Department of Gynecological Oncology, Zhongnan Hospital of Wuhan University, Hubei Clinical Cancer Study Center, Hubei Key Laboratory of Tumor Biological Behaviors, Wuhan, China.
Department of Obstetrics and Gynecology, Xiangyang No. 1 People's Hospital, Jinzhou Medical University Union Training Base, China.
J Immunol Res. 2022 Jul 28;2022:2148215. doi: 10.1155/2022/2148215. eCollection 2022.
Datasets containing RNA sequencing and corresponding clinical data of cervical cancer patients were obtained from searching publicly accessible databases. The "NMF" R package was conducted to calculate the matrix of the screened prognosis gene expression. Ferroptosis-related differential genes in cervical cancer were detected using the "limma" R function and WGCNA. The least absolute shrinkage and selection operator (LASSO) algorithm and Cox regression analysis were conducted to develop a novel prognostic signature. The prediction model was verified by the nomogram integrating clinical characteristics; the GSE44001 dataset was used as an external verification. Then, the immune status and tumor mutation load were explored. Finally, immunohistochemistry as well as quantitative polymerase chain reaction (RT-qPCR) was utilized to ascertain the expression of FRGs.
Two molecular subgroups (cluster 1 and cluster 2) with different FRG expression patterns were recognized. A ferroptosis-related model based on 4 genes (VEGFA, CA9, DERL3, and RNF130) was developed through TCGA database to identify the unfavorable prognosis cases. Patients in cluster 1 showed significantly decreased overall survival in contrast with those in cluster 2 ( < 0.05). The LASSO technique and Cox regression analysis were both utilized to establish the independence of the prognostic model. The validity of nomogram prognostic predictions has been well demonstrated for 3- and 5-year survival in both internal and external data validation cohorts. These two subgroups showed striking differences in tumor-infiltrating leukocytes and tumor mutation burden. The low-risk subgroup showed a longer overall survival time with a higher immune cell score and higher tumor mutation rate. Gene functional enrichment analyses revealed predominant enrichment in various tumor-associated signaling pathways. Finally, the expression of each gene was confirmed by immunohistochemistry and RT-qPCR.
A novel and comprehensive ferroptosis-related gene model was proposed for cervical cancer which was capable of distinguishing the patients independently with high risk for poor survival, and targeting ferroptosis may represent a promising approach for the treatment of CC.
从公共访问数据库中搜索获得包含宫颈癌患者 RNA 测序和相应临床数据的数据集。使用“NMF”R 包计算筛选预后基因表达的矩阵。使用“limma”R 函数和 WGCNA 检测宫颈癌中的铁死亡相关差异基因。使用最小绝对收缩和选择算子（LASSO）算法和 Cox 回归分析构建新的预后特征。通过整合临床特征的诺模图验证预测模型；使用 GSE44001 数据集进行外部验证。然后，探索免疫状态和肿瘤突变负荷。最后，通过免疫组织化学和实时定量聚合酶链反应（RT-qPCR）确定 FRG 的表达。
鉴定出具有不同 FRG 表达模式的两个分子亚群（簇 1 和簇 2）。通过 TCGA 数据库建立了基于 4 个基因（VEGFA、CA9、DERL3 和 RNF130）的铁死亡相关模型，以识别预后不良的病例。与簇 2 相比，簇 1 中的患者总生存率明显降低（<0.05）。LASSO 技术和 Cox 回归分析均用于建立预后模型的独立性。内部和外部数据验证队列中，诺模图预测的生存结果均具有良好的有效性。这两个亚组在肿瘤浸润白细胞和肿瘤突变负荷方面存在显著差异。低风险亚组的总生存时间更长，免疫细胞评分更高，肿瘤突变率更高。基因功能富集分析显示，各种肿瘤相关信号通路存在明显富集。最后，通过免疫组织化学和 RT-qPCR 验证了每个基因的表达。
提出了一种新的、全面的宫颈癌铁死亡相关基因模型，能够独立区分具有高不良预后风险的患者，靶向铁死亡可能是治疗宫颈癌的一种有前途的方法。
