Discovery of Novel Biomarkers with Extended Non-Coding RNA Interactor Networks from Genetic and Protein Biomarkers

Curated online interaction databases and gene ontology tools have streamlined the analysis of highly complex gene/protein networks. However, understanding of disease pathogenesis has gradually shifted from a protein-based core to complex interactive networks where non-coding RNA (ncRNA) is thought to play an essential role. As current gene ontology is based predominantly on protein-level information, there is a growing need to analyze networks with ncRNA. In this study, we propose a gene ontology workflow integrating ncRNA using the NPInter V5.0 database. To validate the proposed workflow, we analyzed our previously published curated biomarker datasets for hidden disease susceptibility processes and pharmacogenomics. Our results show a novel involvement of melanogenesis in psoriasis response to biological drugs in general. Hyperpigmentation has been previously observed in psoriasis following treatment with currently indicated biological drugs, thus calling attention to melanogenesis research as a response biomarker in psoriasis. Moreover, our proposed workflow highlights the need to critically evaluate computed ncRNA interactions within databases and a demand for gene ontology analysis of large miRNA blocks.

Authors

Institutions

Publication Details

Journal
International Journal of Molecular Sciences
Published
2024-09-23
DOI
https://doi.org/10.3390/ijms251810210
Citations
2
Primary Topic
RNA Research and Splicing
Type
article
Field-Weighted Citation Impact
0.47
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Discovery of Novel Biomarkers with Extended Non-Coding RNA Interactor Networks from Genetic and Protein Biomarkers

Mario Gorenjak, Uroš Potočnik, Pavel Skok, Martina Krušič et al.
2 citations
International Journal of Molecular Sciences
RNA Research and Splicing
0.47
article

Discovery of Novel Biomarkers with Extended Non-Coding RNA Interactor Networks from Genetic and Protein Biomarkers

Mario Gorenjak, Uroš Potočnik, Pavel Skok, Martina Krušič, Gregor Jezernik, Damjan Glavač
article en
2 citations

Abstract

Curated online interaction databases and gene ontology tools have streamlined the analysis of highly complex gene/protein networks. However, understanding of disease pathogenesis has gradually shifted from a protein-based core to complex interactive networks where non-coding RNA (ncRNA) is thought to play an essential role. As current gene ontology is based predominantly on protein-level information, there is a growing need to analyze networks with ncRNA. In this study, we propose a gene ontology workflow integrating ncRNA using the NPInter V5.0 database. To validate the proposed workflow, we analyzed our previously published curated biomarker datasets for hidden disease susceptibility processes and pharmacogenomics. Our results show a novel involvement of melanogenesis in psoriasis response to biological drugs in general. Hyperpigmentation has been previously observed in psoriasis following treatment with currently indicated biological drugs, thus calling attention to melanogenesis research as a response biomarker in psoriasis. Moreover, our proposed workflow highlights the need to critically evaluate computed ncRNA interactions within databases and a demand for gene ontology analysis of large miRNA blocks.

International Journal of Molecular SciencesVol. 25(18)
University of Ljubljana (SI), University of Maribor (SI), University Clinical Centre Maribor (SI)
Good health and well-being
Openalex Percentile: Top 35%
RNA Research and Splicing
0.47
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.