RNACOREX: The Open-Source Tool Unlocking Cancer's Hidden Genetic Networks

In the complex world of cancer research, understanding how genes communicate within tumors has long represented a significant challenge. Traditional analysis methods often struggle to map the intricate molecular networks that drive disease progression, leaving researchers with incomplete pictures of how cancers develop and spread. Now, a breakthrough from Spanish scientists offers a new way forward. Researchers at the University of Navarra have developed RNACOREX, an open-source software platform that reveals the hidden genetic architecture of cancer while providing clear, interpretable insights into how these networks relate to patient outcomes.

Decoding Cancer's Molecular Language

Within human cells, biological molecules like microRNAs (miRNAs) and messenger RNAs (mRNAs) communicate through highly complex regulatory networks. When these networks malfunction, diseases including cancer can develop. As explained by Rubén Armañanzas, head of the Digital Medicine Laboratory at the University's Institute of Data Science and Artificial Intelligence (DATAI), understanding this architecture is crucial for detecting, studying, and classifying different tumor types. However, reliably identifying these networks presents significant challenges due to the vast amount of available data, the presence of false signals, and the lack of accessible tools capable of distinguishing which molecular interactions truly matter for each disease.

How RNACOREX Works

RNACOREX was specifically designed to overcome these challenges by integrating curated information from international biological databases with real-world gene expression data. The software ranks the most biologically meaningful miRNA-mRNA interactions, then builds progressively more complex regulatory networks that can function as probabilistic models for studying disease behavior. This approach allows researchers to analyze thousands of biological molecules simultaneously, detecting important molecular interactions that traditional methods often miss. By producing a clear molecular "map," the software helps researchers better understand how tumors function and offers new ways to explore the biological processes driving cancer progression.

Predictive Power with Interpretable Results

The research team rigorously tested RNACOREX using data from thirteen different cancers provided by The Cancer Genome Atlas (TCGA) international consortium. These included breast, colon, lung, stomach, melanoma, and head and neck tumors. According to Aitor Oviedo-Madrid, first author of the study and researcher at DATAI's Digital Medicine Laboratory, the software predicted patient survival with accuracy on par with sophisticated AI models. What sets RNACOREX apart is its ability to provide clear, interpretable explanations of the molecular interactions behind these predictions—something many advanced AI systems lack. This transparency allows researchers to understand not just what the predictions are, but why the tool makes them.

Beyond Survival Prediction

RNACOREX's capabilities extend well beyond survival prediction. The tool can identify regulatory networks linked to clinical outcomes, detect molecular patterns shared across multiple tumor types, and spotlight individual molecules with strong biomedical relevance. These insights help researchers generate new hypotheses about how tumors grow and progress while pointing toward promising future diagnostic markers or treatment targets. As Oviedo-Madrid notes, the tool provides a reliable molecular "map" that helps prioritize new biological targets, potentially speeding up cancer research and drug development processes.

An Open-Source Solution for the Research Community

In keeping with principles of scientific collaboration and accessibility, RNACOREX is freely available as an open-source program on GitHub and PyPI (Python Package Index). The platform includes automated tools for downloading databases, making it easier for laboratories and research institutions worldwide to integrate the software into their workflows. This open approach reflects a growing movement in biomedical research toward transparent, reproducible tools that can be validated and improved by the broader scientific community. The project has received partial funding from the Government of Navarra (ANDIA 2021 program) and the ERA PerMed JTC2022 PORTRAIT initiative.

The Future of Explainable AI in Cancer Research

As artificial intelligence in genomics accelerates, RNACOREX positions itself as an explainable, easy-to-interpret alternative to "black-box" models, helping bring omics data into practical biomedical practice. The University of Navarra team is already working on expanding the software's capabilities, with planned additions including pathway analysis and new layers of molecular interaction data. These enhancements aim to create models that more fully explain the biological mechanisms behind tumor growth and progression. This work highlights the institution's broader commitment to interdisciplinary research that combines biomedicine, artificial intelligence, and data science to advance personalized and precision cancer medicine.

The development of RNACOREX represents a significant step forward in making complex cancer biology more accessible and understandable to researchers. By providing both predictive power and interpretable results, this tool bridges the gap between advanced computational methods and practical biomedical insights. As cancer research continues to evolve toward more personalized approaches, tools like RNACOREX that offer transparency alongside technical sophistication will likely play increasingly important roles in helping scientists understand—and ultimately combat—this complex disease.