THRESHOLD: A Comprehensive Transcriptomic Analysis Tool for Evaluating Gene Saturation and Impact in Disease Progression

Gene expression studies serve as a foundational tool in molecular biology, providing insights into developmental, physiological, and pathological processes. Variations in gene expression can indicate disease states, which are vital in understanding disease progression, subtype manifestations, and identifying therapeutic targets based on detailed expression patterns. To effectively investigate gene expression patterns, especially in large datasets, a robust and precise analysis tool is crucial. In response to this critical analytical need, we developed THRESHOLD, a novel tool that goes beyond traditional gene expression analysis by introducing the concept of gene saturation. Unlike conventional methods that focus on absolute expression levels or binary differential expression, THRESHOLD quantifies the consistency of gene expression across patients, revealing co-regulation patterns that may otherwise be overlooked. This novel metric offers a unique perspective on gene expression patterns by highlighting consistent trends across patient samples, which are critical for understanding disease mechanisms and stratifying patients based on 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 (p < 0.00001). Moreover, THRESHOLD identified heightened gene saturation in patients with earlier onset of prostate adenocarcinoma (p < 0.0001) 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. Collectively, THRESHOLD advances our understanding of patient stratification and molecular signatures by offering a more detailed view of gene expression dynamics. The THRESHOLD tool is publicly available at: https://github.com/alperuzun/THRESHOLD .