@article{4587b6f32c014dc39842de64323d99a6,
title = "Analysis of lages family gene signature and prognostic relevance in breast cancer",
abstract = "Breast cancer (BRCA) is one of the most complex diseases and involves several biological processes. Members of the L-antigen (LAGE) family participate in the development of various cancers, but their expressions and prognostic values in breast cancer remain to be clarified. High-throughput methods for exploring disease progression mechanisms might play a pivotal role in the improvement of novel therapeutics. Therefore, gene expression profiles and clinical data of LAGE family members were acquired from the cBioportal database, followed by verification using the Oncomine and The Cancer Genome Atlas (TCGA) databases. In addition, the Kaplan-Meier method was applied to explore correlations between expressions of LAGE family members and prognoses of breast cancer patients. MetaCore, GlueGo, and GluePedia were used to comprehensively study the transcript expression signatures of LAGEs and their co-expressed genes together with LAGE-related signal transduction pathways in BRCA. The result indicated that higher LAGE3 messenger (m)RNA expressions were observed in BRCA tissues than in normal tissues, and they were also associated with the stage of BRCA patients. Kaplan-Meier plots showed that overexpression of LAGE1, LAGE2A, LAGE2B, and LAGE3 were highly correlated to poor survival in most types of breast cancer. Significant associations of LAGE family genes were correlated with the cell cycle, focal adhesion, and extracellular matrix (ECM) receptor interactions as indicated by functional enrichment analyses. Collectively, LAGE family members{\textquoteright} gene expression levels were related to adverse clinicopathological factors and prognoses of BRCA patients; therefore, LAGEs have the potential to serve as prognosticators of BRCA patients.",
keywords = "Bioinformatics, Breast cancer, LAGE1, LAGE2A, LAGE2B, LAGE3",
author = "Ta, {Hoang Dang Khoa} and Tang, {Wan Chun} and Phan, {Nam Nhut} and Gangga Anuraga and Hou, {Sz Ying} and Chiao, {Chung Chieh} and Liu, {Yen Hsi} and Wu, {Yung Fu} and Lee, {Kuen Haur} and Wang, {Chih Yang}",
note = "Funding Information: Funding: This research was funded by grants from the Ministry of Science and Technology (MOST) of Taiwan (MOST109-2320-B-038-009-MY2 to C.-Y.W.), the Ministry of Education of Taiwan (DP2-109-21121-03-C-03-03 to K.-H.L.), Taipei Medical University (TMU-108-AE1-B16 to C.-Y.W.), and the TMU Research Center of Cancer Translational Medicine from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan. Funding Information: Acknowledgments: Bioinformatics analyses and data mining were conducted by the Bioinformatics Core Facility at Taipei Medical University. The study was supported by grants from the Ministry of Science and Technology (MOST) of Taiwan (MOST109-2320-B-038-009-MY2 to C.-Y.W.), the Ministry of Education of Taiwan (DP2-109-21121-03-C-03-03 to K.-H.L.), Taipei Medical University (TMU-108-AE1-B16 to C.-Y.W.), and the TMU Research Center of Cancer Translational Medicine from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan. The authors give special thanks to Daniel P. Chamberlin for his professional English editing from the Office of Research and Development at Taipei Medical University. Publisher Copyright: {\textcopyright} 2021 by the authors. Licensee MDPI, Basel, Switzerland.",
year = "2021",
month = apr,
doi = "10.3390/DIAGNOSTICS11040726",
language = "English",
volume = "11",
journal = "Diagnostics",
issn = "2075-4418",
publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",
number = "4",
}