Identification of Copper Homeostasis-Related Gene Signature for Predicting Prognosis in Patients with Epithelial Ovarian Cancer.

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Cancer Informatics Pub Date : 2024-08-13 eCollection Date: 2024-01-01 DOI:10.1177/11769351241272400

Ping Yan, Yueqin Tian, Xiaojing Li, Shuangmei Li, Haidong Wu, Tong Wang

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Abstract

Objectives: This research aims to establish a copper homeostasis-related gene signature for predicting the prognosis of epithelial ovarian cancer and to investigate its underlying mechanisms.

Methods: We mainly constructed the copper homeostasis-related gene signature by LASSO regression analysis. Then multiple methods were used to evaluate the independent predictive ability of the model and explored the mechanisms.

Results: The 15-copper homeostasis-related gene (15-CHRG) signature was successfully established. Utilizing an optimal cut-off value of 0.35, we divided the training dataset into high-risk and low-risk subgroups. Kaplan-Meier analysis revealed that survival times for the high-risk subgroup were significantly shorter than those in the low-risk group (P < .05). Additionally, the Area Under the Curve (AUC) of the 15-CHRG signature achieved 0.822 at 1 year, 0.762 at 3 years, and 0.696 at 5 years in the training set. COX regression analysis confirmed the 15-CHRG signature as both accurate and independent. Gene set enrichment (GSEA), Kyoto Encyclopedia of Gene and Genome (KEGG) and Gene Ontology (GO) analysis showed that there were significant differences in apoptosis, p53 pathway, protein synthesis, hydrolase and transport-related pathways between high-risk group and low-risk group. In tumor immune cell (TIC) analysis, the increased expression of resting mast cells was positively correlated with the risk score.

Conclusion: Consequently, the 15-CHRG signature shows significant potential as a method for accurately predicting clinical outcomes and treatment responses in patients with epithelial ovarian cancer.

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鉴定铜平衡相关基因特征以预测上皮性卵巢癌患者的预后

研究目的本研究旨在建立预测上皮性卵巢癌预后的铜稳态相关基因特征，并探讨其潜在机制：方法：主要通过 LASSO 回归分析构建铜稳态相关基因特征。方法：我们主要通过 LASSO 回归分析法构建了铜稳态相关基因特征，然后采用多种方法评估了模型的独立预测能力并探讨了其机制：结果：成功建立了15个铜稳态相关基因（15-CHRG）特征。利用0.35的最佳临界值，我们将训练数据集分为高危和低危亚组。Kaplan-Meier 分析显示，高风险亚组的生存时间明显短于低风险组（P 结论：高风险亚组的生存时间明显短于低风险组）：因此，15-CHRG 特征在准确预测上皮性卵巢癌患者的临床结局和治疗反应方面显示出巨大的潜力。

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

Cancer Informatics Medicine-Oncology

CiteScore

3.00

自引率

5.00%

发文量

审稿时长

8 weeks

期刊介绍： The field of cancer research relies on advances in many other disciplines, including omics technology, mass spectrometry, radio imaging, computer science, and biostatistics. Cancer Informatics provides open access to peer-reviewed high-quality manuscripts reporting bioinformatics analysis of molecular genetics and/or clinical data pertaining to cancer, emphasizing the use of machine learning, artificial intelligence, statistical algorithms, advanced imaging techniques, data visualization, and high-throughput technologies. As the leading journal dedicated exclusively to the report of the use of computational methods in cancer research and practice, Cancer Informatics leverages methodological improvements in systems biology, genomics, proteomics, metabolomics, and molecular biochemistry into the fields of cancer detection, treatment, classification, risk-prediction, prevention, outcome, and modeling.