ONCO.IO

ONCO.IO integrates an experimentally curated knowledgebase with network analysis to identify and analyze co-deregulated microRNAs (miRNAs), long non-coding RNAs (lncRNAs), messenger RNAs (mRNAs), signaling proteins, and transcription factors involved in cancer.

Key Features:

• Curated Database Content: Manually curated, experimentally verified molecular interactions sourced from scientific literature covering miRNAs, lncRNAs, mRNAs, signaling proteins, and transcription factors.
• Network Analysis Capabilities: Network analysis tools for exploring and analyzing relationships among RNAs, proteins, and transcription factors in oncogenic regulatory networks.
• Focus on miRNA Regulation: Identification and analysis of co-deregulated miRNA genes and characterization of their associated biological processes and pathways.
• Meta-analysis Tools: Support for meta-analysis of miRNA expression datasets using the MetaDE package with combined P-value approaches including adaptive weight and Fisher's methods.
• Pathway and PPI Analysis: Integration of Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and protein-protein interaction (PPI) hub protein analysis.
• Biomarker and Prognostic Marker Identification: Identification of miRNA panels predictive of tumor recurrence after radical prostatectomy and biomarkers for biochemical recurrence (BCR), including gene interactions involving CTNNB1, TCF3, MAX, MYC, CYP26A1, and SREBF1.
• Network-Based Prognostic Analysis: Network-based analyses to assess prognostic significance and involvement of identified miRNAs in tumor growth and processes such as regulation of epithelial cell proliferation and tissue morphogenesis.

Scientific Applications:

• Prostate Cancer Research: Application to prostate cancer (PCa) for identifying predictive miRNA markers of tumor recurrence following radical prostatectomy via meta-analysis of miRNA expression datasets.
• Meta-Analysis and Predictive Marker Identification: Use of MetaDE combined P-value methods (adaptive weight and Fisher's) to derive panels of prognostic miRNAs across cancer datasets.
• Network-Based Prognostic Analysis: Determination of prognostic significance of miRNAs and their network roles in tumor growth and specific biological processes such as epithelial cell proliferation and tissue morphogenesis.
• Pathway and Protein Interaction Analysis: Elucidation of miRNA regulatory roles using GO enrichment, KEGG pathway mapping, and PPI hub protein analysis.
• Biomarker Discovery for Cancer Detection: Discovery of biomarkers for more specific cancer detection and prediction of biochemical recurrence (BCR), including identification of key interacting genes (CTNNB1, TCF3, MAX, MYC, CYP26A1, SREBF1).

Methodology:

Meta-analysis of miRNA expression datasets using the MetaDE package with combined P-value approaches (adaptive weight and Fisher's methods); network analysis of experimentally verified molecular interactions; Gene Ontology (GO) enrichment; KEGG pathway analysis; protein-protein interaction (PPI) hub protein analysis; identification of co-deregulated miRNA genes.