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系统构建和验证新型铁死亡相关基因模型预测宫颈癌预后

Systematic Construction and Validation of a Novel Ferroptosis-Related Gene Model for Predicting Prognosis in Cervical Cancer.

机构信息

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.

出版信息

J Immunol Res. 2022 Jul 28;2022:2148215. doi: 10.1155/2022/2148215. eCollection 2022.

Abstract

METHODS

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.

RESULTS

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.

CONCLUSION

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.

摘要

方法

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。

结果

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。

结论

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。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c132/9352469/4ba3bcd53b5f/JIR2022-2148215.001.jpg

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