Details

Autor(en) / Beteiligte

• Zhu, Songling
• Bao, Hongxia
• Zhang, Meng-Chun
• Liu, Huidi
• Wang, Yao
• Lin, Caiji
• Zhao, Xingjuan
• Liu, Shu-Lin

Titel

KAZN as a diagnostic marker in ovarian cancer: a comprehensive analysis based on microarray, mRNA-sequencing, and methylation data

Ist Teil von

• BMC cancer, 2022-06, Vol.22 (1), p.1-662, Article 662

Ort / Verlag

London: BioMed Central Ltd

Erscheinungsjahr

2022

Quelle

SpringerLink

Beschreibungen/Notizen

• Background Ovarian cancer (OC) is among the deadliest malignancies in women and the lack of appropriate markers for early diagnosis leads to poor prognosis in most cases. Previous studies have shown that KAZN is involved in multiple biological processes during development, such as cell proliferation, differentiation, and apoptosis, so defects or aberrant expression of KAZN might cause queer cell behaviors such as malignancy. Here we evaluated the KAZN expression and methylation levels for possible use as an early diagnosis marker for OC. Methods We used data from Gene Expression Omnibus (GEO) microarrays, The Cancer Genome Atlas (TCGA), and Clinical Proteomic Tumor Analysis Consortium (CPTAC) to investigate the correlations between KAZN expression and clinical characteristics of OC by comparing methylation levels of normal and OC samples. The relationships among differentially methylated sites in the KAZN gene, corresponding KAZN mRNA expression levels and prognosis were analyzed. Results KAZN was up-regulated in ovarian epithelial tumors and the expression of KAZN was correlated with the patients' survival time. KAZN CpG site cg17657618 was positively correlated with the expression of mRNA and the methylation levels were significantly differential between the group of stage "I and II" and the group of stage "III and IV". This study also presents a new method to classify tumor and normal tissue in OC using DNA methylation pattern in the KAZN gene body region. Conclusions KAZN was involved in ovarian cancer pathogenesis. Our results demonstrate a new direction for ovarian cancer research and provide a potential diagnostic biomarker as well as a novel therapeutic target for clinical application. Keywords: KAZN, Ovarian cancer, Methylation, Bioinformatics, Survival