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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 < 0.05 ). KIF4A overexpression was correlated with the grade, histological type, and pathological stage according to logistic regression analysis ( p < 0.05 ). Meanwhile, survival analysis and meta-analysis revealed that KIF4A was associated with a poor prognosis and acted as an independent prognostic marker in EC patients ( p < 0.05 ). KIF4A is associated to immune response and may have a function in controlling immune cell infiltration in EC (20/24, p < 0.05 ). This is noteworthy given that gene enrichment analysis suggested KIF4A may be involved in the neuroactive ligand-receptor interaction pathway, etc. Finally, we identified transcription factors which have a potential interaction with KIF4A. Conclusion. We provided robust evidences that KIF4A is an indicator of poor prognosis and a potential target for immunotherapy in patients with EC.

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 < 0.05). KIF4A overexpression was correlated with the grade, histological type, and pathological stage according to logistic regression analysis (p < 0.05). Meanwhile, survival analysis and meta-analysis revealed that KIF4A was associated with a poor prognosis and acted as an independent prognostic marker in EC patients (p < 0.05). KIF4A is associated to immune response and may have a function in controlling immune cell infiltration in EC (20/24, p < 0.05). This is noteworthy given that gene enrichment analysis suggested KIF4A may be involved in the neuroactive ligand-receptor interaction pathway, etc. Finally, we identified transcription factors which have a potential interaction with KIF4A. Conclusion We provided robust evidences that KIF4A is an indicator of poor prognosis and a potential target for immunotherapy in patients with EC.