Prognostic Value and Immune-Infiltration Pattern of KIF4A in Patients with Endometrial Carcinoma

Background. With the development of sequencing technology, an increasing number of biomarkers has been identified in endometrial carcinoma (EC). However, there have been few comprehensive analyses of the KIF4A gene in patients with EC. Methods. Based on raw data in public databases, the KIF4A gene a...

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Bibliographic Details
Published in:Disease markers 2022, Vol.2022, p.9621701-18
Main Authors: Yang, Zhujuan, Shen, Xiaoqing, Luo, Shanhui, Li, Yi
Format: Article
Language:English
Subjects:
Algorithms
Annotations
Biomarkers
Biomarkers, Tumor - metabolism
Cancer
Carcinoma
Cell cycle
Datasets
DNA damage
Endometrial cancer
Endometrial Neoplasms - diagnosis
Endometrial Neoplasms - metabolism
Endometrium
Female
Gene expression
Genes
Humans
Immune response
Immune system
Immunohistochemistry
Immunotherapy
Infiltration
Kinesins - metabolism
Medical prognosis
Meta-analysis
Metastases
Nomograms
Patients
Predictive Value of Tests
Prognosis
Protein interaction
Proteins
Regression analysis
Sequences
Software
Subgroups
Survival
Survival analysis
Transcription factors
Tumors
Uterine cancer
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Summary:Background. With the development of sequencing technology, an increasing number of biomarkers has been identified in endometrial carcinoma (EC). However, there have been few comprehensive analyses of the KIF4A gene in patients with EC. Methods. Based on raw data in public databases, the KIF4A gene and protein expression in EC were validated. Logistic regression analysis was conducted to analyze the correlations between clinical characteristics and the KIF4A expression. Kaplan-Meier analysis was used to explore the difference in survival in clinical subgroups. Meanwhile, we used meta-analysis in multiple datasets to investigate the prognostic value of KIF4A. In addition, Cox regression analysis was used to confirm the independent prognostic value of KIF4A, and we constructed a nomogram based on KIF4A expression. Subsequently, we used ESTIMATE and ssGSEA algorithms to excavate the correlation between KIF4A, tumour-infiltrating immune cells, and related gene markers of immune cells. Moreover, the potential biological functions of KIF4A were investigated by gene function annotation. Finally, we identified the hub genes interacting with KIF4A by constructing a protein-protein interaction (PPI) network and screening differential genes (DEGs). Results. In the pan-cancer analysis, KIF4A was upregulated in most tumors (21/33). Similarly, the overexpression of KIF4A in EC patients was confirmed in the TCGA cohort, the GEO cohort, and immunohistochemistry. In addition, upregulated KIF4A is associated with age, survival status, grade, FIGO stage, histological type, tumour invasion, and TCGA molecular subtypes (p
ISSN:0278-0240
1875-8630
DOI:10.1155/2022/9621701