ArticlePeerJ2025
LncRNACNVIntegrateR: a novel framework for correlating long non-coding RNAs with copy number variation abnormalities and disease progression.
Article in PeerJ, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.