解码卵巢癌中铁死亡相关基因特征及免疫浸润模式：生物信息学预测与实验验证相结合

Decoding the Ferroptosis-Related Gene Signatures and Immune Infiltration Patterns in Ovarian Cancer: Bioinformatic Prediction Integrated with Experimental Validation.

作者信息

Zhang Beilei, Guo Bin, Kong Hancun, Yang Linwei, Yan Hui, Liu Jierui, Zhou Yichen, An Ruifang, Wang Fu

机构信息

Department of Obstetrics and Gynecology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710061, People's Republic of China.

Department of Medical Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710004, People's Republic of China.

出版信息

J Inflamm Res. 2024 Dec 4;17:10333-10346. doi: 10.2147/JIR.S498740. eCollection 2024.

DOI:10.2147/JIR.S498740

PMID:39654865

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11626233/

Abstract

BACKGROUND

Ovarian cancer is a type of gynecological cancer with extremely high fatality rate. Ferroptosis, an iron-dependent regulated cell death, inhibits the immune infiltration of tumor cells. Therefore, it is worthwhile to explore the effects of ferroptosis-related gene signatures and immune infiltration patterns on the clinical prognosis of ovarian cancer.

METHODS

In this study, we used the mRNA expression matrix and related medical information of those who suffer from ovarian cancer in the TCGA database. After that, we established a ferroptosis-related gene signature based on LASSO Cox regression model, and employed several specific enrichment analyses to explore the bioinformatics functions of differentially expressed genes (DEGs). Additionally, we analyzed the link between ferroptosis and immune cells by single-sample gene set enrichment analysis (ssGSEA) to create a heatmap of gene-immune cell correlation. We then examined the expression of immune checkpoints and verified the gene expression in ovarian cancer tissues by qPCR assays. Finally, we induced ferroptosis in ovarian cancer cells using drugs and analyzed their migration, invasion and gene expression.

RESULTS

According to LASSO Cox regression analysis, 9 prognostic DEGs were in association with overall survival (OS), which was utilized to construct a 9-gene signature for patients. Patients were divided into two groups, in which high-risk group's OS was markedly shorter than that of low-risk group (Log-rank p<0.001). KEGG enrichment analysis showed that these DEGs were linked to human cytomegalovirus (HCMV) infection. The ssGSEA analysis revealed significant differences in immune cell type and expression between ALOX12 and GLRX5 groups (p<0.05). Heatmap showed high correlation of prognostic genes with various immune cells. qPCR assay confirmed the 9 gene expression signature in ovarian cancer tissues. The ovarian cancer cell invasion and migration were significantly inhibited after induction of ferroptosis.

CONCLUSION

We decoded the ferroptosis-related gene signatures and immune infiltration patterns that can be used to predict the prognosis of ovarian cancer patients.

摘要

背景

卵巢癌是一种致死率极高的妇科癌症。铁死亡是一种铁依赖性的程序性细胞死亡，可抑制肿瘤细胞的免疫浸润。因此，探索铁死亡相关基因特征和免疫浸润模式对卵巢癌临床预后的影响具有重要意义。

方法

在本研究中，我们使用了TCGA数据库中卵巢癌患者的mRNA表达矩阵和相关医学信息。之后，我们基于LASSO Cox回归模型建立了铁死亡相关基因特征，并采用多种特定的富集分析来探索差异表达基因（DEG）的生物信息学功能。此外，我们通过单样本基因集富集分析（ssGSEA）分析铁死亡与免疫细胞之间的联系，以创建基因-免疫细胞相关性热图。然后，我们检测了免疫检查点的表达，并通过qPCR分析验证了卵巢癌组织中的基因表达。最后，我们使用药物诱导卵巢癌细胞发生铁死亡，并分析其迁移、侵袭和基因表达情况。

结果

根据LASSO Cox回归分析，9个预后DEG与总生存期（OS）相关，利用这些基因构建了患者的9基因特征。患者被分为两组，其中高危组的OS明显短于低危组（对数秩检验p<0.001）。KEGG富集分析表明，这些DEG与人巨细胞病毒（HCMV）感染有关。ssGSEA分析显示，ALOX12和GLRX5组之间的免疫细胞类型和表达存在显著差异（p<0.05）。热图显示预后基因与各种免疫细胞高度相关。qPCR分析证实了卵巢癌组织中的9基因表达特征。诱导铁死亡后，卵巢癌细胞的侵袭和迁移受到显著抑制。