Topological Network Analysis of Differentially Expressed Genes in Cancer Cells with Acquired Gefitinib Resistance

Cancer Genomics - Proteomics

KANG KWON⁴  SUNG YOUNG KIM³  CHEOL SEONG JANG¹  HO SUNG MYEONG³  YOUNG RAE KIM³  YOUNG SEOK LEE³  TAE HWAN PARK²  YUN HEE NOH³  JIN KI KIM³  SUN GOO HWANG¹ 

[1]Plant Genomics Laboratory, Department of Applied Plant Science, Kangwon National University, Chuncheon, Republic of KorePlant Genomics Laboratory, Department of Applied Plant Science, Kangwon National University, Chuncheon, Republic of KorePlant Genomics Laboratory, Department of Applied Plant Science, Kangwon National University, Chuncheon, Republic of Kore

[2]Department of Plastic and Reconstructive Surgery, College of Medicine, Yonsei University, Seoul, Republic of KoreDepartment of Plastic and Reconstructive Surgery, College of Medicine, Yonsei University, Seoul, Republic of KoreDepartment of Plastic and Reconstructive Surgery, College of Medicine, Yonsei University, Seoul, Republic of Kore

[3]Department of Biochemistry, School of Medicine, Konkuk University, Seoul, Republic of KoreDepartment of Biochemistry, School of Medicine, Konkuk University, Seoul, Republic of KoreDepartment of Biochemistry, School of Medicine, Konkuk University, Seoul, Republic of Kore

[4]School of Korean Medicine, Pusan National University, Yangsan, Republic of KoreSchool of Korean Medicine, Pusan National University, Yangsan, Republic of KoreSchool of Korean Medicine, Pusan National University, Yangsan, Republic of Kore

关键词: Meta-analysis;    microarray;    differentially expressed genes;    DEGs;    acquired drug resistance;    gefitinib;   

DOI  :  

来源: Delinasios GJ CO

【 摘 要 】

Background/Aim: Despite great effort to elucidate the process of acquired gefitinib resistance (AGR) in order to develop successful chemotherapy, the precise mechanisms and genetic factors of such resistance have yet to be elucidated. Materials and Methods: We performed a cross-platform meta-analysis of three publically available microarray datasets related to cancer with AGR. For the top 100 differentially expressed genes (DEGs), we clustered functional modules of hub genes in a gene co-expression network and a protein-protein interaction network. We conducted a weighted correlation network analysis of total DEGs in microarray dataset GSE 34228. The identified DEGs were functionally enriched by Gene Ontology (GO) function and KEGG pathway. Results: We identified a total of 1,033 DEGs (510 up-regulated, 523 down-regulated, and 109 novel genes). Among the top 100 up- or down-regulated DEGs, many genes were found in different types of cancers and tumors. Through integrative analysis of two systemic networks, we selected six hub DEGs (Pre-B-cell leukemia homeobox1, Transient receptor potential cation channel subfamily C member 1, AXL receptor tyrosine kinase, S100 calcium binding protein A9, S100 calcium binding protein A8, and Nucleotide-binding oligomerization domain containing 2) associated with calcium homeostasis and signaling, apoptosis, transcriptional regulation, or chemoresistance. We confirmed a correlation of expression of these genes in the microarray dataset. Conclusion: Our study may lead to comprehensive insights into the complex mechanism of AGR and to novel gene expression signatures useful for further clinical studies.

【 授权许可】

Unknown