Literature DB >> 33374439

miR-497-5p Decreased Expression Associated with High-Risk Endometrial Cancer.

Ivana Fridrichova1, Lenka Kalinkova1, Miloslav Karhanek2, Bozena Smolkova3, Katarina Machalekova4, Lenka Wachsmannova1, Nataliia Nikolaieva1, Karol Kajo4.   

Abstract

The current guidelines for diagnosis, prognosis, and treatment of endometrial cancer (EC), based on clinicopathological factors, are insufficient for numerous reasons; therefore, we investigated the relevance of miRNA expression profiles for the discrimination of different EC subtypes. Among the miRNAs previously predicted to allow distinguishing of endometrioid ECs (EECs) according to different grades (G) and from serous subtypes (SECs), we verified the utility of miR-497-5p. In ECs, we observed downregulated miR-497-5p levels that were significantly decreased in SECs, clear cell carcinomas (CCCs), and carcinosarcomas (CaSas) compared to EECs, thereby distinguishing EEC from SEC and rare EC subtypes. Significantly reduced miR-497-5p expression was found in high-grade ECs (EEC G3, SEC, CaSa, and CCC) compared to low-grade carcinomas (EEC G1 and mucinous carcinoma) and ECs classified as being in advanced FIGO (International Federation of Gynecology and Obstetrics) stages, that is, with loco-regional and distant spread compared to cancers located only in the uterus. Based on immunohistochemical features, lower miR-497-5p levels were observed in hormone-receptor-negative, p53-positive, and highly Ki-67-expressing ECs. Using a machine learning method, we showed that consideration of miR-497-5p expression, in addition to the traditional clinical and histopathologic parameters, slightly improves the prediction accuracy of EC diagnosis. Our results demonstrate that changes in miR-497-5p expression influence endometrial tumorigenesis and its evaluation may contribute to more precise diagnoses.

Entities:  

Keywords:  endometrioid endometrial carcinoma; machine learning evaluation; miR-497-5p expression; rare subtypes of endometrial carcinoma; serous endometrial carcinoma

Year:  2020        PMID: 33374439     DOI: 10.3390/ijms22010127

Source DB:  PubMed          Journal:  Int J Mol Sci        ISSN: 1422-0067            Impact factor:   5.923


  8 in total

Review 1.  Can miRNAs be useful biomarkers in improving prognostic stratification in endometrial cancer patients? An update review.

Authors:  Gloria Ravegnini; Francesca Gorini; Eugenia De Crescenzo; Antonio De Leo; Dario De Biase; Marco Di Stanislao; Patrizia Hrelia; Sabrina Angelini; Pierandrea De Iaco; Anna Myriam Perrone
Journal:  Int J Cancer       Date:  2021-11-17       Impact factor: 7.316

2.  Long noncoding RNA SNHG25 promotes the malignancy of endometrial cancer by sponging microRNA-497-5p and increasing FASN expression.

Authors:  Yuhua He; Shuifang Xu; Yi Qi; Jinfang Tian; Fengying Xu
Journal:  J Ovarian Res       Date:  2021-11-18       Impact factor: 4.234

3.  Identification of miR-499a-5p as a Potential Novel Biomarker for Risk Stratification in Endometrial Cancer.

Authors:  Gloria Ravegnini; Antonio De Leo; Camelia Coada; Francesca Gorini; Dario de Biase; Claudio Ceccarelli; Giulia Dondi; Marco Tesei; Eugenia De Crescenzo; Donatella Santini; Angelo Gianluca Corradini; Giovanni Tallini; Patrizia Hrelia; Pierandrea De Iaco; Sabrina Angelini; Anna Myriam Perrone
Journal:  Front Oncol       Date:  2021-10-29       Impact factor: 6.244

4.  miR-497-5p/SALL4 axis promotes stemness phenotype of choriocarcinoma and forms a feedback loop with DNMT-mediated epigenetic regulation.

Authors:  Zheng Peng; Yi Zhang; Dazun Shi; Yanyan Jia; Huirong Shi; Huining Liu
Journal:  Cell Death Dis       Date:  2021-11-03       Impact factor: 8.469

5.  Circular RNA hsa_circ_0011324 is involved in endometrial cancer progression and the evolution of its mechanism.

Authors:  Dajiang Liu; Xuehan Bi; Yongxiu Yang
Journal:  Bioengineered       Date:  2022-03       Impact factor: 3.269

6.  miR-497-5p-RSPO2 axis inhibits cell growth and metastasis in glioblastoma.

Authors:  Kun Chen; Zheng Wang; Qi-Bei Zong; Meng-Ying Zhou; Qing-Fa Chen
Journal:  J Cancer       Date:  2022-01-24       Impact factor: 4.207

7.  Discovering Common miRNA Signatures Underlying Female-Specific Cancers via a Machine Learning Approach Driven by the Cancer Hallmark ERBB.

Authors:  Katia Pane; Mario Zanfardino; Anna Maria Grimaldi; Gustavo Baldassarre; Marco Salvatore; Mariarosaria Incoronato; Monica Franzese
Journal:  Biomedicines       Date:  2022-06-02

8.  Long Noncoding RNA LINC00473 Ameliorates Depression-Like Behaviors in Female Mice by Acting as a Molecular Sponge to Regulate miR-497-5p/BDNF Axis.

Authors:  Bo Li; Hongxia Zhao; Junxia Sun
Journal:  Comput Math Methods Med       Date:  2022-08-28       Impact factor: 2.809

  8 in total

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