# LncRNACNVIntegrateR: a novel framework for correlating long non-coding RNAs with copy number variation abnormalities and disease progression

## TL;DR

This paper introduces lncRNACNVIntegrateR, an R package that integrates multi-omics data to study the relationship between long non-coding RNAs and copy number variations in cancer.

## Contribution

The novel contribution is an R package that provides a complete pipeline for analyzing lncRNA-CNV correlations and building prognostic models.

## Key findings

- The package was validated on TCGA datasets for Glioblastoma and Colorectal Adenocarcinoma.
- Risk score models achieved AUCs of 0.80 and 0.71 for GBM and COAD, respectively.
- Functional enrichment analyses revealed biological insights into disease progression.

## Abstract

Understanding complex biological systems and disease mechanisms necessitates the integration of multiple molecular layers, making multi-omics data integration a cornerstone of modern biomedical research. By combining datasets from different omics domains, researchers can uncover intricate molecular relationships, discover robust biomarkers, and advance precision medicine. Despite advancements in high-throughput technologies that have increased the availability of multi-omics datasets, challenges such as sample consistency and the development of reliable analytical frameworks hinder their full potential. Addressing these challenges is crucial for achieving a comprehensive understanding of biological systems and leveraging multi-omics data to drive breakthroughs in healthcare. lncRNACNVIntegrateR is an R package that facilitates multi-omics data integration to explore the interplay between long non-coding RNAs (lncRNAs) and copy number variations (CNVs). The package integrates transcriptomic data, CNV profiles, and clinical information from matched samples, providing a complete pipeline for data preprocessing, lncRNA-CNV correlation analysis, and identification of CNV-driven prognostic signatures. Additionally, the package supports the construction of risk score models based on CNV-associated lncRNAs and functional enrichment analyses to reveal the role of corresponding target genes in disease progression. We validated lncRNACNVIntegrateR using The Cancer Genome Atlas (TCGA) Glioblastoma (GBM) and Colorectal Adenocarcinoma (COAD) datasets. The risk score models developed by the package demonstrated promising predictive performance, achieving an area under the receiver operating characteristic curve (AUC) of 0.80 for GBM and 0.71 for COAD. Functional enrichment analyses further highlighted the biological significance of the identified prognostic CNV-driven lncRNA signatures, providing insights into disease progression, risk stratification, and potential therapeutic targets to support clinical decision-making and personalized treatment approaches.