lncAPNet enables the deciphering of lncRNA–mRNA connections in patient transcriptomic data

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ID: 321774
2026
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Abstract
Abstract Motivation Long non-coding RNAs regulate gene expression through chromatin remodeling, transcriptional control, and post-transcriptional modulation, influencing physiological cell homeostasis but also disease onset. Yet most transcriptomic and network-based studies rely on descriptive linear co-expression analyses, missing nonlinear and mechanistic insights. Emerging ML/DL methods offer promise but remain limited by data sparsity, noise, insufficient biological priors, and poor interpretability, constraining systems-level lncRNA-mRNA motif discovery. Results In this manuscript, we introduce lncAPNet, an extended version of the APNet workflow, which integrates graph-based nonlinear inference of lncRNA–mRNA interactions using NetBID2's and scMINERs activity logic within a lncRNA-focused SJARACNe co-expression network, coupled with PASNet, a biologically informed sparse deep learning model. This framework enables explainable identification of lncRNA drivers in three different cancer type case studies, two with bulk RNA-seq datasets [Chronic Lymphocytic Leukemia and Prostate Adenocarcinoma] and one by combining bulk RNA-seq and scRNA-seq omics datasets [Breast Invasive Carcinoma], uncovering lncRNA drivers that illuminate lncRNA-mediated programs in cancer progression. Availability and implementation lncAPNet's R scripts, Python scripts, and Nextflow pipeline are available at the GitHub repository: https://github.com/BiodataAnalysisGroup/lncAPNet
Reference Key
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Authors Vasileios Vasileiou, George Gavriilidis, Pedro Faria Zeni, Marek Mráz, Evangelos Karatzas, Antonis Giakountis, Georgios A. Pavlopoulos, Antonis Giannakakis, Fotis Psomopoulos
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Journal Bioinformatics advances
Year 2026
DOI
10.1093/bioadv/vbag204
URL
https://doi.org/10.1093/bioadv/vbag204
Keywords Keywords not found

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