LncRNACNVIntegrateR: a novel framework for correlating long non-coding RNAs with copy number variation abnormalities...

Area of Science:

• Bioinformatics
• Genomics
• Cancer Research

Background:

• Multi-omics data integration is crucial for understanding complex biological systems and disease mechanisms.
• Challenges in sample consistency and analytical frameworks limit the potential of multi-omics data.
• Identifying molecular relationships and biomarkers is key for advancing precision medicine.

Purpose of the Study:

• To develop an R package, lncRNACNVIntegrateR, for integrating multi-omics data.
• To explore the interplay between long non-coding RNAs (lncRNAs) and copy number variations (CNVs).
• To identify CNV-driven prognostic signatures and build predictive models for cancer.

Main Methods:

• The lncRNACNVIntegrateR package integrates transcriptomic data, CNV profiles, and clinical information.
• It provides a pipeline for data preprocessing, lncRNA-CNV correlation analysis, and signature identification.
• Risk score models and functional enrichment analyses are employed to assess biological significance.

Main Results:

• The package was validated using The Cancer Genome Atlas (TCGA) Glioblastoma (GBM) and Colorectal Adenocarcinoma (COAD) datasets.
• Predictive models achieved AUCs of 0.80 for GBM and 0.71 for COAD.
• Functional enrichment analyses revealed the biological significance of identified prognostic signatures.

Conclusions:

• lncRNACNVIntegrateR facilitates multi-omics data integration to uncover lncRNA-CNV interactions.
• The identified signatures and predictive models offer insights into disease progression and risk stratification.
• The package supports the discovery of potential therapeutic targets for personalized medicine.