Renal malignant tumor with the loss of INI1 expression and WT1 positive in a 3-year-old boy: a case report

Identification of Prognostic Biomarkers in Patients With Malignant Rhabdoid Tumor of the Kidney Based on mTORC1 Signaling Pathway-Related Genes

Background: Malignant rhabdoid tumor of the kidney (MRTK) is an infrequent malignant tumor in childhood, accounting for approximately 2% of all childhood kidney tumors. Although the development of current treatments, the overall survival (OS) rate of MRTK patients is only 25%. The aim of this research was to explore the prognostic value of genes associated with the mTORC1 signaling pathway in MRTK.

Methods: The transcriptome data of MRTK samples were downloaded from the TARGET database. The 200 genes of HALLMARK_MTORC1_SIGNALING were downloaded from the Molecular Signatures Database (MSigDB). Furthermore, we applied gene set variation analysis (GSVA) to screen differentially expressed gene sets between the MRTK and normal samples. The 200 genes were combined with differentially expressed genes (DEGs) identified from differentially expressed gene sets. Then, a gene signature of mTORC1 pathway-related genes (mTRGs) was constructed in MRTK. The molecular mechanism of prognostic factors in MRTK was further analyzed using gene set enrichment analysis (GSEA). The target drugs based on these prognostic factors were explored from The Comparative Toxicogenomics Database (CTD). Moreover, six paired fresh tumor tissues and paraneoplastic tissues from children with MRTK were collected to validate the expressions of P4HA1, MLLT11, AURKA, and GOT1 in clinical samples via real-time fluorescence quantitative PCR and Western blot.

Results: A four-gene signature (P4HA1, MLLT11, AURKA, and GOT1) related to the mTORC1 pathway was developed in MRTK, which divided the MRTK patients into high-risk and low-risk groups. The patients with high-risk scores were strongly associated with reduced OS. Receiver operating characteristic (ROC) analysis indicated a good prediction performance of the four biomarker signatures. GSEA revealed that the mTOR signaling pathway was significantly enriched. The risk score was demonstrated to be an independent predictor for MRTK outcome. According to the correlation of tumor stem cell index and prognostic factors, the target drugs were obtained for the treatment of MRTK patients. Furthermore, the expressions of RT-qPCR and Western blot were consistent with RNA-sequencing data such that their expressions were significantly elevated in tumor tissues.

Conclusion: A total of four genes (P4HA1, MLLT11, AURKA, and GOT1) were screened as prognostic markers, further providing a new understanding for the treatment of patients with MRTK.