基于线粒体代谢相关基因建立卵巢癌预后模型。
Establishment of a prognostic model for ovarian cancer based on mitochondrial metabolism-related genes.
作者信息
Meng Chao, Sun Yue, Liu Guoyan
机构信息
Tianjin Medical University General Hospital, Department of Gynecology and Obstetrics, Tianjin Key Laboratory of Female Reproductive Health and Eugenics, Tianjin, China.
Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.
出版信息
Front Oncol. 2023 May 15;13:1144430. doi: 10.3389/fonc.2023.1144430. eCollection 2023.
BACKGROUND
Mitochondrial metabolism and mitochondrial structure were found to be altered in high-grade serous ovarian cancer (HGSOC). The intent of this exploration was to systematically depict the relevance between mitochondrial metabolism-related genes (MMRGs) and the prognosis of HGSOC patients by bioinformatics analysis and establish a prognostic model for HGSOC.
METHODS
First of all, screened differentially expressed genes (DEGs) between TCGA-HGSOC and GTEx-normal by limma, with RNA-seq related HGSOC sourced from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) database. Subsequently, expressed MMRGs (DE-MMRGs) were acquired by overlapping DEGs with MMRGs, and an enrichment analysis of DE-MMRGs was performed. Kaplan-Meier (K-M) survival analysis and Cox regression analysis were conducted to validate the genes' prognostic value, Gene Set Enrichment Analysis (GSEA) to elucidate the molecular mechanisms of the risk score, and CIBERSORT algorithm to explore the immuno landscape of HGSOC patients. Finally, a drug sensitivity analysis was made the Drug Sensitivity in Cancer (GDSC) database.
RESULTS
436 HGSOC-related DE-MMRGs (222 up-regulated and 214 down-regulated) were observed to participate in multiple metabolic pathways. The study structured a MMRGs-related prognostic signature on the basis of IDO1, TNFAIP8L3, GPAT4, SLC27A1, ACSM3, ECI2, PPT2, and PMVK. Risk score was the independent prognostic element for HGSOC. Highly dangerous population was characterized by significant association with mitochondria-related biological processes, lower immune cell abundance, lower expression of immune checkpoint and antigenic molecules. Besides, 54 drugs associated with eight prognostic genes were obtained. Furthermore, copy number variation was bound up with the 8 prognostic genes in expression levels.
CONCLUSION
We have preliminarily determined the prognostic value of MMRGs in HGSOC as well as relationship between MMRGs and the tumor immune microenvironment.
背景
在高级别浆液性卵巢癌（HGSOC）中发现线粒体代谢和线粒体结构发生改变。本研究旨在通过生物信息学分析系统描述线粒体代谢相关基因（MMRGs）与HGSOC患者预后之间的相关性，并建立HGSOC的预后模型。
方法
首先，利用limma软件筛选来自癌症基因组图谱（TCGA）和基因型-组织表达（GTEx）数据库中RNA-seq相关的HGSOC与GTEx-正常样本之间的差异表达基因（DEGs）。随后，通过将DEGs与MMRGs重叠获得差异表达的MMRGs（DE-MMRGs），并对DE-MMRGs进行富集分析。进行Kaplan-Meier（K-M）生存分析和Cox回归分析以验证基因的预后价值，基因集富集分析（GSEA）以阐明风险评分的分子机制，以及CIBERSORT算法以探索HGSOC患者的免疫格局。最后，利用癌症药物敏感性（GDSC）数据库进行药物敏感性分析。
结果
观察到436个与HGSOC相关的DE-MMRGs（222个上调和214个下调）参与多个代谢途径。该研究基于IDO1、TNFAIP8L3、GPAT4、SLC27A1、ACSM3、ECI2、PPT2和PMVK构建了一个与MMRGs相关的预后特征。风险评分是HGSOC的独立预后因素。高危人群的特征是与线粒体相关的生物学过程显著相关、免疫细胞丰度较低、免疫检查点和抗原分子表达较低。此外，获得了与8个预后基因相关的54种药物。此外，拷贝数变异与8个预后基因的表达水平相关。
结论
我们初步确定了MMRGs在HGSOC中的预后价值以及MMRGs与肿瘤免疫微环境之间的关系。
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