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GMPS、PR、CD40和p21在卵巢癌中的预后价值。

Prognostic values of GMPS, PR, CD40, and p21 in ovarian cancer.

作者信息

Wang Ping, Zhang Zengli, Ma Yujie, Lu Jun, Zhao Hu, Wang Shuiliang, Tan Jianming, Li Bingyan

机构信息

Fujian Key Laboratory of Transplant Biology, Fuzhou General Hospital, Fuzhou, Fujian, China.

Department of Nutrition and Food Hygiene, School of Public Health, Soochow University, Suzhou, Jiangsu, China.

出版信息

PeerJ. 2019 Jan 25;7:e6301. doi: 10.7717/peerj.6301. eCollection 2019.

DOI:10.7717/peerj.6301
PMID:30701134
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6348951/
Abstract

Early detection and prediction of prognosis and treatment responses are all the keys in improving survival of ovarian cancer patients. This study profiled an ovarian cancer progression model to identify prognostic biomarkers for ovarian cancer patients. Mouse ovarian surface epithelial cells (MOSECs) can undergo spontaneous malignant transformation cell culture. These were used as a model of ovarian cancer progression for alterations in gene expression and signaling detected using the Illumina HiSeq2000 Next-Generation Sequencing platform and bioinformatical analyses. The differential expression of four selected genes was identified using the gene expression profiling interaction analysis (http://gepia.cancer-pku.cn/) and then associated with survival in ovarian cancer patients using the Cancer Genome Atlas dataset and the online Kaplan-Meier Plotter (http://www.kmplot.com) data. The data showed 263 aberrantly expressed genes, including 182 up-regulated and 81 down-regulated genes between the early and late stages of tumor progression in MOSECs. The bioinformatic data revealed four genes (i.e., guanosine 5'-monophosphate synthase (GMPS), progesterone receptor (PR), CD40, and p21 (cyclin-dependent kinase inhibitor 1A)) to play an important role in ovarian cancer progression. Furthermore, the Cancer Genome Atlas dataset validated the differential expression of these four genes, which were associated with prognosis in ovarian cancer patients. In conclusion, this study profiled differentially expressed genes using the ovarian cancer progression model and identified four (i.e., GMPS, PR, CD40, and p21) as prognostic markers for ovarian cancer patients. Future studies of prospective patients could further verify the clinical usefulness of this four-gene signature.

摘要

早期检测、预后预测以及治疗反应预测都是提高卵巢癌患者生存率的关键。本研究构建了一个卵巢癌进展模型，以识别卵巢癌患者的预后生物标志物。小鼠卵巢表面上皮细胞（MOSEC）在细胞培养中可发生自发恶性转化。这些细胞被用作卵巢癌进展模型，通过Illumina HiSeq2000下一代测序平台和生物信息学分析来检测基因表达和信号传导的变化。使用基因表达谱交互分析（http://gepia.cancer-pku.cn/）确定四个选定基因的差异表达，然后使用癌症基因组图谱数据集和在线Kaplan-Meier Plotter（http://www.kmplot.com）数据将其与卵巢癌患者的生存率相关联。数据显示，在MOSEC肿瘤进展的早期和晚期之间有263个异常表达基因，包括182个上调基因和81个下调基因。生物信息学数据揭示了四个基因（即鸟苷5'-单磷酸合酶（GMPS）、孕激素受体（PR）、CD40和p21（细胞周期蛋白依赖性激酶抑制剂1A））在卵巢癌进展中起重要作用。此外，癌症基因组图谱数据集验证了这四个基因的差异表达，它们与卵巢癌患者的预后相关。总之，本研究使用卵巢癌进展模型分析了差异表达基因，并确定了四个基因（即GMPS、PR、CD40和p21）作为卵巢癌患者的预后标志物。对前瞻性患者的未来研究可以进一步验证这个四基因特征的临床实用性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8abb/6348951/de89991b16e9/peerj-07-6301-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8abb/6348951/b947c9531a86/peerj-07-6301-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8abb/6348951/357a11199210/peerj-07-6301-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8abb/6348951/415eb8f61a3d/peerj-07-6301-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8abb/6348951/f0391a826855/peerj-07-6301-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8abb/6348951/b22cd0934e93/peerj-07-6301-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8abb/6348951/de89991b16e9/peerj-07-6301-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8abb/6348951/b947c9531a86/peerj-07-6301-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8abb/6348951/357a11199210/peerj-07-6301-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8abb/6348951/415eb8f61a3d/peerj-07-6301-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8abb/6348951/f0391a826855/peerj-07-6301-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8abb/6348951/b22cd0934e93/peerj-07-6301-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8abb/6348951/de89991b16e9/peerj-07-6301-g006.jpg

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