Literature DB >> 30883337

Co-expression network analysis identified KIF2C in association with progression and prognosis in lung adenocarcinoma.

Yuquan Bai, Lecai Xiong, Minglin Zhu, Zetian Yang, Jinping Zhao, Hexiao Tang.   

Abstract

Lung cancer is a malignant tumor with high morbidity and mortality, of which 80% is non-small cell lung cancer (NSCLC). And lung adenocarcinoma (LUAD) is the most important and common subtype in the NSCLC. In current study, the microarray data GSE31210 containing LUAD (n= 226) and normal lung tissue (n= 20) was analyzed to identify 965 differentially expressed genes, on which weighted gene co-expression network analysis was performed. Finally, it was confirmed that there was a significant correlation between brown module and LUAD stage. In the significant module, a total of 54 network hub genes were identified, and six of them were also identified as hub genes of the protein-protein interaction network. In validation, KIF2C showed a higher correlation with disease stage than other hub genes (p< 0.001, R2 = 0.955). Functional enrichment suggests that KIF2C is associated with cell mitosis and cell cycle. Combined with clinicopathological parameters, we found that the high expression of KIF2C is closely related to the relapse and tumor stage of LUAD. Survival analysis showed a significant reduction in overall survival in LUAD patients with high expression of KIF2C. Gene set enrichment analysis (GSEA) also showed that the "cell cycle signaling pathway" and "P53 related pathway" were significantly enriched in LUAD samples with high expression of KIF2C (FDR < 0.05). In conclusion, based on the co-expression analysis, KIF2C was identified in the association with progression and prognosis of LUAD, which might refer a poor prognosis probably by regulating cell cycle signaling pathway.

Entities:  

Keywords:  KIF2C; Lung adenocarcinoma; cell cycle; co-expression analysis

Mesh:

Substances:

Year:  2019        PMID: 30883337     DOI: 10.3233/CBM-181512

Source DB:  PubMed          Journal:  Cancer Biomark        ISSN: 1574-0153            Impact factor:   4.388


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