Decoding the Possible Molecular Mechanisms in Pediatric Wilms Tumor and Rhabdoid Tumor of the Kidney through Machine Learning Approaches

Abstract

Objective: Wilms tumor (WT) and Rhabdoid tumor (RT) are pediatric renal tumors and their differentiation is based on histopathological and molecular analysis. The present study aimed to introduce the panels of mRNAs and microRNAs involved in the pathogenesis of these cancers using deep learning algorithms. Methods: Filter, graph, and association rule mining algorithms were applied to the mRNAs/microRNAs data. Results: Candidate miRNAs and mRNAs with high accuracy (AUC: 97%/93% and 94%/97%, respectively) could differentiate the WT and RT classes in training and test data. Let-7a-2 and C19orf24 were identified in the WT, while miR-199b and RP1-3E10.2 were detected in the RT by analysis of Association Rule Mining. Conclusion: The application of the machine learning methods could identify mRNA/miRNA patterns to discriminate WT from RT. The identified miRNAs/mRNAs panels could offer novel insights into the underlying molecular mechanisms that are responsible for the initiation and development of these cancers. They may provide further insight into the pathogenesis, prognosis, diagnosis, and molecular-targeted therapy in pediatric renal tumors.

Graphical Abstract

Keywords:

• Renal neoplasms
• nephroblastoma
• pediatric kidney tumors
• machine learning
• graph algorithms
• association rule mining

Acknowledgments

This work was supported by the Kidney Research Center, Tabriz University of Medical Sciences, Tabriz, Iran. Also, the authors would like to thank the Clinical Research Development Unit of Tabriz Valiasr Hospital for their assistance in this research.

Ethical approval

This study was approved by the Ethics Committee of Tabriz University of Medical Sciences, Tabriz, Iran (Ethical code: IR.TBZMED.VCR.REC.1400.261).

Authors’ contributions

Conception and design study: SZV, SMHK, and SP; Data analysis: SP, SMHK; Data interpretation: SZV, SP, SMHK; Writing original draft: YR, SSH, SMH; Review and editing: all authors; Final Revision: SZV, MRA and MT.

Disclosure statement

No potential conflict of interest was reported by the authors.

Availability of data and materials

The data obtained from the artificial intelligence approaches will be available from the corresponding authors upon request.

Additional information

Funding

This research was funded by Tabriz University of Medical Sciences (Grant No: 68440).