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解析卵巢癌淋巴结转移的分子机制:聚焦 MEOX1。

Unraveling the molecular mechanisms of lymph node metastasis in ovarian cancer: focus on MEOX1.

机构信息

Department of Gynecology Oncology, Obstetrics & Gynecology Hospital, Fudan University, Shanghai, 200011, China.

Department of Pathology, Obstetrics and Gynecology Hospital of Fudan University, Shanghai, 200011, China.

出版信息

J Ovarian Res. 2024 Mar 14;17(1):61. doi: 10.1186/s13048-024-01384-6.

DOI:10.1186/s13048-024-01384-6
PMID:38486335
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10938838/
Abstract

BACKGROUND

Lymph node metastasis (LNM) is a major factor contributing to the high mortality rate of ovarian cancer, making the treatment of this disease challenging. However, the molecular mechanism underlying LNM in ovarian cancer is still not well understood, posing a significant obstacle to overcome.

RESULTS

Through data mining from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, we have identified MEOX1 as a specific gene associated with LNM in ovarian cancer. The expression of MEOX1 was found to be relatively high in serous ovarian adenocarcinoma, and its higher expression were associated with increased tumor grade and poorer clinical prognosis for ovarian cancer patients. Bioinformatics analysis revealed that MEOX1 exhibited the highest mRNA levels among all cancer types in ovarian cancer tissues and cell lines. Furthermore, gene set enrichment analysis (GSEA) and pathway analysis demonstrated that MEOX1 was involved in various LNM-related biological activities, such as lymphangiogenesis, lymphatic vessel formation during metastasis, epithelial-mesenchymal transition (EMT), G2/M checkpoint, degradation of extracellular matrix, and collagen formation. Additionally, the expression of MEOX1 was positively correlated with the expression of numerous prolymphangiogenic factors in ovarian cancer. To validate our findings, we conducted experiments using clinical tissue specimens and cell lines, which confirmed that MEOX1 was highly expressed in high-grade serous ovarian cancer (HGSOC) tissues and various ovarian cancer cell lines (A2780, SKOV3, HO8910, and OVCAR5) compared to normal ovarian tissues and normal ovarian epithelial cell line IOSE-80, respectively. Notably, we observed a higher protein level of MEOX1 in tumor tissues of LNM-positive HGSOC compared to LNM-negative HGSOC. Moreover, our fundamental experiments demonstrated that suppression of MEOX1 led to inhibitory effects on ovarian cancer cell proliferation and EMT, while overexpression of MEOX1 enhanced the proliferation and EMT capacities of ovarian cancer cells.

CONCLUSIONS

The results of our study indicate that MEOX1 plays a role in the lymph node metastasis of ovarian cancer by regulating multiple biological activities, including the proliferation and EMT of ovarian cancer, lymphangiogenesis, and ECM remodeling. Our findings suggest that MEOX1 could serve as a potential biomarker for the diagnosis and treatment of ovarian cancer with LNM.

摘要

背景

淋巴结转移(LNM)是导致卵巢癌高死亡率的主要因素,这使得该疾病的治疗极具挑战性。然而,卵巢癌中 LNM 的分子机制仍未得到充分理解,这成为了克服这一问题的重大障碍。

结果

通过对癌症基因组图谱(TCGA)和基因表达综合数据库(GEO)的数据挖掘,我们鉴定到 MEOX1 是卵巢癌中与 LNM 相关的特异性基因。在浆液性卵巢腺癌中,MEOX1 的表达相对较高,且其高表达与肿瘤分级增加和卵巢癌患者临床预后不良相关。生物信息学分析显示,在卵巢癌组织和细胞系中,MEOX1 的 mRNA 水平在所有癌症类型中均最高。此外,基因集富集分析(GSEA)和通路分析表明,MEOX1 参与了各种与 LNM 相关的生物学活动,如淋巴管生成、转移过程中的淋巴管形成、上皮间质转化(EMT)、G2/M 检查点、细胞外基质降解和胶原形成。此外,MEOX1 的表达与卵巢癌中许多促淋巴管生成因子的表达呈正相关。为了验证我们的发现,我们使用临床组织标本和细胞系进行了实验,结果证实,与正常卵巢组织和正常卵巢上皮细胞系 IOSE-80 相比,MEOX1 在高级别浆液性卵巢癌(HGSOC)组织和各种卵巢癌细胞系(A2780、SKOV3、HO8910 和 OVCAR5)中表达较高。值得注意的是,我们观察到 LNM 阳性 HGSOC 肿瘤组织中 MEOX1 的蛋白水平高于 LNM 阴性 HGSOC。此外,我们的基础实验表明,抑制 MEOX1 可抑制卵巢癌细胞增殖和 EMT,而过表达 MEOX1 则增强了卵巢癌细胞的增殖和 EMT 能力。

结论

本研究结果表明,MEOX1 通过调节多种生物学活动,包括卵巢癌细胞的增殖和 EMT、淋巴管生成和 ECM 重塑,在卵巢癌的淋巴结转移中发挥作用。我们的研究结果表明,MEOX1 可能成为诊断和治疗伴有 LNM 的卵巢癌的潜在生物标志物。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0024/10938838/8d0e4f2e5589/13048_2024_1384_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0024/10938838/289b36e2ea20/13048_2024_1384_Fig1_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0024/10938838/4aed65f97ef0/13048_2024_1384_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0024/10938838/7c03dc52b2cd/13048_2024_1384_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0024/10938838/77f7e9af09bd/13048_2024_1384_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0024/10938838/8d0e4f2e5589/13048_2024_1384_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0024/10938838/289b36e2ea20/13048_2024_1384_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0024/10938838/036d52ab417e/13048_2024_1384_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0024/10938838/5ccc183cc832/13048_2024_1384_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0024/10938838/4aed65f97ef0/13048_2024_1384_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0024/10938838/7c03dc52b2cd/13048_2024_1384_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0024/10938838/77f7e9af09bd/13048_2024_1384_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0024/10938838/8d0e4f2e5589/13048_2024_1384_Fig7_HTML.jpg

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Cancer statistics, 2022.癌症统计数据,2022 年。
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