Bioinformatical analysis of the key differentially expressed genes for screening potential biomarkers in Wilms tumor

• Published in: Scientific reports 2023-09, Vol.13 (1), p.15404-15404, Article 15404
• Main Authors: Cai, Linghao, Shi, Bo, Zhu, Kun, Zhong, Xiaohui, Lai, Dengming, Wang, Jinhu, Tou, Jinfa
• Format: Article
• Language: English
• Subjects: 631/67/1857; 631/67/2332; Bioinformatics; Biomarkers; Gene expression; Genomics; Histology; Humanities and Social Sciences; Immunohistochemistry; Infiltration; Kidney cancer; Kinesin; Medical prognosis; Metastases; Molecular biology; Molecular modelling; multidisciplinary; Patients; Pediatrics; Prognosis; Science; Science (multidisciplinary); Tumors
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-023-42730-w

Wilms tumor (WT) is the most common pediatric renal malignant tumor in the world. Overall, the prognosis of Wilms tumor is very good. However, the prognosis of patients with anaplastic tumor histology or disease relapse is still poor, and their recurrence rate, metastasis rate and mortality are significantly increased compared with others. Currently, the combination of histopathological examination and molecular biology is essential to predict prognosis and guide the treatment. However, the molecular mechanism has not been well studied. Genetic profiling may be helpful in some way. Hence, we sought to identify novel promising biomarkers of WT by integrating bioinformatics analysis and to identify genes associated with the pathogenesis of WT. In the presented study, the NCBI Gene Expression Omnibus was used to download two datasets of gene expression profiles related to WT patients for the purpose of detecting overlapped differentially expressed genes (DEGs). The DEGs were then uploaded to DAVID database for enrichment analysis. In addition, the functional interactions between proteins were evaluated by simulating the protein–protein interaction (PPI) network of DEGs. The impact of selected hub genes on survival in WT patients was analyzed by using the online tool R2: Genomics Analysis and Visualization Platform. The correlation between gene expression and the degree of immune infiltration was assessed by the Estimation of Stromal and Immune cells in Malignant Tumor tissues using the Expression (ESTIMATE) algorithm and the single sample GSEA. Top 12 genes were identified for further study after constructing a PPI network and screening hub gene modules. Kinesin family member 2C ( KIF2C ) was identified as the most significant gene predicting the overall survival of WT patients. The expression of KIF2C in WT was further verified by quantitative real-time polymerase chain reaction and immunohistochemistry. Furthermore, we found that KIF2C was significantly correlated with immune cell infiltration in WT. Our present study demonstrated that altered expression of KIF2C may be involved in WT and serve as a potential prognostic biomarker for WT patients.