Dataset Information

Exploring lncRNA-Mediated Regulatory Networks in Endometrial Cancer Cells and the Tumor Microenvironment: Advances and Challenges.

ABSTRACT: Recent studies have revealed both the promise and challenges of targeting long non-coding RNAs (lncRNAs) to diagnose and treat endometrial cancer (EC). LncRNAs are upregulated or downregulated in ECs compared to normal tissues and their dysregulation has been linked to tumor grade, FIGO stage, the depth of myometrial invasion, lymph node metastasis and patient survival. Tumor suppressive lncRNAs (GAS5, MEG3, FER1L4 and LINC00672) and oncogenic lncRNAs (CCAT2, BANCR, NEAT1, MALAT1, H19 and Linc-RoR) have been identified as upstream modulators or downstream effectors of major signaling pathways influencing EC metastasis, including the PTEN/PI3K/AKT/mTOR, RAS/RAF/MEK/ERK, WNT/β-catenin and p53 signaling pathways. TUG1 and TDRG1 stimulate the VEGF-A pathway. PCGEM1 is implicated in activating the JAK/STAT3 pathway. Here, we present an overview of the expression pattern, prognostic value, biological function of lncRNAs in EC cells and their roles within the tumor microenvironment, focusing on the influence of lncRNAs on established EC-relevant pathways. We also describe the emerging classification of EC subtypes based on their lncRNA signature and discuss the clinical implications of lncRNAs as valuable biomarkers for EC diagnosis and potential targets for EC treatment.

SUBMITTER: Dong P

PROVIDER: S-EPMC6406952 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Exploring lncRNA-Mediated Regulatory Networks in Endometrial Cancer Cells and the Tumor Microenvironment: Advances and Challenges.

Dong Peixin P Xiong Ying Y Yue Junming J J B Hanley Sharon S Kobayashi Noriko N Todo Yukiharu Y Watari Hidemichi H

Cancers 20190216 2

Recent studies have revealed both the promise and challenges of targeting long non-coding RNAs (lncRNAs) to diagnose and treat endometrial cancer (EC). LncRNAs are upregulated or downregulated in ECs compared to normal tissues and their dysregulation has been linked to tumor grade, FIGO stage, the depth of myometrial invasion, lymph node metastasis and patient survival. Tumor suppressive lncRNAs (GAS5, MEG3, FER1L4 and LINC00672) and oncogenic lncRNAs (CCAT2, BANCR, NEAT1, MALAT1, H19 and Linc-R ...[more]

PMID: 30781521

Similar Datasets

Project description:BackgroundThe limitations of traditional computer-aided detection (CAD) systems for mammography, the extreme importance of early detection of breast cancer and the high impact of the false diagnosis of patients drive researchers to investigate deep learning (DL) methods for mammograms (MGs). Recent breakthroughs in DL, in particular, convolutional neural networks (CNNs) have achieved remarkable advances in the medical fields. Specifically, CNNs are used in mammography for lesion localization and detection, risk assessment, image retrieval, and classification tasks. CNNs also help radiologists providing more accurate diagnosis by delivering precise quantitative analysis of suspicious lesions.ResultsIn this survey, we conducted a detailed review of the strengths, limitations, and performance of the most recent CNNs applications in analyzing MG images. It summarizes 83 research studies for applying CNNs on various tasks in mammography. It focuses on finding the best practices used in these research studies to improve the diagnosis accuracy. This survey also provides a deep insight into the architecture of CNNs used for various tasks. Furthermore, it describes the most common publicly available MG repositories and highlights their main features and strengths.ConclusionsThe mammography research community can utilize this survey as a basis for their current and future studies. The given comparison among common publicly available MG repositories guides the community to select the most appropriate database for their application(s). Moreover, this survey lists the best practices that improve the performance of CNNs including the pre-processing of images and the use of multi-view images. In addition, other listed techniques like transfer learning (TL), data augmentation, batch normalization, and dropout are appealing solutions to reduce overfitting and increase the generalization of the CNN models. Finally, this survey identifies the research challenges and directions that require further investigations by the community.

Dataset Information

Exploring lncRNA-Mediated Regulatory Networks in Endometrial Cancer Cells and the Tumor Microenvironment: Advances and Challenges.

Publications

Exploring lncRNA-Mediated Regulatory Networks in Endometrial Cancer Cells and the Tumor Microenvironment: Advances and Challenges.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets