THRESHOLD: a comprehensive transcriptomic analysis tool for evaluating gene saturation and impact in disease progression

Gene expression studies are fundamental in molecular biology, offering insights into development, disease progression, and therapeutic targets. To address the need for precise analysis of large datasets, we developed THRESHOLD, a novel tool that introduces the concept of gene saturation. Unlike traditional methods focused on absolute or binary expression levels, THRESHOLD quantifies the consistency of gene expression across patients, revealing co-regulation patterns critical for understanding disease mechanisms and stratifying patients by molecular signatures. The tool offers several features, including user-defined parameters, statistical comparisons, and interactive data visualization. THRESHOLD has uncovered compelling insights into disease progression using TCGA cancer datasets. For instance, bladder urothelial carcinoma demonstrated increasing upregulated gene saturation in progressive cancer stages (<i>P</i> < .00001). Moreover, THRESHOLD identified heightened gene saturation in patients with earlier onset of prostate adenocarcinoma (<i>P</i> < .0001) and revealed a critical fusion transcript, SLC45A2-AMACR, implicated in prostate adenocarcinoma progression, recurrence, and metastasis. Additionally, novel biomarkers and potential candidates for drug therapies were identified through protein-protein interaction networks and functional analyses of saturation data in colon adenocarcinoma and breast invasive carcinoma. THRESHOLD offers a new approach for studying gene expression dynamics and patient stratification. The tool is publicly available at Zenodo: https://zenodo.org/records/15287195.

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Publication Details

Journal
NAR Cancer
Published
2025-09-09
DOI
https://doi.org/10.1093/narcan/zcaf032
Primary Topic
Bioinformatics and Genomic Networks
Type
article
Field-Weighted Citation Impact
0.00

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article

THRESHOLD: a comprehensive transcriptomic analysis tool for evaluating gene saturation and impact in disease progression

Jessica Plavicki, Jennifer Li, Christopher P. Elco, Alper Uzun et al.
NAR Cancer
Bioinformatics and Genomic Networks
article

THRESHOLD: a comprehensive transcriptomic analysis tool for evaluating gene saturation and impact in disease progression

Jessica Plavicki, Jennifer Li, Christopher P. Elco, Alper Uzun, Finan M Gammell
article en

Abstract

Gene expression studies are fundamental in molecular biology, offering insights into development, disease progression, and therapeutic targets. To address the need for precise analysis of large datasets, we developed THRESHOLD, a novel tool that introduces the concept of gene saturation. Unlike traditional methods focused on absolute or binary expression levels, THRESHOLD quantifies the consistency of gene expression across patients, revealing co-regulation patterns critical for understanding disease mechanisms and stratifying patients by molecular signatures. The tool offers several features, including user-defined parameters, statistical comparisons, and interactive data visualization. THRESHOLD has uncovered compelling insights into disease progression using TCGA cancer datasets. For instance, bladder urothelial carcinoma demonstrated increasing upregulated gene saturation in progressive cancer stages (<i>P</i> < .00001). Moreover, THRESHOLD identified heightened gene saturation in patients with earlier onset of prostate adenocarcinoma (<i>P</i> < .0001) and revealed a critical fusion transcript, SLC45A2-AMACR, implicated in prostate adenocarcinoma progression, recurrence, and metastasis. Additionally, novel biomarkers and potential candidates for drug therapies were identified through protein-protein interaction networks and functional analyses of saturation data in colon adenocarcinoma and breast invasive carcinoma. THRESHOLD offers a new approach for studying gene expression dynamics and patient stratification. The tool is publicly available at Zenodo: https://zenodo.org/records/15287195.

NAR CancerVol. 7(4)
Brown University (US), New England Institute of Technology (US), Greenwich Public Schools (US), University of Greenwich (GB)
Brown University, Warren Alpert Medical School, Brown University
Openalex Percentile: Top 71%
Bioinformatics and Genomic Networks
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