into the refinement of personalized therapy and immunotherapy for OV in the future.
据统计，卵巢癌（OV）是最常见的妇科恶性肿瘤类型，在所有妇科肿瘤中死亡率最高。尽管多项研究表明氧化应激（OS）对癌症的发生和发展有显著影响，但OS在OV中的作用仍需进一步研究。因此，了解OS相关基因在OV中的功能至关重要。
在本研究中，所有与患者转录组和临床状态相关的数据均从“癌症基因组图谱”（TCGA）和“基因表达综合数据库”（GEO）中检索。使用无监督聚类分析技术，根据OS基因将所有OV患者分为两种不同的亚型（类别）。使用加权基因共表达网络分析（WGCNA）筛选所有枢纽基因。由于发现两类中的枢纽基因和差异表达基因（DEG）存在交集，因此进行了单变量Cox回归分析。还进行了多变量Cox分析以构建新的临床预后模型，并使用来自GEO队列的数据进行验证。此外，使用CIBERSORT评估风险评分与免疫细胞浸润水平之间的关系。最后，使用qRT-PCR确认用于构建模型的基因的表达。
获得了OS的两种亚型。研究结果表明，OS-C1的生存结果优于OS-C2。WGCNA的结果产生了112个枢纽基因。对于单变量COX回归分析，获得了49个与OS相关的性状基因。最后，构建了一个包含两个基因的临床预后模型。该模型可以区分TCGA和GEO队列中生存年限不同的OV患者。模型风险评分被验证为独立的预后指标。根据CIBERSORT的结果，发现许多肿瘤浸润免疫细胞与风险评分显著相关。此外，结果显示CTLA4治疗组中低风险OV患者从免疫治疗中获益的可能性很高。qRT-PCR结果还表明， 和 在OV样本中的表达较高。
对结果的分析表明，新开发的包含两个与OS相关特征基因的模型可以成功预测所有OV患者的生存结果。本研究结果可为未来OV个性化治疗和免疫治疗的优化提供有价值的信息和见解。
