Author Archives: Hyunju Lee

Yeonghun Lee, Sehhoon Park, Se-Hoon Lee$, Hyunju Lee$ (2017) Characterization of Genetic Aberrations in a Single Case of Metastatic Thymic Adenocarcinoma. BMC Cancer, 2017 May 15;17(1):330. (IF: 3.265) (JCR 2015: 85/213, 39.9%, Oncology)

Characterization of Genetic Aberrations in a Single Case of Metastatic Thymic Adenocarcinoma.

  • Author : Yeonghun Lee, Sehhoon Park, Se-Hoon Lee$, and Hyunju Lee$
  • Published Date : 2017
  • Category : Bioinformatics and Text Mining 
  • Place of publication : BMC Cancer

 

BACKGROUND:

Thymic adenocarcinoma is an extremely rare subtype of thymic epithelial tumors. Due to its rarity, there is currently no sequencing approach for thymic adenocarcinoma.

METHODS:

We performed whole exome and transcriptome sequencing on a case of thymic adenocarcinoma and performed subsequent validation using Sanger sequencing.

RESULTS:

The case of thymic adenocarcinoma showed aggressive behaviors with systemic bone metastases. We identified a high incidence of genetic aberrations, which included somatic mutations in RNASEL, PEG10, TNFSF15, TP53, TGFB2, and FAT1. Copy number analysis revealed a complex chromosomal rearrangement of chromosome 8, which resulted in gene fusion between MCM4 and SNTB1 and dramatic amplification of MYC and NDRG1. Focal deletion was detected at human leukocyte antigen (HLA) class II alleles, which was previously observed in thymic epithelial tumors. We further investigated fusion transcripts using RNA-seq data and found an intergenic splicing event between the CTBS and GNG5 transcript. Finally, enrichment analysis using all the variants represented the immune system dysfunction in thymic adenocarcinoma.

CONCLUSION:

Thymic adenocarcinoma shows highly malignant characteristics with alterations in several cancer-related genes.

Jaeyong Kang and Hyunju Lee* (2017) Modeling User Interest in Social Media using News Media and Wikipedia. Information Systems, 2017 April 01; 65:52-64 (IF: 1.832) (JCR 2016: 34/144, 23.61%, COMPUTER SCIENCE, INFORMATION SYSTEMS).

Modeling User Interest in Social Media using News Media and Wikipedia.

  • Author : Jaeyong Kang and Hyunju Lee
  • Published Date : 2017
  • Category : Social Media
  • Place of publication : Information Systems

 

Abstract

Social media has become an important source of information and a medium for following and spreading trends, news, and ideas all over the world. Although determining the subjects of individual posts is important to extract users’ interests from social media, this task is nontrivial because posts are highly contextualized and informal and have limited length. To address this problem, we propose a user modeling framework that maps the content of texts in social media to relevant categories in news media. In our framework, the semantic gaps between social media and news media are reduced by using Wikipedia as an external knowledge base. We map term-based features from a short text and a news category into Wikipedia-based features such as Wikipedia categories and article entities. A user’s microposts are thus represented in a rich feature space of words. Experimental results show that our proposed method using Wikipedia-based features outperforms other existing methods of identifying users’ interests from social media.

Jeongkyun Kim, Jung-jae Kim and Hyunju Lee* (2017) An analysis of disease-gene relationship from Medline abstracts by DigSee. Scientific Reports, 2017 January 05; 7:40154 (IF: 5.228) (JCR 2015: 7/63, 11.3%, MULTIDISCIPLINARY SCIENCES).

An analysis of disease-gene relationship from Medline abstracts by DigSee.

  • Author : Jeongkyun Kim, Jung-jae Kim and Hyunju Lee
  • Published Date : 2017
  • Category : Bioinformatics and Text Mining 
  • Place of publication : Scientific Reports

 

Abstract

Diseases are developed by abnormal behavior of genes in biological events such as gene regulation, mutation, phosphorylation, and epigenetics and post-translational modification. Many studies of text mining attempted to identify the relationship between gene and disease by mining the literature, but they did not consider the biological events in which genes show abnormal behaviour in response to diseases. In this study, we propose to identify disease-related genes that are involved in the development of disease through biological events from Medline abstracts. We identified associations between 13,054 genes and 4,494 disease types, which cover more disease-related genes than manually curated databases for all disease types (e.g., Online Mendelian Inheritance in Man) and also than those for specific diseases (e.g., Alzheimer’s disease and hypertension). We show that the text mining findings are reliable, as per the PubMed scale, in that the disease-disease relationships inferred from the literature-wide findings are similar to those inferred from manually curated databases in a well-known study. In addition, literature-wide distribution of biological events across disease types reveals different characteristics of disease types.

Jiyoun Seo, Daeyong Jin, Chan-Hun Choi and Hyunju Lee* (2017) Integration of MicroRNA, mRNA, and Protein Expression Data for the Identification of Cancer-Related MicroRNAs. PLoS One, 2017 January 5; 12(1):e0168412 (IF: 3.057) (JCR 2015: 11/63, 17.5%, MULTIDISCIPLINARY SCIENCES).

Integration of MicroRNA, mRNA, and Protein Expression Data for the Identification of Cancer-Related MicroRNAs.

  • Author : Jiyoun SeoDaeyong Jin,  Chan-Hun Choi, and Hyunju Lee
  • Published Date : 2017
  • Category : Bioinformatics and Text Mining 
  • Place of publication : PLoS One

 

Abstract

MicroRNAs (miRNAs) are responsible for the regulation of target genes involved in various biological processes, and may play oncogenic or tumor suppressive roles. Many studies have investigated the relationships between miRNAs and their target genes, using mRNA and miRNA expression data. However, mRNA expression levels do not necessarily represent the exact gene expression profiles, since protein translation may be regulated in several different ways. Despite this, large-scale protein expression data have been integrated rarely when predicting gene-miRNA relationships. This study explores two approaches for the investigation of gene-miRNA relationships by integrating mRNA expression and protein expression data. First, miRNAs were ranked according to their effects on cancer development. We calculated influence scores for each miRNA, based on the number of significant mRNA-miRNA and protein-miRNA correlations. Furthermore, we constructed modules containing mRNAs, proteins, and miRNAs, in which these three molecular types are highly correlated. The regulatory interactions between miRNA and genes in these modules have been validated based on the direct regulations, indirect regulations, and co-regulations through transcription factors. We applied our approaches to glioblastomas (GBMs), ranked miRNAs depending on their effects on GBM, and obtained 52 GBM-related modules. Compared with the miRNA rankings and modules constructed using only mRNA expression data, the rankings and modules constructed using mRNA and protein expression data were shown to have better performance. Additionally, we experimentally verified that miR-504, highly ranked and included in the identified modules, plays a suppressive role in GBM development. We demonstrated that the integration of both expression profiles allows a more precise analysis of gene-miRNA interactions and the identification of a higher number of cancer-related miRNAs and regulatory mechanisms.