Department of Gynecology, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, China.
Department of Urology, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, China.
J Obstet Gynaecol Res. 2023 Aug;49(8):2135-2150. doi: 10.1111/jog.15720. Epub 2023 Jun 20.
Epidemiological studies reported that patients with endometriosis had an increased risk of developing endometriosis-associated ovarian cancer (EAOC). The present study aimed to identify shared genes and key pathways that commonly interacted between EAOC and endometriosis.
The expression matrix of ovarian cancer and endometriosis were collected from the Gene Expression Omnibus database. The weighted gene co-expression network analysis (WGCNA) was used to construct co-expression gene network. Machine learning algorithms were applied to identify characteristic genes. CIBERSORT deconvolution algorithm was used to explore the difference in tumor immune microenvironment. Furthermore, diagnostic nomogram was constructed and evaluated for supporting clinical practicality.
We identified 262 shared genes between EAOC and endometriosis via WGCNA analysis. They were mainly enriched in cytokine-cytokine receptor interaction. After protein-protein interaction network and machine learning algorithms, we recognized two characteristic genes (EDNRA, OCLN) and established a nomogram that presented an outstanding predictive performance. The hub genes demonstrated remarkable associations with immunological functions. Survival analysis indicated that dysregulated expressions of EDNRA and OCLN were closely correlated with prognosis of ovarian cancer patients. gene set enrichment analyses revealed that the two characteristic genes were mainly enriched in the cancer- and immune-related pathways.
Our findings pave the way for further investigation of potential candidate genes and will aid in improving the diagnosis and treatment of EAOC in endometriosis patients. More research is required to determine the exact mechanisms by which these two hub genes affecting the development and progression of EAOC from endometriosis.
流行病学研究报告称，子宫内膜异位症患者发生与子宫内膜异位症相关的卵巢癌（EAOC）的风险增加。本研究旨在确定 EAOC 和子宫内膜异位症之间共同相互作用的共享基因和关键途径。
从基因表达综合数据库中收集卵巢癌和子宫内膜异位症的表达矩阵。使用加权基因共表达网络分析（WGCNA）构建共表达基因网络。应用机器学习算法识别特征基因。使用 CIBERSORT 去卷积算法探索肿瘤免疫微环境的差异。此外，构建并评估诊断列线图以支持临床实用性。
通过 WGCNA 分析，我们在 EAOC 和子宫内膜异位症之间鉴定出 262 个共享基因。它们主要富集在细胞因子-细胞因子受体相互作用中。经过蛋白质-蛋白质相互作用网络和机器学习算法，我们识别出两个特征基因（EDNRA、OCLN），并建立了一个具有出色预测性能的列线图。枢纽基因与免疫功能具有显著关联。生存分析表明，EDNRA 和 OCLN 的失调表达与卵巢癌患者的预后密切相关。基因集富集分析表明，这两个特征基因主要富集在癌症和免疫相关途径中。
我们的研究结果为进一步研究潜在的候选基因铺平了道路，并将有助于改善子宫内膜异位症患者 EAOC 的诊断和治疗。需要进一步研究以确定这两个枢纽基因影响 EAOC 从子宫内膜异位症发展和进展的确切机制。
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