Machine learning analysis of oxidative stress-related phenotypes for specific gene screening in ovarian cancer

IF 4.4 3区医学 Q2 ENVIRONMENTAL SCIENCES

Environmental Toxicology Pub Date : 2024-08-20 DOI:10.1002/tox.24321

Chenxiang Pan, Chunyu Pan, Lili Chen, Aidi Lin

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引用次数: 0

Abstract

Background

Oxidative stress serves a crucial role in tumor development. However, the relationship between ovarian cancer and oxidative stress remains unknown. We aimed to create an oxidative stress-related prognostic signature to enhance the prognosis prediction of CC patients using bioinformatics.

Methods

The genes differentially expressed and associated with oxidative stress were extracted with the help of “limma” packages. The model for prognosis was created using Multivariate Cox regression analysis to determine the risk related to the genes related to oxidative stress. Patients were categorized as low-risk or high-risk based on the median score. The receiver operation characteristic (ROC) and survival curves were used to evaluate the predictive effect of the prognostic signature. We utilized quantitative real-time PCR to assess the expression levels of key genes associated with oxidative stress in ovarian cancer cell lines (SKOV3, OVCAR3, and HeyA8) and normal ovarian epithelial cells (HOSEpiC).

Results

A signature comprising seven genes associated with oxidative stress was developed to prognosticate patients with ovarian cancer. Overall survival (OS) of the patient having CC was determined using Kaplan–Meier analysis. It was found that patient with a higher risk score had lower OS than the low-risk score. The signature of genes associated with oxidative stress was found to be independently prognostic for 1, 2, and 3 years. Further research found that the expression levels of nine hub genes had a strong association with patient outcomes. Our analysis revealed a higher expression of CX3CR1 in ovarian cancer cell lines compared with normal cells.

Conclusions

To deploy a novel oxidative stress-related prognostic signature as an independent biomarker in cervical cancer, we developed and validated it.

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对氧化应激相关表型进行机器学习分析，以筛查卵巢癌中的特异性基因。

背景：氧化应激在肿瘤发生发展过程中起着至关重要的作用。然而，卵巢癌与氧化应激之间的关系仍然未知。我们的目的是利用生物信息学建立一个与氧化应激相关的预后特征，以加强对卵巢癌患者的预后预测：方法：利用 "limma "软件包提取与氧化应激相关的差异表达基因。方法：利用 "limma "软件包提取了与氧化应激相关的差异表达基因，并利用多变量 Cox 回归分析建立了预后模型，以确定与氧化应激相关基因有关的风险。根据得分中位数将患者分为低风险和高风险。接受者操作特征曲线（ROC）和生存曲线用于评估预后特征的预测效果。我们利用定量实时 PCR 技术评估了卵巢癌细胞系（SKOV3、OVCAR3 和 HeyA8）和正常卵巢上皮细胞（HOSEpiC）中与氧化应激相关的关键基因的表达水平：结果：由七个与氧化应激相关的基因组成的特征被用来预测卵巢癌患者的预后。采用卡普兰-梅耶分析法确定了CC患者的总生存期（OS）。结果发现，风险评分较高的患者的 OS 低于风险评分较低的患者。研究发现，与氧化应激相关的基因特征可独立预测1年、2年和3年的预后。进一步的研究发现，九个枢纽基因的表达水平与患者的预后密切相关。我们的分析表明，与正常细胞相比，卵巢癌细胞系中CX3CR1的表达量更高：为了将新型氧化应激相关预后特征作为宫颈癌的独立生物标志物，我们开发并验证了该特征。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Environmental Toxicology 环境科学-毒理学

CiteScore

7.10

自引率

8.90%

发文量

261

审稿时长

4.5 months

期刊介绍： The journal publishes in the areas of toxicity and toxicology of environmental pollutants in air, dust, sediment, soil and water, and natural toxins in the environment.Of particular interest are: Toxic or biologically disruptive impacts of anthropogenic chemicals such as pharmaceuticals, industrial organics, agricultural chemicals, and by-products such as chlorinated compounds from water disinfection and waste incineration; Natural toxins and their impacts; Biotransformation and metabolism of toxigenic compounds, food chains for toxin accumulation or biodegradation; Assays of toxicity, endocrine disruption, mutagenicity, carcinogenicity, ecosystem impact and health hazard; Environmental and public health risk assessment, environmental guidelines, environmental policy for toxicants.