Zhang Xianzhen, Li Aihua, Zhu Wanqi, Guo Qiufen, Wu Qian, Zhao Hong, Yu Yunbei, Xie Peng, Li Xiaolin
Department of Oncology, Liaocheng People's Hospital, Liaocheng, Shandong, 252000, People's Republic of China.
Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250000, People's Republic of China.
Cancer Manag Res. 2025 Mar 12;17:557-575. doi: 10.2147/CMAR.S501663. eCollection 2025.
The aim of this study was to clarify the genome of ferroptosis in the genes involved in radiotherapy resistance and regulation of tumor immune microenvironment by multigene analysis of cervical cancer (CC) patients.
Different radiation sensitivity samples from CC patients were collected for RNA sequencing. Differentially expressed genes (DEGs) between the RNA dataset and the GSE9750 dataset were considered as radiotherapy-DEGs. The intersection genes of radiotherapy-DEGs with ferroptosis-related genes (FRGs) and the intersection genes of radiotherapy-DEGs with immune-related genes (IRGs) were labeled as FRGs-IRGs-DEGs (FIGs). A risk model was established by prognostic genes selected from FIGs by univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) analysis. The results were further validated using samples from CC tissue samples.
The 329 DEGs related to CC radiotherapy were identified. LSAAO analysis was utilized to identify five prognostic genes (, and ) from six candidate prognosis genes and construct a risk model. The risk model demonstrated favorable effectiveness in predicting outcomes at 1, 3, and 5 years, as evidenced by ROC curves. Univariate and multivariate Cox regression analysis demonstrated that , and were independent prognostic factors. The results of functional similarity analysis showed that and had high average functional similarity. The results of PCR and IHC showed the same trend with the results above.
A novel prognostic model related to ferroptosis and immune microenvironment in CC radiotherapy was developed and validated, providing valuable guidance for personalized anti-cancer therapy.
本研究旨在通过对宫颈癌（CC）患者进行多基因分析，阐明参与放疗抵抗和肿瘤免疫微环境调节的铁死亡基因。
收集CC患者不同辐射敏感性样本进行RNA测序。RNA数据集与GSE9750数据集之间的差异表达基因（DEG）被视为放疗相关DEG。放疗相关DEG与铁死亡相关基因（FRG）的交集基因以及放疗相关DEG与免疫相关基因（IRG）的交集基因被标记为FRG-IRG-DEG（FIG）。通过单因素Cox分析和最小绝对收缩和选择算子（LASSO）分析从FIG中选择的预后基因建立风险模型。使用CC组织样本中的样本进一步验证结果。
鉴定出329个与CC放疗相关的DEG。利用LSAAO分析从六个候选预后基因中鉴定出五个预后基因（……）并构建风险模型。ROC曲线证明，该风险模型在预测1年、3年和5年的结果方面显示出良好的有效性。单因素和多因素Cox回归分析表明……是独立的预后因素。功能相似性分析结果表明……具有较高的平均功能相似性。PCR和IHC结果与上述结果趋势相同。
开发并验证了一种与CC放疗中铁死亡和免疫微环境相关的新型预后模型，为个性化抗癌治疗提供了有价值的指导。
