前列腺素-内过氧化物合酶-2在人乳腺癌中表达的预后意义:一种多组分析方法。

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Cancer Informatics Pub Date : 2020-11-06 eCollection Date: 2020-01-01 DOI:10.1177/1176935120969696
Madhuri Saindane, Harikrishna Reddy Rallabandi, Kyoung Sik Park, Alexander Heil, Sang Eun Nam, Young Bum Yoo, Jung-Hyun Yang, Ik Jin Yun
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引用次数: 4

摘要

前列腺素内过氧化物合成酶-2 (PTGS2)在人类乳腺癌的炎症和癌变中起关键作用。我们的研究目的是发现PTGS2在乳腺癌中的预后价值。我们进行了一项多组学分析,以确定PTGS2是否作为人类乳腺癌的预后生物标志物。我们利用不同的公共生物信息学门户研究了PTGS2 mRNA的表达。使用Oncomine、SAGE、GEPIA、ULCAN、PrognoScan数据库、Kaplan-Meier Plotter、bc-GenExMiner、USC XENA和Cytoscape/STRING DB来确定PTGS2在乳腺癌中的预后作用。基于临床病理分析,PTGS2表达降低与年龄、淋巴结状态、人表皮生长因子受体2 (HER2)状态(P P P P P P PTGS2)呈正相关。前列腺素内过氧化物合酶-2在乳腺癌组织中的表达明显低于正常组织。在PrognoScan数据库和Kaplan-Meier扫描仪中,PTGS2表达下调与不良的总生存期(OS)、无复发生存期(RFS)和远端无转移生存期相关。Luminal B亚型的甲基化水平明显更高。通过oncomine共表达基因分析,我们发现PTGS2与乳腺癌组织中白细胞介素-6 (IL-6)的表达呈正相关。这些结果表明,PTGS2的下调表达可以作为一种有希望的预后生物标志物,而Luminal B超甲基化可能在乳腺癌的发展中发挥重要作用。然而,为了澄清我们的结果,需要进行广泛的研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Prognostic Significance of Prostaglandin-Endoperoxide Synthase-2 Expressions in Human Breast Carcinoma: A Multiomic Approach.

Prognostic Significance of Prostaglandin-Endoperoxide Synthase-2 Expressions in Human Breast Carcinoma: A Multiomic Approach.

Prognostic Significance of Prostaglandin-Endoperoxide Synthase-2 Expressions in Human Breast Carcinoma: A Multiomic Approach.

Prognostic Significance of Prostaglandin-Endoperoxide Synthase-2 Expressions in Human Breast Carcinoma: A Multiomic Approach.

Prostaglandin-endoperoxide synthase-2 (PTGS2) plays a pivotal role in inflammation and carcinogenesis in human breast cancer. Our aim of the study is to find the prognostic value of PTGS2 in breast cancer. We conducted a multiomic analysis to determine whether PTGS2 functions as a prognostic biomarker in human breast cancer. We explored PTGS2 mRNA expressions using different public bioinformatics portals. Oncomine, Serial Analysis of Gene Expression (SAGE), GEPIA, ULCAN, PrognoScan database, Kaplan-Meier Plotter, bc-GenExMiner, USC XENA, and Cytoscape/STRING DB were used to identify the prognostic roles of PTGS2 in breast cancer. Based on the clinicopathological analysis, decreased PTGS2 expressions correlated positively with older age, lymph node status, the human epidermal growth factor receptor 2 (HER2) status (P < .0001), estrogen receptor (ER+) expression (P < .0001) Luminal A (P < .0001), and Luminal B (P < .0001). Interestingly, progesterone receptor (PR) (P < .0001) negative showed a high expression of PTGS2. Prostaglandin-endoperoxide synthase-2 was downregulated in breast cancer tissues than in normal tissues. In the PrognoScan database and, Kaplan-Meier Scanner, downregulated expressions of PTGS2 associated with poor overall survival (OS), relapse-free survival (RFS), and distant metastasis-free survival. The methylation levels were significantly higher in the Luminal B subtype. Through oncomine coexpressed gene analysis, we found a positive correlation between PTGS2 and interleukin-6 (IL-6) expression in breast cancer tissues. These results indicate that downregulated expressions of PTGS2 can be used as a promising prognostic biomarker and Luminal B hyper methylation may play an important role in the development of breast cancers. However, to clarify our results, extensive study is required.

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来源期刊
Cancer Informatics
Cancer Informatics Medicine-Oncology
CiteScore
3.00
自引率
5.00%
发文量
30
审稿时长
8 weeks
期刊介绍: The field of cancer research relies on advances in many other disciplines, including omics technology, mass spectrometry, radio imaging, computer science, and biostatistics. Cancer Informatics provides open access to peer-reviewed high-quality manuscripts reporting bioinformatics analysis of molecular genetics and/or clinical data pertaining to cancer, emphasizing the use of machine learning, artificial intelligence, statistical algorithms, advanced imaging techniques, data visualization, and high-throughput technologies. As the leading journal dedicated exclusively to the report of the use of computational methods in cancer research and practice, Cancer Informatics leverages methodological improvements in systems biology, genomics, proteomics, metabolomics, and molecular biochemistry into the fields of cancer detection, treatment, classification, risk-prediction, prevention, outcome, and modeling.
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