Bioinformatics Analysis of KIF1A Expression and Gene Regulation Network in Ovarian Carcinoma.

Abstract

BACKGROUND:

The study aims to analyze the expression levels of kinesin family member 1A (KIF1A) in ovarian cancer (OC) and explore its clinical significance in the development of OC and its potential regulatory network.

METHODS:

The Cancer Genome Atlas (TCGA) OC data was used to examine the expression differences between OC and normal tissue and explore the correlation with tumor stage. The relationship between KIF1A expression and prognosis was analyzed using Oncomine and Kaplan-Meier plotter tools. The co-expression network of KIF1A in TCGA OC was analyzed based on the application of cBioPortal, GO cluster, and KEGG analyses were performed based on the co-expression network. Immune-infiltration analysis were used to analyze the significant involvement of KIF1A in function.

RESULTS:

KIF1A was highly elevated in OC tissues and KIF1A expression was significantly correlated with the FIGO stage (P=0.015) and age (P=0.020). High KIF1A expression of OC predicted the poor prognosis including overall survival (OS) (HR: 1.27; 95% CI: 1.11-1.45; P=0.00046) and post-progression survival (PFS) (HR: 1.18; 95% CI: 1.03-1.35; P=0.015). GO and KEGG analysis showed KIF1A had a potential role in the biological process of ATP-dependent chromatin remodeling, transcription, DNA-templated cytolysis, positive regulation of T cell proliferation, positive regulation of transcription, DNA-templated via cell adhesion molecules (CAMs), primary immunodeficiency, oxidative phosphorylation, NF-kappa B signaling pathway, pathways in cancer and Wnt signaling pathway, and immune infiltrating cells.

CONCLUSION:

KIF1A was highly expressed and correlated with poor survival and immune infiltration in OC, and it may be a prognostic biomarker in OC.

• Authors: Xiaoyuan Lu, Guilin Li, Sicong Liu, Haihong Wang, Zhengzheng Zhang, Buze Chen
• Journal: International journal of general medicine (Int J Gen Med) Vol. 14 Pg. 3707-3717 ( 2021) ISSN: 1178-7074 [Print] New Zealand
• PMID: 34321916 (Publication Type: Journal Article)
• Copyright: © 2021 Lu et al.