MBMethPred: a computational framework for the accurate classification of childhood medulloblastoma subgroups using data integration and AI-based approaches

Childhood medulloblastoma is a malignant form of brain tumor that is widely classified into four subgroups based on molecular and genetic characteristics. Accurate classification of these subgroups is crucial for appropriate treatment, monitoring plans, and targeted therapies. However, misclassification between groups 3 and 4 is common. To address this issue, an AI-based R package called MBMethPred was developed based on DNA methylation and gene expression profiles of 763 medulloblastoma samples to classify subgroups using machine learning and neural network models. The developed prediction models achieved a classification accuracy of over 96% for subgroup classification by using 399 CpGs as prediction biomarkers. We also assessed the prognostic relevance of prediction biomarkers using survival analysis. Furthermore, we identified subgroup-specific drivers of medulloblastoma using functional enrichment analysis, Shapley values, and gene network analysis. In particular, the genes involved in the nervous system development process have the potential to separate medulloblastoma subgroups with 99% accuracy. Notably, our analysis identified 16 genes that were specifically significant for subgroup classification, including EP300, CXCR4, WNT4, ZIC4, MEIS1, SLC8A1, NFASC, ASCL2, KIF5C, SYNGAP1, SEMA4F, ROR1, DPYSL4, ARTN, RTN4RL1, and TLX2. Our findings contribute to enhanced survival outcomes for patients with medulloblastoma. Continued research and validation efforts are needed to further refine and expand the utility of our approach in other cancer types, advancing personalized medicine in pediatric oncology

ExplORRNet: An interactive web tool to explore stage-wise miRNA expression profiles and their interactions with mRNA and lncRNA in human breast and gynecological cancers

Background: MicroRNAs (miRNAs) are key regulators of gene expression that have been implicated in gynecological and breast cancers. Understanding the cancer stage-wise expression patterns of miRNAs and their interactions with other RNA molecules in cancer is crucial to improve cancer diagnosis and treatment planning. Comprehensive web tools that integrate data on the transcriptome, circulating miRNAs, and their validated targets to derive beneficial conclusions in cancer research are lacking. Methods: Using the Shiny R package, we developed a web tool called ExplORRNet that integrates transcriptomic profiles from The Cancer Genome Atlas and miRNA expression data derived from various sources, including tissues, cell lines, exosomes, serum, and plasma, available in the Gene Expression Omnibus database. Differential expression analyses between normal and tumor tissue samples as well as different stages of cancer, accompanied by gene enrichment and survival analyses, can be performed using specialized R packages. Additionally, a miRNA-messenger RNA (mRNA)-long non-coding RNA (lncRNA) networks are constructed to identify regulatory modules. Results: Our tool identifies cancer stage-wise differentially regulated miRNAs, mRNAs, and lncRNAs in gynecological and breast cancers. Survival analysis identifies miRNAs associated with patient survival, and functional enrichment analysis provides insights into dysregulated miRNA-related biological processes and pathways. The miRNA–mRNA–lncRNA networks highlight interconnected regulatory molecular modules driving cancer progression. Case studies demonstrate the utility of the ExplORRNet for studying gynecological and breast cancers. Conclusion: ExplORRNet is an intuitive and user-friendly web tool that provides a deeper understanding of dysregulated miRNAs and their functional implications in gynecological and breast cancers. We hope our ExplORRNet tool has potential utility among the clinical and basic researchers and will be beneficial to the entire cancer genomics community to encourage and facilitate mining the rapidly growing public databases to progress the field of precision oncology. The ExplORRNet is available at https://mirna.cs.ut.ee

Novel Mutation C.7348C>T in NF1 Gene Identified by Whole-Exome Sequencing in Patient with Overlapping Clinical Symptoms of Neurofibromatosis Type 1 and Bannayan�Riley�Ruvalcaba Syndrome

Abstract: Neurofibromatosis type 1 (NF-1) is an autosomal dominant disorder provoking benign cutaneous and nerve sheath tumors. The cutaneous tumors termed as plexiform neurofibromas, which some of them are extremely visible, and can influence the quality of life. They can also develop into invasive forms of carcinomas and infiltrate into multiple tissues, thus endangering the patient�s life. The loss-of-function mutations in NF1 gene are responsible for NF-1 type. Due to the large size of NF1 gene (~350 kb and 60 exons), exist some pseudogenes on another locus, and lack mutation hotspot the molecular characterizing of patients is complex. In this study, we reported a patient showed symptoms of both NF-1 and Bannayan�Riley�Ruvalcaba syndrome (BRRS), then performed a whole-exome sequencing (WES) and a data analysis for molecular characterization. These results showed a single heterozygous nucleotide variant (c.7348C>T) in NF1 gene, which results in a premature stop codon (p.Arg2450Ter) and a truncated protein, causing clinical symptoms of the patient. According to the results, WES is a quick and cost-effective approach for molecular diagnosis of the mixed phenotype of NF-1. © 2020, Allerton Press, Inc